Imported Upstream version 1.4.0upstream/1.4.0submit/tizen/20200423.054851

559 files changed, 0 insertions, 71905 deletions

diff --git a/runtime/neurun/CMakeLists.txt b/runtime/neurun/CMakeLists.txt
deleted file mode 100644
index 359006d54..000000000
--- a/runtime/neurun/CMakeLists.txt
+++ /dev/null
@@ -1,16 +0,0 @@
-if(NOT BUILD_NEURUN)
-  return()
-endif(NOT BUILD_NEURUN)
-
-# Add cpu
-
-# TODO Remove this variable as adding include dirs is done with target_link_libraries
-#      (currently used by cpu/acl_cl kernel module which is not proper)
-set(NEURUN_INCLUDE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/core/include)
-
-add_subdirectory(backend)
-add_subdirectory(frontend)
-add_subdirectory(core)
-add_subdirectory(api)
-add_subdirectory(test)
-add_subdirectory(sample)
diff --git a/runtime/neurun/api/CMakeLists.txt b/runtime/neurun/api/CMakeLists.txt
deleted file mode 100644
index c3f7702ad..000000000
--- a/runtime/neurun/api/CMakeLists.txt
+++ /dev/null
@@ -1,21 +0,0 @@
-file(GLOB_RECURSE API_SRC "*.cc")
-
-set(NEURUN_DEV nnfw-dev)
-add_library(${NEURUN_DEV} SHARED ${API_SRC})
-
-# Public headers to publish
-# nnfw_debug.h is header for runtime developer, so it will not be installed
-# But runtime developer can use nnfw_debug.h by linking nnfw-dev
-set(NNFW_API_HEADERS include/nnfw.h include/nnfw_dev.h)
-
-target_link_libraries(${NEURUN_DEV} PUBLIC nnfw-nnapi-header)
-target_link_libraries(${NEURUN_DEV} PRIVATE neurun_core)
-target_link_libraries(${NEURUN_DEV} PRIVATE jsoncpp tflite_loader circle_loader ${LIB_PTHREAD})
-target_link_libraries(${NEURUN_DEV} PRIVATE nnfw_common)
-target_link_libraries(${NEURUN_DEV} PRIVATE nnfw_coverage)
-target_include_directories(${NEURUN_DEV} PUBLIC include)
-set_target_properties(${NEURUN_DEV} PROPERTIES PUBLIC_HEADER "${NNFW_API_HEADERS}")
-
-install(TARGETS ${NEURUN_DEV}
-        LIBRARY DESTINATION lib
-        PUBLIC_HEADER DESTINATION include/nnfw)
diff --git a/runtime/neurun/api/include/nnfw.h b/runtime/neurun/api/include/nnfw.h
deleted file mode 100644
index c903fbcad..000000000
--- a/runtime/neurun/api/include/nnfw.h
+++ /dev/null
@@ -1,378 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/**
- * @file  nnfw.h
- * @brief This file describes runtime API
- */
-#ifndef __NNFW_H__
-#define __NNFW_H__
-
-#include <stddef.h>
-#include <stdint.h>
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-/**
- * @brief Session to query with runtime
- *
- * <p>nnfw_session is started and passed by calling {@link nnfw_create_session}.
- * Each session has its own inference environment, such as model to inference, backend usage, etc.
- *
- * <p>Load model by calling {@link nnfw_load_model_from_file}
- *
- * <p>After loading, prepare inference by calling {@link nnfw_prepare}.
- * Application can set runtime environment before prepare by calling
- * {@link nnfw_set_available_backends} and {@link nnfw_set_op_backend}, and it is optional.
- *
- * <p>Application can inference by calling {@link nnfw_run}.
- * Before inference, application has responsibility to set input tensor to set input data by calling
- * {@link nnfw_set_output}, and output tensor to get output by calling {@link nnfw_set_input}
- *
- * <p>To support input and output setting, application can get
- * input and output tensor information by calling<ul>
- * <li>{@link nnfw_input_size}</li>
- * <li>{@link nnfw_output_size}</li>
- * <li>{@link nnfw_input_tensorinfo}</li>
- * <li>{@link nnfw_output_tensorinfo}</li>
- * </ul>
- *
- * <p>Application can inference many times using one session,
- * but next inference can do after prior inference end
- *
- * <p>Application cannot use muitiple model using one session
- */
-typedef struct nnfw_session nnfw_session;
-
-/**
- * @brief Tensor types
- *
- * The type of tensor represented in {@link nnfw_tensorinfo}
- */
-typedef enum {
-  /** A tensor of 32 bit floating point */
-  NNFW_TYPE_TENSOR_FLOAT32 = 0,
-  /** A tensor of 32 bit signed integer */
-  NNFW_TYPE_TENSOR_INT32 = 1,
-  /**
-   * A tensor of 8 bit integers that represent real numbers.
-   *
-   * real_value = (integer_value - zeroPoint) * scale.
-   */
-  NNFW_TYPE_TENSOR_QUANT8_ASYMM = 2,
-  /** A tensor of boolean */
-  NNFW_TYPE_TENSOR_BOOL = 3,
-  /** A tensor of 8 bit unsigned integer */
-  NNFW_TYPE_TENSOR_UINT8 = 4,
-} NNFW_TYPE;
-
-/**
- * @brief Result Values
- */
-typedef enum {
-  /** Successful */
-  NNFW_STATUS_NO_ERROR = 0,
-  /** Failed */
-  NNFW_STATUS_ERROR = 1,
-} NNFW_STATUS;
-
-/**
- * @brief Data format of a tensor
- */
-typedef enum {
-  /** Don't care layout */
-  NNFW_LAYOUT_NONE = 0,
-  /**
-   * Channel last layout
-   * If rank is 4, layout is NHWC
-   */
-  NNFW_LAYOUT_CHANNELS_LAST = 1,
-  /**
-   * Channel first layout
-   * If rank is 4, layout is NCHW
-   */
-  NNFW_LAYOUT_CHANNELS_FIRST = 2,
-} NNFW_LAYOUT;
-
-/**
- * @brief tensor info describes the type and shape of tensors
- *
- * <p>This structure is used to describe input and output tensors.
- * Application can get input and output tensor type and shape described in model by using
- * {@link nnfw_input_tensorinfo} and {@link nnfw_output_tensorinfo}
- *
- * <p>Maximum rank is 6. And tensor's dimension value is filled in 'dims' field from index 0.
- * For example, if tensor's rank is 4,
- * application can get dimension value from dims[0], dims[1], dims[2], and dims[3]
- */
-typedef struct nnfw_tensorinfo
-{
-  /** The data type */
-  NNFW_TYPE dtype;
-  /** The number of dimensions (rank) */
-  int32_t rank;
-  /**
-   * The dimension of tensor.
-   * Maximum rank is 6.
-   */
-  int32_t dims[6];
-} nnfw_tensorinfo;
-
-/**
- * @brief Create a new session instance.
- *
- * <p>This only creates a session.
- * Model is loaded after {@link nnfw_load_model_from_file} is invoked.
- * And inference is performed after {@link nnfw_run} is invoked.
- *
- * <p>{@link nnfw_close_session} should be called once
- * if session is no longer need
- *
- * @param[out]  session The session to be created
- * @return      NNFW_STATUS_NO_ERROR if successful
- */
-NNFW_STATUS nnfw_create_session(nnfw_session **session);
-
-/**
- * @brief Close a session instance
- *
- * After called, access to closed session by application will be invalid
- *
- * @param[in] session The session to be closed
- * @return    @c NNFW_STATUS_NO_ERROR if successful
- */
-NNFW_STATUS nnfw_close_session(nnfw_session *session);
-
-/**
- * @brief     Load model from nnpackage file or directory
- *
- * @param[in] session           nnfw_session loading the given nnpackage file/dir
- * @param[in] package_file_path Path to the nnpackage file or unzipped directory to be loaded
- *
- * @return    @c NNFW_STATUS_NO_ERROR if successful
- */
-NNFW_STATUS nnfw_load_model_from_file(nnfw_session *session, const char *package_file_path);
-
-/**
- * @brief     Apply i-th input's tensor info to resize input tensor
- *
- * This function should be called before {@link nnfw_prepare} is invoked, and
- * should be called after {@link nnfw_load_model_from_file} is invoked
- * See {@link nnfw_prepare} for information applying updated tensor info
- * If this function is called many times for same index, tensor info is overwritten
- *
- * @param[in] session     Session to the input tensor info is to be set
- * @param[in] index       Index of input to be applied (0-indexed)
- * @param[in] tensor_info Tensor info to be applied
- * @return    @c NNFW_STATUS_NO_ERROR if successful, otherwise return @c NNFW_STATUS_ERROR
- */
-NNFW_STATUS nnfw_apply_tensorinfo(nnfw_session *session, uint32_t index,
-                                  nnfw_tensorinfo tensor_info);
-
-/**
- * @brief     Prepare session to be ready for inference
- *
- * This phase may finalize model compilation, scheduling, and additional settings.
- * If {@link nnfw_apply_tensor} is called to apply input tensor info different with model
- * before this function, tries to resize all tensors.
- *
- * @param[in] session the session to be prepared
- * @return    @c NNFW_STATUS_NO_ERROR if successful, otherwise return @c NNFW_STATUS_ERROR
- */
-NNFW_STATUS nnfw_prepare(nnfw_session *session);
-
-/**
- * @brief     Run inference
- *
- * <p>This function should be called after model is loaded by {@link nnfw_load_model_from_file},
- * session is prepared for inference by {@link nnfw_prepare}, set input and output buffers
- * by {@link nnfw_set_input} and {@link nnfw_set_output}.</p>
- *
- * <p>This function return after inference is finished.</p>
- *
- * @param[in] session The session to run inference
- * @return    @c NNFW_STATUS_NO_ERROR if successful
- */
-NNFW_STATUS nnfw_run(nnfw_session *session);
-
-/**
- * @brief     Set input buffer
- *
- * This function should be called after {@link nnfw_prepare}, and before first inference
- * on session by {@link nnfw_run}. Application can reuse buffer for many inferences.
- *
- * @param[in] session Session to the input is to be set
- * @param[in] index   Index of input to be set (0-indexed)
- * @param[in] type    Type of the input
- * @param[in] buffer  Raw buffer for input
- * @param[in] length  Size of bytes of input buffer
- *
- * @return    @c NNFW_STATUS_NO_ERROR if successful
- */
-NNFW_STATUS nnfw_set_input(nnfw_session *session, uint32_t index, NNFW_TYPE type,
-                           const void *buffer, size_t length);
-
-/**
- * @brief       Set output buffer
- *
- * This function should be called after {@link nnfw_prepare}, and before first inference
- * on session by {@link nnfw_run}. Application can reuse buffer for many inferences.
- *
- * @param[in]   session Session from inference output is to be extracted
- * @param[in]   index   Index of output to be set (0-indexed)
- * @param[in]   type    Type of the output
- * @param[out]  buffer  Raw buffer for output
- * @param[in]   length  Size of bytes of output buffer
- *
- * @return      @c NNFW_STATUS_NO_ERROR if successful
- */
-NNFW_STATUS nnfw_set_output(nnfw_session *session, uint32_t index, NNFW_TYPE type, void *buffer,
-                            size_t length);
-
-/**
- * @brief       Get the number of inputs
- *
- * Application can call this function to get number of inputs defined in loaded model.
- * This function should be called after {@link nnfw_load_model_from_file} is invoked to load model
- *
- * @param[in]   session Session from input information is to be extracted
- * @param[out]  number  Variable which the number of inputs is put into
- *
- * @return      @c NNFW_STATUS_NO_ERROR if successful
- */
-NNFW_STATUS nnfw_input_size(nnfw_session *session, uint32_t *number);
-
-/**
- * @brief       Get the number of outputs
- *
- * Application can call this function to get number of outputs defined in loaded model.
- * This function should be called after {@link nnfw_load_model_from_file} is invoked to load model
- *
- * @param[in]   session Session from output information is to be extracted
- * @param[out]  number  Variable which the number of outputs is put into
- *
- * @return      @c NNFW_STATUS_NO_ERROR if successful
- */
-NNFW_STATUS nnfw_output_size(nnfw_session *session, uint32_t *number);
-
-/**
- * @brief Set the layout of an input
- *
- * The input that does not call this has NNFW_LAYOUT_NHWC layout
- *
- * @param[in] session session from inference input is to be extracted
- * @param[in] index   index of input to be set (0-indexed)
- * @param[in] layout  layout to set to target input
- *
- * @return NNFW_STATUS_NO_ERROR if successful
- */
-NNFW_STATUS nnfw_set_input_layout(nnfw_session *session, uint32_t index, NNFW_LAYOUT layout);
-
-/**
- * @brief Set the layout of an output
- *
- * The output that does not call this has NNFW_LAYOUT_NHWC layout
- *
- * @param[in] session session from inference output is to be extracted
- * @param[in] index   index of output to be set (0-indexed)
- * @param[in] layout  layout to set to target output
- *
- * @return NNFW_STATUS_NO_ERROR if successful
- */
-NNFW_STATUS nnfw_set_output_layout(nnfw_session *session, uint32_t index, NNFW_LAYOUT layout);
-
-/**
- * @brief       Get i-th input tensor info
- *
- * <p>Before {@link nnfw_prepare} is invoked, this function return tensor info in model,
- * so updated tensor info by {@link nnfw_apply_tensorinfo} is not returned.</p>
- *
- * <p>After {@link nnfw_prepare} is invoked, this function return updated tensor info
- * if tensor info is updated by {@link nnfw_apply_tensorinfo}.</p>
- *
- * @param[in]   session     Session from input information is to be extracted
- * @param[in]   index       Index of input
- * @param[out]  tensor_info Tensor info (shape, type, etc)
- *
- * @return      @c NNFW_STATUS_NO_ERROR if successful
- */
-NNFW_STATUS nnfw_input_tensorinfo(nnfw_session *session, uint32_t index,
-                                  nnfw_tensorinfo *tensor_info);
-
-/**
- * @brief     Get i-th output tensor info
- *
- * <p>Before {@link nnfw_prepare} is invoked, this function return tensor info in model,
- * so updated tensor info by {@link nnfw_apply_tensorinfo} is not returned.</p>
- *
- * <p>After {@link nnfw_prepare} is invoked, this function return updated tensor info
- * if tensor info is updated by {@link nnfw_apply_tensorinfo}.</p>
- *
- * @param[in]   session     Session from output information is to be extracted
- * @param[in]   index       Index of output
- * @param[out]  tensor_info Tensor info (shape, type, etc)
- *
- * @return      @c NNFW_STATUS_NO_ERROR if successful
- */
-NNFW_STATUS nnfw_output_tensorinfo(nnfw_session *session, uint32_t index,
-                                   nnfw_tensorinfo *tensor_info);
-
-/**
- * @brief     Set available backends
- *
- * This function should be called before {@link nnfw_prepare} is invoked.
- *
- * <p>Supported backends differs on each platforms.
- * For example, `x86_64` supports "cpu" only.
- * Can set multiple backends by semicolon (ex: "acl_cl;cpu").
- * Among the multiple backends, the 1st element is used as default backend.</p>
- *
- * @note      Possible backend strings are: "cpu", "acl_cl", "acl_neon", "srcn"
- *
- * @param[in] session session to which avilable backends are set
- * @param[in] backends available backends on which nnfw uses
- *
- * @return @c NNFW_STATUS_NO_ERROR if successful
- */
-NNFW_STATUS nnfw_set_available_backends(nnfw_session *session, const char *backends);
-
-/**
- * @brief     Set the operation's backend
- *
- * This function should be called before {@link nnfw_prepare} is invoked.
- *
- * <p>Supported backends differs on each platforms.
- * For example, `x86_64` supports "cpu" only.
- * The backend for op has higher priority than default backend specified by
- * nnfw_set_default_backend.</p>
- *
- * @note      Possible backend strings are: "cpu", "acl_cl", "acl_neon"
- *
- * @param[in] session session to be modified
- * @param[in] op operation to be set
- * @param[in] backend bakcend on which operation run
- *
- * @return @c NNFW_STATUS_NO_ERROR if successful
- */
-NNFW_STATUS nnfw_set_op_backend(nnfw_session *session, const char *op, const char *backend);
-
-#ifdef __cplusplus
-}
-#endif
-
-#endif
diff --git a/runtime/neurun/api/include/nnfw_debug.h b/runtime/neurun/api/include/nnfw_debug.h
deleted file mode 100644
index eefca0d29..000000000
--- a/runtime/neurun/api/include/nnfw_debug.h
+++ /dev/null
@@ -1,24 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NNFW_DEBUG_H__
-#define __NNFW_DEBUG_H__
-
-#include "nnfw.h"
-
-NNFW_STATUS nnfw_create_debug_session(nnfw_session **session);
-
-#endif // __NNFW_DEBUG_H__
diff --git a/runtime/neurun/api/include/nnfw_dev.h b/runtime/neurun/api/include/nnfw_dev.h
deleted file mode 100644
index ecf0597cf..000000000
--- a/runtime/neurun/api/include/nnfw_dev.h
+++ /dev/null
@@ -1,65 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NNFW_DEV_H__
-#define __NNFW_DEV_H__
-
-#include "nnfw.h"
-
-// Used for custom kernel development
-
-/*
- * operand type, used only for custom operations
- */
-typedef struct
-{
-  nnfw_tensorinfo type;
-  void *allocation;
-} nnfw_operand;
-
-/*
- * Used as input to custom operation eval function
- */
-typedef struct
-{
-  size_t ninputs;
-  nnfw_operand *inputs;
-
-  size_t noutputs;
-  nnfw_operand *outputs;
-} nnfw_custom_kernel_params;
-
-/*
- * Custom kernel evaluation function
- *
- * param[in] params custom operation parameters
- * param[in] userdata pointer to user-specified buffer( kernel instance specific )
- */
-typedef void (*nnfw_custom_eval)(nnfw_custom_kernel_params *params, char *userdata,
-                                 size_t userdata_size);
-
-/*
- * custom operation registration info
- */
-typedef struct
-{
-  nnfw_custom_eval eval_function;
-} custom_kernel_registration_info;
-
-NNFW_STATUS nnfw_register_custom_op_info(nnfw_session *session, const char *id,
-                                         custom_kernel_registration_info *info);
-
-#endif // __NNFW_DEV_H__
diff --git a/runtime/neurun/api/src/CustomKernel.cc b/runtime/neurun/api/src/CustomKernel.cc
deleted file mode 100644
index 60ddeedc2..000000000
--- a/runtime/neurun/api/src/CustomKernel.cc
+++ /dev/null
@@ -1,98 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "CustomKernel.h"
-
-namespace neurun
-{
-namespace frontend
-{
-namespace custom
-{
-
-using namespace backend::custom;
-
-class APIConverter
-{
-public:
-  static nnfw_operand convertOperand(void *alloc, const TypeInfo &type)
-  {
-    nnfw_operand api_operand;
-    api_operand.allocation = alloc;
-    api_operand.type = convertType(type);
-    return api_operand;
-  }
-
-  static nnfw_tensorinfo convertType(const TypeInfo &type)
-  {
-    nnfw_tensorinfo api_type;
-    api_type.rank = type.shape.rank();
-    assert(type.shape.rank() <= 6);
-    std::copy(type.shape.dims().begin(), type.shape.dims().end(), std::begin(api_type.dims));
-
-    switch (type.dtype)
-    {
-      case ir::DataType::FLOAT32:
-        api_type.dtype = NNFW_TYPE_TENSOR_FLOAT32;
-        break;
-      case ir::DataType::INT32:
-        api_type.dtype = NNFW_TYPE_TENSOR_INT32;
-        break;
-      case ir::DataType::QUANT8_ASYMM:
-        api_type.dtype = NNFW_TYPE_TENSOR_QUANT8_ASYMM;
-        break;
-      case ir::DataType::BOOL8:
-        api_type.dtype = NNFW_TYPE_TENSOR_BOOL;
-        break;
-      default:
-        throw std::runtime_error("Unsupported tensor datatype");
-    }
-    return api_type;
-  }
-};
-
-Kernel::Kernel(const nnfw_custom_eval evalFunction)
-    : _params(), _userdata(nullptr), _userdata_size(0), _evalFunction(evalFunction)
-{
-}
-
-void Kernel::configure(CustomKernelConfigParams &&inParams)
-{
-  _userdata = inParams.userdata;
-  _userdata_size = inParams.userdata_size;
-
-  _params.ninputs = inParams.input_allocations.size();
-  _params.inputs = new nnfw_operand[_params.ninputs];
-  for (size_t i = 0; i < _params.ninputs; ++i)
-  {
-    _params.inputs[i] =
-        APIConverter::convertOperand(inParams.input_allocations[i], inParams.input_types[i]);
-  }
-
-  _params.noutputs = inParams.output_allocations.size();
-  _params.outputs = new nnfw_operand[_params.noutputs];
-  for (size_t i = 0; i < _params.noutputs; ++i)
-  {
-    _params.outputs[i] =
-        APIConverter::convertOperand(inParams.output_allocations[i], inParams.output_types[i]);
-  }
-}
-
-void Kernel::run() { _evalFunction(&_params, _userdata, _userdata_size); }
-
-} // namespace custom
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/api/src/CustomKernel.h b/runtime/neurun/api/src/CustomKernel.h
deleted file mode 100644
index 8cafc2061..000000000
--- a/runtime/neurun/api/src/CustomKernel.h
+++ /dev/null
@@ -1,59 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_CUSTOM_KERNEL_H__
-#define __NEURUN_BACKEND_CUSTOM_KERNEL_H__
-
-#include "nnfw_dev.h"
-
-#include "backend/CustomKernelBuilder.h"
-
-#include <vector>
-
-namespace neurun
-{
-namespace frontend
-{
-namespace custom
-{
-
-class Kernel : public ::neurun::exec::IFunction
-{
-public:
-  explicit Kernel(nnfw_custom_eval evalFunction);
-
-  nnfw_custom_kernel_params _params;
-  char *_userdata;
-  size_t _userdata_size;
-
-  nnfw_custom_eval _evalFunction;
-  // nnfw_custom_type_infer _type_infer_function; //Unused for now
-
-  /**
-   * Fills _params field used later by user specified eval function
-   * @param inParams custom kernel parameters
-   */
-  virtual void configure(backend::custom::CustomKernelConfigParams &&inParams);
-
-  void run() override;
-  void runSync() override { run(); }
-};
-
-} // namespace custom
-} // namespace frontend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_CUSTOM_KERNEL_H__
diff --git a/runtime/neurun/api/src/CustomKernelRegistry.cc b/runtime/neurun/api/src/CustomKernelRegistry.cc
deleted file mode 100644
index b223682b8..000000000
--- a/runtime/neurun/api/src/CustomKernelRegistry.cc
+++ /dev/null
@@ -1,64 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "CustomKernelRegistry.h"
-
-#include "cpp14/memory.h"
-
-namespace neurun
-{
-namespace frontend
-{
-namespace custom
-{
-
-void KernelRegistry::registerKernel(const std::string &id, nnfw_custom_eval evalFunction)
-{
-  _storage.emplace(id, evalFunction);
-}
-
-std::shared_ptr<backend::custom::IKernelBuilder> KernelRegistry::getBuilder()
-{
-  return nnfw::cpp14::make_unique<KernelBuilder>(this);
-}
-
-std::unique_ptr<Kernel> KernelRegistry::buildKernelForOp(const std::string &id)
-{
-  auto it = _storage.find(id);
-  if (it == _storage.end())
-  {
-    throw std::runtime_error("Unable to find associated kernel for op");
-  }
-
-  return nnfw::cpp14::make_unique<Kernel>(it->second);
-}
-
-// Kernel builder
-std::unique_ptr<exec::IFunction>
-KernelBuilder::buildKernel(const std::string &id,
-                           backend::custom::CustomKernelConfigParams &&params) const
-{
-  auto kernel = _registry->buildKernelForOp(id);
-  kernel->configure(std::move(params));
-
-  return kernel;
-}
-
-KernelBuilder::KernelBuilder(KernelRegistry *registry) : _registry(registry) {}
-
-} // namespace custom
-} // namespace frontend
-} // namespace neurun
diff --git a/runtime/neurun/api/src/CustomKernelRegistry.h b/runtime/neurun/api/src/CustomKernelRegistry.h
deleted file mode 100644
index 207a82a0a..000000000
--- a/runtime/neurun/api/src/CustomKernelRegistry.h
+++ /dev/null
@@ -1,64 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_CUSTOM_KERNEL_REGISTRY_H__
-#define __NEURUN_BACKEND_CUSTOM_KERNEL_REGISTRY_H__
-
-#include "CustomKernel.h"
-
-#include <unordered_map>
-#include <functional>
-#include <memory>
-
-#include <iostream>
-
-namespace neurun
-{
-namespace frontend
-{
-namespace custom
-{
-
-class KernelRegistry
-{
-public:
-  void registerKernel(const std::string &id, nnfw_custom_eval evalFunction);
-
-  std::shared_ptr<backend::custom::IKernelBuilder> getBuilder();
-  std::unique_ptr<Kernel> buildKernelForOp(const std::string &id);
-
-private:
-  std::unordered_map<std::string, nnfw_custom_eval> _storage;
-};
-
-class KernelBuilder : public backend::custom::IKernelBuilder
-{
-public:
-  KernelBuilder(KernelRegistry *registry);
-
-  std::unique_ptr<exec::IFunction>
-  buildKernel(const std::string &id,
-              backend::custom::CustomKernelConfigParams &&params) const override;
-
-private:
-  KernelRegistry *_registry;
-};
-
-} // namespace custom
-} // namespace frontend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_CUSTOM_KERNEL_REGISTRY_H__
diff --git a/runtime/neurun/api/src/OpMap.lst b/runtime/neurun/api/src/OpMap.lst
deleted file mode 100644
index 5e93275b8..000000000
--- a/runtime/neurun/api/src/OpMap.lst
+++ /dev/null
@@ -1,89 +0,0 @@
-/*
- * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef MAP_MACRO
-#error  Define MAP_MACRO before including this file
-#endif
-
-//        circle operation  | neurun internal operation
-MAP_MACRO(ADD               , Add)
-MAP_MACRO(SUB               , Sub)
-MAP_MACRO(BATCH_TO_SPACE_ND , BatchToSpaceND)
-MAP_MACRO(CAST              , Cast)
-MAP_MACRO(CONV_2D           , Conv2D)
-MAP_MACRO(DEPTHWISE_CONV_2D , DepthwiseConv2D)
-MAP_MACRO(AVERAGE_POOL_2D   , AvgPool2D)
-MAP_MACRO(MAX_POOL_2D       , MaxPool2D)
-MAP_MACRO(CONCATENATION     , Concat)
-MAP_MACRO(FULLY_CONNECTED   , FullyConnected)
-MAP_MACRO(SUM               , ReduceSum)
-MAP_MACRO(RESHAPE           , Reshape)
-MAP_MACRO(MUL               , Mul)
-MAP_MACRO(SOFTMAX           , Softmax)
-MAP_MACRO(SQUEEZE           , Squeeze)
-MAP_MACRO(SLICE             , Slice)
-MAP_MACRO(STRIDED_SLICE     , StridedSlice)
-MAP_MACRO(TANH              , Tanh)
-MAP_MACRO(LOGISTIC          , Logistic)
-MAP_MACRO(DIV               , Div)
-MAP_MACRO(TRANSPOSE         , Transpose)
-MAP_MACRO(EXP               , Exp)
-MAP_MACRO(REDUCE_MAX        , ReduceMax)
-// UNMATCHED
-//MAP_MACRO(Comparison)
-MAP_MACRO(LOGICAL_AND       , LogicalAnd)
-MAP_MACRO(LOGICAL_OR        , LogicalOr)
-MAP_MACRO(LOGICAL_NOT       , LogicalNot)
-MAP_MACRO(LSTM              , LSTM)
-MAP_MACRO(RSQRT             , RSQRT)
-MAP_MACRO(RELU              , ReLU)
-MAP_MACRO(RESIZE_BILINEAR   , ResizeBilinear)
-MAP_MACRO(RELU_N1_TO_1      , ReLU1)
-MAP_MACRO(RELU6             , ReLU6)
-MAP_MACRO(RNN , RNN)
-MAP_MACRO(FLOOR , Floor)
-MAP_MACRO(SPACE_TO_BATCH_ND , SpaceToBatchND)
-MAP_MACRO(SPACE_TO_DEPTH  , SpaceToDepth)
-MAP_MACRO(L2_POOL_2D  , L2Pool2D)
-MAP_MACRO(EMBEDDING_LOOKUP  , EmbeddingLookup)
-MAP_MACRO(L2_NORMALIZATION  , L2Normalization)
-MAP_MACRO(HASHTABLE_LOOKUP  , HashtableLookup)
-MAP_MACRO(INSTANCE_NORM , InstanceNorm)
-MAP_MACRO(PRELU , PReLU)
-MAP_MACRO(TRANSPOSE_CONV  , TransposeConv)
-MAP_MACRO(SQRT  , SQRT)
-MAP_MACRO(SQUARED_DIFFERENCE  , SquaredDifference)
-MAP_MACRO(TOPK_V2 , TopKV2)
-MAP_MACRO(GATHER  , Gather)
-MAP_MACRO(NEG , Neg)
-MAP_MACRO(ABS , Abs)
-MAP_MACRO(ARG_MAX , ArgMax)
-MAP_MACRO(DEQUANTIZE  , Dequantize)
-MAP_MACRO(MEAN  , Mean)
-MAP_MACRO(LOCAL_RESPONSE_NORMALIZATION  , LocalResponseNormalization)
-// UNDEFINED IN CIRCLE
-//MAP_MACRO(DepthToSpace)
-MAP_MACRO(PACK  , Pack)
-MAP_MACRO(REDUCE_MIN  , ReduceMin)
-MAP_MACRO(SPLIT , Split)
-MAP_MACRO(UNPACK  , Unpack)
-MAP_MACRO(PAD , Pad)
-MAP_MACRO(CUSTOM  , Custom)
-// UNDEFINED IN CIRCLE
-//MAP_MACRO(Permute)
-MAP_MACRO(MINIMUM , Min)
-MAP_MACRO(MAXIMUM , Max)
-MAP_MACRO(ONE_HOT , OneHot)
diff --git a/runtime/neurun/api/src/nnfw_api.cc b/runtime/neurun/api/src/nnfw_api.cc
deleted file mode 100644
index bdac4c89b..000000000
--- a/runtime/neurun/api/src/nnfw_api.cc
+++ /dev/null
@@ -1,267 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "nnfw_api_internal.h"
-
-/*
- * Create a new session instance
- *
- * @param session the session to be created
- * @return NNFW_STATUS_NO_ERROR if successful
- */
-NNFW_STATUS nnfw_create_session(nnfw_session **session)
-{
-  *session = new nnfw_session();
-
-  return NNFW_STATUS_NO_ERROR;
-}
-
-/*
- * Close a session instance
- *
- * @param session the session to be closed
- * @return NNFW_STATUS_NO_ERROR if successful
- */
-NNFW_STATUS nnfw_close_session(nnfw_session *session)
-{
-  delete session;
-  return NNFW_STATUS_NO_ERROR;
-}
-
-#define NNFW_RETURN_ERROR_IF_NULL(p) \
-  do                                 \
-  {                                  \
-    if ((p) == NULL)                 \
-      return NNFW_STATUS_ERROR;      \
-  } while (0)
-
-/*
- * Load model from nnpackage file or directory
- *
- * @param session nnfw_session loading the given nnpackage file/dir
- * @param package_file_path path to the nnpackage file or unzipped directory to be loaded
- *
- * @return NNFW_STATUS_NO_ERROR if successful
- */
-NNFW_STATUS nnfw_load_model_from_file(nnfw_session *session, const char *pacakge_file_path)
-{
-  NNFW_RETURN_ERROR_IF_NULL(session);
-  return session->load_model_from_file(pacakge_file_path);
-}
-
-/*
- * Prepare session to be ready for inference
- * This phase may finalize model compilation, scheduling, and additional settings.
- *
- * @param session the session to be prepared
- * @return NNFW_STATUS_NO_ERROR if successful
- */
-NNFW_STATUS nnfw_prepare(nnfw_session *session)
-{
-  NNFW_RETURN_ERROR_IF_NULL(session);
-  return session->prepare();
-}
-
-/*
- * Run inference
- *
- * @param session the session to run inference
- * @return NNFW_STATUS_NO_ERROR if successful
- */
-NNFW_STATUS nnfw_run(nnfw_session *session)
-{
-  NNFW_RETURN_ERROR_IF_NULL(session);
-  return session->run();
-}
-
-/*
- * Set input
- *
- * @param session session to the input is to be set
- * @param index index of input to be set (0-indexed)
- * @param type type of the input
- * @param buffer raw buffer for input
- * @param length size of bytes of output
- *
- * @return NNFW_STATUS_NO_ERROR if successful
- */
-
-NNFW_STATUS nnfw_set_input(nnfw_session *session, uint32_t index, NNFW_TYPE type,
-                           const void *buffer, size_t length)
-{
-  NNFW_RETURN_ERROR_IF_NULL(session);
-  return session->set_input(index, type, buffer, length);
-}
-
-/*
- * Set output
- *
- * @param session session from inference output is to be extracted
- * @param index index of output to be set (0-indexed)
- * @param type type of the output
- * @param buffer raw buffer for output
- * @param length size of bytes of output
- *
- * @return NNFW_STATUS_NO_ERROR if successful
- */
-
-NNFW_STATUS nnfw_set_output(nnfw_session *session, uint32_t index, NNFW_TYPE type, void *buffer,
-                            size_t length)
-{
-  NNFW_RETURN_ERROR_IF_NULL(session);
-  return session->set_output(index, type, buffer, length);
-}
-
-/*
- * Get the number of inputs
- *
- * @param[in] session session from input information is to be extracted
- * @param[out] number variable which the number of inputs is put into
- *
- * @return NNFW_STATUS_NO_ERROR if successful
- */
-
-NNFW_STATUS nnfw_input_size(nnfw_session *session, uint32_t *number)
-{
-  NNFW_RETURN_ERROR_IF_NULL(session);
-  return session->input_size(number);
-}
-
-/*
- * Get the number of outputs
- *
- * @param[in] session session from output information is to be extracted
- * @param[out] number variable which the number of outputs is put into
- *
- * @return NNFW_STATUS_NO_ERROR if successful
- */
-NNFW_STATUS nnfw_output_size(nnfw_session *session, uint32_t *number)
-{
-  NNFW_RETURN_ERROR_IF_NULL(session);
-  return session->output_size(number);
-}
-
-/*
- * Set the layout of an input
- * @note The input that does not call this has NNFW_LAYOUT_CHANNELS_LAST layout
- *
- * @param[in] session session from inference input is to be extracted
- * @param[in] index   index of input to be set (0-indexed)
- * @param[in] layout  layout to set to target input
- *
- * @return NNFW_STATUS_NO_ERROR if successful
- */
-NNFW_STATUS nnfw_set_input_layout(nnfw_session *session, uint32_t index, NNFW_LAYOUT layout)
-{
-  NNFW_RETURN_ERROR_IF_NULL(session);
-  return session->set_input_layout(index, layout);
-}
-
-/*
- * Set the layout of an output
- * @note The output that does not call this has NNFW_LAYOUT_CHANNELS_LAST layout
- *
- * @param[in] session session from inference output is to be extracted
- * @param[in] index   index of output to be set (0-indexed)
- * @param[in] layout  layout to set to target output
- *
- * @return NNFW_STATUS_NO_ERROR if successful
- */
-NNFW_STATUS nnfw_set_output_layout(nnfw_session *session, uint32_t index, NNFW_LAYOUT layout)
-{
-  NNFW_RETURN_ERROR_IF_NULL(session);
-  return session->set_output_layout(index, layout);
-}
-
-/*
- * Get i-th input tensor info
- *
- * @param[in] session session from input information is to be extracted
- * @param[in] index index of input
- * @param[out] tensor_info nnfw_tensor_info
- *
- * @return NNFW_STATUS_NO_ERROR if successful
- */
-NNFW_STATUS nnfw_input_tensorinfo(nnfw_session *session, uint32_t index,
-                                  nnfw_tensorinfo *tensor_info)
-{
-  NNFW_RETURN_ERROR_IF_NULL(session);
-  return session->input_tensorinfo(index, tensor_info);
-}
-
-/*
- * Get i-th output tensor info
- *
- * @param[in] session session from output information is to be extracted
- * @param[in] index index of output
- * @param[out] tensor_info nnfw_tensor_info
- *
- * @return NNFW_STATUS_NO_ERROR if successful
- */
-NNFW_STATUS nnfw_output_tensorinfo(nnfw_session *session, uint32_t index,
-                                   nnfw_tensorinfo *tensor_info)
-{
-  NNFW_RETURN_ERROR_IF_NULL(session);
-  return session->output_tensorinfo(index, tensor_info);
-}
-
-/*
- * Register custom operation
- * @param session session to register this operation
- * @param id operation id
- * @param info registration info ( eval function, etc. )
- * @return NNFW_STATUS_NO_ERROR if successful
- */
-NNFW_STATUS nnfw_register_custom_op_info(nnfw_session *session, const char *id,
-                                         custom_kernel_registration_info *info)
-{
-  NNFW_RETURN_ERROR_IF_NULL(session);
-  return session->register_custom_operation(id, info->eval_function);
-}
-
-NNFW_STATUS nnfw_apply_tensorinfo(nnfw_session *session, uint32_t index,
-                                  nnfw_tensorinfo tensor_info)
-{
-  NNFW_RETURN_ERROR_IF_NULL(session);
-  return session->apply_tensorinfo(index, tensor_info);
-}
-
-/*
- * Set available backends
- *
- * @param[in] session session to which a avilable backends are set
- * @param[in] backends available backends on which nnfw uses
- */
-NNFW_STATUS nnfw_set_available_backends(nnfw_session *session, const char *backends)
-{
-  NNFW_RETURN_ERROR_IF_NULL(session);
-  return session->set_available_backends(backends);
-}
-
-/*
- * Set the operation's backend
- *
- * @param[in] session session to be modified
- * @param[in] op operation to be set
- * @param[in] backend bakcend on which operation run
- *
- * @return NNFW_STATUS_NO_ERROR if successful
- */
-NNFW_STATUS nnfw_set_op_backend(nnfw_session *session, const char *op, const char *backend)
-{
-  NNFW_RETURN_ERROR_IF_NULL(session);
-  return session->set_op_backend(op, backend);
-}
diff --git a/runtime/neurun/api/src/nnfw_api_internal.cc b/runtime/neurun/api/src/nnfw_api_internal.cc
deleted file mode 100644
index 037cd3bca..000000000
--- a/runtime/neurun/api/src/nnfw_api_internal.cc
+++ /dev/null
@@ -1,435 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "nnfw_api_internal.h"
-#include "CustomKernelRegistry.h"
-#include "compiler/Compiler.h"
-#include "exec/Execution.h"
-#include "circle_loader.h"
-#include "tflite_loader.h"
-#include "json/json.h"
-#include <fstream>
-#include <iostream>
-#include <string>
-#include <dirent.h>
-#include <util/ConfigSource.h>
-
-/*
- * API does not accept string argument longer than max length below
- */
-#define MAX_BACKEND_NAME_LENGTH 32
-#define MAX_OP_NAME_LENGTH 64
-
-// Is null-terminating in length ?
-static bool null_terminating(const char *str, uint32_t length)
-{
-  for (uint32_t i = 0; i < length; i++)
-  {
-    if (*(str + i) == '\0')
-    {
-      return true;
-    }
-  }
-  return false;
-}
-
-static neurun::ir::Layout convertLayout(NNFW_LAYOUT layout)
-{
-  if (layout == NNFW_LAYOUT_CHANNELS_LAST)
-  {
-    return neurun::ir::Layout::NHWC;
-  }
-  else if (layout == NNFW_LAYOUT_CHANNELS_FIRST)
-  {
-    return neurun::ir::Layout::NCHW;
-  }
-  return neurun::ir::Layout::UNKNOWN;
-}
-
-nnfw_session::nnfw_session()
-    : _graph{nullptr}, _execution{nullptr},
-      _kernel_registry{std::make_shared<neurun::frontend::custom::KernelRegistry>()},
-      _source{nnfw::cpp14::make_unique<neurun::util::GeneralConfigSource>()}
-{
-  // DO NOTHING
-}
-
-NNFW_STATUS nnfw_session::load_model_from_file(const char *package_dir)
-{
-  // TODO : add support for zipped package file load
-  DIR *dir;
-  if (!(dir = opendir(package_dir)))
-  {
-    std::cerr << "invalid nnpackge directory: " << package_dir << std::endl;
-    return NNFW_STATUS_ERROR;
-  }
-  closedir(dir);
-
-  try
-  {
-    std::string manifest_file_name(package_dir);
-    manifest_file_name += "/metadata/MANIFEST";
-    std::ifstream mfs(manifest_file_name);
-
-    // extract the filename of the first(index 0) model
-    // e.g. In MANIFEST file, { "models" : [ "firstmodel.tflite", "2nd.tflite" ] }
-    Json::Value root;
-    mfs >> root;
-    Json::Value models = root["models"];
-    Json::Value model_types = root["model-types"];
-
-    auto model_file_path = package_dir + std::string("/") + models[0].asString(); // first model
-    auto model_type = model_types[0].asString(); // first model's type
-    if (model_type == "tflite")
-    {
-      _graph = neurun::tflite_loader::loadModel(model_file_path.c_str());
-    }
-    else if (model_type == "circle")
-    {
-      _graph = neurun::circle_loader::loadModel(model_file_path.c_str());
-    }
-    else
-    {
-      std::cerr << "Unsupported model type in MANIFEST" << std::endl;
-      return NNFW_STATUS_ERROR;
-    }
-    _graph->bindKernelBuilder(_kernel_registry->getBuilder());
-  }
-  catch (const std::exception &e)
-  {
-    std::cerr << "Error during model loading : " << e.what() << std::endl;
-    return NNFW_STATUS_ERROR;
-  }
-  return NNFW_STATUS_NO_ERROR;
-}
-
-NNFW_STATUS nnfw_session::prepare()
-{
-  // TODO : add additional setting routine(executor type, backend)
-  // Note that we assume acl_cl backend
-
-  try
-  {
-    // config_source setting
-    using neurun::util::config_source;
-    config_source(std::move(_source));
-
-    auto compiler = nnfw::cpp14::make_unique<neurun::compiler::Compiler>(_graph);
-    compiler->compile();
-    std::shared_ptr<neurun::exec::IExecutor> executor;
-    compiler->release(executor);
-    _execution = std::make_shared<neurun::exec::Execution>(executor);
-  }
-  catch (const std::exception &e)
-  {
-    std::cerr << "Error during model prepare : " << e.what() << std::endl;
-    return NNFW_STATUS_ERROR;
-  }
-  return NNFW_STATUS_NO_ERROR;
-}
-
-NNFW_STATUS nnfw_session::run()
-{
-  try
-  {
-    _execution->execute();
-  }
-  catch (const std::exception &e)
-  {
-    std::cerr << "Error during nnfw_session::run : " << e.what() << std::endl;
-    return NNFW_STATUS_ERROR;
-  }
-  return NNFW_STATUS_NO_ERROR;
-}
-
-NNFW_STATUS nnfw_session::set_input(uint32_t index, NNFW_TYPE /*type*/, const void *buffer,
-                                    size_t length)
-{
-  try
-  {
-    _execution->setInput(neurun::ir::IOIndex(index), buffer, length);
-  }
-  catch (const std::exception &e)
-  {
-    std::cerr << "Error during nnfw_session::set_input : " << e.what() << std::endl;
-    return NNFW_STATUS_ERROR;
-  }
-  return NNFW_STATUS_NO_ERROR;
-}
-
-NNFW_STATUS nnfw_session::set_output(uint32_t index, NNFW_TYPE /*type*/, void *buffer,
-                                     size_t length)
-{
-  try
-  {
-    _execution->setOutput(neurun::ir::IOIndex(index), buffer, length);
-  }
-  catch (const std::exception &e)
-  {
-    std::cerr << "Error during nnfw_session::set_output : " << e.what() << std::endl;
-    return NNFW_STATUS_ERROR;
-  }
-  return NNFW_STATUS_NO_ERROR;
-}
-
-NNFW_STATUS nnfw_session::input_size(uint32_t *number)
-{
-  try
-  {
-    if (number == nullptr)
-    {
-      std::cerr << "Error during nnfw_session::input_size, number is null pointer." << std::endl;
-      return NNFW_STATUS_ERROR;
-    }
-    *number = _graph->getInputs().size();
-  }
-  catch (const std::exception &e)
-  {
-    std::cerr << "Error during nnfw_session::input_size : " << e.what() << std::endl;
-    return NNFW_STATUS_ERROR;
-  }
-  return NNFW_STATUS_NO_ERROR;
-}
-
-NNFW_STATUS nnfw_session::output_size(uint32_t *number)
-{
-  try
-  {
-    if (number == nullptr)
-    {
-      std::cerr << "Error during nnfw_session::output_size, number is null pointer." << std::endl;
-      return NNFW_STATUS_ERROR;
-    }
-    *number = _graph->getOutputs().size();
-  }
-  catch (const std::exception &e)
-  {
-    std::cerr << "Error during nnfw_session::output_size" << e.what() << std::endl;
-    return NNFW_STATUS_ERROR;
-  }
-  return NNFW_STATUS_NO_ERROR;
-}
-
-NNFW_STATUS nnfw_session::set_input_layout(uint32_t index, NNFW_LAYOUT layout)
-{
-  try
-  {
-    if (layout != NNFW_LAYOUT_NONE && layout != NNFW_LAYOUT_CHANNELS_FIRST &&
-        layout != NNFW_LAYOUT_CHANNELS_LAST)
-    {
-      std::cerr << "Error during nnfw_session::set_input_layout, not supported layout" << std::endl;
-      return NNFW_STATUS_ERROR;
-    }
-    _execution->setInputLayout(neurun::ir::IOIndex(index), convertLayout(layout));
-  }
-  catch (const std::exception &e)
-  {
-    std::cerr << "Error during nnfw_session::set_input_layout : " << e.what() << std::endl;
-    return NNFW_STATUS_ERROR;
-  }
-  return NNFW_STATUS_NO_ERROR;
-}
-
-NNFW_STATUS nnfw_session::set_output_layout(uint32_t index, NNFW_LAYOUT layout)
-{
-  try
-  {
-    if (layout != NNFW_LAYOUT_NONE && layout != NNFW_LAYOUT_CHANNELS_FIRST &&
-        layout != NNFW_LAYOUT_CHANNELS_LAST)
-    {
-      std::cerr << "Error during nnfw_session::set_output_layout, not supported layout"
-                << std::endl;
-      return NNFW_STATUS_ERROR;
-    }
-    _execution->setOutputLayout(neurun::ir::IOIndex(index), convertLayout(layout));
-  }
-  catch (const std::exception &e)
-  {
-    std::cerr << "Error during nnfw_session::set_output_layout : " << e.what() << std::endl;
-    return NNFW_STATUS_ERROR;
-  }
-  return NNFW_STATUS_NO_ERROR;
-}
-
-static NNFW_TYPE datatype_to_nnfw_dtype(neurun::ir::DataType dt)
-{
-  using neurun::ir::DataType;
-  switch (dt)
-  {
-    case DataType::FLOAT32:
-      return NNFW_TYPE_TENSOR_FLOAT32;
-    case DataType::INT32:
-      return NNFW_TYPE_TENSOR_INT32;
-    case DataType::QUANT8_ASYMM:
-      return NNFW_TYPE_TENSOR_QUANT8_ASYMM;
-    case DataType::BOOL8:
-      return NNFW_TYPE_TENSOR_BOOL;
-    case DataType::UINT8:
-      return NNFW_TYPE_TENSOR_UINT8;
-    case DataType::UINT32:
-    case DataType::QUANT8_SYMM:
-    default:
-      std::cerr << "Error: Model has type that runtime API does not support." << std::endl;
-      exit(-1);
-  }
-}
-
-NNFW_STATUS nnfw_session::apply_tensorinfo(uint32_t /*index*/, nnfw_tensorinfo /*ti*/)
-{
-  std::cerr << "Error: NYI" << std::endl;
-  return NNFW_STATUS_ERROR;
-}
-
-NNFW_STATUS nnfw_session::input_tensorinfo(uint32_t index, nnfw_tensorinfo *ti)
-{
-  try
-  {
-    if (ti == nullptr)
-    {
-      std::cerr << "Error during nnfw_session::input_tensorinfo, tensorinfo is null pointer."
-                << std::endl;
-      return NNFW_STATUS_ERROR;
-    }
-    if (index >= _graph->getInputs().size())
-    {
-      std::cerr << "Error during nnfw_session::input_tensorinfo, index is out of range."
-                << std::endl;
-      return NNFW_STATUS_ERROR;
-    }
-    auto opidx = _graph->getInputs().at(index);
-    auto shape = _graph->operands().at(opidx).shape();
-    ti->rank = shape.rank();
-    for (int j = 0; j < ti->rank; ++j)
-    {
-      ti->dims[j] = shape.dim(j);
-    }
-    ti->dtype = datatype_to_nnfw_dtype(_graph->operands().at(opidx).typeInfo().type());
-  }
-  catch (const std::exception &e)
-  {
-    std::cerr << "Error during nnfw_session::input_tensorinfo : " << e.what() << std::endl;
-    return NNFW_STATUS_ERROR;
-  }
-  return NNFW_STATUS_NO_ERROR;
-}
-
-NNFW_STATUS nnfw_session::output_tensorinfo(uint32_t index, nnfw_tensorinfo *ti)
-{
-  try
-  {
-    if (ti == nullptr)
-    {
-      std::cerr << "Error during nnfw_session::output_tensorinfo, tensorinfo is null pointer."
-                << std::endl;
-      return NNFW_STATUS_ERROR;
-    }
-    if (index >= _graph->getOutputs().size())
-    {
-      std::cerr << "Error during nnfw_session::output_tensorinfo, index is out of range."
-                << std::endl;
-      return NNFW_STATUS_ERROR;
-    }
-    auto opidx = _graph->getOutputs().at(index);
-    auto shape = _graph->operands().at(opidx).shape();
-    ti->rank = shape.rank();
-    for (int j = 0; j < ti->rank; ++j)
-    {
-      ti->dims[j] = shape.dim(j);
-    }
-    ti->dtype = datatype_to_nnfw_dtype(_graph->operands().at(opidx).typeInfo().type());
-  }
-  catch (const std::exception &e)
-  {
-    std::cerr << "Error during nnfw_session::output_tensorinfo : " << e.what() << std::endl;
-    return NNFW_STATUS_ERROR;
-  }
-  return NNFW_STATUS_NO_ERROR;
-}
-NNFW_STATUS nnfw_session::register_custom_operation(const std::string &id,
-                                                    nnfw_custom_eval eval_func)
-{
-  _kernel_registry->registerKernel(id, eval_func);
-  return NNFW_STATUS_NO_ERROR;
-}
-
-static std::string get_op_backend_string(std::string op)
-{
-#define MAP_MACRO(CircleName, NeurunName) {#CircleName, "OP_BACKEND_" #NeurunName},
-
-  static std::unordered_map<std::string, std::string> operation_map = {
-#include "OpMap.lst"
-  };
-
-#undef MAP_MACRO
-
-  auto n = operation_map.find(op);
-
-  if (n == operation_map.end())
-  {
-    // this return value is handled by a caller to return error code
-    return std::string("");
-  }
-  else
-  {
-    return n->second;
-  }
-}
-
-NNFW_STATUS nnfw_session::set_available_backends(const char *backends)
-{
-  try
-  {
-    if (!backends || null_terminating(backends, MAX_BACKEND_NAME_LENGTH) == false)
-    {
-      return NNFW_STATUS_ERROR;
-    }
-
-    _source->set("BACKENDS", backends);
-  }
-  catch (const std::exception &e)
-  {
-    std::cerr << "Error during nnfw_session::set_available_backends : " << e.what() << std::endl;
-    return NNFW_STATUS_ERROR;
-  }
-  return NNFW_STATUS_NO_ERROR;
-}
-
-NNFW_STATUS nnfw_session::set_op_backend(const char *op, const char *backend)
-{
-  try
-  {
-    if (!op || !null_terminating(op, MAX_OP_NAME_LENGTH) || !backend ||
-        !null_terminating(backend, MAX_BACKEND_NAME_LENGTH))
-    {
-      return NNFW_STATUS_ERROR;
-    }
-
-    auto key = get_op_backend_string(op);
-
-    if (key.empty())
-    {
-      return NNFW_STATUS_ERROR;
-    }
-
-    _source->set(key, backend);
-  }
-  catch (const std::exception &e)
-  {
-    std::cerr << "Error during nnfw_session::set_op_backend : " << e.what() << std::endl;
-    return NNFW_STATUS_ERROR;
-  }
-  return NNFW_STATUS_NO_ERROR;
-}
diff --git a/runtime/neurun/api/src/nnfw_api_internal.h b/runtime/neurun/api/src/nnfw_api_internal.h
deleted file mode 100644
index 40069cc55..000000000
--- a/runtime/neurun/api/src/nnfw_api_internal.h
+++ /dev/null
@@ -1,84 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __API_NNFW_API_INTERNAL_H__
-#define __API_NNFW_API_INTERNAL_H__
-
-#include "nnfw.h"
-#include "nnfw_dev.h"
-
-#include <util/GeneralConfigSource.h>
-
-#include <string>
-#include <memory>
-
-namespace neurun
-{
-namespace frontend
-{
-namespace custom
-{
-class KernelRegistry;
-}
-} // namespace frontend
-namespace exec
-{
-class Execution;
-}
-namespace ir
-{
-class Graph;
-} // namespace ir
-} // namespace neurun
-
-struct nnfw_session
-{
-public:
-  nnfw_session();
-
-  NNFW_STATUS load_model_from_file(const char *package_file_path);
-  NNFW_STATUS prepare();
-  NNFW_STATUS run();
-
-  NNFW_STATUS set_input(uint32_t index, NNFW_TYPE type, const void *buffer, size_t length);
-  NNFW_STATUS set_output(uint32_t index, NNFW_TYPE type, void *buffer, size_t length);
-
-  NNFW_STATUS input_size(uint32_t *number);
-  NNFW_STATUS output_size(uint32_t *number);
-
-  NNFW_STATUS set_input_layout(uint32_t index, NNFW_LAYOUT layout);
-  NNFW_STATUS set_output_layout(uint32_t index, NNFW_LAYOUT layout);
-
-  NNFW_STATUS apply_tensorinfo(uint32_t index, nnfw_tensorinfo ti);
-
-  NNFW_STATUS input_tensorinfo(uint32_t index, nnfw_tensorinfo *ti);
-  NNFW_STATUS output_tensorinfo(uint32_t index, nnfw_tensorinfo *ti);
-
-  NNFW_STATUS register_custom_operation(const std::string &id, nnfw_custom_eval eval_func);
-
-  NNFW_STATUS set_available_backends(const char *backends);
-  NNFW_STATUS set_op_backend(const char *op, const char *backend);
-
-private:
-  std::shared_ptr<neurun::ir::Graph> _graph;
-  std::shared_ptr<neurun::exec::Execution> _execution;
-  std::shared_ptr<neurun::frontend::custom::KernelRegistry> _kernel_registry;
-
-protected:
-  std::unique_ptr<neurun::util::GeneralConfigSource> _source;
-};
-
-#endif // __API_NNFW_API_INTERNAL_H__
diff --git a/runtime/neurun/api/src/nnfw_debug.cc b/runtime/neurun/api/src/nnfw_debug.cc
deleted file mode 100644
index 4ea0a203f..000000000
--- a/runtime/neurun/api/src/nnfw_debug.cc
+++ /dev/null
@@ -1,24 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "nnfw_debug_internal.h"
-
-NNFW_STATUS nnfw_create_debug_session(nnfw_session **session)
-{
-  *session = new nnfw_debug_session();
-
-  return NNFW_STATUS_NO_ERROR;
-}
diff --git a/runtime/neurun/api/src/nnfw_debug_internal.cc b/runtime/neurun/api/src/nnfw_debug_internal.cc
deleted file mode 100644
index 778efbc5c..000000000
--- a/runtime/neurun/api/src/nnfw_debug_internal.cc
+++ /dev/null
@@ -1,25 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "nnfw_debug_internal.h"
-#include "util/EnvConfigSource.h"
-
-#include <cpp14/memory.h>
-
-nnfw_debug_session::nnfw_debug_session() : nnfw_session()
-{
-  _source = nnfw::cpp14::make_unique<neurun::util::EnvConfigSource>();
-}
diff --git a/runtime/neurun/api/src/nnfw_debug_internal.h b/runtime/neurun/api/src/nnfw_debug_internal.h
deleted file mode 100644
index f4984e7a1..000000000
--- a/runtime/neurun/api/src/nnfw_debug_internal.h
+++ /dev/null
@@ -1,29 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __API_NNFW_DEBUG_INTERNAL_H__
-#define __API_NNFW_DEBUG_INTERNAL_H__
-
-#include "nnfw_debug.h"
-#include "nnfw_api_internal.h"
-
-class nnfw_debug_session : public nnfw_session
-{
-public:
-  nnfw_debug_session();
-};
-
-#endif // __API_NNFW_DEBUG_INTERNAL_H__
diff --git a/runtime/neurun/backend/CMakeLists.txt b/runtime/neurun/backend/CMakeLists.txt
deleted file mode 100644
index fc363e031..000000000
--- a/runtime/neurun/backend/CMakeLists.txt
+++ /dev/null
@@ -1,10 +0,0 @@
-set(LIB_NEURUN_BACKEND_ACL_COMMON neurun_backend_acl_common)
-set(LIB_NEURUN_BACKEND_CPU_COMMON neurun_backend_cpu_common)
-
-add_subdirectory(cpu)
-add_subdirectory(cpu_common)
-add_subdirectory(acl_cl)
-add_subdirectory(acl_neon)
-add_subdirectory(acl_common)
-add_subdirectory(hi_perf_cpu)
-add_subdirectory(srcn)
diff --git a/runtime/neurun/backend/acl_cl/Backend.h b/runtime/neurun/backend/acl_cl/Backend.h
deleted file mode 100644
index 2033b42e7..000000000
--- a/runtime/neurun/backend/acl_cl/Backend.h
+++ /dev/null
@@ -1,65 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_CL_BACKEND_H__
-#define __NEURUN_BACKEND_ACL_CL_BACKEND_H__
-
-#include <memory>
-#include <backend/Backend.h>
-#include <ir/Operands.h>
-
-#include "Config.h"
-#include "ConstantInitializer.h"
-#include "KernelGenerator.h"
-#include "ShapeFixer.h"
-#include "TensorManager.h"
-#include "TensorRegister.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_cl
-{
-
-class Backend : public ::neurun::backend::Backend
-{
-public:
-  Backend() : _config{std::make_shared<Config>()} {}
-
-  std::shared_ptr<IConfig> config() const override { return _config; }
-
-  std::unique_ptr<BackendContext>
-  newContext(const ir::Operands &operands,
-             const std::shared_ptr<custom::IKernelBuilder> &) const override
-  {
-    auto tensor_builder = std::make_shared<TensorBuilder>(createTensorManager());
-    return std::unique_ptr<BackendContext>{new BackendContext{
-        this, tensor_builder, std::make_shared<ConstantInitializer>(operands, tensor_builder),
-        std::make_shared<KernelGenerator>(operands, tensor_builder),
-        std::make_shared<ShapeFixer>(operands, tensor_builder),
-        std::make_shared<TensorRegister>(operands, tensor_builder)}};
-  }
-
-private:
-  std::shared_ptr<IConfig> _config;
-};
-
-} // namespace acl_cl
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ACL_CL_BACKEND_H__
diff --git a/runtime/neurun/backend/acl_cl/CLTimer.h b/runtime/neurun/backend/acl_cl/CLTimer.h
deleted file mode 100644
index 3939ee722..000000000
--- a/runtime/neurun/backend/acl_cl/CLTimer.h
+++ /dev/null
@@ -1,108 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_CL_CLTIMER_H__
-#define __NEURUN_BACKEND_ACL_CL_CLTIMER_H__
-
-#include <util/ITimer.h>
-#include <arm_compute/core/CL/OpenCL.h>
-#include <arm_compute/runtime/CL/CLScheduler.h>
-#include <chrono>
-#include <list>
-#include <sstream>
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_cl
-{
-
-/**
- * @brief Class to measure CL kernels execution time
- */
-class CLTimer : public util::ITimer
-{
-public:
-  /**
-   * @brief This function replaces CL function, which enqueues a command to execute a kernel
-   *        with a wrapper which remembers enqueued kernels
-   */
-  void handleBegin() override
-  {
-    _measured_events.clear();
-
-    _origin_enqueue_function = arm_compute::CLSymbols::get().clEnqueueNDRangeKernel_ptr;
-
-    auto _timer_enqueue_function = [this](cl_command_queue command_queue, cl_kernel kernel,
-                                          cl_uint work_dim, const size_t *gwo, const size_t *gws,
-                                          const size_t *lws, cl_uint num_events_in_wait_list,
-                                          const cl_event *event_wait_list, cl_event *usr_event) {
-      cl_event event;
-      cl_int enqueue_res =
-          this->_origin_enqueue_function(command_queue, kernel, work_dim, gwo, gws, lws,
-                                         num_events_in_wait_list, event_wait_list, &event);
-      this->_measured_events.emplace_back(event);
-
-      // According to spec, if NULL was provided in usr_event - event shouldn't be returned
-      if (usr_event != nullptr)
-      {
-        clRetainEvent(event);
-        *usr_event = event;
-      }
-      return enqueue_res;
-    };
-    arm_compute::CLSymbols::get().clEnqueueNDRangeKernel_ptr = _timer_enqueue_function;
-
-    // Set CL_QUEUE_PROFILING_ENABLE flag for the CL command-queue, if it isn't already set
-    auto &cl_scheduler = arm_compute::CLScheduler::get();
-    auto props = cl_scheduler.queue().getInfo<CL_QUEUE_PROPERTIES>();
-    if ((props & CL_QUEUE_PROFILING_ENABLE) == 0)
-    {
-      cl_scheduler.set_queue(
-          cl::CommandQueue(cl_scheduler.context(), props | CL_QUEUE_PROFILING_ENABLE));
-    }
-  };
-
-  /**
-   * @brief Get timer result by addition executed CL kernels durations
-   */
-  void handleEnd() override
-  {
-    _timer_res = 0;
-    for (auto const &event : _measured_events)
-    {
-      cl_ulong start;
-      cl_ulong end;
-      event.getProfilingInfo(CL_PROFILING_COMMAND_START, &start);
-      event.getProfilingInfo(CL_PROFILING_COMMAND_END, &end);
-      _timer_res += (end - start) / 1000.f; // nanoseconds -> microseconds
-    }
-
-    // Restore origin CL enqueue function
-    arm_compute::CLSymbols::get().clEnqueueNDRangeKernel_ptr = _origin_enqueue_function;
-  };
-
-private:
-  std::function<decltype(clEnqueueNDRangeKernel)> _origin_enqueue_function;
-  std::list<::cl::Event> _measured_events;
-};
-
-} // namespace acl_cl
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ACL_CL_CLTIMER_H__
diff --git a/runtime/neurun/backend/acl_cl/CMakeLists.txt b/runtime/neurun/backend/acl_cl/CMakeLists.txt
deleted file mode 100644
index aaf6a4d62..000000000
--- a/runtime/neurun/backend/acl_cl/CMakeLists.txt
+++ /dev/null
@@ -1,21 +0,0 @@
-# Unsupported architecture
-nnas_find_package(ARMCompute QUIET)
-if(NOT ARMCompute_FOUND)
-  return()
-endif(NOT ARMCompute_FOUND)
-
-set(LIB_NEURUN_BACKEND_ACL_CL neurun_backend_acl_cl)
-
-file(GLOB_RECURSE SOURCES "*.cc")
-
-add_library(${LIB_NEURUN_BACKEND_ACL_CL} SHARED ${SOURCES})
-
-target_include_directories(${LIB_NEURUN_BACKEND_ACL_CL} PRIVATE ${CMAKE_CURRENT_SOURCE_DIR})
-target_link_libraries(${LIB_NEURUN_BACKEND_ACL_CL} PRIVATE neurun_core)
-target_link_libraries(${LIB_NEURUN_BACKEND_ACL_CL} PRIVATE ${LIB_NEURUN_BACKEND_ACL_COMMON})
-target_link_libraries(${LIB_NEURUN_BACKEND_ACL_CL} PRIVATE nnfw_common)
-target_link_libraries(${LIB_NEURUN_BACKEND_ACL_CL} PRIVATE nnfw_coverage)
-
-set_target_properties(${LIB_NEURUN_BACKEND_ACL_CL} PROPERTIES OUTPUT_NAME backend_acl_cl)
-
-install(TARGETS ${LIB_NEURUN_BACKEND_ACL_CL} DESTINATION lib)
diff --git a/runtime/neurun/backend/acl_cl/Config.cc b/runtime/neurun/backend/acl_cl/Config.cc
deleted file mode 100644
index 36bf83686..000000000
--- a/runtime/neurun/backend/acl_cl/Config.cc
+++ /dev/null
@@ -1,50 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-// For CLKernelLibraryEx initialization
-#include "arm_compute/core/CL/CLHelpers.h"
-#include "arm_compute/core/CL/CLKernelLibrary.h"
-#include "arm_compute/core/CL/CLKernelLibraryEx.h"
-
-#include <arm_compute/runtime/CL/CLScheduler.h>
-
-#include "Config.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_cl
-{
-
-bool Config::initialize()
-{
-  if (!arm_compute::opencl_is_available())
-  {
-    return false;
-  }
-  arm_compute::CLScheduler::get().default_init();
-  // NOTE CLKernelLibraryEx must use the same context as CLScheduler
-  // It did not check whether another device is available.
-  arm_compute::CLKernelLibraryEx::get().init(
-      "./cl_kernels/", arm_compute::CLScheduler::get().context(), cl::Device::getDefault());
-
-  return true;
-}
-
-} // namespace acl_cl
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/acl_cl/Config.h b/runtime/neurun/backend/acl_cl/Config.h
deleted file mode 100644
index a7ceaac26..000000000
--- a/runtime/neurun/backend/acl_cl/Config.h
+++ /dev/null
@@ -1,45 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_CL_CONFIG_H__
-#define __NEURUN_BACKEND_ACL_CL_CONFIG_H__
-
-#include "CLTimer.h"
-#include <cpp14/memory.h>
-#include <backend/IConfig.h>
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_cl
-{
-
-class Config : public IConfig
-{
-public:
-  std::string id() override { return "acl_cl"; }
-  bool initialize() override;
-  bool SupportPermutation() override { return true; }
-  bool SupportSubTensorAlloc() override { return true; }
-  std::unique_ptr<util::ITimer> timer() override { return nnfw::cpp14::make_unique<CLTimer>(); }
-};
-
-} // namespace acl_cl
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ACL_CL_CONFIG_H__
diff --git a/runtime/neurun/backend/acl_cl/ConstantInitializer.cc b/runtime/neurun/backend/acl_cl/ConstantInitializer.cc
deleted file mode 100644
index 165b17cd1..000000000
--- a/runtime/neurun/backend/acl_cl/ConstantInitializer.cc
+++ /dev/null
@@ -1,266 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ConstantInitializer.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_cl
-{
-
-ConstantInitializer::ConstantInitializer(const ir::Operands &operands,
-                                         const std::shared_ptr<TensorBuilder> &tensor_builder)
-    : _operands{operands}, _tensor_builder{tensor_builder}
-{
-  // DO NOTHING
-}
-
-void ConstantInitializer::visit(const ir::operation::BatchToSpaceND &node)
-{
-  const auto &block_size_index = node.getInputs().at(ir::operation::BatchToSpaceND::BLOCK_SIZE);
-  const auto &block_size_obj = _operands.at(block_size_index);
-
-  if (block_size_obj.isConstant())
-  {
-    _init_map[block_size_index] = [](const ir::Operand &model_obj, backend::operand::ITensor &obj) {
-      const auto &shape = model_obj.shape();
-      const auto base = reinterpret_cast<const int32_t *>(model_obj.data().base());
-      assert(model_obj.shape().rank() == 1);
-      obj.access([&](::neurun::backend::operand::ITensor &tensor) {
-        for (size_t i = 0; i < shape.num_elements(); ++i)
-        {
-          const int32_t value = base[shape.num_elements() - i - 1];
-          int32_t *into = reinterpret_cast<int32_t *>(tensor.buffer() +
-                                                      tensor.calcOffset({static_cast<int32_t>(i)}));
-          *into = value;
-        }
-      });
-    };
-  }
-}
-
-void ConstantInitializer::visit(const ir::operation::Conv2D &node)
-{
-  const auto &kernel_index = node.getInputs().at(ir::operation::Conv2D::KERNEL);
-  const auto &kernel_obj = _operands.at(kernel_index);
-  registerPermuteInitializer(kernel_index, kernel_obj);
-
-  const auto &bias_index = node.getInputs().at(ir::operation::Conv2D::BIAS);
-  const auto &bias_obj = _operands.at(bias_index);
-  registerCopyInitializer(bias_index, bias_obj);
-}
-
-void ConstantInitializer::visit(const ir::operation::DepthwiseConv2D &node)
-{
-  const auto &kernel_index = node.getInputs().at(ir::operation::DepthwiseConv2D::KERNEL);
-  const auto &kernel_obj = _operands.at(kernel_index);
-  registerPermuteInitializer(kernel_index, kernel_obj);
-
-  const auto &bias_index = node.getInputs().at(ir::operation::DepthwiseConv2D::BIAS);
-  const auto &bias_obj = _operands.at(bias_index);
-  registerCopyInitializer(bias_index, bias_obj);
-}
-
-void ConstantInitializer::visit(const ir::operation::EmbeddingLookup &node)
-{
-  const auto &lookups_index = node.getInputs().at(ir::operation::EmbeddingLookup::LOOKUPS);
-  const auto &lookups_obj = _operands.at(lookups_index);
-  registerCopyInitializer(lookups_index, lookups_obj);
-}
-
-void ConstantInitializer::visit(const ir::operation::FullyConnected &node)
-{
-  const auto &weight_index = node.getInputs().at(ir::operation::FullyConnected::WEIGHT);
-  const auto &weight_obj = _operands.at(weight_index);
-  registerCopyInitializer(weight_index, weight_obj);
-
-  const auto &bias_index = node.getInputs().at(ir::operation::FullyConnected::BIAS);
-  const auto &bias_obj = _operands.at(bias_index);
-  registerCopyInitializer(bias_index, bias_obj);
-}
-
-void ConstantInitializer::visit(const ir::operation::Gather &node)
-{
-  const auto &indices_index = node.getInputs().at(ir::operation::Gather::INDICES);
-  const auto &indices_obj = _operands.at(indices_index);
-  registerCopyInitializer(indices_index, indices_obj);
-}
-
-void ConstantInitializer::visit(const ir::operation::HashtableLookup &node)
-{
-  const auto &lookups_index = node.getInputs().at(ir::operation::HashtableLookup::LOOKUPS);
-  const auto &lookups_obj = _operands.at(lookups_index);
-  registerCopyInitializer(lookups_index, lookups_obj);
-
-  const auto &keys_index = node.getInputs().at(ir::operation::HashtableLookup::KEYS);
-  const auto &keys_obj = _operands.at(keys_index);
-  registerCopyInitializer(keys_index, keys_obj);
-}
-
-void ConstantInitializer::visit(const ir::operation::LSTM &node)
-{
-  const auto &input_to_input_weights_index =
-      node.getInputs().at(ir::operation::LSTM::INPUT_TO_INPUT_WEIGHTS);
-  const auto &input_to_input_weights_obj = _operands.at(input_to_input_weights_index);
-  registerCopyInitializer(input_to_input_weights_index, input_to_input_weights_obj);
-
-  const auto &input_to_forget_weights_index =
-      node.getInputs().at(ir::operation::LSTM::INPUT_TO_FORGET_WEIGHTS);
-  const auto &input_to_forget_weights_obj = _operands.at(input_to_forget_weights_index);
-  registerCopyInitializer(input_to_forget_weights_index, input_to_forget_weights_obj);
-
-  const auto &input_to_cell_weights_index =
-      node.getInputs().at(ir::operation::LSTM::INPUT_TO_CELL_WEIGHTS);
-  const auto &input_to_cell_weights_obj = _operands.at(input_to_cell_weights_index);
-  registerCopyInitializer(input_to_cell_weights_index, input_to_cell_weights_obj);
-
-  const auto &input_to_output_weights_index =
-      node.getInputs().at(ir::operation::LSTM::INPUT_TO_OUTPUT_WEIGHTS);
-  const auto &input_to_output_weights_obj = _operands.at(input_to_output_weights_index);
-  registerCopyInitializer(input_to_output_weights_index, input_to_output_weights_obj);
-
-  const auto &recurrent_to_input_weights_index =
-      node.getInputs().at(ir::operation::LSTM::RECURRENT_TO_INPUT_WEIGHTS);
-  const auto &recurrent_to_input_weights_obj = _operands.at(recurrent_to_input_weights_index);
-  registerCopyInitializer(recurrent_to_input_weights_index, recurrent_to_input_weights_obj);
-
-  const auto &recurrent_to_forget_weights_index =
-      node.getInputs().at(ir::operation::LSTM::RECURRENT_TO_FORGET_WEIGHTS);
-  const auto &recurrent_to_forget_weights_obj = _operands.at(recurrent_to_forget_weights_index);
-  registerCopyInitializer(recurrent_to_forget_weights_index, recurrent_to_forget_weights_obj);
-
-  const auto &recurrent_to_cell_weights_index =
-      node.getInputs().at(ir::operation::LSTM::RECURRENT_TO_CELL_WEIGHTS);
-  const auto &recurrent_to_cell_weights_obj = _operands.at(recurrent_to_cell_weights_index);
-  registerCopyInitializer(recurrent_to_cell_weights_index, recurrent_to_cell_weights_obj);
-
-  const auto &recurrent_to_output_weights_index =
-      node.getInputs().at(ir::operation::LSTM::RECURRENT_TO_OUTPUT_WEIGHTS);
-  const auto &recurrent_to_output_weights_obj = _operands.at(recurrent_to_output_weights_index);
-  registerCopyInitializer(recurrent_to_output_weights_index, recurrent_to_output_weights_obj);
-
-  const auto &cell_to_input_weights_index =
-      node.getInputs().at(ir::operation::LSTM::CELL_TO_INPUT_WEIGHTS);
-  const auto &cell_to_input_weights_obj = _operands.at(cell_to_input_weights_index);
-  registerCopyInitializer(cell_to_input_weights_index, cell_to_input_weights_obj);
-
-  const auto &cell_to_forget_weights_index =
-      node.getInputs().at(ir::operation::LSTM::CELL_TO_FORGET_WEIGHTS);
-  const auto &cell_to_forget_weights_obj = _operands.at(cell_to_forget_weights_index);
-  registerCopyInitializer(cell_to_forget_weights_index, cell_to_forget_weights_obj);
-
-  const auto &cell_to_output_weights_index =
-      node.getInputs().at(ir::operation::LSTM::CELL_TO_OUTPUT_WEIGHTS);
-  const auto &cell_to_output_weights_obj = _operands.at(cell_to_output_weights_index);
-  registerCopyInitializer(cell_to_output_weights_index, cell_to_output_weights_obj);
-
-  const auto &input_gate_bias_index = node.getInputs().at(ir::operation::LSTM::INPUT_GATE_BIAS);
-  const auto &input_gate_bias_obj = _operands.at(input_gate_bias_index);
-  registerCopyInitializer(input_gate_bias_index, input_gate_bias_obj);
-
-  const auto &forget_gate_bias_index = node.getInputs().at(ir::operation::LSTM::FORGET_GATE_BIAS);
-  const auto &forget_gate_bias_obj = _operands.at(forget_gate_bias_index);
-  registerCopyInitializer(forget_gate_bias_index, forget_gate_bias_obj);
-
-  const auto &output_gate_bias_index = node.getInputs().at(ir::operation::LSTM::OUTPUT_GATE_BIAS);
-  const auto &output_gate_bias_obj = _operands.at(output_gate_bias_index);
-  registerCopyInitializer(output_gate_bias_index, output_gate_bias_obj);
-
-  const auto &projection_weights_index =
-      node.getInputs().at(ir::operation::LSTM::PROJECTION_WEIGHTS);
-  const auto &projection_weights_obj = _operands.at(projection_weights_index);
-  registerCopyInitializer(projection_weights_index, projection_weights_obj);
-
-  const auto &projection_bias_index = node.getInputs().at(ir::operation::LSTM::PROJECTION_BIAS);
-  const auto &projection_bias_obj = _operands.at(projection_bias_index);
-  registerCopyInitializer(projection_bias_index, projection_bias_obj);
-}
-
-void ConstantInitializer::visit(const ir::operation::RNN &node)
-{
-  const auto &weights_index = node.getInputs().at(ir::operation::RNN::WEIGHTS);
-  const auto &weights_obj = _operands.at(weights_index);
-  registerCopyInitializer(weights_index, weights_obj);
-
-  const auto &recurrent_weights_index = node.getInputs().at(ir::operation::RNN::RECURRENT_WEIGHTS);
-  const auto &recurrent_weights_obj = _operands.at(recurrent_weights_index);
-  registerCopyInitializer(recurrent_weights_index, recurrent_weights_obj);
-
-  const auto &bias_index = node.getInputs().at(ir::operation::RNN::BIAS);
-  const auto &bias_obj = _operands.at(bias_index);
-  registerCopyInitializer(bias_index, bias_obj);
-}
-
-void ConstantInitializer::visit(const ir::operation::SpaceToBatchND &node)
-{
-  const auto &block_size_index = node.getInputs().at(ir::operation::SpaceToBatchND::BLOCK_SIZE);
-  const auto &block_size_obj = _operands.at(block_size_index);
-
-  if (block_size_obj.isConstant())
-  {
-    _init_map[block_size_index] = [](const ir::Operand &model_obj, backend::operand::ITensor &obj) {
-      const auto &shape = model_obj.shape();
-      const auto base = reinterpret_cast<const int32_t *>(model_obj.data().base());
-      assert(model_obj.shape().rank() == 1);
-      obj.access([&](::neurun::backend::operand::ITensor &tensor) {
-        for (size_t i = 0; i < shape.num_elements(); ++i)
-        {
-          const int32_t value = base[shape.num_elements() - i - 1];
-          int32_t *into = reinterpret_cast<int32_t *>(tensor.buffer() +
-                                                      tensor.calcOffset({static_cast<int32_t>(i)}));
-          *into = value;
-        }
-      });
-    };
-  }
-
-  const auto &paddings_index = node.getInputs().at(ir::operation::SpaceToBatchND::PADDINGS);
-  const auto &paddings_obj = _operands.at(paddings_index);
-  if (paddings_obj.isConstant())
-  {
-    _init_map[paddings_index] = [](const ir::Operand &model_obj, backend::operand::ITensor &obj) {
-      const auto &shape = model_obj.shape();
-      const auto base = reinterpret_cast<const int32_t *>(model_obj.data().base());
-      assert(model_obj.shape().rank() == 2);
-      assert(obj.dimension(0) == 2);
-      obj.access([&](::neurun::backend::operand::ITensor &tensor) {
-        for (auto i = 0; i < shape.dim(0); ++i)
-        {
-          for (auto j = 0; j < shape.dim(1); ++j)
-          {
-            const int32_t value = base[i * 2 + j];
-            int32_t *into = reinterpret_cast<int32_t *>(
-                tensor.buffer() + tensor.calcOffset({shape.dim(0) - i - 1, j}));
-            *into = value;
-          }
-        }
-      });
-    };
-  }
-}
-
-void ConstantInitializer::visit(const ir::operation::TransposeConv &node)
-{
-  const auto &kernel_index = node.getInputs().at(ir::operation::TransposeConv::KERNEL);
-  const auto &kernel_obj = _operands.at(kernel_index);
-  registerPermuteInitializer(kernel_index, kernel_obj);
-}
-
-} // namespace acl_cl
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/acl_cl/ConstantInitializer.h b/runtime/neurun/backend/acl_cl/ConstantInitializer.h
deleted file mode 100644
index 5965d2046..000000000
--- a/runtime/neurun/backend/acl_cl/ConstantInitializer.h
+++ /dev/null
@@ -1,63 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_COMPILER_ACL_CL_CONSTANT_INITIALIZER_H__
-#define __NEURUN_COMPILER_ACL_CL_CONSTANT_INITIALIZER_H__
-
-#include <backend/IConstantInitializer.h>
-#include <ir/Operands.h>
-#include "TensorBuilder.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_cl
-{
-
-class ConstantInitializer : public IConstantInitializer
-{
-public:
-  ConstantInitializer(const ir::Operands &operands,
-                      const std::shared_ptr<TensorBuilder> &tensor_builder);
-
-public:
-  void visit(const ir::operation::BatchToSpaceND &) override;
-  void visit(const ir::operation::Conv2D &) override;
-  void visit(const ir::operation::DepthwiseConv2D &) override;
-  void visit(const ir::operation::EmbeddingLookup &) override;
-  void visit(const ir::operation::FullyConnected &) override;
-  void visit(const ir::operation::Gather &) override;
-  void visit(const ir::operation::HashtableLookup &) override;
-  void visit(const ir::operation::LSTM &) override;
-  void visit(const ir::operation::RNN &) override;
-  void visit(const ir::operation::SpaceToBatchND &) override;
-  void visit(const ir::operation::TransposeConv &) override;
-
-private:
-  const ir::Operands &operands() const override { return _operands; }
-  std::shared_ptr<ITensorBuilder> tensor_builder() const override { return _tensor_builder; }
-
-private:
-  const ir::Operands &_operands;
-  std::shared_ptr<TensorBuilder> _tensor_builder;
-};
-
-} // namespace acl_cl
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_COMPILER_ACL_CL_CONSTANT_INITIALIZER_H__
diff --git a/runtime/neurun/backend/acl_cl/KernelGenerator.cc b/runtime/neurun/backend/acl_cl/KernelGenerator.cc
deleted file mode 100644
index bffb60b61..000000000
--- a/runtime/neurun/backend/acl_cl/KernelGenerator.cc
+++ /dev/null
@@ -1,2151 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "KernelGenerator.h"
-
-#include <arm_compute/runtime/CL/CLFunctions.h>   // Include all ARM Compute CL functions
-#include <arm_compute/runtime/CL/CLFunctionsEx.h> // Include all ARM Compute EX CL functions
-
-#include <AclFunction.h>
-#include <Convert.h>
-#include <Swizzle.h>
-
-#include "ir/Index.h"
-#include "ir/DataType.h"
-#include "ir/InternalType.h"
-#include "compiler/IExecutionBuilder.h"
-#include "exec/NopFunction.h"
-#include "util/logging.h"
-#include "util/Utils.h"
-#include "util/Padding.h"
-
-using ::neurun::compiler::IExecutionBuilder;
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_cl
-{
-
-using ::neurun::backend::acl_common::asAclFunction;
-
-//
-// ActivationBuilder
-//
-class ActivationBuilder
-{
-public:
-  explicit ActivationBuilder(IExecutionBuilder &builder) : _builder(builder)
-  {
-    // DO NOTHING
-  }
-
-private:
-  void appendReLU(::arm_compute::ICLTensor *ifm_alloc);
-  void appendReLU1(::arm_compute::ICLTensor *ifm_alloc);
-  void appendReLU6(::arm_compute::ICLTensor *ifm_alloc);
-
-public:
-  void append(ir::Activation code, ::arm_compute::ICLTensor *ifm_alloc);
-
-private:
-  IExecutionBuilder &_builder;
-};
-
-void ActivationBuilder::appendReLU(::arm_compute::ICLTensor *ifm_alloc)
-{
-  const ::arm_compute::ActivationLayerInfo act_info{
-      ::arm_compute::ActivationLayerInfo::ActivationFunction::RELU};
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLActivationLayer>();
-
-  fn->configure(ifm_alloc, nullptr, act_info);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _builder.append(std::move(acl_fn));
-}
-
-void ActivationBuilder::appendReLU1(::arm_compute::ICLTensor *ifm_alloc)
-{
-  const ::arm_compute::ActivationLayerInfo act_info{
-      ::arm_compute::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 1.0f, -1.0f};
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLActivationLayer>();
-
-  fn->configure(ifm_alloc, nullptr, act_info);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _builder.append(std::move(acl_fn));
-}
-
-void ActivationBuilder::appendReLU6(::arm_compute::ICLTensor *ifm_alloc)
-{
-  const ::arm_compute::ActivationLayerInfo act_info{
-      ::arm_compute::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.0f, 0.0f};
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLActivationLayer>();
-
-  fn->configure(ifm_alloc, nullptr, act_info);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _builder.append(std::move(acl_fn));
-}
-
-void ActivationBuilder::append(ir::Activation code, ::arm_compute::ICLTensor *ifm_alloc)
-{
-  switch (code)
-  {
-    case ir::Activation::NONE:
-    {
-      // DO NOTHING
-      break;
-    }
-    case ir::Activation::RELU:
-    {
-      appendReLU(ifm_alloc);
-      break;
-    }
-    case ir::Activation::RELU1:
-    {
-      appendReLU1(ifm_alloc);
-      break;
-    }
-    case ir::Activation::RELU6:
-    {
-      appendReLU6(ifm_alloc);
-      break;
-    }
-    default:
-    {
-      throw std::runtime_error("Not supported, yet");
-    }
-  }
-}
-
-//
-// KernelGenerator
-//
-KernelGenerator::KernelGenerator(const ir::Operands &ctx,
-                                 const std::shared_ptr<TensorBuilder> &tensor_builder)
-    : _ctx(ctx), _tensor_builder(tensor_builder), _current_subg_layout(ir::Layout::UNKNOWN)
-{
-  // DO NOTHING
-}
-
-void KernelGenerator::visit(const ir::OpSequence &op_seq)
-{
-  _current_subg_layout = op_seq.getLayout();
-  for (const auto &e : op_seq.operations())
-  {
-    const auto &node = *(e.node);
-    _tensor_builder->preVisit(node);
-    node.accept(*this);
-    _tensor_builder->postVisit(node);
-  }
-}
-
-void KernelGenerator::visit(const ir::operation::BatchToSpaceND &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::BatchToSpaceND::Input::INPUT)};
-  const auto block_size_index{
-      node.getInputs().at(ir::operation::BatchToSpaceND::Input::BLOCK_SIZE)};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-  auto block_size_alloc = _tensor_builder->at(block_size_index).get();
-
-  assert(_ctx.at(block_size_index).isConstant());
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLBatchToSpaceLayer>();
-
-  fn->configure(ifm_alloc->handle(), block_size_alloc->handle(), ofm_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Cast &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::Cast::Input::INPUT)};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-  const auto input_sub_type = _ctx.at(ifm_index).typeInfo().type() == ir::DataType::BOOL8
-                                  ? arm_compute::SubDataType::BOOL
-                                  : arm_compute::SubDataType::NONE;
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLCast>();
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle(), input_sub_type);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Conv2D &node)
-{
-  using ir::operation::Conv2D;
-
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(Conv2D::Input::INPUT)};
-  const auto ker_index{node.getInputs().at(Conv2D::Input::KERNEL)};
-  const auto bias_index{node.getInputs().at(Conv2D::Input::BIAS)};
-
-  const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(_current_subg_layout);
-  const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(_current_subg_layout);
-  // Kernel format is [depth_out, kernel_height, kernel_width, depth_in].
-  const auto &ker_shape = _ctx.at(ker_index).shape();
-  const auto ker_height = ker_shape.dim(1);
-  const auto ker_width = ker_shape.dim(2);
-
-  const auto stride = node.param().stride;
-  const auto padding = neurun::util::calculatePadding(node.param().padding, ifm_shape, ofm_shape,
-                                                      stride, ker_width, ker_height);
-  const auto activation = node.param().activation;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-  auto ker_alloc = _tensor_builder->at(ker_index).get();
-  auto bias_alloc = _tensor_builder->at(bias_index).get();
-
-  const auto conv_info = acl_common::asPadStrideInfo(padding, stride);
-  const auto act_info = acl_common::asActivationLayerInfo(activation);
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLConvolutionLayer>(
-      _tensor_builder->acl_tensor_manager()->internal_buffer_manager());
-
-  fn->configure(ifm_alloc->handle(), ker_alloc->handle(), bias_alloc->handle(), ofm_alloc->handle(),
-                conv_info, ::arm_compute::WeightsInfo(), ::arm_compute::Size2D(1U, 1U), act_info);
-
-  _execution_builder->append(asAclFunction(std::move(fn)));
-}
-
-void KernelGenerator::visit(const ir::operation::DepthwiseConv2D &node)
-{
-  using ir::operation::DepthwiseConv2D;
-
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(DepthwiseConv2D::Input::INPUT)};
-  const auto ker_index{node.getInputs().at(DepthwiseConv2D::Input::KERNEL)};
-  const auto bias_index{node.getInputs().at(DepthwiseConv2D::Input::BIAS)};
-
-  const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(_current_subg_layout);
-  const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(_current_subg_layout);
-  // Kernel format is [1, kernel_height, kernel_width, depth_out].
-  const auto &ker_shape = _ctx.at(ker_index).shape();
-  const auto ker_height = ker_shape.dim(1);
-  const auto ker_width = ker_shape.dim(2);
-
-  const auto stride = node.param().stride;
-  const auto padding = neurun::util::calculatePadding(node.param().padding, ifm_shape, ofm_shape,
-                                                      stride, ker_width, ker_height);
-  const auto multiplier = node.param().multiplier;
-  const auto activation = node.param().activation;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-  auto ker_alloc = _tensor_builder->at(ker_index).get();
-  auto bias_alloc = _tensor_builder->at(bias_index).get();
-
-  const auto conv_info = acl_common::asPadStrideInfo(padding, stride);
-  const auto act_info = acl_common::asActivationLayerInfo(activation);
-
-  if (ker_height == 3 && ker_width == 3)
-  {
-    auto fn = nnfw::cpp14::make_unique<::arm_compute::CLDepthwiseConvolutionLayer3x3>(
-        _tensor_builder->acl_tensor_manager()->internal_buffer_manager());
-
-    fn->configure(ifm_alloc->handle(), ker_alloc->handle(), bias_alloc->handle(),
-                  ofm_alloc->handle(), conv_info, multiplier, act_info);
-
-    _execution_builder->append(asAclFunction(std::move(fn)));
-  }
-  else
-  {
-    auto fn = nnfw::cpp14::make_unique<::arm_compute::CLDepthwiseConvolutionLayer>();
-
-    fn->configure(ifm_alloc->handle(), ker_alloc->handle(), bias_alloc->handle(),
-                  ofm_alloc->handle(), conv_info, multiplier, act_info);
-
-    _execution_builder->append(asAclFunction(std::move(fn)));
-  }
-}
-
-void KernelGenerator::visit(const ir::operation::MaxPool2D &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::MaxPool2D::Input::INPUT)};
-
-  const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(_current_subg_layout);
-  const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(_current_subg_layout);
-
-  const auto kh = node.param().kh;
-  const auto kw = node.param().kw;
-  const auto stride = node.param().stride;
-  const auto padding =
-      neurun::util::calculatePadding(node.param().padding, ifm_shape, ofm_shape, stride, kw, kh);
-  const auto activation = node.param().activation;
-
-  VERBOSE(MaxPool2D) << "IFM_H: " << ifm_shape.H << std::endl;
-  VERBOSE(MaxPool2D) << "IFM_W: " << ifm_shape.W << std::endl;
-  VERBOSE(MaxPool2D) << "OFM_H: " << ofm_shape.H << std::endl;
-  VERBOSE(MaxPool2D) << "OFM_W: " << ofm_shape.W << std::endl;
-  VERBOSE(MaxPool2D) << "KER_H: " << kh << std::endl;
-  VERBOSE(MaxPool2D) << "KER_W: " << kw << std::endl;
-  VERBOSE(MaxPool2D) << "STRIDE_H: " << stride.vertical << std::endl;
-  VERBOSE(MaxPool2D) << "STRIDE_W: " << stride.horizontal << std::endl;
-  VERBOSE(MaxPool2D) << "PAD(T): " << padding.top << std::endl;
-  VERBOSE(MaxPool2D) << "PAD(B): " << padding.bottom << std::endl;
-  VERBOSE(MaxPool2D) << "PAD(L): " << padding.left << std::endl;
-  VERBOSE(MaxPool2D) << "PAD(R): " << padding.right << std::endl;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-
-  ::arm_compute::PoolingLayerInfo info{::arm_compute::PoolingType::MAX,
-                                       ::arm_compute::Size2D{kw, kh},
-                                       acl_common::asPadStrideInfo(padding, stride)};
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLPoolingLayer>();
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle(), info);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append((std::move(acl_fn)));
-
-  ActivationBuilder{*_execution_builder}.append(activation, ofm_alloc->handle());
-}
-
-void KernelGenerator::visit(const ir::operation::AvgPool2D &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::AvgPool2D::Input::INPUT)};
-
-  const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(_current_subg_layout);
-  const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(_current_subg_layout);
-
-  const auto kh = node.param().kh;
-  const auto kw = node.param().kw;
-  const auto stride = node.param().stride;
-  const auto padding =
-      neurun::util::calculatePadding(node.param().padding, ifm_shape, ofm_shape, stride, kw, kh);
-  const auto activation = node.param().activation;
-
-  VERBOSE(AvgPool2D) << "IFM_H: " << ifm_shape.H << std::endl;
-  VERBOSE(AvgPool2D) << "IFM_W: " << ifm_shape.W << std::endl;
-  VERBOSE(AvgPool2D) << "OFM_H: " << ofm_shape.H << std::endl;
-  VERBOSE(AvgPool2D) << "OFM_W: " << ofm_shape.W << std::endl;
-  VERBOSE(AvgPool2D) << "KER_H: " << kh << std::endl;
-  VERBOSE(AvgPool2D) << "KER_W: " << kw << std::endl;
-  VERBOSE(AvgPool2D) << "STRIDE_H: " << stride.vertical << std::endl;
-  VERBOSE(AvgPool2D) << "STRIDE_W: " << stride.horizontal << std::endl;
-  VERBOSE(AvgPool2D) << "PAD(T): " << padding.top << std::endl;
-  VERBOSE(AvgPool2D) << "PAD(B): " << padding.bottom << std::endl;
-  VERBOSE(AvgPool2D) << "PAD(L): " << padding.left << std::endl;
-  VERBOSE(AvgPool2D) << "PAD(R): " << padding.right << std::endl;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-
-  ::arm_compute::PoolingLayerInfo info{
-      ::arm_compute::PoolingType::AVG, ::arm_compute::Size2D{kw, kh},
-      acl_common::asPadStrideInfo(padding, stride), true /* exclude_padding */};
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLPoolingLayer>();
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle(), info);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append((std::move(acl_fn)));
-
-  ActivationBuilder{*_execution_builder}.append(activation, ofm_alloc->handle());
-}
-
-void KernelGenerator::visit(const ir::operation::Concat &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-
-  std::vector<ir::OperandIndex> input_indexes;
-
-  for (const auto &input : node.getInputs())
-    input_indexes.emplace_back(input);
-
-  const auto axis = node.param().axis;
-
-  // If tensor allocator allocate as subtensor
-  bool canEliminate = true;
-  for (auto &ifm_ind : input_indexes)
-  {
-    if (!_tensor_builder->isSubTensorOf(ofm_index, ifm_ind))
-    {
-      canEliminate = false;
-      break;
-    }
-  }
-  if (canEliminate)
-  {
-    // If concat eliminated, return a NOP IFunction
-    _execution_builder->append(nnfw::cpp14::make_unique<exec::NopFunction>());
-    return;
-  }
-
-  auto output_alloc = _tensor_builder->at(ofm_index).get();
-  std::vector<::arm_compute::ICLTensor *> input_tensors;
-  for (auto &ifm_ind : input_indexes)
-    input_tensors.emplace_back(_tensor_builder->at(ifm_ind)->handle());
-
-  std::unique_ptr<::arm_compute::IFunction> fn;
-  if (input_indexes.size() < 2)
-  {
-    auto l = nnfw::cpp14::make_unique<::arm_compute::CLCopy>();
-    l->configure(input_tensors.at(0), output_alloc->handle());
-    fn = std::move(l);
-  }
-  else
-  {
-    auto l = nnfw::cpp14::make_unique<::arm_compute::CLConcatenateLayer>();
-    const auto rank = node.param().rank;
-    const auto frontend_layout = _current_subg_layout;
-    const auto backend_layout = output_alloc->layout();
-    const auto fixed_axis =
-        acl_common::ToARMComputeAxis(rank, axis, frontend_layout, backend_layout).value();
-    l->configure(input_tensors, output_alloc->handle(), fixed_axis);
-    fn = std::move(l);
-  }
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::FullyConnected &node)
-{
-  using ir::operation::FullyConnected;
-
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(FullyConnected::Input::INPUT)};
-  const auto weight_index{node.getInputs().at(FullyConnected::Input::WEIGHT)};
-  const auto bias_index{node.getInputs().at(FullyConnected::Input::BIAS)};
-
-  const auto input_rank = _ctx.at(input_index).shape().rank();
-  // TODO Currently we are not handling where the case is that the input's rank is 3.
-  // The handling should be added in the future.
-  assert(input_rank != 3);
-
-  const auto output_size =
-      _ctx.at(output_index).shape().dim(_ctx.at(output_index).shape().rank() - 1);
-  UNUSED_RELEASE(output_size);
-  assert(_ctx.at(bias_index).shape().dim(0) == output_size);
-  assert(_ctx.at(weight_index).shape().dim(0) == output_size);
-  const auto batch_size =
-      _ctx.at(output_index).shape().dim(_ctx.at(output_index).shape().rank() - 2);
-  const auto input_size =
-      _ctx.at(weight_index).shape().dim(_ctx.at(weight_index).shape().rank() - 1);
-
-  // Check for reshaping input's shape into rank-2
-  bool needs_reshape = false;
-  ir::Shape reshape(2);
-  if (input_rank == 4)
-  {
-    const auto feature_size = _ctx.at(input_index).shape().num_elements();
-
-    UNUSED_RELEASE(feature_size);
-    assert(batch_size >= 0 && input_size >= 0);
-    assert(feature_size == static_cast<uint64_t>(batch_size) * static_cast<uint64_t>(input_size));
-
-    // for reshaping
-    needs_reshape = true;
-    reshape.dim(0) = batch_size; /* H */
-    reshape.dim(1) = input_size; /* W */
-  }
-
-  const auto activation = node.param().activation;
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  const auto input_alloc = _tensor_builder->at(input_index).get();
-  const auto weight_alloc = _tensor_builder->at(weight_index).get();
-  const auto bias_alloc = _tensor_builder->at(bias_index).get();
-  const auto frontend_layout = _current_subg_layout;
-  const auto acl_layout = output_alloc->handle()->info()->data_layout();
-
-  auto fn = nnfw::cpp14::make_unique<arm_compute::CLFullyConnectedReshapingLayer>(
-      _tensor_builder->acl_tensor_manager()->internal_buffer_manager());
-
-  fn->configure(
-      input_alloc->handle(), weight_alloc->handle(), bias_alloc->handle(), output_alloc->handle(),
-      needs_reshape,
-      ::neurun::backend::acl_common::asTensorShape(
-          reshape, frontend_layout, ::neurun::backend::acl_common::asRuntimeLayout(acl_layout)));
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-
-  ActivationBuilder{*_execution_builder}.append(activation, output_alloc->handle());
-}
-
-void KernelGenerator::visit(const ir::operation::Mul &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto lhs_index{node.getInputs().at(ir::operation::Mul::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::Mul::Input::RHS)};
-
-  const auto activation = node.param().activation;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto lhs_alloc = _tensor_builder->at(lhs_index).get();
-  auto rhs_alloc = _tensor_builder->at(rhs_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLPixelWiseMultiplication>();
-
-  fn->configure(lhs_alloc->handle(), rhs_alloc->handle(), ofm_alloc->handle(), 1.0, // scale
-                arm_compute::ConvertPolicy::SATURATE, arm_compute::RoundingPolicy::TO_NEAREST_EVEN);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-
-  ActivationBuilder{*_execution_builder}.append(activation, ofm_alloc->handle());
-}
-
-void KernelGenerator::visit(const ir::operation::ReduceSum &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::ReduceSum::Input::INPUT)};
-  const auto &axes{node.param().axes};
-  const auto keep_dims{node.param().keep_dims};
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input_alloc = _tensor_builder->at(input_index).get();
-  const auto frontend_layout = _current_subg_layout;
-  const auto backend_layout = input_alloc->layout();
-
-  // Convert to ACL axes taking into account negative values and possible duplicates.
-  std::set<std::uint32_t> acl_axes;
-  const int input_rank = node.param().rank;
-  for (int axis : axes)
-  {
-    if (axis < 0)
-      axis += input_rank;
-    acl_axes.insert(
-        acl_common::ToARMComputeAxis(input_rank, axis, frontend_layout, backend_layout).value());
-  }
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLReduceOperation>(
-      _tensor_builder->acl_tensor_manager()->internal_buffer_manager());
-
-  fn->configure(input_alloc->handle(), output_alloc->handle(), acl_axes, keep_dims,
-                ::arm_compute::ReduceOperation::SUM);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Reshape &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::Reshape::Input::INPUT)};
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input_alloc = _tensor_builder->at(input_index).get();
-
-  // NOTE This operation must not be changed the layout from frontend to backend
-  //      So, PermutationOperationPass makes layouts of frontend and backend the same.
-  const auto frontend_layout = _current_subg_layout;
-  const auto backend_layout = output_alloc->layout();
-  assert((_ctx.at(input_index).shape().rank() < 4 && _ctx.at(output_index).shape().rank() < 4) ||
-         frontend_layout == backend_layout);
-  UNUSED_RELEASE(frontend_layout);
-  UNUSED_RELEASE(backend_layout);
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLReshapeLayer>();
-
-  fn->configure(input_alloc->handle(), output_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Squeeze &node)
-{
-  // Squeeze is identical to reshape except that it has an optional dimensions input.
-  // In addition, optional dims_index is ignored since output tensor already has squeezed shape
-  // by freezer and toco
-  // TODO Support multi-layout for frontend and backend
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::Squeeze::Input::INPUT)};
-  const auto dims{node.param().dims};
-  const auto ndim{node.param().ndim};
-  (void)dims;
-  (void)ndim;
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input_alloc = _tensor_builder->at(input_index).get();
-  auto fn = nnfw::cpp14::make_unique<arm_compute::CLReshapeLayer>();
-  fn->configure(input_alloc->handle(), output_alloc->handle());
-  auto acl_fn = asAclFunction(std::move(fn));
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Tanh &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::Tanh::Input::INPUT)};
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input_alloc = _tensor_builder->at(input_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<arm_compute::CLActivationLayer>();
-
-  const ::arm_compute::ActivationLayerInfo act_info{
-      ::arm_compute::ActivationLayerInfo::ActivationFunction::TANH, 1.0f, 1.0f};
-
-  fn->configure(input_alloc->handle(), output_alloc->handle(), act_info);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Softmax &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::Softmax::Input::INPUT)};
-
-  const auto beta = node.param().beta;
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input_alloc = _tensor_builder->at(input_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLSoftmaxLayer>(
-      _tensor_builder->acl_tensor_manager()->internal_buffer_manager());
-
-  fn->configure(input_alloc->handle(), output_alloc->handle(), beta);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Slice &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::Slice::Input::INPUT)};
-  const auto begins_index{node.getInputs().at(ir::operation::Slice::Input::BEGINS)};
-  const auto sizes_index{node.getInputs().at(ir::operation::Slice::Input::SIZES)};
-
-  auto outputData_alloc = _tensor_builder->at(output_index).get();
-  auto inputData_alloc = _tensor_builder->at(input_index).get();
-  const auto frontend_layout = _current_subg_layout;
-  const auto backend_layout = inputData_alloc->layout();
-
-  // Set initializers for indices data such as order of inputData
-  int input_rank = node.param().rank;
-  std::vector<int32_t> starts;
-  std::vector<int32_t> ends;
-  starts.resize(input_rank, 0);
-  ends.resize(input_rank, 0);
-  {
-    auto beginData_base = _ctx.at(begins_index).data().base();
-    auto sizeData_base = _ctx.at(sizes_index).data().base();
-    const int beginData_size = _ctx.at(begins_index).shape().num_elements();
-    const int sizeData_size = _ctx.at(sizes_index).shape().num_elements();
-
-    using ir::DataType;
-
-    UNUSED_RELEASE(beginData_size);
-    UNUSED_RELEASE(sizeData_size);
-
-    assert(_ctx.at(begins_index).typeInfo().type() == DataType::INT32);
-    assert(_ctx.at(sizes_index).typeInfo().type() == DataType::INT32);
-    assert(beginData_size == input_rank);
-    assert(sizeData_size == input_rank);
-
-    assert(beginData_base != nullptr);
-    for (int n = 0; n < input_rank; ++n)
-    {
-      auto axis = ::neurun::backend::acl_common::ToARMComputeAxis(input_rank, n, frontend_layout,
-                                                                  backend_layout)
-                      .value();
-
-      int32_t begin_value = *(reinterpret_cast<const int32_t *>(beginData_base) + n);
-      starts[axis] = begin_value;
-
-      int32_t size_value = *(reinterpret_cast<const int32_t *>(sizeData_base) + n);
-      ends[axis] = begin_value + size_value;
-    }
-  }
-
-  ::arm_compute::Coordinates starts_set;
-  ::arm_compute::Coordinates ends_set;
-
-  for (size_t i = 0; i < starts.size(); ++i)
-  {
-    starts_set.set(i, starts[i]);
-    ends_set.set(i, ends[i]);
-  }
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLSlice>();
-
-  fn->configure(inputData_alloc->handle(), outputData_alloc->handle(), starts_set, ends_set);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::StridedSlice &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::StridedSlice::Input::INPUT)};
-  const auto starts_index{node.getInputs().at(ir::operation::StridedSlice::Input::STARTS)};
-  const auto ends_index{node.getInputs().at(ir::operation::StridedSlice::Input::ENDS)};
-  const auto strides_index{node.getInputs().at(ir::operation::StridedSlice::Input::STRIDES)};
-
-  auto outputData_alloc = _tensor_builder->at(output_index).get();
-  auto inputData_alloc = _tensor_builder->at(input_index).get();
-  const auto frontend_layout = _current_subg_layout;
-  const auto backend_layout = inputData_alloc->layout();
-
-  // Set initializers for indices data such as order of inputData
-  int input_rank = node.param().rank;
-  std::vector<int32_t> starts;
-  std::vector<int32_t> ends;
-  std::vector<int32_t> strides;
-  starts.resize(input_rank, 0);
-  ends.resize(input_rank, 0);
-  strides.resize(input_rank, 0);
-  {
-    auto startData_base = _ctx.at(starts_index).data().base();
-    auto endData_base = _ctx.at(ends_index).data().base();
-    auto stridesData_base = _ctx.at(strides_index).data().base();
-    const int startData_size = _ctx.at(starts_index).shape().num_elements();
-    const int endData_size = _ctx.at(ends_index).shape().num_elements();
-    const int stridesData_size = _ctx.at(strides_index).shape().num_elements();
-
-    using ir::DataType;
-
-    UNUSED_RELEASE(startData_size);
-    UNUSED_RELEASE(endData_size);
-    UNUSED_RELEASE(stridesData_size);
-
-    assert(_ctx.at(starts_index).typeInfo().type() == DataType::INT32);
-    assert(_ctx.at(ends_index).typeInfo().type() == DataType::INT32);
-    assert(_ctx.at(strides_index).typeInfo().type() == DataType::INT32);
-    assert(startData_size == input_rank);
-    assert(endData_size == input_rank);
-    assert(stridesData_size == input_rank);
-
-    assert(startData_base != nullptr);
-    for (int n = 0; n < input_rank; ++n)
-    {
-      auto axis = ::neurun::backend::acl_common::ToARMComputeAxis(input_rank, n, frontend_layout,
-                                                                  backend_layout)
-                      .value();
-
-      int32_t start_value = *(reinterpret_cast<const int32_t *>(startData_base) + n);
-      starts[axis] = start_value;
-
-      int32_t end_value = *(reinterpret_cast<const int32_t *>(endData_base) + n);
-      ends[axis] = end_value;
-
-      int32_t strides_value = *(reinterpret_cast<const int32_t *>(stridesData_base) + n);
-      strides[axis] = strides_value;
-    }
-  }
-
-  // Set mask bits such as order of inputData
-  const auto begin_mask = acl_common::ReorderBits<int32_t>(node.param().begin_mask, input_rank,
-                                                           frontend_layout, backend_layout);
-  const auto end_mask = acl_common::ReorderBits<int32_t>(node.param().end_mask, input_rank,
-                                                         frontend_layout, backend_layout);
-  const auto shrink_axis_mask = acl_common::ReorderBits<int32_t>(
-      node.param().shrink_axis_mask, input_rank, frontend_layout, backend_layout);
-
-  ::arm_compute::Coordinates starts_set;
-  ::arm_compute::Coordinates ends_set;
-  ::arm_compute::BiStrides strides_set;
-
-  for (size_t i = 0; i < starts.size(); ++i)
-  {
-    starts_set.set(i, starts[i]);
-    ends_set.set(i, ends[i]);
-    strides_set.set(i, strides[i]);
-  }
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLStridedSlice>();
-
-  fn->configure(inputData_alloc->handle(), outputData_alloc->handle(), starts_set, ends_set,
-                strides_set, begin_mask, end_mask, shrink_axis_mask);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Transpose &node)
-{
-  const auto ofm_idx{node.getOutputs().at(0)};
-  const auto ifm_idx{node.getInputs().at(ir::operation::Transpose::Input::INPUT)};
-  const auto &perm{node.param().perm};
-
-  const auto rank = node.param().rank;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_idx).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_idx).get();
-  const auto frontend_layout = _current_subg_layout;
-  const auto backend_layout = ifm_alloc->layout();
-
-  std::vector<std::int32_t> pv(perm.cbegin(), perm.cend());
-  // Reversed
-  auto backend_pv = ::neurun::backend::acl_common::getARMComputePermutationVector(
-      rank, pv, frontend_layout, backend_layout);
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLPermute>();
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle(), backend_pv);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Add &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto lhs_index{node.getInputs().at(ir::operation::Add::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::Add::Input::RHS)};
-
-  const auto activation = node.param().activation;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto lhs_alloc = _tensor_builder->at(lhs_index).get();
-  auto rhs_alloc = _tensor_builder->at(rhs_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLArithmeticAddition>();
-
-  fn->configure(lhs_alloc->handle(), rhs_alloc->handle(), ofm_alloc->handle(),
-                arm_compute::ConvertPolicy::SATURATE);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-
-  ActivationBuilder{*_execution_builder}.append(activation, ofm_alloc->handle());
-}
-
-void KernelGenerator::visit(const ir::operation::Sub &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto lhs_index{node.getInputs().at(ir::operation::Sub::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::Sub::Input::RHS)};
-
-  const auto activation = node.param().activation;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto lhs_alloc = _tensor_builder->at(lhs_index).get();
-  auto rhs_alloc = _tensor_builder->at(rhs_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLArithmeticSubtraction>();
-
-  fn->configure(lhs_alloc->handle(), rhs_alloc->handle(), ofm_alloc->handle(),
-                arm_compute::ConvertPolicy::SATURATE);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-
-  ActivationBuilder{*_execution_builder}.append(activation, ofm_alloc->handle());
-}
-
-void KernelGenerator::visit(const ir::operation::Div &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto lhs_index{node.getInputs().at(ir::operation::Div::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::Div::Input::RHS)};
-
-  const auto activation = node.param().activation;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto lhs_alloc = _tensor_builder->at(lhs_index).get();
-  auto rhs_alloc = _tensor_builder->at(rhs_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLArithmeticDivision>();
-
-  fn->configure(lhs_alloc->handle(), rhs_alloc->handle(), ofm_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-
-  ActivationBuilder{*_execution_builder}.append(activation, ofm_alloc->handle());
-}
-
-void KernelGenerator::visit(const ir::operation::Exp &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::Exp::Input::INPUT)};
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input_alloc = _tensor_builder->at(input_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLExpLayer>();
-
-  fn->configure(input_alloc->handle(), output_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::InstanceNorm &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::InstanceNorm::Input::INPUT)};
-  const auto gamma_index{node.getInputs().at(ir::operation::InstanceNorm::Input::GAMMA)};
-  const auto beta_index{node.getInputs().at(ir::operation::InstanceNorm::Input::BETA)};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-  auto gamma_alloc = _tensor_builder->at(gamma_index).get();
-  auto beta_alloc = _tensor_builder->at(beta_index).get();
-  auto epsilon = node.param().epsilon;
-  auto activation = node.param().activation;
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLInstanceNormalizationLayerEx>();
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle(), gamma_alloc->handle(),
-                beta_alloc->handle(), epsilon);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-
-  ActivationBuilder{*_execution_builder}.append(activation, ofm_alloc->handle());
-}
-
-void KernelGenerator::visit(const ir::operation::Logistic &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::Logistic::Input::INPUT)};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-
-  const ::arm_compute::ActivationLayerInfo act_info{
-      ::arm_compute::ActivationLayerInfo::ActivationFunction::LOGISTIC};
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLActivationLayer>();
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle(), act_info);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::LogicalAnd &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input0_index{node.getInputs().at(ir::operation::LogicalAnd::Input::INPUT0)};
-  const auto input1_index{node.getInputs().at(ir::operation::LogicalAnd::Input::INPUT1)};
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input0_alloc = _tensor_builder->at(input0_index).get();
-  auto input1_alloc = _tensor_builder->at(input1_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLBinaryLogicalOp>();
-
-  fn->configure(input0_alloc->handle(), input1_alloc->handle(), output_alloc->handle(),
-                ::arm_compute::BinaryLogicalOperation::AND);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::LSTM &node)
-{
-  // TODO Support dynamic rnn
-  // TODO Fix subtle error in the case of non-CIFG, non-peephole and No Projection.
-  const auto scratch_buffer_index{
-      node.getOutputs().at(ir::operation::LSTM::Output::SCRATCH_BUFFER)};
-  const auto output_state_out_index{
-      node.getOutputs().at(ir::operation::LSTM::Output::OUTPUT_STATE_OUT)};
-  const auto cell_state_out_index{
-      node.getOutputs().at(ir::operation::LSTM::Output::CELL_STATE_OUT)};
-  const auto output_index{node.getOutputs().at(ir::operation::LSTM::Output::OUTPUT)};
-
-  const auto input_index{node.getInputs().at(ir::operation::LSTM::Input::INPUT)};
-  const auto input_to_input_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::INPUT_TO_INPUT_WEIGHTS)}; // optional
-  const auto input_to_forget_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::INPUT_TO_FORGET_WEIGHTS)};
-  const auto input_to_cell_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::INPUT_TO_CELL_WEIGHTS)};
-  const auto input_to_output_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::INPUT_TO_OUTPUT_WEIGHTS)};
-  const auto recurrent_to_input_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::RECURRENT_TO_INPUT_WEIGHTS)}; // optional
-  const auto recurrent_to_forget_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::RECURRENT_TO_FORGET_WEIGHTS)};
-  const auto recurrent_to_cell_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::RECURRENT_TO_CELL_WEIGHTS)};
-  const auto recurrent_to_output_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::RECURRENT_TO_OUTPUT_WEIGHTS)};
-  const auto cell_to_input_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::CELL_TO_INPUT_WEIGHTS)}; // optional
-  const auto cell_to_forget_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::CELL_TO_FORGET_WEIGHTS)}; // optional
-  const auto cell_to_output_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::CELL_TO_OUTPUT_WEIGHTS)}; // optional
-  const auto input_gate_bias_index{
-      node.getInputs().at(ir::operation::LSTM::Input::INPUT_GATE_BIAS)};
-  const auto forget_gate_bias_index{
-      node.getInputs().at(ir::operation::LSTM::Input::FORGET_GATE_BIAS)};
-  const auto cell_bias_index{node.getInputs().at(ir::operation::LSTM::Input::CELL_BIAS)};
-  const auto output_gate_bias_index{
-      node.getInputs().at(ir::operation::LSTM::Input::OUTPUT_GATE_BIAS)};
-  const auto projection_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::PROJECTION_WEIGHTS)}; // optional
-  const auto projection_bias_index{
-      node.getInputs().at(ir::operation::LSTM::Input::PROJECTION_BIAS)}; // optional
-  const auto output_state_in_index{
-      node.getInputs().at(ir::operation::LSTM::Input::OUTPUT_STATE_IN)};
-  const auto cell_state_in_index{node.getInputs().at(ir::operation::LSTM::Input::CELL_STATE_IN)};
-  const auto cell_threshold = node.param().cell_threshold;
-  const auto projection_threshold = node.param().projection_threshold;
-
-  bool has_input_to_input_weights = _ctx.at(input_to_input_weights_index).shape().dim(0) != 0 &&
-                                    _ctx.at(input_to_input_weights_index).shape().dim(1) != 0;
-  bool has_recurrent_to_input_weights =
-      _ctx.at(recurrent_to_input_weights_index).shape().dim(0) != 0 &&
-      _ctx.at(recurrent_to_input_weights_index).shape().dim(1) != 0;
-  bool has_cell_to_forget_weights = _ctx.at(cell_to_forget_weights_index).shape().dim(0) != 0;
-  bool has_cell_to_output_weights = _ctx.at(cell_to_output_weights_index).shape().dim(0) != 0;
-  bool has_projection_weights = _ctx.at(projection_weights_index).shape().dim(0) != 0 &&
-                                _ctx.at(projection_weights_index).shape().dim(1) != 0;
-  bool has_projection_bias = _ctx.at(projection_bias_index).shape().dim(0);
-
-  // NOTE The input_to_input_weights and the recurrent_to_input_weights do not exist in CIFG.
-  // true: no CIFG
-  // false: CIFG
-  // NOTE The cell_to_input_weights does not exist in non-peephole although regular LSTM(non-CIFG).
-  bool has_cifg_param = has_input_to_input_weights && has_recurrent_to_input_weights;
-
-  // NOTE The cell_to_forget_weights and the cell_to_output_weights exist in peephole.
-  // But the cell_to_input_weights does not exist in regular CIFG although peephole.
-  // true: peephole
-  // false: no peephole
-  bool has_peephole_param = has_cell_to_forget_weights && has_cell_to_output_weights;
-
-  // NOTE Although the projection weights has data the projection bias may not have data.
-  bool has_projection_param = has_projection_weights;
-
-  const auto activation = node.param().activation;
-  const auto cell_clip = cell_threshold;
-  const auto projection_clip = projection_threshold;
-  assert(cell_clip >= 0.f && projection_clip >= 0.f);
-
-  auto scratch_buffer_alloc = _tensor_builder->at(scratch_buffer_index).get();
-  auto output_state_out_alloc = _tensor_builder->at(output_state_out_index).get();
-  auto cell_state_out_alloc = _tensor_builder->at(cell_state_out_index).get();
-  auto output_alloc = _tensor_builder->at(output_index).get();
-
-  auto input_alloc = _tensor_builder->at(input_index).get();
-
-  auto input_to_forget_weights_alloc = _tensor_builder->at(input_to_forget_weights_index).get();
-  auto input_to_cell_weights_alloc = _tensor_builder->at(input_to_cell_weights_index).get();
-  auto input_to_output_weights_alloc = _tensor_builder->at(input_to_output_weights_index).get();
-  auto recurrent_to_forget_weights_alloc =
-      _tensor_builder->at(recurrent_to_forget_weights_index).get();
-  auto recurrent_to_cell_weights_alloc = _tensor_builder->at(recurrent_to_cell_weights_index).get();
-  auto recurrent_to_output_weights_alloc =
-      _tensor_builder->at(recurrent_to_output_weights_index).get();
-
-  auto forget_gate_bias_alloc = _tensor_builder->at(forget_gate_bias_index).get();
-  auto cell_bias_alloc = _tensor_builder->at(cell_bias_index).get();
-  auto output_gate_bias_alloc = _tensor_builder->at(output_gate_bias_index).get();
-  auto output_state_in_alloc = _tensor_builder->at(output_state_in_index).get();
-  auto cell_state_in_alloc = _tensor_builder->at(cell_state_in_index).get();
-
-  auto act_info = ::neurun::backend::acl_common::asActivationLayerInfo(activation);
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLLSTMLayer>();
-
-  ::arm_compute::LSTMParams<::arm_compute::ICLTensor> lstm_params{};
-  if (has_cifg_param)
-  {
-    auto input_to_input_weights_alloc =
-        _tensor_builder->at(input_to_input_weights_index).get(); // optional
-    auto recurrent_to_input_weights_alloc =
-        _tensor_builder->at(recurrent_to_input_weights_index).get(); // optional
-    auto cell_to_input_weights_handle =
-        has_peephole_param ? _tensor_builder->at(cell_to_input_weights_index).get()->handle()
-                           : nullptr; // optional (non-cifg && peephole)
-    auto input_gate_bias_alloc = _tensor_builder->at(input_gate_bias_index).get(); // optional
-    lstm_params.set_cifg_params(input_to_input_weights_alloc->handle(),
-                                recurrent_to_input_weights_alloc->handle(),
-                                cell_to_input_weights_handle, input_gate_bias_alloc->handle());
-  }
-  if (has_peephole_param)
-  {
-    auto cell_to_forget_weights_alloc =
-        _tensor_builder->at(cell_to_forget_weights_index).get(); // optional
-    auto cell_to_output_weights_alloc =
-        _tensor_builder->at(cell_to_output_weights_index).get(); // optional
-    lstm_params.set_peephole_params(cell_to_forget_weights_alloc->handle(),
-                                    cell_to_output_weights_alloc->handle());
-  }
-  if (has_projection_param)
-  {
-    auto projection_weights_alloc = _tensor_builder->at(projection_weights_index).get(); // optional
-    auto projection_bias_handle = has_projection_bias
-                                      ? _tensor_builder->at(projection_bias_index).get()->handle()
-                                      : nullptr; // optional
-    lstm_params.set_projection_params(projection_weights_alloc->handle(), projection_bias_handle);
-  }
-
-  fn->configure(
-      input_alloc->handle(), input_to_forget_weights_alloc->handle(),
-      input_to_cell_weights_alloc->handle(), input_to_output_weights_alloc->handle(),
-      recurrent_to_forget_weights_alloc->handle(), recurrent_to_cell_weights_alloc->handle(),
-      recurrent_to_output_weights_alloc->handle(), forget_gate_bias_alloc->handle(),
-      cell_bias_alloc->handle(), output_gate_bias_alloc->handle(), output_state_in_alloc->handle(),
-      cell_state_in_alloc->handle(), scratch_buffer_alloc->handle(),
-      output_state_out_alloc->handle(), cell_state_out_alloc->handle(), output_alloc->handle(),
-      lstm_params, act_info, cell_clip, projection_clip);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::ReduceMax &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::ReduceMax::Input::INPUT)};
-  const auto &axes{node.param().axes};
-  const auto keep_dims{node.param().keep_dims};
-
-  auto ofm_alloc = _tensor_builder->at(output_index).get();
-  auto ifm_alloc = _tensor_builder->at(input_index).get();
-  const auto frontend_layout = _current_subg_layout;
-  const auto backend_layout = ifm_alloc->layout();
-
-  // Convert to ACL axes taking into account negative values and possible duplicates.
-  std::set<std::uint32_t> acl_axes;
-  const int ifm_rank = node.param().rank;
-  for (int axis : axes)
-  {
-    if (axis < 0)
-      axis += ifm_rank;
-    acl_axes.insert(
-        acl_common::ToARMComputeAxis(ifm_rank, axis, frontend_layout, backend_layout).value());
-  }
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLReduceOperation>(
-      _tensor_builder->acl_tensor_manager()->internal_buffer_manager());
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle(), acl_axes, keep_dims,
-                arm_compute::ReduceOperation::MAX);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Comparison &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input0_index{node.getInputs().at(ir::operation::Comparison::Input::INPUT0)};
-  const auto input1_index{node.getInputs().at(ir::operation::Comparison::Input::INPUT1)};
-
-  const auto comparison_type = node.param().comparison_type;
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input0_alloc = _tensor_builder->at(input0_index).get();
-  auto input1_alloc = _tensor_builder->at(input1_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLComparison>();
-
-  fn->configure(input0_alloc->handle(), input1_alloc->handle(), output_alloc->handle(),
-                (arm_compute::ComparisonOperation)comparison_type);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Pack &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  auto axis{node.param().axis};
-
-  const auto output_rank = node.param().rank;
-
-  std::vector<ir::OperandIndex> input_indexes;
-  for (const auto &input_index : node.getInputs())
-    input_indexes.emplace_back(input_index);
-
-  auto output = _tensor_builder->at(output_index).get()->handle();
-  std::vector<arm_compute::ICLTensor *> inputs;
-  for (const auto &input_index : input_indexes)
-    inputs.emplace_back(_tensor_builder->at(input_index)->handle());
-
-  const auto frontend_layout = _current_subg_layout;
-  const auto backend_layout = _tensor_builder->at(output_index).get()->layout();
-
-  if (axis < 0)
-    axis += output_rank;
-  axis = acl_common::ToARMComputeAxis(output_rank, axis, frontend_layout, backend_layout).value();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLStackLayer>();
-
-  fn->configure(inputs, axis, output);
-
-  _execution_builder->append(asAclFunction(std::move(fn)));
-}
-
-void KernelGenerator::visit(const ir::operation::Permute &node)
-{
-  const auto ofm_idx{node.getOutputs().at(0)};
-  const auto ifm_idx{node.getInputs().at(0)};
-  const auto permute_type = node.getPermuteType();
-  auto ofm_alloc = _tensor_builder->at(ofm_idx).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_idx).get();
-  const auto rank = _ctx.at(ofm_idx).shape().rank();
-  assert(_ctx.at(ifm_idx).shape().rank() == _ctx.at(ofm_idx).shape().rank());
-
-  std::unique_ptr<::arm_compute::IFunction> fn;
-  arm_compute::PermutationVector pv;
-  if (permute_type == ir::operation::Permute::Type::NCHW_TO_NHWC && rank == 4)
-  {
-    // WHCN -> CWHN
-    pv = arm_compute::PermutationVector{2, 0, 1};
-
-    auto l = nnfw::cpp14::make_unique<::arm_compute::CLPermute>();
-
-    l->configure(ifm_alloc->handle(), ofm_alloc->handle(), pv);
-
-    fn = std::move(l);
-  }
-  else if (permute_type == ir::operation::Permute::Type::NHWC_TO_NCHW && rank == 4)
-  {
-    // CWHN -> WHCN
-    pv = arm_compute::PermutationVector{1, 2, 0};
-
-    auto l = nnfw::cpp14::make_unique<::arm_compute::CLPermute>();
-
-    l->configure(ifm_alloc->handle(), ofm_alloc->handle(), pv);
-
-    fn = std::move(l);
-  }
-  else
-  {
-    auto l = nnfw::cpp14::make_unique<::arm_compute::CLCopy>();
-
-    l->configure(ifm_alloc->handle(), ofm_alloc->handle());
-
-    fn = std::move(l);
-  }
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::RSQRT &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::RSQRT::Input::INPUT)};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLRsqrtLayer>();
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle());
-
-  _execution_builder->append(asAclFunction(std::move(fn)));
-}
-
-void KernelGenerator::visit(const ir::operation::ReLU &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::ReLU::Input::INPUT)};
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input_alloc = _tensor_builder->at(input_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<arm_compute::CLActivationLayer>();
-
-  const ::arm_compute::ActivationLayerInfo act_info{
-      ::arm_compute::ActivationLayerInfo::ActivationFunction::RELU};
-
-  fn->configure(input_alloc->handle(), output_alloc->handle(), act_info);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::ResizeBilinear &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-
-  const auto ifm_index{node.getInputs().at(ir::operation::ResizeBilinear::Input::INPUT)};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLScale>();
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle(),
-                ::arm_compute::InterpolationPolicy::BILINEAR, ::arm_compute::BorderMode::REPLICATE,
-                ::arm_compute::PixelValue(0.f), ::arm_compute::SamplingPolicy::TOP_LEFT);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::ReLU1 &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::ReLU1::Input::INPUT)};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-
-  const ::arm_compute::ActivationLayerInfo act_info{
-      ::arm_compute::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 1.0f, -1.0f};
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLActivationLayer>();
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle(), act_info);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::ReLU6 &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::ReLU6::Input::INPUT)};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-
-  const ::arm_compute::ActivationLayerInfo act_info{
-      ::arm_compute::ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.0f};
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLActivationLayer>();
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle(), act_info);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::RNN &node)
-{
-  const auto output_index{node.getOutputs().at(ir::operation::RNN::Output::OUTPUT)};
-  const auto hidden_state_out_index{
-      node.getOutputs().at(ir::operation::RNN::Output::HIDDEN_STATE_OUT)};
-
-  const auto input_index{node.getInputs().at(ir::operation::RNN::Input::INPUT)};
-  const auto weights_index{node.getInputs().at(ir::operation::RNN::Input::WEIGHTS)};
-  const auto recurrent_weights_index{
-      node.getInputs().at(ir::operation::RNN::Input::RECURRENT_WEIGHTS)};
-  const auto bias_index{node.getInputs().at(ir::operation::RNN::Input::BIAS)};
-  const auto hidden_state_in_index{node.getInputs().at(ir::operation::RNN::Input::HIDDEN_STATE_IN)};
-
-  const auto activation = node.param().activation;
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto hidden_state_out_alloc = _tensor_builder->at(hidden_state_out_index).get();
-
-  auto input_alloc = _tensor_builder->at(input_index).get();
-  auto weights_alloc = _tensor_builder->at(weights_index).get();
-  auto recurrent_weights_alloc = _tensor_builder->at(recurrent_weights_index).get();
-  auto bias_alloc = _tensor_builder->at(bias_index).get();
-  auto hidden_state_in_alloc = _tensor_builder->at(hidden_state_in_index).get();
-  auto act_info = ::neurun::backend::acl_common::asActivationLayerInfo(activation);
-
-  auto copy_layer = nnfw::cpp14::make_unique<::arm_compute::CLCopy>();
-  copy_layer->configure(hidden_state_in_alloc->handle(), hidden_state_out_alloc->handle());
-  _execution_builder->append(asAclFunction(std::move(copy_layer)));
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLRNNLayerEx>(
-      _tensor_builder->acl_tensor_manager()->internal_buffer_manager());
-  fn->configure(input_alloc->handle(), weights_alloc->handle(), recurrent_weights_alloc->handle(),
-                bias_alloc->handle(), hidden_state_out_alloc->handle(), output_alloc->handle(),
-                act_info);
-  _execution_builder->append(asAclFunction(std::move(fn)));
-}
-
-void KernelGenerator::visit(const ir::operation::Floor &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::Floor::Input::INPUT)};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLFloor>();
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::SpaceToBatchND &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::SpaceToBatchND::Input::INPUT)};
-  const auto block_size_index{
-      node.getInputs().at(ir::operation::SpaceToBatchND::Input::BLOCK_SIZE)};
-  const auto paddings_index{node.getInputs().at(ir::operation::SpaceToBatchND::Input::PADDINGS)};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-  auto block_size_alloc = _tensor_builder->at(block_size_index).get();
-  auto paddings_alloc = _tensor_builder->at(paddings_index).get();
-
-  assert(_ctx.at(block_size_index).isConstant());
-  assert(_ctx.at(paddings_index).isConstant());
-
-  std::unique_ptr<::arm_compute::IFunction> fn;
-  if (_ctx.at(ofm_index).typeInfo().type() == ir::DataType::QUANT8_ASYMM)
-  {
-    // NOTE CLSpaceToBatchLayer has a bug that padding's values are 0 even when zero point of
-    // QASYMM8 is not 0.
-    auto l = nnfw::cpp14::make_unique<::arm_compute::CLSpaceToBatchND>();
-    l->configure(ifm_alloc->handle(), block_size_alloc->handle(), paddings_alloc->handle(),
-                 ofm_alloc->handle());
-    fn = std::move(l);
-  }
-  else
-  {
-    auto l = nnfw::cpp14::make_unique<::arm_compute::CLSpaceToBatchLayer>();
-    l->configure(ifm_alloc->handle(), block_size_alloc->handle(), paddings_alloc->handle(),
-                 ofm_alloc->handle());
-    fn = std::move(l);
-  }
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::SpaceToDepth &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::SpaceToDepth::Input::INPUT)};
-
-  auto block_size = node.param().block_size;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLSpaceToDepth>();
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle(), block_size);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::L2Pool2D &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::L2Pool2D::Input::INPUT)};
-
-  const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(_current_subg_layout);
-  const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(_current_subg_layout);
-
-  uint32_t kw = node.param().kw;
-  uint32_t kh = node.param().kh;
-  const auto stride = node.param().stride;
-  const auto padding =
-      neurun::util::calculatePadding(node.param().padding, ifm_shape, ofm_shape, stride, kw, kh);
-  const auto activation = node.param().activation;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-
-  ::arm_compute::PoolingLayerInfo info{
-      ::arm_compute::PoolingType::L2, ::arm_compute::Size2D{kw, kh},
-      ::neurun::backend::acl_common::asPadStrideInfo(padding, stride)};
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLPoolingLayer>();
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle(), info);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-
-  ActivationBuilder{*_execution_builder}.append(activation, ofm_alloc->handle());
-}
-
-void KernelGenerator::visit(const ir::operation::EmbeddingLookup &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto lookups_index{node.getInputs().at(ir::operation::EmbeddingLookup::Input::LOOKUPS)};
-  const auto values_index{node.getInputs().at(ir::operation::EmbeddingLookup::Input::VALUES)};
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto lookups_alloc = _tensor_builder->at(lookups_index).get();
-  auto values_alloc = _tensor_builder->at(values_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLEmbeddingLookup>();
-
-  fn->configure(values_alloc->handle(), output_alloc->handle(), lookups_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::L2Normalization &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::L2Normalization::Input::INPUT)};
-
-  // {CL|Neon}L2Normalization performs the reduction only along dimension 0
-  // L2 Normalization always performs the reduction along the depth axis
-  // Thus, we repurpose {CL|Neon}NormalizationLayers to act as depthwise L2 normalizations by
-  // choosing normalization parameters as below
-
-  const auto &ifm_shape = _ctx.at(ifm_index).shape();
-  // TODO Support optional constant dimension that normalization would be performed on
-  const auto normalization_axis = node.param().rank - 1;
-  int32_t radius =
-      2 * ifm_shape.dim(normalization_axis) + 1; // normSize = depth(last dimension) * 2 + 1
-  float alpha = 1.0f;                            // In the implementation to make alpha_ become 1
-  float beta = 0.5f;                             // pow(reduction, -0.5) = 1 / sqrt(reduction)
-  float bias = 0.0f;                             // Don't offset the reduction.
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-
-  const auto norm_info = ::arm_compute::NormalizationLayerInfo(::arm_compute::NormType::CROSS_MAP,
-                                                               radius, alpha, beta, bias, false);
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLNormalizationLayer>();
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle(), norm_info);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::HashtableLookup &node)
-{
-  const auto output_index{node.getOutputs().at(ir::operation::HashtableLookup::Output::OUTPUT)};
-  const auto hits_index{node.getOutputs().at(ir::operation::HashtableLookup::Output::HITS)};
-
-  const auto lookups_index{node.getInputs().at(ir::operation::HashtableLookup::Input::LOOKUPS)};
-  const auto keys_index{node.getInputs().at(ir::operation::HashtableLookup::Input::KEYS)};
-  const auto values_index{node.getInputs().at(ir::operation::HashtableLookup::Input::VALUES)};
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto hits_alloc = _tensor_builder->at(hits_index).get();
-
-  auto lookups_alloc = _tensor_builder->at(lookups_index).get();
-  auto keys_alloc = _tensor_builder->at(keys_index).get();
-  auto values_alloc = _tensor_builder->at(values_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLHashtableLookup>();
-
-  fn->configure(lookups_alloc->handle(), keys_alloc->handle(), values_alloc->handle(),
-                output_alloc->handle(), hits_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::PReLU &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::PReLU::Input::INPUT)};
-  const auto alpha_index{node.getInputs().at(ir::operation::PReLU::Input::ALPHA)};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-  auto alpha_alloc = _tensor_builder->at(alpha_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLPReLU>();
-
-  fn->configure(ifm_alloc->handle(), alpha_alloc->handle(), ofm_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::TransposeConv &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto output_shape_index{
-      node.getInputs().at(ir::operation::TransposeConv::Input::OUTPUT_SHAPE)};
-  const auto ker_index{node.getInputs().at(ir::operation::TransposeConv::Input::KERNEL)};
-  const auto ifm_index{node.getInputs().at(ir::operation::TransposeConv::Input::INPUT)};
-
-  const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(_current_subg_layout);
-  const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(_current_subg_layout);
-  const auto ker_shape = _ctx.at(ker_index).shape().asFeature(_current_subg_layout);
-
-  const auto stride = node.param().stride;
-
-  assert((node.param().padding.type == ir::PaddingType::SAME) ||
-         (node.param().padding.type == ir::PaddingType::VALID));
-  auto padding = neurun::util::calculatePadding(node.param().padding, ofm_shape, ifm_shape, stride,
-                                                ker_shape.W, ker_shape.H);
-
-  uint32_t invalid_horizontal = 0;
-  uint32_t invalid_vertical = 0;
-  if (node.param().padding.type == ir::PaddingType::VALID)
-  {
-    invalid_horizontal =
-        ofm_shape.W - (1 + (ifm_shape.W - 1) * stride.horizontal) - (ker_shape.W - 1);
-    invalid_vertical = ofm_shape.H - (1 + (ifm_shape.H - 1) * stride.vertical) - (ker_shape.H - 1);
-  }
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-  auto ker_alloc = _tensor_builder->at(ker_index).get();
-
-  const auto tconv_info = acl_common::asPadStrideInfo(padding, stride);
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLTransposeConvLayer>(
-      _tensor_builder->acl_tensor_manager()->internal_buffer_manager());
-
-  fn->configure(ifm_alloc->handle(), ker_alloc->handle(), nullptr, ofm_alloc->handle(), tconv_info,
-                invalid_horizontal, invalid_vertical);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::SQRT &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::SQRT::Input::INPUT)};
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input_alloc = _tensor_builder->at(input_index).get();
-
-  const ::arm_compute::ActivationLayerInfo act_info{
-      ::arm_compute::ActivationLayerInfo::ActivationFunction::SQRT};
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLActivationLayer>();
-
-  fn->configure(input_alloc->handle(), output_alloc->handle(), act_info);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::LogicalOr &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input0_index{node.getInputs().at(ir::operation::LogicalOr::Input::INPUT0)};
-  const auto input1_index{node.getInputs().at(ir::operation::LogicalOr::Input::INPUT1)};
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input0_alloc = _tensor_builder->at(input0_index).get();
-  auto input1_alloc = _tensor_builder->at(input1_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLBitwiseOr>();
-
-  fn->configure(input0_alloc->handle(), input1_alloc->handle(), output_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::LogicalNot &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::LogicalNot::Input::INPUT)};
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input_alloc = _tensor_builder->at(input_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLBitwiseNot>();
-
-  fn->configure(input_alloc->handle(), output_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::SquaredDifference &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto lhs_index{node.getInputs().at(ir::operation::SquaredDifference::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::SquaredDifference::Input::RHS)};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto lhs_alloc = _tensor_builder->at(lhs_index).get();
-  auto rhs_alloc = _tensor_builder->at(rhs_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLElementwiseSquaredDiff>();
-
-  fn->configure(lhs_alloc->handle(), rhs_alloc->handle(), ofm_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::TopKV2 &node)
-{
-  const auto outputValues_index{node.getOutputs().at(ir::operation::TopKV2::Output::OUTPUT_VALUES)};
-  const auto outputIndices_index{
-      node.getOutputs().at(ir::operation::TopKV2::Output::OUTPUT_INDICES)};
-
-  const auto inputData_index{node.getInputs().at(ir::operation::TopKV2::Input::INPUT)};
-
-  // Currently, we only support the vector input.
-  assert(_ctx.at(inputData_index).shape().rank() == 1 ||
-         _ctx.at(inputData_index).shape().rank() == 2);
-
-  const auto k = node.param().k;
-
-  auto values_alloc = _tensor_builder->at(outputValues_index).get();
-  auto indices_alloc = _tensor_builder->at(outputIndices_index).get();
-  auto input_alloc = _tensor_builder->at(inputData_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLTopKV2>();
-
-  fn->configure(input_alloc->handle(), k, values_alloc->handle(), indices_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Gather &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-
-  const auto ifm_index{node.getInputs().at(ir::operation::Gather::Input::INPUT)};
-  const auto indices_index{node.getInputs().at(ir::operation::Gather::Input::INDICES)};
-
-  const auto ifm_shape = _ctx.at(ifm_index).shape();
-
-  const auto ifm_rank = node.param().rank;
-  const auto axis_raw = node.param().axis;
-  const auto axis_value = (axis_raw < 0 ? (ifm_rank + axis_raw) : axis_raw);
-  const int axis = ::neurun::backend::acl_common::ToARMComputeAxis(ifm_rank, axis_value).value();
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-  auto indices_alloc = _tensor_builder->at(indices_index).get();
-
-  // NOTE The frontend layout and backend layout must be the same for this operation.
-  //      If not the same, we have to add a stage(?) to perform permutation of output tensor. It
-  //      is not not efficient even if it works well. If so, it would be better to set the
-  //      layout of these backend tensors to the same layout.
-  //      There is also one thing we have to think about. This operation depends on the layout of
-  //      a model. For example, if a model in NHWC has this operation as output rank == 4, indices
-  //      rank == 2 and axis == 2, this operation should work as the axis W and C, but the axis W
-  //      and C are not sequential in NCHW. So the backend in NCHW cannot handle this case.
-  const auto backend_layout = ofm_alloc->layout();
-  UNUSED_RELEASE(backend_layout);
-  assert(backend_layout == ifm_alloc->layout());
-  assert(backend_layout == indices_alloc->layout());
-  assert(ifm_rank < 4 || _current_subg_layout == backend_layout);
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLGatherEx>();
-
-  fn->configure(ifm_alloc->handle(), indices_alloc->handle(), ofm_alloc->handle(), axis);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Neg &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::Neg::Input::INPUT)};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLNeg>();
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Abs &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::Abs::Input::INPUT)};
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input_alloc = _tensor_builder->at(input_index).get();
-
-  const ::arm_compute::ActivationLayerInfo act_info{
-      ::arm_compute::ActivationLayerInfo::ActivationFunction::ABS};
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLActivationLayer>();
-
-  fn->configure(input_alloc->handle(), output_alloc->handle(), act_info);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::ArgMax &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::ArgMax::Input::INPUT)};
-
-  auto ifm_shape = _ctx.at(ifm_index).shape();
-  auto ofm_shape = _ctx.at(ofm_index).shape();
-
-  assert((ifm_shape.rank() - 1) == ofm_shape.rank());
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-  const auto ifm_rank = node.param().rank;
-  auto frontend_layout = _current_subg_layout;
-  auto backend_layout = ifm_alloc->layout();
-
-  int axis_value = node.param().axis;
-  if (axis_value < 0)
-  {
-    axis_value += ifm_rank;
-  }
-
-  auto acl_axis =
-      acl_common::ToARMComputeAxis(ifm_rank, axis_value, frontend_layout, backend_layout).value();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLArgOperation>();
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle(), {acl_axis},
-                ::arm_compute::ArgOperation::MAX);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Dequantize &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::Dequantize::Input::INPUT)};
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input_alloc = _tensor_builder->at(input_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLCast>();
-
-  fn->configure(input_alloc->handle(), output_alloc->handle(), arm_compute::SubDataType::NONE);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Mean &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::Mean::Input::INPUT)};
-  const auto &axes{node.param().axes};
-  const auto keep_dims{node.param().keep_dims};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-  const auto frontend_layout = _current_subg_layout;
-  const auto backend_layout = ifm_alloc->layout();
-
-  // Convert to ACL axes taking into account negative values and possible duplicates.
-  std::set<std::uint32_t> acl_axes;
-  const int ifm_rank = node.param().rank;
-  for (int axis : axes)
-  {
-    if (axis < 0)
-      axis += ifm_rank;
-    acl_axes.insert(
-        acl_common::ToARMComputeAxis(ifm_rank, axis, frontend_layout, backend_layout).value());
-  }
-
-  arm_compute::Coordinates reduce_axes;
-  for (const auto axis : acl_axes)
-  {
-    reduce_axes.set(reduce_axes.num_dimensions(), axis);
-  }
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLReduceMean>();
-
-  fn->configure(ifm_alloc->handle(), reduce_axes, keep_dims, ofm_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::LocalResponseNormalization &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{
-      node.getInputs().at(ir::operation::LocalResponseNormalization::Input::INPUT)};
-
-  auto radius = node.param().radius;
-  auto alpha = node.param().alpha;
-  auto beta = node.param().beta;
-  auto bias = node.param().bias;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-
-  const auto norm_info = ::arm_compute::NormalizationLayerInfo(
-      ::arm_compute::NormType::CROSS_MAP, radius * 2 + 1, alpha, beta, bias, false);
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLNormalizationLayer>();
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle(), norm_info);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::DepthToSpace &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::DepthToSpace::Input::INPUT)};
-
-  auto block_size = node.param().block_size;
-  assert(block_size > 0);
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input_alloc = _tensor_builder->at(input_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLDepthToSpace>();
-
-  fn->configure(input_alloc->handle(), output_alloc->handle(), block_size);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::ReduceMin &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::ReduceMin::Input::INPUT)};
-  const auto &axes{node.param().axes};
-  const auto keep_dims{node.param().keep_dims};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-  const auto frontend_layout = _current_subg_layout;
-  const auto backend_layout = ifm_alloc->layout();
-
-  // Convert to ACL axes taking into account negative values and possible duplicates.
-  std::set<std::uint32_t> acl_axes;
-  const int ifm_rank = node.param().rank;
-  for (int axis : axes)
-  {
-    if (axis < 0)
-      axis += ifm_rank;
-    acl_axes.insert(
-        acl_common::ToARMComputeAxis(ifm_rank, axis, frontend_layout, backend_layout).value());
-  }
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLReduceOperation>(
-      _tensor_builder->acl_tensor_manager()->internal_buffer_manager());
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle(), acl_axes, keep_dims,
-                ::arm_compute::ReduceOperation::MIN);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Split &node)
-{
-  const auto ifm_index{node.getInputs().at(ir::operation::Split::Input::INPUT)};
-
-  assert(node.param().num_splits == static_cast<int>(node.getOutputs().size()));
-
-  const auto ifm_rank = node.param().rank;
-  std::vector<ir::OperandIndex> output_indexes;
-  for (const auto &output : node.getOutputs())
-    output_indexes.emplace_back(output);
-
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-  std::vector<arm_compute::ICLTensor *> output_allocs;
-  for (const auto &ofm_ind : output_indexes)
-    output_allocs.emplace_back(_tensor_builder->at(ofm_ind).get()->handle());
-
-  const auto frontend_layout = _current_subg_layout;
-  const auto backend_layout = ifm_alloc->layout();
-  auto axis = node.param().axis;
-  if (axis < 0)
-    axis += ifm_rank;
-  axis = acl_common::ToARMComputeAxis(ifm_rank, axis, frontend_layout, backend_layout).value();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLSplit>();
-
-  fn->configure(ifm_alloc->handle(), output_allocs, axis);
-
-  _execution_builder->append(asAclFunction(std::move(fn)));
-}
-
-void KernelGenerator::visit(const ir::operation::Unpack &node)
-{
-  const auto input_index{node.getInputs().at(ir::operation::Unpack::Input::INPUT)};
-  auto axis{node.param().axis};
-
-  const auto input_rank = node.param().rank;
-
-  std::vector<ir::OperandIndex> output_indexes;
-  for (const auto &output_index : node.getOutputs())
-    output_indexes.emplace_back(output_index);
-
-  auto input = _tensor_builder->at(input_index).get()->handle();
-  std::vector<arm_compute::ICLTensor *> outputs;
-  for (const auto &output_index : output_indexes)
-    outputs.emplace_back(_tensor_builder->at(output_index)->handle());
-
-  const auto frontend_layout = _current_subg_layout;
-  const auto backend_layout = _tensor_builder->at(input_index).get()->layout();
-  if (axis < 0)
-    axis += input_rank;
-  axis = acl_common::ToARMComputeAxis(input_rank, axis, frontend_layout, backend_layout).value();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLUnstack>();
-
-  fn->configure(input, outputs, axis);
-
-  _execution_builder->append(asAclFunction(std::move(fn)));
-}
-
-void KernelGenerator::visit(const ir::operation::Pad &node)
-{
-  const auto input_index{node.getInputs().at(ir::operation::Pad::Input::INPUT)};
-  const auto pad_index{node.getInputs().at(ir::operation::Pad::Input::PAD)};
-  const auto output_index{node.getOutputs().at(0)};
-  assert(_ctx.at(pad_index).isConstant());
-
-  auto rank = node.param().rank;
-  auto pad_base = _ctx.at(pad_index).data().base();
-
-  auto input_type = _ctx.at(input_index).typeInfo();
-  auto data_type = acl_common::asDataType(input_type.type());
-  auto quant_info = ::arm_compute::QuantizationInfo(input_type.scale(), input_type.offset());
-  const auto pixel_value = ::arm_compute::PixelValue(0, data_type, quant_info);
-
-  auto input = _tensor_builder->at(input_index).get()->handle();
-  auto output = _tensor_builder->at(output_index).get()->handle();
-
-  ::arm_compute::PaddingList padding_list;
-  padding_list.resize(rank);
-  for (int32_t n = 0; n < rank; ++n)
-  {
-    const int32_t *from = reinterpret_cast<const int32_t *>(pad_base) + (n * 2);
-
-    const auto frontend_layout = _current_subg_layout;
-    const auto backend_layout = _tensor_builder->at(input_index).get()->layout();
-    const auto axis =
-        acl_common::ToARMComputeAxis(rank, n, frontend_layout, backend_layout).value();
-    padding_list[axis] = ::arm_compute::PaddingInfo{from[0], from[1]};
-  }
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLPadLayer>();
-  fn->configure(input, output, padding_list, pixel_value);
-
-  _execution_builder->append(asAclFunction(std::move(fn)));
-}
-
-void KernelGenerator::visit(const ir::operation::Min &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto lhs_index{node.getInputs().at(ir::operation::Min::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::Min::Input::RHS)};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto lhs_alloc = _tensor_builder->at(lhs_index).get();
-  auto rhs_alloc = _tensor_builder->at(rhs_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLElementwiseMin>();
-
-  fn->configure(lhs_alloc->handle(), rhs_alloc->handle(), ofm_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Max &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto lhs_index{node.getInputs().at(ir::operation::Max::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::Max::Input::RHS)};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto lhs_alloc = _tensor_builder->at(lhs_index).get();
-  auto rhs_alloc = _tensor_builder->at(rhs_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::CLElementwiseMax>();
-
-  fn->configure(lhs_alloc->handle(), rhs_alloc->handle(), ofm_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-} // namespace acl_cl
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/acl_cl/KernelGenerator.h b/runtime/neurun/backend/acl_cl/KernelGenerator.h
deleted file mode 100644
index a577f1ebc..000000000
--- a/runtime/neurun/backend/acl_cl/KernelGenerator.h
+++ /dev/null
@@ -1,112 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_CL_KERNEL_GENERATOR_H__
-#define __NEURUN_BACKEND_ACL_CL_KERNEL_GENERATOR_H__
-
-#include <backend/IKernelGenerator.h>
-
-#include "ir/Operands.h"
-#include "TensorBuilder.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_cl
-{
-
-class KernelGenerator : public IKernelGenerator
-{
-public:
-  KernelGenerator(const ir::Operands &ctx, const std::shared_ptr<TensorBuilder> &tensor_builder);
-
-  void visit(const ir::OpSequence &) override;
-  void visit(const ir::operation::BatchToSpaceND &) override;
-  void visit(const ir::operation::Conv2D &) override;
-  void visit(const ir::operation::DepthwiseConv2D &) override;
-  void visit(const ir::operation::MaxPool2D &) override;
-  void visit(const ir::operation::AvgPool2D &) override;
-  void visit(const ir::operation::Concat &) override;
-  void visit(const ir::operation::FullyConnected &) override;
-  void visit(const ir::operation::Mul &) override;
-  void visit(const ir::operation::ReduceSum &) override;
-  void visit(const ir::operation::Reshape &) override;
-  void visit(const ir::operation::Squeeze &) override;
-  void visit(const ir::operation::Tanh &) override;
-  void visit(const ir::operation::Softmax &) override;
-  void visit(const ir::operation::Slice &) override;
-  void visit(const ir::operation::StridedSlice &) override;
-  void visit(const ir::operation::Transpose &) override;
-  void visit(const ir::operation::Add &) override;
-  void visit(const ir::operation::Sub &) override;
-  void visit(const ir::operation::Cast &) override;
-  void visit(const ir::operation::Div &) override;
-  void visit(const ir::operation::Exp &) override;
-  void visit(const ir::operation::InstanceNorm &) override;
-  void visit(const ir::operation::Logistic &) override;
-  void visit(const ir::operation::ReduceMax &) override;
-  void visit(const ir::operation::Comparison &) override;
-  void visit(const ir::operation::LogicalAnd &) override;
-  void visit(const ir::operation::LSTM &) override;
-  void visit(const ir::operation::Pack &) override;
-  void visit(const ir::operation::Permute &) override;
-  void visit(const ir::operation::RSQRT &) override;
-  void visit(const ir::operation::ReLU &) override;
-  void visit(const ir::operation::ResizeBilinear &) override;
-  void visit(const ir::operation::ReLU1 &) override;
-  void visit(const ir::operation::ReLU6 &) override;
-  void visit(const ir::operation::RNN &) override;
-  void visit(const ir::operation::Floor &) override;
-  void visit(const ir::operation::SpaceToBatchND &) override;
-  void visit(const ir::operation::SpaceToDepth &) override;
-  void visit(const ir::operation::L2Pool2D &) override;
-  void visit(const ir::operation::EmbeddingLookup &) override;
-  void visit(const ir::operation::L2Normalization &) override;
-  void visit(const ir::operation::HashtableLookup &) override;
-  void visit(const ir::operation::PReLU &) override;
-  void visit(const ir::operation::TransposeConv &) override;
-  void visit(const ir::operation::SQRT &) override;
-  void visit(const ir::operation::LogicalOr &) override;
-  void visit(const ir::operation::LogicalNot &) override;
-  void visit(const ir::operation::SquaredDifference &) override;
-  void visit(const ir::operation::TopKV2 &) override;
-  void visit(const ir::operation::Gather &) override;
-  void visit(const ir::operation::Neg &) override;
-  void visit(const ir::operation::Abs &) override;
-  void visit(const ir::operation::ArgMax &) override;
-  void visit(const ir::operation::Dequantize &) override;
-  void visit(const ir::operation::Mean &) override;
-  void visit(const ir::operation::LocalResponseNormalization &) override;
-  void visit(const ir::operation::DepthToSpace &) override;
-  void visit(const ir::operation::ReduceMin &) override;
-  void visit(const ir::operation::Split &) override;
-  void visit(const ir::operation::Unpack &) override;
-  void visit(const ir::operation::Pad &) override;
-  void visit(const ir::operation::Min &) override;
-  void visit(const ir::operation::Max &) override;
-
-private:
-  const ir::Operands &_ctx;
-  std::shared_ptr<TensorBuilder> _tensor_builder;
-  ir::Layout _current_subg_layout;
-};
-
-} // namespace acl_cl
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ACL_CL_KERNEL_GENERATOR_H__
diff --git a/runtime/neurun/backend/acl_cl/PluginClassesAllocator.cc b/runtime/neurun/backend/acl_cl/PluginClassesAllocator.cc
deleted file mode 100644
index ac3f0acff..000000000
--- a/runtime/neurun/backend/acl_cl/PluginClassesAllocator.cc
+++ /dev/null
@@ -1,33 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <util/logging.h>
-
-#include "Backend.h"
-
-extern "C" {
-neurun::backend::Backend *neurun_backend_create()
-{
-  VERBOSE(neurun_backend_create) << "'acl_cl' loaded\n";
-  return new neurun::backend::acl_cl::Backend;
-}
-
-void neurun_backend_destroy(neurun::backend::Backend *backend)
-{
-  VERBOSE(neurun_backend_create) << "'acl_cl' unloaded\n";
-  delete backend;
-}
-}
diff --git a/runtime/neurun/backend/acl_cl/ShapeFixer.cc b/runtime/neurun/backend/acl_cl/ShapeFixer.cc
deleted file mode 100644
index e1cbeab6c..000000000
--- a/runtime/neurun/backend/acl_cl/ShapeFixer.cc
+++ /dev/null
@@ -1,434 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ShapeFixer.h"
-
-#include <arm_compute/runtime/CL/CLFunctions.h>   // Include all ARM Compute CL functions
-#include <arm_compute/runtime/CL/CLFunctionsEx.h> // Include all ARM Compute EX CL functions
-
-#include <AclFunction.h>
-#include <Convert.h>
-#include <Swizzle.h>
-
-#include "ir/Index.h"
-#include "compiler/IExecutionBuilder.h"
-#include "exec/NopFunction.h"
-#include "util/logging.h"
-#include "util/Utils.h"
-#include "util/Padding.h"
-
-using ::neurun::compiler::IExecutionBuilder;
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_cl
-{
-
-using ::neurun::backend::acl_common::asAclFunction;
-
-ShapeFixer::ShapeFixer(const ir::Operands &ctx,
-                       const std::shared_ptr<TensorBuilder> &tensor_builder)
-    : _ctx(ctx), _tensor_builder(tensor_builder)
-{
-  assert(tensor_builder);
-}
-
-void ShapeFixer::visit(const ir::operation::BatchToSpaceND &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::BatchToSpaceND::Input::INPUT)};
-  _tensor_builder->dimCorrection(ofm_index, false);
-  _tensor_builder->dimCorrection(ifm_index, false);
-}
-
-void ShapeFixer::visit(const ir::operation::Cast &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Conv2D &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::DepthwiseConv2D &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::MaxPool2D &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::AvgPool2D &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Concat &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  _tensor_builder->dimCorrection(ofm_index, false);
-  for (const auto &input : node.getInputs())
-    _tensor_builder->dimCorrection(input, false);
-}
-
-void ShapeFixer::visit(const ir::operation::FullyConnected &node)
-{
-  using ir::operation::FullyConnected;
-  const auto input_index{node.getInputs().at(FullyConnected::Input::INPUT)};
-  const auto input_rank = _ctx.at(input_index).shape().rank();
-  if (input_rank == 4)
-    _tensor_builder->dimCorrection(input_index, false);
-}
-
-void ShapeFixer::visit(const ir::operation::Mul &node)
-{
-  const auto lhs_index{node.getInputs().at(ir::operation::Mul::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::Mul::Input::RHS)};
-
-  if (!(_ctx.at(lhs_index).shape() == _ctx.at(rhs_index).shape()))
-  {
-    const auto broadcast_rank =
-        std::max(_ctx.at(lhs_index).shape().rank(), _ctx.at(rhs_index).shape().rank());
-
-    // TODO remove const_cast later. For example, _ctx may need to be a non const variable or
-    //      a node to extend shape may be inserted in front of this operation
-    const_cast<ir::Shape &>(_ctx.at(lhs_index).shape()).extendRank(broadcast_rank);
-    const_cast<ir::Shape &>(_ctx.at(rhs_index).shape()).extendRank(broadcast_rank);
-  }
-}
-
-void ShapeFixer::visit(const ir::operation::ReduceSum &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Reshape &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::Reshape::Input::INPUT)};
-  _tensor_builder->dimCorrection(input_index, false);
-  _tensor_builder->dimCorrection(output_index, false);
-}
-
-void ShapeFixer::visit(const ir::operation::Squeeze &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  if (_ctx.at(output_index).shape().rank() == 0)
-    const_cast<ir::Shape &>(_ctx.at(output_index).shape()).extendRank(1);
-  const auto input_index{node.getInputs().at(ir::operation::Squeeze::Input::INPUT)};
-  _tensor_builder->dimCorrection(input_index, false);
-  _tensor_builder->dimCorrection(output_index, false);
-}
-
-void ShapeFixer::visit(const ir::operation::Tanh &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Softmax &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Slice &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::StridedSlice &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::StridedSlice::Input::INPUT)};
-  _tensor_builder->dimCorrection(ofm_index, false);
-  _tensor_builder->dimCorrection(ifm_index, false);
-}
-
-void ShapeFixer::visit(const ir::operation::Transpose &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Add &node)
-{
-  const auto lhs_index{node.getInputs().at(ir::operation::Add::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::Add::Input::RHS)};
-
-  if (!(_ctx.at(lhs_index).shape() == _ctx.at(rhs_index).shape()))
-  {
-    const auto broadcast_rank =
-        std::max(_ctx.at(lhs_index).shape().rank(), _ctx.at(rhs_index).shape().rank());
-    const_cast<ir::Shape &>(_ctx.at(lhs_index).shape()).extendRank(broadcast_rank);
-    const_cast<ir::Shape &>(_ctx.at(rhs_index).shape()).extendRank(broadcast_rank);
-  }
-}
-
-void ShapeFixer::visit(const ir::operation::Sub &node)
-{
-  const auto lhs_index{node.getInputs().at(ir::operation::Sub::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::Sub::Input::RHS)};
-
-  if (!(_ctx.at(lhs_index).shape() == _ctx.at(rhs_index).shape()))
-  {
-    const auto broadcast_rank =
-        std::max(_ctx.at(lhs_index).shape().rank(), _ctx.at(rhs_index).shape().rank());
-
-    // TODO remove const_cast later. For example, _ctx may need to be a non const variable or
-    //      a node to extend shape may be inserted in front of this operation
-    const_cast<ir::Shape &>(_ctx.at(lhs_index).shape()).extendRank(broadcast_rank);
-    const_cast<ir::Shape &>(_ctx.at(rhs_index).shape()).extendRank(broadcast_rank);
-  }
-}
-
-void ShapeFixer::visit(const ir::operation::Div &node)
-{
-  const auto lhs_index{node.getInputs().at(ir::operation::Div::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::Div::Input::RHS)};
-
-  if (!(_ctx.at(lhs_index).shape() == _ctx.at(rhs_index).shape()))
-  {
-    const auto broadcast_rank =
-        std::max(_ctx.at(lhs_index).shape().rank(), _ctx.at(rhs_index).shape().rank());
-
-    // TODO remove const_cast later. For example, _ctx may need to be a non const variable or
-    //      a node to extend shape may be inserted in front of this operation
-    const_cast<ir::Shape &>(_ctx.at(lhs_index).shape()).extendRank(broadcast_rank);
-    const_cast<ir::Shape &>(_ctx.at(rhs_index).shape()).extendRank(broadcast_rank);
-  }
-}
-
-void ShapeFixer::visit(const ir::operation::Exp &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::InstanceNorm &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Logistic &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::LogicalAnd &node)
-{
-  const auto input0_index{node.getInputs().at(ir::operation::LogicalAnd::Input::INPUT0)};
-  const auto input1_index{node.getInputs().at(ir::operation::LogicalAnd::Input::INPUT1)};
-
-  if (!(_ctx.at(input0_index).shape() == _ctx.at(input1_index).shape()))
-  {
-    const auto broadcast_rank =
-        std::max(_ctx.at(input0_index).shape().rank(), _ctx.at(input1_index).shape().rank());
-
-    // TODO remove const_cast later. For example, _ctx may need to be a non const variable or
-    //      a node to extend shape may be inserted in front of this operation
-    const_cast<ir::Shape &>(_ctx.at(input0_index).shape()).extendRank(broadcast_rank);
-    const_cast<ir::Shape &>(_ctx.at(input1_index).shape()).extendRank(broadcast_rank);
-  }
-}
-
-void ShapeFixer::visit(const ir::operation::LSTM &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::ReduceMax &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Comparison &node)
-{
-  const auto input0_index{node.getInputs().at(ir::operation::Comparison::Input::INPUT0)};
-  const auto input1_index{node.getInputs().at(ir::operation::Comparison::Input::INPUT1)};
-
-  if (!(_ctx.at(input0_index).shape() == _ctx.at(input1_index).shape()))
-  {
-    const auto broadcast_rank =
-        std::max(_ctx.at(input0_index).shape().rank(), _ctx.at(input1_index).shape().rank());
-
-    // TODO remove const_cast later. For example, _ctx may need to be a non const variable or
-    //      a node to extend shape may be inserted in front of this operation
-    const_cast<ir::Shape &>(_ctx.at(input0_index).shape()).extendRank(broadcast_rank);
-    const_cast<ir::Shape &>(_ctx.at(input1_index).shape()).extendRank(broadcast_rank);
-  }
-}
-
-void ShapeFixer::visit(const ir::operation::Pack &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  _tensor_builder->dimCorrection(ofm_index, false);
-  for (const auto &inputs : node.getInputs())
-  {
-    _tensor_builder->dimCorrection(inputs, false);
-    const auto ofm_rank = _ctx.at(ofm_index).shape().rank();
-
-    // TODO remove const_cast later. For example, _ctx may need to be a non const variable or
-    //      a node to extend shape may be inserted in front of this operation
-    const_cast<ir::Shape &>(_ctx.at(inputs).shape()).extendRank(ofm_rank);
-  }
-}
-
-void ShapeFixer::visit(const ir::operation::Permute &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::RSQRT &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::ReLU &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::ResizeBilinear &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::ReLU1 &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::ReLU6 &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::RNN &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Floor &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::SpaceToBatchND &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::SpaceToBatchND::Input::INPUT)};
-  _tensor_builder->dimCorrection(ofm_index, false);
-  _tensor_builder->dimCorrection(ifm_index, false);
-}
-
-void ShapeFixer::visit(const ir::operation::SpaceToDepth &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::SpaceToDepth::Input::INPUT)};
-  _tensor_builder->dimCorrection(ofm_index, false);
-  _tensor_builder->dimCorrection(ifm_index, false);
-}
-
-void ShapeFixer::visit(const ir::operation::L2Pool2D &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::EmbeddingLookup &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto values_index{node.getInputs().at(ir::operation::EmbeddingLookup::Input::VALUES)};
-  _tensor_builder->dimCorrection(values_index, false);
-  _tensor_builder->dimCorrection(output_index, false);
-}
-
-void ShapeFixer::visit(const ir::operation::L2Normalization &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::HashtableLookup &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::PReLU &node)
-{
-  const auto ifm_index{node.getInputs().at(ir::operation::PReLU::Input::INPUT)};
-  const auto alpha_index{node.getInputs().at(ir::operation::PReLU::Input::ALPHA)};
-
-  if (!(_ctx.at(ifm_index).shape() == _ctx.at(alpha_index).shape()))
-  {
-    const auto broadcast_rank =
-        std::max(_ctx.at(ifm_index).shape().rank(), _ctx.at(alpha_index).shape().rank());
-    const_cast<ir::Shape &>(_ctx.at(ifm_index).shape()).extendRank(broadcast_rank);
-    const_cast<ir::Shape &>(_ctx.at(alpha_index).shape()).extendRank(broadcast_rank);
-  }
-}
-
-void ShapeFixer::visit(const ir::operation::TransposeConv &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::SQRT &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::LogicalOr &node)
-{
-  const auto input0_index{node.getInputs().at(ir::operation::LogicalOr::Input::INPUT0)};
-  const auto input1_index{node.getInputs().at(ir::operation::LogicalOr::Input::INPUT1)};
-
-  if (!(_ctx.at(input0_index).shape() == _ctx.at(input1_index).shape()))
-  {
-    const auto broadcast_rank =
-        std::max(_ctx.at(input0_index).shape().rank(), _ctx.at(input1_index).shape().rank());
-    const_cast<ir::Shape &>(_ctx.at(input0_index).shape()).extendRank(broadcast_rank);
-    const_cast<ir::Shape &>(_ctx.at(input1_index).shape()).extendRank(broadcast_rank);
-  }
-}
-
-void ShapeFixer::visit(const ir::operation::LogicalNot &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::SquaredDifference &node)
-{
-  const auto lhs_index{node.getInputs().at(ir::operation::SquaredDifference::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::SquaredDifference::Input::RHS)};
-
-  if (!(_ctx.at(lhs_index).shape() == _ctx.at(rhs_index).shape()))
-  {
-    const auto broadcast_rank =
-        std::max(_ctx.at(lhs_index).shape().rank(), _ctx.at(rhs_index).shape().rank());
-    const_cast<ir::Shape &>(_ctx.at(lhs_index).shape()).extendRank(broadcast_rank);
-    const_cast<ir::Shape &>(_ctx.at(rhs_index).shape()).extendRank(broadcast_rank);
-  }
-}
-
-void ShapeFixer::visit(const ir::operation::TopKV2 &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Gather &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::Gather::Input::INPUT)};
-  const auto indices_index{node.getInputs().at(ir::operation::Gather::Input::INDICES)};
-  _tensor_builder->dimCorrection(ofm_index, false);
-  _tensor_builder->dimCorrection(ifm_index, false);
-  _tensor_builder->dimCorrection(indices_index, false);
-}
-
-void ShapeFixer::visit(const ir::operation::Neg &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Abs &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::ArgMax &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::ArgMax::Input::INPUT)};
-  _tensor_builder->dimCorrection(ofm_index, false);
-  _tensor_builder->dimCorrection(ifm_index, false);
-}
-
-void ShapeFixer::visit(const ir::operation::Dequantize &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Mean &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::LocalResponseNormalization &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::DepthToSpace &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::ReduceMin &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Split &node)
-{
-  const auto input_index{node.getInputs().at(ir::operation::Split::Input::INPUT)};
-  _tensor_builder->dimCorrection(input_index, false);
-  for (const auto &output : node.getOutputs())
-    _tensor_builder->dimCorrection(output, false);
-}
-
-void ShapeFixer::visit(const ir::operation::Unpack &node)
-{
-  const auto input_index{node.getInputs().at(ir::operation::Unpack::Input::INPUT)};
-  _tensor_builder->dimCorrection(input_index, false);
-  for (const auto &output_index : node.getOutputs())
-    _tensor_builder->dimCorrection(output_index, false);
-}
-
-void ShapeFixer::visit(const ir::operation::Pad &node)
-{
-  const auto input_index{node.getInputs().at(ir::operation::Pad::Input::INPUT)};
-  const auto output_index{node.getOutputs().at(0)};
-  _tensor_builder->dimCorrection(input_index, false);
-  _tensor_builder->dimCorrection(output_index, false);
-}
-
-void ShapeFixer::visit(const ir::operation::Min &node)
-{
-  const auto lhs_index{node.getInputs().at(ir::operation::Min::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::Min::Input::RHS)};
-
-  if (!(_ctx.at(lhs_index).shape() == _ctx.at(rhs_index).shape()))
-  {
-    const auto broadcast_rank =
-        std::max(_ctx.at(lhs_index).shape().rank(), _ctx.at(rhs_index).shape().rank());
-
-    // TODO remove const_cast later. For example, _ctx may need to be a non const variable or
-    //      a node to extend shape may be inserted in front of this operation
-    const_cast<ir::Shape &>(_ctx.at(lhs_index).shape()).extendRank(broadcast_rank);
-    const_cast<ir::Shape &>(_ctx.at(rhs_index).shape()).extendRank(broadcast_rank);
-  }
-}
-
-void ShapeFixer::visit(const ir::operation::Max &node)
-{
-  const auto lhs_index{node.getInputs().at(ir::operation::Max::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::Max::Input::RHS)};
-
-  if (!(_ctx.at(lhs_index).shape() == _ctx.at(rhs_index).shape()))
-  {
-    const auto broadcast_rank =
-        std::max(_ctx.at(lhs_index).shape().rank(), _ctx.at(rhs_index).shape().rank());
-
-    // TODO remove const_cast later. For example, _ctx may need to be a non const variable or
-    //      a node to extend shape may be inserted in front of this operation
-    const_cast<ir::Shape &>(_ctx.at(lhs_index).shape()).extendRank(broadcast_rank);
-    const_cast<ir::Shape &>(_ctx.at(rhs_index).shape()).extendRank(broadcast_rank);
-  }
-}
-
-} // namespace acl_cl
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/acl_cl/ShapeFixer.h b/runtime/neurun/backend/acl_cl/ShapeFixer.h
deleted file mode 100644
index ec5f5c896..000000000
--- a/runtime/neurun/backend/acl_cl/ShapeFixer.h
+++ /dev/null
@@ -1,110 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_CL_SHAPE_FIXER_H__
-#define __NEURUN_BACKEND_ACL_CL_SHAPE_FIXER_H__
-
-#include <backend/IShapeFixer.h>
-
-#include "ir/Operands.h"
-#include "TensorBuilder.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_cl
-{
-
-class ShapeFixer : public IShapeFixer
-{
-public:
-  ShapeFixer(const ir::Operands &ctx, const std::shared_ptr<TensorBuilder> &tensor_builder);
-
-  void visit(const ir::operation::BatchToSpaceND &) override;
-  void visit(const ir::operation::Conv2D &) override;
-  void visit(const ir::operation::DepthwiseConv2D &) override;
-  void visit(const ir::operation::MaxPool2D &) override;
-  void visit(const ir::operation::AvgPool2D &) override;
-  void visit(const ir::operation::Concat &) override;
-  void visit(const ir::operation::FullyConnected &) override;
-  void visit(const ir::operation::Mul &) override;
-  void visit(const ir::operation::ReduceSum &) override;
-  void visit(const ir::operation::Reshape &) override;
-  void visit(const ir::operation::Squeeze &) override;
-  void visit(const ir::operation::Tanh &) override;
-  void visit(const ir::operation::Softmax &) override;
-  void visit(const ir::operation::Slice &) override;
-  void visit(const ir::operation::StridedSlice &) override;
-  void visit(const ir::operation::Transpose &) override;
-  void visit(const ir::operation::Add &) override;
-  void visit(const ir::operation::Sub &) override;
-  void visit(const ir::operation::Cast &) override;
-  void visit(const ir::operation::Div &) override;
-  void visit(const ir::operation::Exp &) override;
-  void visit(const ir::operation::InstanceNorm &) override;
-  void visit(const ir::operation::Logistic &) override;
-  void visit(const ir::operation::ReduceMax &) override;
-  void visit(const ir::operation::Comparison &) override;
-  void visit(const ir::operation::LogicalAnd &) override;
-  void visit(const ir::operation::LSTM &) override;
-  void visit(const ir::operation::Pack &) override;
-  void visit(const ir::operation::Permute &) override;
-  void visit(const ir::operation::RSQRT &) override;
-  void visit(const ir::operation::ReLU &) override;
-  void visit(const ir::operation::ResizeBilinear &) override;
-  void visit(const ir::operation::ReLU1 &) override;
-  void visit(const ir::operation::ReLU6 &) override;
-  void visit(const ir::operation::RNN &) override;
-  void visit(const ir::operation::Floor &) override;
-  void visit(const ir::operation::SpaceToBatchND &) override;
-  void visit(const ir::operation::SpaceToDepth &) override;
-  void visit(const ir::operation::L2Pool2D &) override;
-  void visit(const ir::operation::EmbeddingLookup &) override;
-  void visit(const ir::operation::L2Normalization &) override;
-  void visit(const ir::operation::HashtableLookup &) override;
-  void visit(const ir::operation::PReLU &) override;
-  void visit(const ir::operation::TransposeConv &) override;
-  void visit(const ir::operation::SQRT &) override;
-  void visit(const ir::operation::LogicalOr &) override;
-  void visit(const ir::operation::LogicalNot &) override;
-  void visit(const ir::operation::SquaredDifference &) override;
-  void visit(const ir::operation::TopKV2 &) override;
-  void visit(const ir::operation::Gather &) override;
-  void visit(const ir::operation::Neg &) override;
-  void visit(const ir::operation::Abs &) override;
-  void visit(const ir::operation::ArgMax &) override;
-  void visit(const ir::operation::Dequantize &) override;
-  void visit(const ir::operation::Mean &) override;
-  void visit(const ir::operation::LocalResponseNormalization &) override;
-  void visit(const ir::operation::DepthToSpace &) override;
-  void visit(const ir::operation::ReduceMin &) override;
-  void visit(const ir::operation::Split &) override;
-  void visit(const ir::operation::Unpack &) override;
-  void visit(const ir::operation::Pad &) override;
-  void visit(const ir::operation::Min &) override;
-  void visit(const ir::operation::Max &) override;
-
-private:
-  const ir::Operands &_ctx;
-  std::shared_ptr<TensorBuilder> _tensor_builder;
-};
-
-} // namespace acl_cl
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ACL_CL_SHAPE_FIXER_H__
diff --git a/runtime/neurun/backend/acl_cl/TensorBuilder.h b/runtime/neurun/backend/acl_cl/TensorBuilder.h
deleted file mode 100644
index b9a0dd4a6..000000000
--- a/runtime/neurun/backend/acl_cl/TensorBuilder.h
+++ /dev/null
@@ -1,39 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_CL_TENSOR_BUILDER_H__
-#define __NEURUN_BACKEND_ACL_CL_TENSOR_BUILDER_H__
-
-#include <TemplTensorBuilder.h>
-
-#include "operand/CLTensor.h"
-#include "operand/CLSubTensor.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_cl
-{
-
-using TensorBuilder =
-    acl_common::TemplTensorBuilder<operand::ICLTensor, operand::CLTensor, operand::CLSubTensor>;
-
-} // namespace acl_cl
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ACL_CL_TENSOR_BUILDER_H__
diff --git a/runtime/neurun/backend/acl_cl/TensorManager.h b/runtime/neurun/backend/acl_cl/TensorManager.h
deleted file mode 100644
index fd2a9059c..000000000
--- a/runtime/neurun/backend/acl_cl/TensorManager.h
+++ /dev/null
@@ -1,80 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_CL_TENSOR_MANAGER_H__
-#define __NEURUN_BACKEND_ACL_CL_TENSOR_MANAGER_H__
-
-#include <arm_compute/runtime/CL/CLBufferAllocator.h>
-#include <arm_compute/runtime/PoolManager.h>
-#include <arm_compute/runtime/BlobLifetimeManager.h>
-#include <arm_compute/runtime/MemoryManagerOnDemand.h>
-#include <arm_compute/runtime/CL/CLMemoryGroup.h>
-
-#include <AclMemoryManager.h>
-#include <AclLinearMemoryManager.h>
-#include <AclInternalBufferManager.h>
-#include <AclTensorManager.h>
-
-#include "operand/CLTensor.h"
-#include "operand/CLSubTensor.h"
-
-#include "util/logging.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_cl
-{
-
-using MemoryManager =
-    acl_common::AclMemoryManager<operand::ICLTensor, operand::CLTensor, operand::CLSubTensor>;
-
-using LinearMemoryManager = acl_common::AclLinearMemoryManager<
-    operand::ICLTensor, operand::CLTensor, operand::CLSubTensor,
-    ::arm_compute::MemoryManagerOnDemand, ::arm_compute::PoolManager,
-    ::arm_compute::BlobLifetimeManager, ::arm_compute::CLBufferAllocator,
-    ::arm_compute::CLMemoryGroup>;
-
-using InternalBufferManager = acl_common::AclInternalBufferManager<
-    ::arm_compute::MemoryManagerOnDemand, ::arm_compute::PoolManager,
-    ::arm_compute::BlobLifetimeManager, ::arm_compute::CLBufferAllocator>;
-
-using TensorManager =
-    acl_common::AclTensorManager<operand::ICLTensor, operand::CLTensor, operand::CLSubTensor>;
-
-TensorManager *createTensorManager()
-{
-  const std::string executor_str = util::getConfigString(util::config::EXECUTOR);
-
-  if (executor_str == "Linear")
-  {
-    VERBOSE(acl_cl_createTensorManager) << "AclTensorManager as Linear" << std::endl;
-    return new TensorManager(new MemoryManager(), new LinearMemoryManager(),
-                             new InternalBufferManager());
-  }
-  else
-  {
-    VERBOSE(acl_cl_createTensorManager) << "AclTensorManager" << std::endl;
-    return new TensorManager(new MemoryManager(), new MemoryManager(), new InternalBufferManager());
-  }
-}
-
-} // namespace acl_cl
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ACL_CL_TENSOR_MANAGER_H__
diff --git a/runtime/neurun/backend/acl_cl/TensorRegister.h b/runtime/neurun/backend/acl_cl/TensorRegister.h
deleted file mode 100644
index 02de45580..000000000
--- a/runtime/neurun/backend/acl_cl/TensorRegister.h
+++ /dev/null
@@ -1,51 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_CL_TENSOR_REGISTER_H__
-#define __NEURUN_BACKEND_ACL_CL_TENSOR_REGISTER_H__
-
-#include <AclTensorRegister.h>
-#include <misc/polymorphic_downcast.h>
-#include "TensorBuilder.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_cl
-{
-
-class TensorRegister : public acl_common::AclTensorRegister
-{
-public:
-  TensorRegister(const ir::Operands &operands, const std::shared_ptr<TensorBuilder> &tensor_builder)
-      : acl_common::AclTensorRegister{operands, tensor_builder}
-  {
-    // DO NOTHING
-  }
-
-  void setUsesCount(const ir::OperandIndex &ind, size_t num_uses) const override
-  {
-    nnfw::misc::polymorphic_downcast<TensorBuilder *>(tensor_builder().get())
-        ->setUsesCount(ind, num_uses);
-  }
-};
-
-} // namespace acl_cl
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ACL_CL_TENSOR_REGISTER_H__
diff --git a/runtime/neurun/backend/acl_cl/operand/CLSubTensor.cc b/runtime/neurun/backend/acl_cl/operand/CLSubTensor.cc
deleted file mode 100644
index 70c8829d9..000000000
--- a/runtime/neurun/backend/acl_cl/operand/CLSubTensor.cc
+++ /dev/null
@@ -1,44 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "CLSubTensor.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_cl
-{
-namespace operand
-{
-
-CLSubTensor::CLSubTensor(ICLTensor *parent, const arm_compute::TensorShape &tensor_shape,
-                         const arm_compute::Coordinates &coords, size_t rank, bool extend_parent)
-    : _cl_sub_tensor(std::make_shared<arm_compute::CLSubTensor>(parent->handle(), tensor_shape,
-                                                                coords, extend_parent)),
-      _rank{rank}
-{
-  // DO NOTHING
-}
-
-const arm_compute::CLSubTensor *CLSubTensor::handle() const { return _cl_sub_tensor.get(); }
-
-arm_compute::CLSubTensor *CLSubTensor::handle() { return _cl_sub_tensor.get(); }
-
-} // namespace operand
-} // namespace acl_cl
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/acl_cl/operand/CLSubTensor.h b/runtime/neurun/backend/acl_cl/operand/CLSubTensor.h
deleted file mode 100644
index 8eba3760f..000000000
--- a/runtime/neurun/backend/acl_cl/operand/CLSubTensor.h
+++ /dev/null
@@ -1,63 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_CL_OPERAND_CL_SUB_TENSOR_H__
-#define __NEURUN_BACKEND_ACL_CL_OPERAND_CL_SUB_TENSOR_H__
-
-#include <arm_compute/runtime/CL/CLSubTensor.h>
-#include "ICLTensor.h"
-#include "compiler/SubTensorInfo.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_cl
-{
-namespace operand
-{
-
-class CLSubTensor : public ICLTensor
-{
-public:
-  CLSubTensor() = delete;
-
-public:
-  CLSubTensor(ICLTensor *parent, const arm_compute::TensorShape &tensor_shape,
-              const arm_compute::Coordinates &coords, size_t rank, bool extend_parent = false);
-
-public:
-  size_t num_dimensions() const final { return _rank; }
-
-public:
-  const arm_compute::CLSubTensor *handle() const override;
-  arm_compute::CLSubTensor *handle() override;
-
-public:
-  // This method is used to prevent the use of memcpy for SubTensor
-  bool has_padding() const override { return true; }
-
-private:
-  std::shared_ptr<arm_compute::CLSubTensor> _cl_sub_tensor;
-  size_t _rank;
-};
-
-} // namespace operand
-} // namespace acl_cl
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ACL_CL_OPERAND_CL_SUB_TENSOR_H__
diff --git a/runtime/neurun/backend/acl_cl/operand/CLTensor.cc b/runtime/neurun/backend/acl_cl/operand/CLTensor.cc
deleted file mode 100644
index dab74e65f..000000000
--- a/runtime/neurun/backend/acl_cl/operand/CLTensor.cc
+++ /dev/null
@@ -1,62 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "CLTensor.h"
-
-#include <arm_compute/runtime/CL/CLScheduler.h>
-#include <arm_compute/runtime/CL/CLMemory.h>
-#include <arm_compute/runtime/CL/CLMemoryRegion.h>
-
-#include <Convert.h>
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_cl
-{
-namespace operand
-{
-
-CLTensor::CLTensor(const arm_compute::TensorInfo &info, size_t rank, size_t num_uses)
-    : _cl_tensor(std::make_shared<arm_compute::CLTensor>()), _rank{rank}, _num_uses{num_uses}
-{
-  allocator()->init(info);
-}
-
-const arm_compute::CLTensor *CLTensor::handle() const { return _cl_tensor.get(); }
-
-arm_compute::CLTensor *CLTensor::handle() { return _cl_tensor.get(); }
-
-arm_compute::CLTensorAllocator *CLTensor::allocator() { return _cl_tensor->allocator(); }
-
-void CLTensor::map(bool blocking) { _cl_tensor->map(blocking); }
-
-void CLTensor::unmap() { _cl_tensor->unmap(); }
-
-void CLTensor::setBuffer(void *host_ptr)
-{
-  // Constructs a Buffer on a user-supplied memory
-  auto buffer = cl::Buffer(arm_compute::CLScheduler::get().context(),
-                           CL_MEM_USE_HOST_PTR | CL_MEM_READ_WRITE, info()->total_size(), host_ptr);
-  // import memory
-  allocator()->import_memory(buffer);
-}
-
-} // namespace operand
-} // namespace acl_cl
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/acl_cl/operand/CLTensor.h b/runtime/neurun/backend/acl_cl/operand/CLTensor.h
deleted file mode 100644
index 8518bf0c3..000000000
--- a/runtime/neurun/backend/acl_cl/operand/CLTensor.h
+++ /dev/null
@@ -1,75 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_CL_OPERAND_CL_TENSOR_H__
-#define __NEURUN_BACKEND_ACL_CL_OPERAND_CL_TENSOR_H__
-
-#include <arm_compute/core/TensorInfo.h>
-#include <arm_compute/runtime/CL/CLTensor.h>
-#include <arm_compute/runtime/CL/CLScheduler.h>
-#include "arm_compute/runtime/CL/CLTensorAllocator.h"
-#include "ICLTensor.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_cl
-{
-namespace operand
-{
-
-class CLTensor : public ICLTensor
-{
-public:
-  CLTensor() = delete;
-
-public:
-  CLTensor(const arm_compute::TensorInfo &info, size_t rank, size_t num_uses);
-
-public:
-  size_t num_dimensions() const final { return _rank; }
-
-public:
-  const arm_compute::CLTensor *handle() const override;
-  arm_compute::CLTensor *handle() override;
-  size_t num_uses() const { return _num_uses; }
-
-public:
-  arm_compute::CLTensorAllocator *allocator();
-  void map(bool blocking = true);
-  void unmap();
-  /** Set given buffer as the buffer of the tensor
-   *
-   * @note Ownership of the memory is not transferred to this object.
-   *       Thus management (allocate/free) should be done by the client.
-   *
-   * @param[in] host_ptr Storage to be used.
-   */
-  void setBuffer(void *host_ptr);
-
-private:
-  std::shared_ptr<arm_compute::CLTensor> _cl_tensor;
-  size_t _rank;
-  size_t _num_uses;
-};
-
-} // namespace operand
-} // namespace acl_cl
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ACL_CL_OPERAND_CL_TENSOR_H__
diff --git a/runtime/neurun/backend/acl_cl/operand/ICLTensor.cc b/runtime/neurun/backend/acl_cl/operand/ICLTensor.cc
deleted file mode 100644
index 6b14584e0..000000000
--- a/runtime/neurun/backend/acl_cl/operand/ICLTensor.cc
+++ /dev/null
@@ -1,45 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ICLTensor.h"
-
-#include <arm_compute/runtime/CL/CLScheduler.h>
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_cl
-{
-namespace operand
-{
-
-void ICLTensor::access(const std::function<void(ITensor &tensor)> &fn)
-{
-  auto &queue = ::arm_compute::CLScheduler::get().queue();
-
-  // This is an optional input
-  if (total_size() == 0)
-    return;
-
-  map(queue);
-  fn(*this);
-  unmap(queue);
-}
-} // namespace operand
-} // namespace acl_cl
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/acl_cl/operand/ICLTensor.h b/runtime/neurun/backend/acl_cl/operand/ICLTensor.h
deleted file mode 100644
index 68e4e7fc5..000000000
--- a/runtime/neurun/backend/acl_cl/operand/ICLTensor.h
+++ /dev/null
@@ -1,50 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_CL_OPERAND_I_CL_TENSOR_H__
-#define __NEURUN_BACKEND_ACL_CL_OPERAND_I_CL_TENSOR_H__
-
-#include <arm_compute/core/CL/ICLTensor.h>
-
-#include <IACLTensor.h>
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_cl
-{
-namespace operand
-{
-
-class ICLTensor : public acl_common::IACLTensor
-{
-public:
-  const arm_compute::ICLTensor *handle() const override = 0;
-  arm_compute::ICLTensor *handle() override = 0;
-
-public:
-  void map(cl::CommandQueue &q, bool blocking = true) { return handle()->map(q, blocking); }
-  void unmap(cl::CommandQueue &q) { return handle()->unmap(q); }
-  void access(const std::function<void(ITensor &tensor)> &fn) final;
-};
-
-} // namespace operand
-} // namespace acl_cl
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ACL_CL_OPERAND_I_CL_TENSOR_H__
diff --git a/runtime/neurun/backend/acl_common/AclFunction.h b/runtime/neurun/backend/acl_common/AclFunction.h
deleted file mode 100644
index a63f3807b..000000000
--- a/runtime/neurun/backend/acl_common/AclFunction.h
+++ /dev/null
@@ -1,60 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_COMMON_KERNEL_ACL_FUNCTION_H__
-#define __NEURUN_BACKEND_ACL_COMMON_KERNEL_ACL_FUNCTION_H__
-
-#include <exec/IFunction.h>
-#include <arm_compute/runtime/IFunction.h>
-#include <arm_compute/runtime/CL/CLScheduler.h>
-#include <memory>
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_common
-{
-
-class AclFunction : public ::neurun::exec::IFunction
-{
-public:
-  AclFunction() = delete;
-
-public:
-  AclFunction(std::unique_ptr<::arm_compute::IFunction> &&func) : _func(std::move(func))
-  {
-    // DO NOTHING
-  }
-
-public:
-  void run() override { _func->run(); }
-  void runSync() override
-  {
-    run();
-    arm_compute::CLScheduler::get().sync();
-  }
-  void prepare() override { _func->prepare(); }
-
-private:
-  std::unique_ptr<::arm_compute::IFunction> _func;
-};
-
-} // namespace acl_common
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ACL_COMMON_KERNEL_ACL_FUNCTION_H__
diff --git a/runtime/neurun/backend/acl_common/AclInternalBufferManager.h b/runtime/neurun/backend/acl_common/AclInternalBufferManager.h
deleted file mode 100644
index b7183c86e..000000000
--- a/runtime/neurun/backend/acl_common/AclInternalBufferManager.h
+++ /dev/null
@@ -1,97 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_COMMON_INTERNAL_BUFFER_MANAGER_H__
-#define __NEURUN_BACKEND_ACL_COMMON_INTERNAL_BUFFER_MANAGER_H__
-
-#include <arm_compute/runtime/IMemoryManager.h>
-#include <cassert>
-#include <memory>
-#include <backend/IMemoryManager.h>
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_common
-{
-
-// NOTE. If any backend can use something like InternalBufferManager,
-// this interface can be moved to core/include/backend/
-/**
- * @brief Interface for InternalBufferManager which has ::arm_compute::IMemoryManager pointer
- */
-struct IInternalBufferManager : public backend::IMemoryManager
-{
-  virtual ~IInternalBufferManager() = default;
-
-  /**
-   * @brief Get shared_ptr of ::arm_compute::IMemoryManager
-   */
-  virtual std::shared_ptr<::arm_compute::IMemoryManager> internal_buffer_manager(void) = 0;
-};
-
-/**
- * @brief class for InternalBufferManager which has ::arm_compute::IMemoryManager pointer
- */
-template <typename T_MemoryManager, typename T_PoolManager, typename T_LifetimeManager,
-          typename T_Allocator>
-class AclInternalBufferManager : public IInternalBufferManager
-{
-public:
-  AclInternalBufferManager() : _allocator{nullptr}
-  {
-    std::shared_ptr<T_LifetimeManager> lifetime_mgr = std::make_shared<T_LifetimeManager>();
-    std::shared_ptr<T_PoolManager> pool_mgr = std::make_shared<T_PoolManager>();
-
-    _internal_manager = std::make_shared<T_MemoryManager>(lifetime_mgr, pool_mgr);
-    assert(_internal_manager);
-  }
-
-  virtual ~AclInternalBufferManager() = default;
-
-  /**
-   * @brief Allocate the internal buffer manager on acl
-   */
-  void allocate(void) override
-  {
-    _allocator = std::make_shared<T_Allocator>();
-    _internal_manager->populate(*_allocator, 1);
-  }
-
-  /**
-   * @brief Deallocate the internal buffer manager on acl
-   */
-  void deallocate(void) override { _internal_manager->clear(); }
-
-  /**
-   * @brief Get shared_ptr of ::arm_compute::IMemoryManager
-   */
-  std::shared_ptr<::arm_compute::IMemoryManager> internal_buffer_manager(void) override
-  {
-    return _internal_manager;
-  }
-
-private:
-  std::shared_ptr<T_Allocator> _allocator;
-  std::shared_ptr<T_MemoryManager> _internal_manager;
-};
-
-} // namespace acl_common
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ACL_COMMON_INTERNAL_BUFFER_MANAGER_H__
diff --git a/runtime/neurun/backend/acl_common/AclLinearMemoryManager.h b/runtime/neurun/backend/acl_common/AclLinearMemoryManager.h
deleted file mode 100644
index 7ed719bc3..000000000
--- a/runtime/neurun/backend/acl_common/AclLinearMemoryManager.h
+++ /dev/null
@@ -1,110 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_COMMON_LINEAR_MEMORY_MANAGER_H__
-#define __NEURUN_BACKEND_ACL_COMMON_LINEAR_MEMORY_MANAGER_H__
-
-#include <cassert>
-
-#include "AclMemoryManager.h"
-#include "ir/OperandIndexMap.h"
-#include "util/logging.h"
-
-namespace
-{
-
-template <typename T_MemoryManager, typename T_PoolManager, typename T_LifetimeManager>
-std::shared_ptr<T_MemoryManager> createMemoryManager()
-{
-  std::shared_ptr<T_LifetimeManager> lifetime_mgr = std::make_shared<T_LifetimeManager>();
-  std::shared_ptr<T_PoolManager> pool_mgr = std::make_shared<T_PoolManager>();
-
-  std::shared_ptr<T_MemoryManager> mem_mgr =
-      std::make_shared<T_MemoryManager>(lifetime_mgr, pool_mgr);
-  return mem_mgr;
-}
-
-} // namespace anonymous
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_common
-{
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor, typename T_MemoryManager,
-          typename T_PoolManager, typename T_LifetimeManager, typename T_Allocator,
-          typename T_MemoryGroup>
-class AclLinearMemoryManager : public AclMemoryManager<T_ITensor, T_Tensor, T_SubTensor>
-{
-public:
-  AclLinearMemoryManager()
-      : _allocator{nullptr},
-        _io_manager{createMemoryManager<T_MemoryManager, T_PoolManager, T_LifetimeManager>()},
-        _io_group{std::make_shared<T_MemoryGroup>(_io_manager)}
-  {
-    // DO NOTHING
-  }
-
-  virtual ~AclLinearMemoryManager() = default;
-
-  void allocate(void) override
-  {
-    _allocator = std::make_shared<T_Allocator>();
-    _io_manager->populate(*_allocator, 1);
-    _io_group->acquire();
-  }
-
-  void deallocate(void) override
-  {
-    _io_group->release();
-    _io_manager->clear();
-  }
-
-  void startLifetime(const ir::OperandIndex &ind) override
-  {
-    auto &tensors = this->tensors();
-    assert(tensors.find(ind) != tensors.end());
-
-    auto tensor = tensors[ind];
-    assert(tensor->handle());
-
-    _io_group->manage(tensor->handle());
-  }
-
-  void finishLifetime(const ir::OperandIndex &ind) override
-  {
-    auto &tensors = this->tensors();
-    assert(tensors.find(ind) != tensors.end());
-
-    auto tensor = tensors[ind];
-    assert(tensor->allocator());
-
-    tensor->allocator()->allocate();
-  }
-
-private:
-  std::shared_ptr<T_Allocator> _allocator;
-  std::shared_ptr<T_MemoryManager> _io_manager;
-  std::shared_ptr<T_MemoryGroup> _io_group;
-};
-
-} // namespace acl_common
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ACL_COMMON_LINEAR_MEMORY_MANAGER_H__
diff --git a/runtime/neurun/backend/acl_common/AclMemoryManager.h b/runtime/neurun/backend/acl_common/AclMemoryManager.h
deleted file mode 100644
index af7f3a460..000000000
--- a/runtime/neurun/backend/acl_common/AclMemoryManager.h
+++ /dev/null
@@ -1,98 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_COMMON_MEMORY_MANAGER_H__
-#define __NEURUN_BACKEND_ACL_COMMON_MEMORY_MANAGER_H__
-
-#include <arm_compute/core/Types.h>
-#include <arm_compute/runtime/IMemoryManager.h>
-#include <cassert>
-
-#include "backend/IMemoryManager.h"
-#include "ir/OperandIndexMap.h"
-#include "Convert.h"
-#include "util/logging.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_common
-{
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-class AclMemoryManager : public backend::IMemoryManager
-{
-public:
-  AclMemoryManager()
-  {
-    // DO NOTHING
-  }
-
-  virtual ~AclMemoryManager() = default;
-
-  void allocate(void) override
-  {
-    for (const auto &tensor_entry : _tensors)
-    {
-      auto tensor = tensor_entry.second;
-      tensor->allocator()->allocate();
-    }
-  }
-
-  void deallocate(void) override
-  {
-    for (const auto &tensor_entry : _tensors)
-    {
-      auto tensor = tensor_entry.second;
-      tensor->allocator()->free();
-    }
-  }
-
-  virtual void startLifetime(const ir::OperandIndex &) { /* DO NOTHING */}
-  virtual void finishLifetime(const ir::OperandIndex &) { /* DO NOTHING */}
-
-  void buildTensor(const ir::OperandIndex &ind, const ::arm_compute::TensorInfo &info, size_t rank,
-                   size_t num_uses)
-  {
-    auto tensor = std::make_shared<T_Tensor>(info, rank, num_uses);
-    _tensors[ind] = tensor;
-  }
-
-  void buildSubtensor(std::shared_ptr<T_ITensor> parent_tensor, const ir::OperandIndex &child_ind,
-                      const ::arm_compute::TensorShape &shape,
-                      const ::arm_compute::Coordinates &coordinates, size_t rank,
-                      bool extent_parent)
-  {
-    auto subtensor =
-        std::make_shared<T_SubTensor>(parent_tensor.get(), shape, coordinates, rank, extent_parent);
-    _subtensors[child_ind] = subtensor;
-  }
-
-  ir::OperandIndexMap<std::shared_ptr<T_Tensor>> &tensors(void) { return _tensors; }
-
-  ir::OperandIndexMap<std::shared_ptr<T_SubTensor>> &subtensors(void) { return _subtensors; }
-
-private:
-  ir::OperandIndexMap<std::shared_ptr<T_Tensor>> _tensors;
-  ir::OperandIndexMap<std::shared_ptr<T_SubTensor>> _subtensors;
-};
-
-} // namespace acl_common
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ACL_COMMON_MEMORY_MANAGER_H__
diff --git a/runtime/neurun/backend/acl_common/AclTensorManager.h b/runtime/neurun/backend/acl_common/AclTensorManager.h
deleted file mode 100644
index 48a4c2599..000000000
--- a/runtime/neurun/backend/acl_common/AclTensorManager.h
+++ /dev/null
@@ -1,300 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_COMMON_TENSOR_MANAGER_H__
-#define __NEURUN_BACKEND_ACL_COMMON_TENSOR_MANAGER_H__
-
-#include <arm_compute/runtime/IMemoryManager.h>
-
-#include "backend/ITensorManager.h"
-#include "AclMemoryManager.h"
-#include "AclInternalBufferManager.h"
-#include "ir/OperandIndexMap.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_common
-{
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-class AclTensorManager : public backend::ITensorManager
-{
-public:
-  using T_AclMemoryManager = AclMemoryManager<T_ITensor, T_Tensor, T_SubTensor>;
-
-  AclTensorManager(T_AclMemoryManager *const_mgr, T_AclMemoryManager *nonconst_mgr,
-                   IInternalBufferManager *inter_mgr);
-
-  virtual ~AclTensorManager() = default;
-
-  void allocateConsts(void) override;
-  void allocateNonconsts(void) override;
-  void deallocateConsts(void) override;
-  void deallocateNonconsts(void) override;
-
-  void allocateInternalBufferManager(void);
-  void deallocateInternalBufferManager(void);
-
-  void buildTensor(const ir::OperandIndex &ind, const ::arm_compute::TensorInfo &info, size_t rank,
-                   bool as_const, size_t num_uses);
-  void buildSubtensor(const ir::OperandIndex &parent, const ir::OperandIndex &child,
-                      const ::arm_compute::TensorShape &shape,
-                      const ::arm_compute::Coordinates &coordinates, size_t rank,
-                      bool extent_parent);
-
-  std::shared_ptr<T_ITensor> findTensorAsParent(const ir::OperandIndex &ind);
-
-  void startLifetime(const ir::OperandIndex &ind);
-  void finishLifetime(const ir::OperandIndex &ind);
-
-  std::shared_ptr<T_ITensor> at(const ir::OperandIndex &ind);
-
-  ir::OperandIndexMap<std::shared_ptr<T_Tensor>> &constTensors(void);
-  ir::OperandIndexMap<std::shared_ptr<T_Tensor>> &nonconstTensors(void);
-  ir::OperandIndexMap<std::shared_ptr<T_SubTensor>> &nonconstSubtensors(void);
-
-  std::shared_ptr<::arm_compute::IMemoryManager> internal_buffer_manager(void);
-
-  void iterate(const std::function<void(const ir::OperandIndex &)> &fn);
-
-  void tryDeallocConstants(void);
-
-private:
-  std::unique_ptr<T_AclMemoryManager> _const_mgr;
-  std::unique_ptr<T_AclMemoryManager> _nonconst_mgr;
-  std::unique_ptr<IInternalBufferManager> _inter_mgr;
-  ir::OperandIndexMap<T_AclMemoryManager &> _ind_to_mgr;
-};
-
-} // namespace acl_common
-} // namespace backend
-} // namespace neurun
-
-#include <cassert>
-#include "util/logging.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_common
-{
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::AclTensorManager(
-    T_AclMemoryManager *const_mgr, T_AclMemoryManager *nonconst_mgr,
-    IInternalBufferManager *inter_mgr)
-    : _const_mgr{const_mgr}, _nonconst_mgr{nonconst_mgr}, _inter_mgr{inter_mgr}
-{
-  // DO NOTHING
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-void AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::allocateConsts(void)
-{
-  _const_mgr->allocate();
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-void AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::allocateNonconsts(void)
-{
-  _nonconst_mgr->allocate();
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-void AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::deallocateConsts(void)
-{
-  _const_mgr->deallocate();
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-void AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::deallocateNonconsts(void)
-{
-  _nonconst_mgr->deallocate();
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-void AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::allocateInternalBufferManager(void)
-{
-  _inter_mgr->allocate();
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-void AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::deallocateInternalBufferManager(void)
-{
-  _inter_mgr->deallocate();
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-void AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::buildTensor(
-    const ir::OperandIndex &ind, const ::arm_compute::TensorInfo &info, size_t rank, bool as_const,
-    size_t num_uses)
-{
-  assert(_ind_to_mgr.find(ind) == _ind_to_mgr.end());
-  if (as_const)
-  {
-    _const_mgr->buildTensor(ind, info, rank, num_uses);
-    _ind_to_mgr.insert({ind, *_const_mgr});
-  }
-  else
-  {
-    _nonconst_mgr->buildTensor(ind, info, rank, num_uses);
-    _ind_to_mgr.insert({ind, *_nonconst_mgr});
-  }
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-void AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::buildSubtensor(
-    const ir::OperandIndex &parent, const ir::OperandIndex &child,
-    const ::arm_compute::TensorShape &shape, const ::arm_compute::Coordinates &coordinates,
-    size_t rank, bool extent_parent)
-{
-  assert(_ind_to_mgr.find(child) == _ind_to_mgr.end());
-  std::shared_ptr<T_ITensor> parent_tensor = findTensorAsParent(parent);
-  assert(parent_tensor);
-  _nonconst_mgr->buildSubtensor(parent_tensor, child, shape, coordinates, rank, extent_parent);
-  _ind_to_mgr.insert({child, *_nonconst_mgr});
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-std::shared_ptr<T_ITensor>
-AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::findTensorAsParent(const ir::OperandIndex &ind)
-{
-
-  auto &tensors = _nonconst_mgr->tensors();
-  auto &subtensors = _nonconst_mgr->subtensors();
-  if (tensors.find(ind) != tensors.end())
-  {
-    // Parent is allocated as tensor
-    return tensors[ind];
-  }
-  else if (subtensors.find(ind) != subtensors.end())
-  {
-    // Parent is allocated as subtensor
-    return subtensors[ind];
-  }
-  else
-  {
-    return nullptr;
-  }
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-void AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::startLifetime(const ir::OperandIndex &ind)
-{
-  assert(_ind_to_mgr.find(ind) != _ind_to_mgr.end());
-  _ind_to_mgr.at(ind).startLifetime(ind);
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-void AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::finishLifetime(const ir::OperandIndex &ind)
-{
-  assert(_ind_to_mgr.find(ind) != _ind_to_mgr.end());
-  _ind_to_mgr.at(ind).finishLifetime(ind);
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-std::shared_ptr<T_ITensor>
-AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::at(const ir::OperandIndex &ind)
-{
-  assert(_ind_to_mgr.find(ind) != _ind_to_mgr.end());
-
-  auto &tensors = _ind_to_mgr.at(ind).tensors();
-  if (tensors.find(ind) != tensors.end())
-  {
-    return tensors.at(ind);
-  }
-  else
-  {
-    return _ind_to_mgr.at(ind).subtensors().at(ind);
-  }
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-ir::OperandIndexMap<std::shared_ptr<T_Tensor>> &
-AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::constTensors(void)
-{
-  return _const_mgr->tensors();
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-ir::OperandIndexMap<std::shared_ptr<T_Tensor>> &
-AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::nonconstTensors(void)
-{
-  return _nonconst_mgr->tensors();
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-ir::OperandIndexMap<std::shared_ptr<T_SubTensor>> &
-AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::nonconstSubtensors(void)
-{
-  return _nonconst_mgr->subtensors();
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-std::shared_ptr<::arm_compute::IMemoryManager>
-AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::internal_buffer_manager(void)
-{
-  return _inter_mgr->internal_buffer_manager();
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-void AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::iterate(
-    const std::function<void(const ir::OperandIndex &)> &fn)
-{
-  for (auto it : _nonconst_mgr->tensors())
-    fn(it.first);
-
-  for (auto it : _nonconst_mgr->subtensors())
-    fn(it.first);
-
-  for (auto it : _const_mgr->tensors())
-    fn(it.first);
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-void AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::tryDeallocConstants(void)
-{
-  auto &tensors = _const_mgr->tensors();
-
-  for (auto it = tensors.begin(); it != tensors.end();)
-  {
-    const auto &ind = it->first;
-    auto tensor = it->second;
-    // NOTE The condition "tensor->num_uses() < 2" is used to prevent deallocating a constant tensor
-    // used in several nodes.
-    if (tensor->handle() && !tensor->handle()->is_used() && tensor->num_uses() < 2)
-    {
-      VERBOSE(AclTensorManager) << "Tensor #" << ind.value()
-                                << " will be deallocated as an unused constant tensor" << std::endl;
-      tensor->allocator()->free();
-      tensor.reset();
-      it = tensors.erase(it);
-    }
-    else
-    {
-      ++it;
-    }
-  }
-}
-
-} // namespace acl_common
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ACL_COMMON_TENSOR_MANAGER_H__
diff --git a/runtime/neurun/backend/acl_common/AclTensorRegister.cc b/runtime/neurun/backend/acl_common/AclTensorRegister.cc
deleted file mode 100644
index 07cbece1e..000000000
--- a/runtime/neurun/backend/acl_common/AclTensorRegister.cc
+++ /dev/null
@@ -1,49 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "AclTensorRegister.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_common
-{
-
-AclTensorRegister::AclTensorRegister(const ir::Operands &operands,
-                                     const std::shared_ptr<ITensorBuilder> &tensor_builder)
-    : _operands{operands}, _tensor_builder{tensor_builder}
-{
-  assert(tensor_builder != nullptr);
-}
-
-void AclTensorRegister::visit(const ir::OpSequence &op_seq)
-{
-  for (const auto &e : op_seq.operations())
-  {
-    const auto &node = *(e.node);
-    node.accept(*this);
-    // Set count of nodes to use operand
-    for (const auto &input : node.getInputs())
-    {
-      setUsesCount(input, _operands.at(input).getUses().size());
-    }
-  }
-}
-
-} // namespace acl_common
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/acl_common/AclTensorRegister.h b/runtime/neurun/backend/acl_common/AclTensorRegister.h
deleted file mode 100644
index 905e19adb..000000000
--- a/runtime/neurun/backend/acl_common/AclTensorRegister.h
+++ /dev/null
@@ -1,56 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_COMMON_TENSOR_REGISTER_H__
-#define __NEURUN_BACKEND_ACL_COMMON_TENSOR_REGISTER_H__
-
-#include <backend/ITensorRegister.h>
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_common
-{
-
-class AclTensorRegister : public ITensorRegister
-{
-protected:
-  AclTensorRegister(const ir::Operands &operands,
-                    const std::shared_ptr<ITensorBuilder> &tensor_builder);
-
-public:
-  virtual ~AclTensorRegister() = default;
-
-protected:
-  void visit(const ir::OpSequence &op_seq);
-  virtual void setUsesCount(const ir::OperandIndex &ind, size_t num_uses) const = 0;
-
-protected:
-  const ir::Operands &operands() const override { return _operands; }
-  std::shared_ptr<ITensorBuilder> tensor_builder() const override { return _tensor_builder; }
-  bool supportSubTensor() const final { return true; }
-
-private:
-  const ir::Operands &_operands;
-  const std::shared_ptr<ITensorBuilder> _tensor_builder;
-};
-
-} // namespace acl_common
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ACL_COMMON_TENSOR_REGISTER_H__
diff --git a/runtime/neurun/backend/acl_common/CMakeLists.txt b/runtime/neurun/backend/acl_common/CMakeLists.txt
deleted file mode 100644
index 32d01291f..000000000
--- a/runtime/neurun/backend/acl_common/CMakeLists.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-# Unsupported architecture
-nnas_find_package(ARMCompute QUIET)
-if(NOT ARMCompute_FOUND)
-  return()
-endif(NOT ARMCompute_FOUND)
-
-file(GLOB SOURCES "*.cc")
-
-add_library(${LIB_NEURUN_BACKEND_ACL_COMMON} STATIC ${SOURCES})
-
-target_include_directories(${LIB_NEURUN_BACKEND_ACL_COMMON} PUBLIC ${NEURUN_INCLUDE_DIR})
-target_include_directories(${LIB_NEURUN_BACKEND_ACL_COMMON} PUBLIC ${CMAKE_CURRENT_SOURCE_DIR})
-target_link_libraries(${LIB_NEURUN_BACKEND_ACL_COMMON} PUBLIC arm_compute arm_compute_ex)
-target_link_libraries(${LIB_NEURUN_BACKEND_ACL_COMMON} PUBLIC nnfw_lib_misc nnfw_lib_cpp14)
-target_link_libraries(${LIB_NEURUN_BACKEND_ACL_COMMON} PRIVATE nnfw_common)
-target_link_libraries(${LIB_NEURUN_BACKEND_ACL_COMMON} PRIVATE nnfw_coverage)
-
-set_target_properties(${LIB_NEURUN_BACKEND_ACL_COMMON} PROPERTIES POSITION_INDEPENDENT_CODE ON)
-set_target_properties(${LIB_NEURUN_BACKEND_ACL_COMMON} PROPERTIES OUTPUT_NAME backend_acl_common)
diff --git a/runtime/neurun/backend/acl_common/Convert.cc b/runtime/neurun/backend/acl_common/Convert.cc
deleted file mode 100644
index fc5be72a2..000000000
--- a/runtime/neurun/backend/acl_common/Convert.cc
+++ /dev/null
@@ -1,193 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "Convert.h"
-
-#include "Swizzle.h"
-#include "ir/DataType.h"
-#include <cpp14/memory.h>
-
-namespace
-{
-
-::arm_compute::DataLayout asDataLayout(neurun::ir::Layout layout)
-{
-  switch (layout)
-  {
-    case neurun::ir::Layout::NHWC:
-      return ::arm_compute::DataLayout::NHWC;
-    case neurun::ir::Layout::NCHW:
-      return ::arm_compute::DataLayout::NCHW;
-    default:
-      return ::arm_compute::DataLayout::UNKNOWN;
-  }
-}
-
-} // namespace
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_common
-{
-
-::arm_compute::TensorShape asTensorShape(const ir::Shape &shape, ir::Layout frontend_layout,
-                                         ir::Layout backend_layout, bool apply_dim_correction)
-{
-  const uint32_t rank = shape.rank();
-
-  ::arm_compute::TensorShape res{};
-
-  res.set_num_dimensions(rank);
-
-  for (uint32_t axis = 0; axis < rank; ++axis)
-  {
-    // NOTE In some cases, in incorrect dimensions is required.
-    // For example, intput_size is 1 in LSTM. The input-to-input weights([num_units, input_size]) of
-    // LSTM is used as the weight of the FullyConnected.
-    // The FullyConnected's weight must be greater or equal than 2-dimensions.
-    // However, if the dimension correction is applied to input_to_input_weights with input_size
-    // equal to 1, it will be changed to 1-D.
-    // So input_to_input_weights is not used by the weight of FullyConnected.
-    res.set(ToARMComputeAxis(rank, axis, frontend_layout, backend_layout).value(), shape.dim(axis),
-            apply_dim_correction);
-  }
-
-  return res;
-}
-
-::arm_compute::Coordinates asTensorCoordinate(const ::neurun::util::Coordinates &coord,
-                                              ir::Layout frontend_layout, ir::Layout backend_layout)
-{
-  const uint32_t rank = coord.size();
-
-  ::arm_compute::Coordinates res{};
-
-  res.set_num_dimensions(rank);
-
-  for (uint32_t axis = 0; axis < rank; ++axis)
-  {
-    res.set(ToARMComputeAxis(rank, axis, frontend_layout, backend_layout).value(), coord[axis]);
-  }
-
-  return res;
-}
-
-::arm_compute::DataType asDataType(const ir::DataType type)
-{
-  switch (type)
-  {
-    case ir::DataType::FLOAT32:
-      return ::arm_compute::DataType::F32;
-    case ir::DataType::INT32:
-      return ::arm_compute::DataType::S32;
-    case ir::DataType::UINT32:
-      return ::arm_compute::DataType::U32;
-    case ir::DataType::QUANT8_ASYMM:
-      return ::arm_compute::DataType::QASYMM8;
-    case ir::DataType::BOOL8:
-    case ir::DataType::UINT8:
-      return ::arm_compute::DataType::U8;
-    case ir::DataType::QUANT8_SYMM:
-      return ::arm_compute::DataType::S8;
-    default:
-      throw std::runtime_error("Not supported, yet");
-      break;
-  }
-}
-
-::arm_compute::QuantizationInfo asQuantizationInfo(const float scale, const int32_t offset)
-{
-  return ::arm_compute::QuantizationInfo(scale, offset);
-}
-
-::arm_compute::TensorInfo asTensorInfo(const ir::Shape &shape, const ir::TypeInfo &typeInfo,
-                                       ir::Layout frontend_layout, ir::Layout backend_layout,
-                                       bool apply_dim_correction)
-{
-  ::arm_compute::TensorInfo info(
-      asTensorShape(shape, frontend_layout, backend_layout, apply_dim_correction), 1,
-      asDataType(typeInfo.type()), asQuantizationInfo(typeInfo.scale(), typeInfo.offset()));
-  info.set_data_layout(asDataLayout(backend_layout));
-  return info;
-}
-
-::arm_compute::PadStrideInfo asPadStrideInfo(const ir::ExplicitPadding &padding,
-                                             const ir::Stride &stride)
-{
-  return ::arm_compute::PadStrideInfo{stride.horizontal,
-                                      stride.vertical,
-                                      padding.left,
-                                      padding.right,
-                                      padding.top,
-                                      padding.bottom,
-                                      ::arm_compute::DimensionRoundingType::FLOOR};
-}
-
-::arm_compute::ActivationLayerInfo asActivationLayerInfo(const ir::Activation act_code)
-{
-  switch (act_code)
-  {
-    case ir::Activation::NONE:
-      return ::arm_compute::ActivationLayerInfo{};
-    case ir::Activation::RELU:
-      return ::arm_compute::ActivationLayerInfo{
-          ::arm_compute::ActivationLayerInfo::ActivationFunction::RELU};
-    case ir::Activation::RELU1:
-      return ::arm_compute::ActivationLayerInfo{
-          ::arm_compute::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 1.0f, -1.0f};
-    case ir::Activation::RELU6:
-      return ::arm_compute::ActivationLayerInfo{
-          ::arm_compute::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.0f, 0.0f};
-    // Cases for activation of LSTM.
-    case ir::Activation::TANH:
-      return ::arm_compute::ActivationLayerInfo{
-          ::arm_compute::ActivationLayerInfo::ActivationFunction::TANH, 1.0f, 1.0f};
-    case ir::Activation::SIGMOID:
-      // NOTE The sigmoid function is a special case of the Logistic function when L=1, k=1, x0=0.
-      // TODO In ACL and nnapi sepc, currently, Logistic's L always is 1, k always is 1, x0 always
-      // 0(always sigmoid) regardless of values of the parameter.
-      //      If ACL support non-sigmoid logistic, should fix param values.
-      return ::arm_compute::ActivationLayerInfo{
-          ::arm_compute::ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.0f, 0.0f};
-    default:
-      throw std::runtime_error{"Not supported, yet"};
-      break;
-  }
-}
-
-std::unique_ptr<AclFunction> asAclFunction(std::unique_ptr<::arm_compute::IFunction> &&layer)
-{
-  return nnfw::cpp14::make_unique<AclFunction>(std::move(layer));
-}
-
-ir::Layout asRuntimeLayout(::arm_compute::DataLayout data_layout)
-{
-  switch (data_layout)
-  {
-    case ::arm_compute::DataLayout::NHWC:
-      return ir::Layout::NHWC;
-    case ::arm_compute::DataLayout::NCHW:
-      return ir::Layout::NCHW;
-    default:
-      return ir::Layout::UNKNOWN;
-  }
-}
-
-} // namespace acl_common
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/acl_common/Convert.h b/runtime/neurun/backend/acl_common/Convert.h
deleted file mode 100644
index 4c638157b..000000000
--- a/runtime/neurun/backend/acl_common/Convert.h
+++ /dev/null
@@ -1,68 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_COMMON_CONVERT_H__
-#define __NEURUN_BACKEND_ACL_COMMON_CONVERT_H__
-
-#include <arm_compute/core/TensorInfo.h>
-#include <arm_compute/core/SubTensorInfo.h>
-#include <arm_compute/core/TensorShape.h>
-
-#include "ir/Layout.h"
-#include "ir/InternalType.h"
-#include "ir/Operand.h"
-#include "ir/Shape.h"
-#include "ir/TypeInfo.h"
-#include "misc/feature/Shape.h"
-#include "misc/kernel/Shape.h"
-
-#include "util/Padding.h"
-#include "util/Coordinates.h"
-
-#include "AclFunction.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_common
-{
-
-::arm_compute::TensorShape asTensorShape(const ir::Shape &shape, ir::Layout frontend_layout,
-                                         ir::Layout backend_layout,
-                                         bool apply_dim_correction = true);
-::arm_compute::Coordinates asTensorCoordinate(const ::neurun::util::Coordinates &coord,
-                                              ir::Layout frontend_layout,
-                                              ir::Layout backend_layout);
-::arm_compute::DataType asDataType(ir::DataType type);
-::arm_compute::TensorInfo asTensorInfo(const ir::Shape &shape, const ir::TypeInfo &typeInfo,
-                                       ir::Layout frontend_layout, ir::Layout backend_layout,
-                                       bool apply_dim_correction = true);
-
-::arm_compute::PadStrideInfo asPadStrideInfo(const ir::ExplicitPadding &padding,
-                                             const ir::Stride &stride);
-
-::arm_compute::ActivationLayerInfo asActivationLayerInfo(ir::Activation act_code);
-
-std::unique_ptr<AclFunction> asAclFunction(std::unique_ptr<::arm_compute::IFunction> &&layer);
-
-ir::Layout asRuntimeLayout(::arm_compute::DataLayout data_layout);
-
-} // namespace acl_common
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ACL_COMMON_CONVERT_H__
diff --git a/runtime/neurun/backend/acl_common/IACLTensor.cc b/runtime/neurun/backend/acl_common/IACLTensor.cc
deleted file mode 100644
index 70ffbdcf0..000000000
--- a/runtime/neurun/backend/acl_common/IACLTensor.cc
+++ /dev/null
@@ -1,63 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "IACLTensor.h"
-#include "Convert.h"
-#include "Swizzle.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_common
-{
-
-size_t IACLTensor::num_dimensions() const
-{
-  throw std::runtime_error("No definition of num_dimensions()");
-  return 0;
-}
-
-size_t IACLTensor::dimension(size_t index) const
-{
-  // Assume that the front is higher dimensional.
-  // i.g. N: 0, C: 1, H: 2, W: 3 for NCHW layout
-  // NOTE This tensor must not be applied dim correction
-  assert(num_dimensions() > index);
-  const ARMComputeAxis reversed{(static_cast<uint32_t>(num_dimensions() - index) - 1)};
-  return info()->dimension(reversed.value());
-}
-
-size_t IACLTensor::calcOffset(const neurun::util::Coordinates &coords) const
-{
-  const auto rank = num_dimensions();
-  assert(rank == coords.size());
-
-  ::arm_compute::Coordinates acl_coords;
-  for (uint32_t i = 0; i < rank; ++i)
-  {
-    const ARMComputeAxis reversed{static_cast<uint32_t>((rank - i) - 1)};
-    acl_coords.set(reversed.value(), coords[i]);
-  }
-
-  return info()->offset_element_in_bytes(acl_coords);
-}
-
-ir::Layout IACLTensor::layout() const { return acl_common::asRuntimeLayout(info()->data_layout()); }
-
-} // namespace acl_common
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/acl_common/IACLTensor.h b/runtime/neurun/backend/acl_common/IACLTensor.h
deleted file mode 100644
index b0dcbb409..000000000
--- a/runtime/neurun/backend/acl_common/IACLTensor.h
+++ /dev/null
@@ -1,62 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_COMMON_I_ACL_TENSOR_H__
-#define __NEURUN_BACKEND_ACL_COMMON_I_ACL_TENSOR_H__
-
-#include <backend/operand/ITensor.h>
-#include <arm_compute/core/ITensor.h>
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_common
-{
-
-class IACLTensor : public operand::ITensor
-{
-public:
-  IACLTensor() = default;
-  IACLTensor(const IACLTensor &) = delete;
-  IACLTensor &operator=(const IACLTensor &) = delete;
-  IACLTensor(IACLTensor &&) = default;
-  IACLTensor &operator=(IACLTensor &&) = default;
-
-public:
-  uint8_t *buffer() const final { return handle()->buffer(); }
-  size_t total_size() const final { return info()->total_size(); }
-  size_t dimension(size_t index) const final;
-  size_t num_dimensions() const override;
-  size_t calcOffset(const neurun::util::Coordinates &coords) const final;
-  ir::Layout layout() const final;
-  bool has_padding() const override { return info()->has_padding(); }
-
-public:
-  virtual const arm_compute::ITensor *handle() const = 0;
-  virtual arm_compute::ITensor *handle() = 0;
-
-  const arm_compute::ITensorInfo *info() const { return handle()->info(); }
-  arm_compute::ITensorInfo *info() { return handle()->info(); }
-
-  arm_compute::DataType data_type() const { return info()->data_type(); }
-};
-
-} // namespace acl_common
-} // namespace backend
-} // namespace neurun
-
-#endif //__NEURUN_BACKEND_ACL_COMMON_I_ACL_TENSOR_H__
diff --git a/runtime/neurun/backend/acl_common/Swizzle.h b/runtime/neurun/backend/acl_common/Swizzle.h
deleted file mode 100644
index 11874b592..000000000
--- a/runtime/neurun/backend/acl_common/Swizzle.h
+++ /dev/null
@@ -1,160 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_COMMON_SWIZZLE_H__
-#define __NEURUN_BACKEND_ACL_COMMON_SWIZZLE_H__
-
-#include <cassert>
-#include <ir/Layout.h>
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_common
-{
-
-class ARMComputeAxis
-{
-public:
-  ARMComputeAxis() = default;
-
-public:
-  explicit ARMComputeAxis(uint32_t value) : _value{value}
-  {
-    // DO NOTHING
-  }
-
-public:
-  uint32_t value(void) const { return _value; }
-
-private:
-  uint32_t _value;
-};
-
-// Convert axis in acl order
-inline ARMComputeAxis ToARMComputeAxis(uint32_t rank, uint32_t axis,
-                                       const ir::Layout org_layout = ir::Layout::UNKNOWN,
-                                       const ir::Layout acl_layout = ir::Layout::UNKNOWN)
-{
-  assert(rank > axis);
-
-  const ARMComputeAxis reversed{(rank - axis) - 1};
-
-  if (rank >= 4 && org_layout == ir::Layout::NHWC && acl_layout == ir::Layout::NCHW)
-  {
-    // NHWC -> WHCN
-    // DEPTH
-    if (0 == reversed.value())
-    {
-      return ARMComputeAxis{2};
-    }
-    // WIDTH
-    if (1 == reversed.value())
-    {
-      return ARMComputeAxis{0};
-    }
-    // HEIGHT
-    if (2 == reversed.value())
-    {
-      return ARMComputeAxis{1};
-    }
-  }
-  if (rank >= 4 && org_layout == ir::Layout::NCHW && acl_layout == ir::Layout::NHWC)
-  {
-    // NCHW -> CWHN
-    // WIDTH
-    if (0 == reversed.value())
-    {
-      return ARMComputeAxis{1};
-    }
-    // HEIGHT
-    if (1 == reversed.value())
-    {
-      return ARMComputeAxis{2};
-    }
-    // DEPTH
-    if (2 == reversed.value())
-    {
-      return ARMComputeAxis{0};
-    }
-  }
-
-  return reversed;
-}
-
-inline ::arm_compute::Coordinates
-getARMComputeAxises(uint32_t rank, const ir::Layout org_layout = ir::Layout::UNKNOWN,
-                    const ir::Layout acl_layout = ir::Layout::UNKNOWN)
-{
-  ::arm_compute::Coordinates res{};
-
-  res.set_num_dimensions(rank);
-
-  for (uint32_t axis = 0; axis < rank; ++axis)
-  {
-    res.set(axis, ToARMComputeAxis(rank, axis, org_layout, acl_layout).value());
-  }
-
-  return res;
-}
-
-// Restructure runtime_permutationVector to ACL_permutationVector
-inline ::arm_compute::PermutationVector
-getARMComputePermutationVector(uint32_t rank, const std::vector<int32_t> runtime_pv,
-                               const ir::Layout org_layout = ir::Layout::UNKNOWN,
-                               const ir::Layout acl_layout = ir::Layout::UNKNOWN)
-{
-  // rank upto 4 is supported
-  assert(rank <= 4);
-  assert(runtime_pv.size() > 0);
-
-  int new_pv[4] = {0};
-  ::arm_compute::Coordinates axises = getARMComputeAxises(rank, org_layout, acl_layout);
-
-  for (uint32_t i = 0; i < rank; ++i)
-  {
-    new_pv[axises[i]] = ToARMComputeAxis(rank, runtime_pv[i], org_layout, acl_layout).value();
-  }
-
-  ::arm_compute::PermutationVector ACL_PV =
-      ::arm_compute::PermutationVector{new_pv[0], new_pv[1], new_pv[2], new_pv[3]};
-  ACL_PV.set_num_dimensions(rank);
-
-  return ACL_PV;
-}
-
-template <typename T>
-inline T ReorderBits(T in, size_t numOfBits, const ir::Layout org_layout = ir::Layout::UNKNOWN,
-                     const ir::Layout acl_layout = ir::Layout::UNKNOWN)
-{
-  assert(numOfBits > 0);
-  T out = 0;
-  for (int32_t i = numOfBits - 1; i >= 0; --i)
-  {
-    const uint32_t toShift =
-        numOfBits - ToARMComputeAxis(numOfBits, i, org_layout, acl_layout).value() - 1;
-    out += ((in & 1) << toShift);
-    in >>= 1;
-  }
-  return out;
-}
-
-} // namespace acl_common
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ACL_COMMON_SWIZZLE_H__
diff --git a/runtime/neurun/backend/acl_common/TemplTensorBuilder.h b/runtime/neurun/backend/acl_common/TemplTensorBuilder.h
deleted file mode 100644
index bb43823ed..000000000
--- a/runtime/neurun/backend/acl_common/TemplTensorBuilder.h
+++ /dev/null
@@ -1,612 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_COMMON_TEMPL_TENSOR_BUILDER_H__
-#define __NEURUN_BACKEND_ACL_COMMON_TEMPL_TENSOR_BUILDER_H__
-
-#include <memory>
-#include <queue>
-
-#include <arm_compute/core/Types.h>
-#include <backend/ITensorBuilder.h>
-#include "ir/OperandIndexMap.h"
-#include "AclTensorManager.h"
-#include "cpp14/memory.h"
-#include <util/Utils.h>
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_common
-{
-
-enum class UsesType
-{
-  FIRST,
-  LAST
-};
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-class TemplTensorBuilder : public ITensorBuilder
-{
-public:
-  using T_AclTensorManager = AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>;
-
-  TemplTensorBuilder(T_AclTensorManager *tensor_mgr);
-
-  /**
-   * @brief     Register tensor information to allocate on ACL-CL backend
-   * @param[in] ind    Operand index
-   * @param[in] info   Tensor information
-   * @param[in] layout Tensor data layout
-   */
-  void registerTensorInfo(const ir::OperandIndex &ind, const ir::OperandInfo &info,
-                          ir::Layout backend_layout, bool as_const) override;
-  /**
-   * @brief     Register subtensor information to allocate on ACL-CL backend
-   * @param[in] ind   Operand index
-   * @param[in] info  Tensor information
-   */
-  void registerSubTensorInfo(const ir::OperandIndex &ind,
-                             const compiler::SubTensorInfo &info) override;
-
-  void notifyFirstUse(const ir::OperandIndex &) override;
-  void notifyLastUse(const ir::OperandIndex &) override;
-
-  bool isRegistered(const ir::OperandIndex &) const override;
-
-  void prepare(void) override;
-  void allocateConsts() override;
-  void allocateNonconsts() override;
-  void postFunctionPrepare() override;
-  void finalize() override;
-
-  std::shared_ptr<::neurun::backend::operand::ITensor>
-  tensorAt(const ir::OperandIndex &ind) override;
-  void iterate(const IterateFunction &fn) override;
-
-  void preVisit(const ir::Operation &node) override;
-  void postVisit(const ir::Operation &node) override;
-
-  std::unique_ptr<ITensorManager> releaseTensorManager(void) override;
-
-  std::shared_ptr<T_ITensor> at(const ir::OperandIndex &ind);
-  /**
-   * @brief     Check child tensor is allocated as subtensor of parent tensor
-   * @param[in] parent  Index of parent
-   * @param[in] child   Index of child
-   * @return    @c true if child is allocated as subtensor of parent, otherwise @c false
-   */
-  bool isSubTensorOf(const ir::OperandIndex &parent, const ir::OperandIndex &child);
-
-  void dimCorrection(const ir::OperandIndex &index, bool apply_dim_correction);
-
-  T_AclTensorManager *acl_tensor_manager(void) { return _tensor_mgr.get(); }
-
-  void setUsesCount(const ir::OperandIndex &index, size_t num_uses)
-  {
-    assert(_uses_count_map.find(index) != _uses_count_map.end() ? _uses_count_map[index] == num_uses
-                                                                : true);
-    _uses_count_map[index] = num_uses;
-  }
-
-private:
-  void buildTensors(void);
-  void buildSubtensors(void);
-  void validate(void);
-  ir::OperandIndex findRootParent(ir::OperandIndex index);
-
-private:
-  ir::OperandIndexMap<ir::OperandInfo> _tensor_info_map;
-  ir::OperandIndexMap<compiler::SubTensorInfo> _subtensor_info_map;
-  ir::OperandIndexMap<bool> _apply_dim_correction_map;
-  ir::OperandIndexMap<ir::Layout> _tensor_layout_map;
-  ir::OperandIndexMap<size_t> _uses_count_map;
-
-  std::unique_ptr<T_AclTensorManager> _tensor_mgr;
-  ir::OperandIndexSequence _constants;
-
-  // TODO Consider dividing TensorBuilder into Linear and others
-  const std::string _executor_str;
-
-  // for linear executor
-  std::queue<std::pair<UsesType, ir::OperandIndex>> _uses_queue;
-  uint32_t _first_uses_num;
-  ir::OperandIndexMap<bool> _first_uses_visit;
-
-  // for subtensors
-  ir::OperandIndexMap<uint32_t> _parent_def;
-  ir::OperandIndexMap<uint32_t> _parent_uses;
-};
-
-} // namespace acl_common
-} // namespace backend
-} // namespace neurun
-
-#include <cassert>
-#include <stack>
-
-#include "Convert.h"
-
-#include "util/logging.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_common
-{
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-TemplTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::TemplTensorBuilder(
-    T_AclTensorManager *tensor_mgr)
-    : _tensor_mgr{tensor_mgr}, _executor_str(util::getConfigString(util::config::EXECUTOR)),
-      _first_uses_num(0)
-{
-  assert(_tensor_mgr);
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-void TemplTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::registerTensorInfo(
-    const ir::OperandIndex &ind, const ir::OperandInfo &info, ir::Layout backend_layout,
-    bool as_const)
-{
-  assert(_tensor_mgr->constTensors().size() == 0);
-  assert(_tensor_mgr->nonconstTensors().size() == 0);
-
-  _tensor_info_map.emplace(ind, info);
-  _apply_dim_correction_map.emplace(ind, true);
-  _tensor_layout_map.insert({ind, backend_layout});
-  if (as_const)
-    _constants.append(ind);
-
-  assert(_first_uses_visit.find(ind) == _first_uses_visit.end());
-  _first_uses_visit[ind] = false;
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-void TemplTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::registerSubTensorInfo(
-    const ir::OperandIndex &ind, const compiler::SubTensorInfo &info)
-{
-  assert(_tensor_mgr->constTensors().size() == 0);
-  assert(_tensor_mgr->nonconstTensors().size() == 0);
-
-  _subtensor_info_map.emplace(ind, info);
-  _apply_dim_correction_map.emplace(ind, true);
-
-  assert(_first_uses_visit.find(ind) == _first_uses_visit.end());
-  _first_uses_visit[ind] = false;
-
-  const auto &parent_ind = info.parent();
-
-  // parent_def
-  _parent_def[parent_ind] = 1;
-
-  // parent_use
-  if (_parent_uses.find(parent_ind) == _parent_uses.end())
-    _parent_uses[parent_ind] = 1; // 1 means including parent it-self
-  _parent_uses[parent_ind]++;
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-void TemplTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::notifyFirstUse(
-    const ir::OperandIndex &ind)
-{
-  _first_uses_num++;
-  _uses_queue.emplace(UsesType::FIRST, ind);
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-void TemplTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::notifyLastUse(
-    const ir::OperandIndex &ind)
-{
-  _uses_queue.emplace(UsesType::LAST, ind);
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-bool TemplTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::isRegistered(
-    const ir::OperandIndex &ind) const
-{
-  return _tensor_info_map.find(ind) != _tensor_info_map.end() ||
-         _subtensor_info_map.find(ind) != _subtensor_info_map.end();
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-void TemplTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::prepare(void)
-{
-  buildTensors();
-  buildSubtensors();
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-void TemplTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::allocateConsts(void)
-{
-  assert(_constants.size() == _tensor_mgr->constTensors().size());
-  _tensor_mgr->allocateConsts();
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-void TemplTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::allocateNonconsts(void)
-{
-  assert(_tensor_info_map.size() == _tensor_mgr->nonconstTensors().size() + _constants.size());
-  _tensor_mgr->allocateNonconsts();
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-void TemplTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::postFunctionPrepare(void)
-{
-  _tensor_mgr->tryDeallocConstants();
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-void TemplTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::finalize(void)
-{
-  validate();
-  _tensor_mgr->allocateInternalBufferManager();
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-std::shared_ptr<::neurun::backend::operand::ITensor>
-TemplTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::tensorAt(const ir::OperandIndex &ind)
-{
-  return _tensor_mgr->at(ind);
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-void TemplTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::iterate(const IterateFunction &fn)
-{
-  _tensor_mgr->iterate(fn);
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-std::shared_ptr<T_ITensor>
-TemplTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::at(const ir::OperandIndex &ind)
-{
-  return _tensor_mgr->at(ind);
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-bool TemplTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::isSubTensorOf(
-    const ir::OperandIndex &parent, const ir::OperandIndex &child)
-{
-  if (_subtensor_info_map.find(child) == _subtensor_info_map.end())
-  {
-    return false;
-  }
-
-  auto &subtensors = _tensor_mgr->nonconstSubtensors();
-  if (subtensors.find(child) == subtensors.end())
-  {
-    return false;
-  }
-
-  if (_subtensor_info_map.at(child).parent() != parent)
-  {
-    return false;
-  }
-
-  return true;
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-void TemplTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::dimCorrection(
-    const ir::OperandIndex &index, bool apply_dim_correction)
-{
-  _apply_dim_correction_map[index] = apply_dim_correction;
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-std::unique_ptr<ITensorManager>
-TemplTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::releaseTensorManager(void)
-{
-  return std::move(_tensor_mgr);
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-void TemplTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::buildTensors(void)
-{
-  assert(_tensor_mgr->constTensors().size() == 0);
-  assert(_tensor_mgr->nonconstTensors().size() == 0);
-
-  for (auto &entry : _tensor_info_map)
-  {
-    auto ind = entry.first;
-    const auto &info = entry.second;
-    // NOTE SubTensor's layout must be the same with layout of parent tensor
-    const auto &root_parent = findRootParent(ind);
-    const auto &backend_layout = _tensor_layout_map[root_parent];
-    auto tensor_info = asTensorInfo(info.shape(), info.typeInfo(), ir::Layout::UNKNOWN,
-                                    backend_layout, _apply_dim_correction_map[ind]);
-    _tensor_mgr->buildTensor(ind, tensor_info, info.shape().rank(), _constants.contains(ind),
-                             _uses_count_map[ind]);
-  }
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-void TemplTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::buildSubtensors(void)
-{
-  // TODO Handle SubTensor(subsumption)
-  //      Currently this TemplTensorBuilder does not have subsumption info yet
-  //      Allocated subtensor will be mapped to _subtensors instead of _tensors
-  assert(_tensor_mgr->nonconstSubtensors().size() == 0);
-
-  // To make subtensor, parent tensor must be made first
-  // For this condition, use stack
-  //  1) Push one subtensor index to stack (iterate subtensors)
-  //  2) If tensor at stack top is already made, pop and go to 4)
-  //  3) If tensor pushed at 1) is not made, check parent tensor
-  //    3-1) If parent tensor is already made, we can make child tensor
-  //         Make child tensor and pop, go to 4)
-  //    3-2) If parent tensor is not made, we can't make child tensor yet
-  //         Push parent tensor index to stack and return to 4)
-  //  4) If stack is empty, return to 1), else return to 2)
-  auto &subtensors = _tensor_mgr->nonconstSubtensors();
-  for (auto &entry : _subtensor_info_map)
-  {
-    ir::OperandIndex ind = entry.first;
-
-    std::stack<ir::OperandIndex> stack;
-    stack.push(ind);
-
-    while (!stack.empty())
-    {
-      const auto current = stack.top();
-      const auto &info = _subtensor_info_map.at(current);
-
-      // Already generated SubTensor
-      if (subtensors.find(current) != subtensors.end())
-      {
-        stack.pop();
-        continue;
-      }
-
-      auto parent = info.parent();
-      std::shared_ptr<T_ITensor> parent_tensor = _tensor_mgr->findTensorAsParent(parent);
-      if (!parent_tensor)
-      {
-        // Cannot find allocated parent tensor: allocate parent first
-        assert(_subtensor_info_map.find(parent) != _subtensor_info_map.end());
-        stack.push(parent);
-        continue;
-      }
-      assert(parent_tensor != nullptr);
-
-      // Child's type should be same with parent
-      assert(info.type().offset() == parent_tensor->info()->quantization_info().offset);
-      assert(info.type().scale() == parent_tensor->info()->quantization_info().scale);
-      assert(asDataType(info.type().type()) == parent_tensor->info()->data_type());
-
-      // NOTE SubTensor's layout must be the same with layout of parent tensor
-      const auto &root_parent = findRootParent(parent);
-      const auto &backend_layout = _tensor_layout_map[root_parent];
-
-      auto shape = asTensorShape(info.shape(), ir::Layout::UNKNOWN, backend_layout,
-                                 _apply_dim_correction_map[current]);
-      ::arm_compute::Coordinates coordinates =
-          asTensorCoordinate(info.offset(), ir::Layout::UNKNOWN, backend_layout);
-      _tensor_mgr->buildSubtensor(parent, current, shape, coordinates, info.shape().rank(), true);
-      stack.pop();
-    }
-  }
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-void TemplTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::preVisit(const ir::Operation &node)
-{
-  // For now others executor doesn't need this step
-  if (_executor_str != "Linear")
-  {
-    return;
-  }
-
-  std::function<void(const ir::OperandIndex &ind)> def_handler =
-      [this, &def_handler](const ir::OperandIndex &ind) {
-        bool is_subtensor = _subtensor_info_map.find(ind) != _subtensor_info_map.end();
-        bool is_parent = _parent_def.find(ind) != _parent_def.end();
-        if (!is_subtensor && !is_parent)
-        {
-          _tensor_mgr->startLifetime(ind);
-          return;
-        }
-
-        if (is_parent)
-        {
-          if (_parent_def[ind] == 0)
-            return;
-
-          _parent_def[ind] = 0;
-
-          if (is_subtensor)
-          {
-            const auto &it = _parent_def.find(ind);
-            _parent_def.erase(it);
-            def_handler(ind);
-          }
-          else
-          {
-            _tensor_mgr->startLifetime(ind);
-          }
-        }
-        else if (is_subtensor)
-        {
-          const ir::OperandIndex &parent_ind = _subtensor_info_map.at(ind).parent();
-          if (_parent_def[parent_ind] == 0)
-            return;
-          def_handler(parent_ind);
-        }
-      };
-
-  // See #5642
-  ir::OperandIndexMap<bool> outputs_map;
-  for (const auto &ind : node.getOutputs())
-  {
-    assert(_first_uses_visit.find(ind) != _first_uses_visit.end());
-    outputs_map[ind] = _first_uses_visit[ind];
-  }
-
-  // outputs_map's all elements are true?
-  auto outputs_map_all_check = [&outputs_map]() {
-    return std::all_of(outputs_map.begin(), outputs_map.end(),
-                       [](std::pair<const ir::OperandIndex, bool> it) { return it.second; });
-  };
-
-  std::pair<UsesType, ir::OperandIndex> peak;
-  while (!outputs_map_all_check() && (peak = _uses_queue.front()).first == UsesType::FIRST)
-  {
-    _uses_queue.pop();
-    _first_uses_num--;
-
-    const auto &popped_idx = peak.second;
-    def_handler(popped_idx);
-
-    outputs_map[popped_idx] = true;
-    _first_uses_visit[popped_idx] = true;
-  }
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-void TemplTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::postVisit(const ir::Operation &node)
-{
-  // For now others executor doesn't need this step
-  if (_executor_str != "Linear")
-  {
-    return;
-  }
-
-  std::function<void(const ir::OperandIndex &ind)> use_handler =
-      [this, &use_handler](const ir::OperandIndex &ind) {
-        bool is_subtensor = _subtensor_info_map.find(ind) != _subtensor_info_map.end();
-        bool is_parent = _parent_uses.find(ind) != _parent_uses.end();
-        if (!is_subtensor && !is_parent)
-        {
-          _tensor_mgr->finishLifetime(ind);
-          return;
-        }
-
-        // This handler shall be executed by the linear executor so that
-        // The parent operand will always be done after the subtensor
-        if (is_parent)
-        {
-          --_parent_uses[ind];
-          assert(_parent_uses[ind] == 0);
-
-          if (is_subtensor)
-          {
-            const auto &it = _parent_uses.find(ind);
-            _parent_uses.erase(it);
-            use_handler(ind);
-          }
-          else
-          {
-            _tensor_mgr->finishLifetime(ind);
-          }
-        }
-        else if (is_subtensor)
-        {
-          const ir::OperandIndex &parent_ind = _subtensor_info_map.at(ind).parent();
-          --_parent_uses[parent_ind];
-          assert(_parent_uses[parent_ind] > 0);
-        }
-      };
-
-  // See #5642
-  const auto &inputs = node.getInputs();
-  std::pair<UsesType, ir::OperandIndex> peak;
-  while ((peak = _uses_queue.front()).first == UsesType::LAST)
-  {
-    const auto &popped_idx = peak.second;
-    if (inputs.contains(popped_idx))
-    {
-      _uses_queue.pop();
-      use_handler(popped_idx);
-    }
-    else
-    {
-      break;
-    }
-  }
-
-  if (_first_uses_num == 0)
-  {
-    while (!_uses_queue.empty())
-    {
-      peak = _uses_queue.front();
-      assert(peak.first == UsesType::LAST);
-
-      _uses_queue.pop();
-
-      use_handler(peak.second);
-    }
-  }
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-void TemplTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::validate(void)
-{
-  // For now others executor doesn't need this step
-  if (_executor_str != "Linear")
-  {
-    return;
-  }
-
-  for (auto it : _tensor_info_map)
-  {
-    assert(_first_uses_visit.find(it.first) != _first_uses_visit.end());
-    assert(_first_uses_visit[it.first]);
-  }
-
-  for (auto it : _subtensor_info_map)
-  {
-    assert(_first_uses_visit.find(it.first) != _first_uses_visit.end());
-    assert(_first_uses_visit[it.first]);
-  }
-
-  for (auto it : _tensor_layout_map)
-  {
-    assert(_first_uses_visit.find(it.first) != _first_uses_visit.end());
-    assert(_first_uses_visit[it.first]);
-    UNUSED_RELEASE(it);
-  }
-
-  assert(_uses_queue.size() == 0);
-  assert(_first_uses_num == 0);
-
-  assert(
-      std::all_of(_parent_def.begin(), _parent_def.end(),
-                  [](std::pair<const ir::OperandIndex, uint32_t> it) { return it.second == 0; }));
-
-  assert(
-      std::all_of(_parent_uses.begin(), _parent_uses.end(),
-                  [](std::pair<const ir::OperandIndex, uint32_t> it) { return it.second == 0; }));
-}
-
-template <typename T_ITensor, typename T_Tensor, typename T_SubTensor>
-ir::OperandIndex
-TemplTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::findRootParent(ir::OperandIndex ind)
-{
-  if (_subtensor_info_map.find(ind) == _subtensor_info_map.end())
-    return ind;
-
-  const auto &parent_ind = _subtensor_info_map.at(ind).parent();
-  return findRootParent(parent_ind);
-}
-
-} // namespace acl_common
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ACL_COMMON_TEMPL_TENSOR_BUILDER_H__
diff --git a/runtime/neurun/backend/acl_neon/Backend.h b/runtime/neurun/backend/acl_neon/Backend.h
deleted file mode 100644
index 2fcf66933..000000000
--- a/runtime/neurun/backend/acl_neon/Backend.h
+++ /dev/null
@@ -1,65 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_NEON_BACKEND_H__
-#define __NEURUN_BACKEND_ACL_NEON_BACKEND_H__
-
-#include <memory>
-#include <backend/Backend.h>
-#include <ir/Operands.h>
-
-#include "Config.h"
-#include "ConstantInitializer.h"
-#include "KernelGenerator.h"
-#include "ShapeFixer.h"
-#include "TensorManager.h"
-#include "TensorRegister.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_neon
-{
-
-class Backend : public ::neurun::backend::Backend
-{
-public:
-  Backend() : _config{std::make_shared<Config>()} {}
-
-  std::shared_ptr<IConfig> config() const override { return _config; }
-
-  std::unique_ptr<BackendContext>
-  newContext(const ir::Operands &operands,
-             const std::shared_ptr<custom::IKernelBuilder> &) const override
-  {
-    auto tensor_builder = std::make_shared<TensorBuilder>(createTensorManager());
-    return std::unique_ptr<BackendContext>{new BackendContext{
-        this, tensor_builder, std::make_shared<ConstantInitializer>(operands, tensor_builder),
-        std::make_shared<KernelGenerator>(operands, tensor_builder),
-        std::make_shared<ShapeFixer>(operands, tensor_builder),
-        std::make_shared<TensorRegister>(operands, tensor_builder)}};
-  }
-
-private:
-  std::shared_ptr<IConfig> _config;
-};
-
-} // namespace acl_neon
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ACL_NEON_BACKEND_H__
diff --git a/runtime/neurun/backend/acl_neon/CMakeLists.txt b/runtime/neurun/backend/acl_neon/CMakeLists.txt
deleted file mode 100644
index 061246d36..000000000
--- a/runtime/neurun/backend/acl_neon/CMakeLists.txt
+++ /dev/null
@@ -1,21 +0,0 @@
-# Unsupported architecture
-nnas_find_package(ARMCompute QUIET)
-if(NOT ARMCompute_FOUND)
-  return()
-endif(NOT ARMCompute_FOUND)
-
-set(LIB_NEURUN_BACKEND_ACL_NEON neurun_backend_acl_neon)
-
-file(GLOB_RECURSE SOURCES "*.cc")
-
-add_library(${LIB_NEURUN_BACKEND_ACL_NEON} SHARED ${SOURCES})
-
-target_include_directories(${LIB_NEURUN_BACKEND_ACL_NEON} PRIVATE ${CMAKE_CURRENT_SOURCE_DIR})
-target_link_libraries(${LIB_NEURUN_BACKEND_ACL_NEON} PRIVATE neurun_core)
-target_link_libraries(${LIB_NEURUN_BACKEND_ACL_NEON} PRIVATE ${LIB_NEURUN_BACKEND_ACL_COMMON})
-target_link_libraries(${LIB_NEURUN_BACKEND_ACL_NEON} PRIVATE nnfw_common)
-target_link_libraries(${LIB_NEURUN_BACKEND_ACL_NEON} PRIVATE nnfw_coverage)
-
-set_target_properties(${LIB_NEURUN_BACKEND_ACL_NEON} PROPERTIES OUTPUT_NAME backend_acl_neon)
-
-install(TARGETS ${LIB_NEURUN_BACKEND_ACL_NEON} DESTINATION lib)
diff --git a/runtime/neurun/backend/acl_neon/Config.cc b/runtime/neurun/backend/acl_neon/Config.cc
deleted file mode 100644
index 352bc0b41..000000000
--- a/runtime/neurun/backend/acl_neon/Config.cc
+++ /dev/null
@@ -1,30 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "Config.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_neon
-{
-
-bool Config::initialize() { return true; }
-
-} // namespace acl_neon
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/acl_neon/Config.h b/runtime/neurun/backend/acl_neon/Config.h
deleted file mode 100644
index 430c194ee..000000000
--- a/runtime/neurun/backend/acl_neon/Config.h
+++ /dev/null
@@ -1,49 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_NEON_CONFIG_H__
-#define __NEURUN_BACKEND_ACL_NEON_CONFIG_H__
-
-#include <backend/IConfig.h>
-#include <cpp14/memory.h>
-#include <util/ITimer.h>
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_neon
-{
-
-class Config : public IConfig
-{
-public:
-  std::string id() override { return "acl_neon"; }
-  bool initialize() override;
-  bool SupportPermutation() override { return true; }
-  bool SupportSubTensorAlloc() override { return true; }
-
-  std::unique_ptr<util::ITimer> timer() override
-  {
-    return nnfw::cpp14::make_unique<util::CPUTimer>();
-  }
-};
-
-} // namespace acl_neon
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ACL_NEON_CONFIG_H__
diff --git a/runtime/neurun/backend/acl_neon/ConstantInitializer.cc b/runtime/neurun/backend/acl_neon/ConstantInitializer.cc
deleted file mode 100644
index 9a74bda29..000000000
--- a/runtime/neurun/backend/acl_neon/ConstantInitializer.cc
+++ /dev/null
@@ -1,246 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ConstantInitializer.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_neon
-{
-
-ConstantInitializer::ConstantInitializer(const ir::Operands &operands,
-                                         const std::shared_ptr<TensorBuilder> &tensor_builder)
-    : _operands{operands}, _tensor_builder{tensor_builder}
-{
-  // DO NOTHING
-}
-
-void ConstantInitializer::visit(const ir::operation::BatchToSpaceND &node)
-{
-  const auto &block_size_index = node.getInputs().at(ir::operation::BatchToSpaceND::BLOCK_SIZE);
-  const auto &block_size_obj = _operands.at(block_size_index);
-
-  if (block_size_obj.isConstant())
-  {
-    _init_map[block_size_index] = [](const ir::Operand &model_obj, backend::operand::ITensor &obj) {
-      const auto &shape = model_obj.shape();
-      const auto base = reinterpret_cast<const int32_t *>(model_obj.data().base());
-      assert(model_obj.shape().rank() == 1);
-      obj.access([&](::neurun::backend::operand::ITensor &tensor) {
-        for (size_t i = 0; i < shape.num_elements(); ++i)
-        {
-          const int32_t value = base[shape.num_elements() - i - 1];
-          int32_t *into = reinterpret_cast<int32_t *>(tensor.buffer() +
-                                                      tensor.calcOffset({static_cast<int32_t>(i)}));
-          *into = value;
-        }
-      });
-    };
-  }
-}
-
-void ConstantInitializer::visit(const ir::operation::Conv2D &node)
-{
-  const auto &kernel_index = node.getInputs().at(ir::operation::Conv2D::KERNEL);
-  const auto &kernel_obj = _operands.at(kernel_index);
-  registerPermuteInitializer(kernel_index, kernel_obj);
-
-  const auto &bias_index = node.getInputs().at(ir::operation::Conv2D::BIAS);
-  const auto &bias_obj = _operands.at(bias_index);
-  registerCopyInitializer(bias_index, bias_obj);
-}
-
-void ConstantInitializer::visit(const ir::operation::DepthwiseConv2D &node)
-{
-  const auto &kernel_index = node.getInputs().at(ir::operation::DepthwiseConv2D::KERNEL);
-  const auto &kernel_obj = _operands.at(kernel_index);
-  registerPermuteInitializer(kernel_index, kernel_obj);
-
-  const auto &bias_index = node.getInputs().at(ir::operation::DepthwiseConv2D::BIAS);
-  const auto &bias_obj = _operands.at(bias_index);
-  registerCopyInitializer(bias_index, bias_obj);
-}
-
-void ConstantInitializer::visit(const ir::operation::FullyConnected &node)
-{
-  const auto &weight_index = node.getInputs().at(ir::operation::FullyConnected::WEIGHT);
-  const auto &weight_obj = _operands.at(weight_index);
-  registerCopyInitializer(weight_index, weight_obj);
-
-  const auto &bias_index = node.getInputs().at(ir::operation::FullyConnected::BIAS);
-  const auto &bias_obj = _operands.at(bias_index);
-  registerCopyInitializer(bias_index, bias_obj);
-}
-
-void ConstantInitializer::visit(const ir::operation::LSTM &node)
-{
-  const auto &input_to_input_weights_index =
-      node.getInputs().at(ir::operation::LSTM::INPUT_TO_INPUT_WEIGHTS);
-  const auto &input_to_input_weights_obj = _operands.at(input_to_input_weights_index);
-  registerCopyInitializer(input_to_input_weights_index, input_to_input_weights_obj);
-
-  const auto &input_to_forget_weights_index =
-      node.getInputs().at(ir::operation::LSTM::INPUT_TO_FORGET_WEIGHTS);
-  const auto &input_to_forget_weights_obj = _operands.at(input_to_forget_weights_index);
-  registerCopyInitializer(input_to_forget_weights_index, input_to_forget_weights_obj);
-
-  const auto &input_to_cell_weights_index =
-      node.getInputs().at(ir::operation::LSTM::INPUT_TO_CELL_WEIGHTS);
-  const auto &input_to_cell_weights_obj = _operands.at(input_to_cell_weights_index);
-  registerCopyInitializer(input_to_cell_weights_index, input_to_cell_weights_obj);
-
-  const auto &input_to_output_weights_index =
-      node.getInputs().at(ir::operation::LSTM::INPUT_TO_OUTPUT_WEIGHTS);
-  const auto &input_to_output_weights_obj = _operands.at(input_to_output_weights_index);
-  registerCopyInitializer(input_to_output_weights_index, input_to_output_weights_obj);
-
-  const auto &recurrent_to_input_weights_index =
-      node.getInputs().at(ir::operation::LSTM::RECURRENT_TO_INPUT_WEIGHTS);
-  const auto &recurrent_to_input_weights_obj = _operands.at(recurrent_to_input_weights_index);
-  registerCopyInitializer(recurrent_to_input_weights_index, recurrent_to_input_weights_obj);
-
-  const auto &recurrent_to_forget_weights_index =
-      node.getInputs().at(ir::operation::LSTM::RECURRENT_TO_FORGET_WEIGHTS);
-  const auto &recurrent_to_forget_weights_obj = _operands.at(recurrent_to_forget_weights_index);
-  registerCopyInitializer(recurrent_to_forget_weights_index, recurrent_to_forget_weights_obj);
-
-  const auto &recurrent_to_cell_weights_index =
-      node.getInputs().at(ir::operation::LSTM::RECURRENT_TO_CELL_WEIGHTS);
-  const auto &recurrent_to_cell_weights_obj = _operands.at(recurrent_to_cell_weights_index);
-  registerCopyInitializer(recurrent_to_cell_weights_index, recurrent_to_cell_weights_obj);
-
-  const auto &recurrent_to_output_weights_index =
-      node.getInputs().at(ir::operation::LSTM::RECURRENT_TO_OUTPUT_WEIGHTS);
-  const auto &recurrent_to_output_weights_obj = _operands.at(recurrent_to_output_weights_index);
-  registerCopyInitializer(recurrent_to_output_weights_index, recurrent_to_output_weights_obj);
-
-  const auto &cell_to_input_weights_index =
-      node.getInputs().at(ir::operation::LSTM::CELL_TO_INPUT_WEIGHTS);
-  const auto &cell_to_input_weights_obj = _operands.at(cell_to_input_weights_index);
-  registerCopyInitializer(cell_to_input_weights_index, cell_to_input_weights_obj);
-
-  const auto &cell_to_forget_weights_index =
-      node.getInputs().at(ir::operation::LSTM::CELL_TO_FORGET_WEIGHTS);
-  const auto &cell_to_forget_weights_obj = _operands.at(cell_to_forget_weights_index);
-  registerCopyInitializer(cell_to_forget_weights_index, cell_to_forget_weights_obj);
-
-  const auto &cell_to_output_weights_index =
-      node.getInputs().at(ir::operation::LSTM::CELL_TO_OUTPUT_WEIGHTS);
-  const auto &cell_to_output_weights_obj = _operands.at(cell_to_output_weights_index);
-  registerCopyInitializer(cell_to_output_weights_index, cell_to_output_weights_obj);
-
-  const auto &input_gate_bias_index = node.getInputs().at(ir::operation::LSTM::INPUT_GATE_BIAS);
-  const auto &input_gate_bias_obj = _operands.at(input_gate_bias_index);
-  registerCopyInitializer(input_gate_bias_index, input_gate_bias_obj);
-
-  const auto &forget_gate_bias_index = node.getInputs().at(ir::operation::LSTM::FORGET_GATE_BIAS);
-  const auto &forget_gate_bias_obj = _operands.at(forget_gate_bias_index);
-  registerCopyInitializer(forget_gate_bias_index, forget_gate_bias_obj);
-
-  const auto &output_gate_bias_index = node.getInputs().at(ir::operation::LSTM::OUTPUT_GATE_BIAS);
-  const auto &output_gate_bias_obj = _operands.at(output_gate_bias_index);
-  registerCopyInitializer(output_gate_bias_index, output_gate_bias_obj);
-
-  const auto &projection_weights_index =
-      node.getInputs().at(ir::operation::LSTM::PROJECTION_WEIGHTS);
-  const auto &projection_weights_obj = _operands.at(projection_weights_index);
-  registerCopyInitializer(projection_weights_index, projection_weights_obj);
-
-  const auto &projection_bias_index = node.getInputs().at(ir::operation::LSTM::PROJECTION_BIAS);
-  const auto &projection_bias_obj = _operands.at(projection_bias_index);
-  registerCopyInitializer(projection_bias_index, projection_bias_obj);
-}
-
-void ConstantInitializer::visit(const ir::operation::RNN &node)
-{
-  const auto &weights_index = node.getInputs().at(ir::operation::RNN::WEIGHTS);
-  const auto &weights_obj = _operands.at(weights_index);
-  registerCopyInitializer(weights_index, weights_obj);
-
-  const auto &recurrent_weights_index = node.getInputs().at(ir::operation::RNN::RECURRENT_WEIGHTS);
-  const auto &recurrent_weights_obj = _operands.at(recurrent_weights_index);
-  registerCopyInitializer(recurrent_weights_index, recurrent_weights_obj);
-
-  const auto &bias_index = node.getInputs().at(ir::operation::RNN::BIAS);
-  const auto &bias_obj = _operands.at(bias_index);
-  registerCopyInitializer(bias_index, bias_obj);
-}
-
-void ConstantInitializer::visit(const ir::operation::SpaceToBatchND &node)
-{
-  const auto &block_size_index = node.getInputs().at(ir::operation::SpaceToBatchND::BLOCK_SIZE);
-  const auto &block_size_obj = _operands.at(block_size_index);
-
-  if (block_size_obj.isConstant())
-  {
-    _init_map[block_size_index] = [](const ir::Operand &model_obj, backend::operand::ITensor &obj) {
-      const auto &shape = model_obj.shape();
-      const auto base = reinterpret_cast<const int32_t *>(model_obj.data().base());
-      assert(model_obj.shape().rank() == 1);
-      obj.access([&](::neurun::backend::operand::ITensor &tensor) {
-        for (size_t i = 0; i < shape.num_elements(); ++i)
-        {
-          const int32_t value = base[shape.num_elements() - i - 1];
-          int32_t *into = reinterpret_cast<int32_t *>(tensor.buffer() +
-                                                      tensor.calcOffset({static_cast<int32_t>(i)}));
-          *into = value;
-        }
-      });
-    };
-  }
-
-  const auto &paddings_index = node.getInputs().at(ir::operation::SpaceToBatchND::PADDINGS);
-  const auto &paddings_obj = _operands.at(paddings_index);
-  if (paddings_obj.isConstant())
-  {
-    _init_map[paddings_index] = [](const ir::Operand &model_obj, backend::operand::ITensor &obj) {
-      const auto &shape = model_obj.shape();
-      const auto base = reinterpret_cast<const int32_t *>(model_obj.data().base());
-      assert(model_obj.shape().rank() == 2);
-      assert(shape.dim(0) == 2);
-      assert(shape.dim(1) == 2);
-      obj.access([&](::neurun::backend::operand::ITensor &tensor) {
-        for (auto i = 0; i < shape.dim(0); ++i)
-        {
-          for (auto j = 0; j < shape.dim(1); ++j)
-          {
-            const int32_t value = base[i * 2 + j];
-            int32_t *into = reinterpret_cast<int32_t *>(
-                // The coordinates of NETensor are different from the coordiantes of CLTensor in
-                // this operand.
-                // NEON : {j, reversed i}
-                // CL : {reversed i, j}
-                tensor.buffer() + tensor.calcOffset({j, shape.dim(0) - i - 1}));
-            *into = value;
-          }
-        }
-      });
-    };
-  }
-}
-
-void ConstantInitializer::visit(const ir::operation::TransposeConv &node)
-{
-  const auto &kernel_index = node.getInputs().at(ir::operation::TransposeConv::KERNEL);
-  const auto &kernel_obj = _operands.at(kernel_index);
-  registerPermuteInitializer(kernel_index, kernel_obj);
-}
-
-} // namespace acl_neon
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/acl_neon/ConstantInitializer.h b/runtime/neurun/backend/acl_neon/ConstantInitializer.h
deleted file mode 100644
index 0f2b2d05b..000000000
--- a/runtime/neurun/backend/acl_neon/ConstantInitializer.h
+++ /dev/null
@@ -1,60 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_COMPILER_ACL_NEON_CONSTANT_INITIALIZER_H__
-#define __NEURUN_COMPILER_ACL_NEON_CONSTANT_INITIALIZER_H__
-
-#include <backend/IConstantInitializer.h>
-#include <ir/Operands.h>
-#include "TensorBuilder.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_neon
-{
-
-class ConstantInitializer : public IConstantInitializer
-{
-public:
-  ConstantInitializer(const ir::Operands &operands,
-                      const std::shared_ptr<TensorBuilder> &tensor_builder);
-
-public:
-  void visit(const ir::operation::BatchToSpaceND &) override;
-  void visit(const ir::operation::Conv2D &) override;
-  void visit(const ir::operation::DepthwiseConv2D &) override;
-  void visit(const ir::operation::FullyConnected &) override;
-  void visit(const ir::operation::LSTM &) override;
-  void visit(const ir::operation::RNN &) override;
-  void visit(const ir::operation::SpaceToBatchND &) override;
-  void visit(const ir::operation::TransposeConv &) override;
-
-private:
-  const ir::Operands &operands() const override { return _operands; }
-  std::shared_ptr<ITensorBuilder> tensor_builder() const override { return _tensor_builder; }
-
-private:
-  const ir::Operands &_operands;
-  std::shared_ptr<TensorBuilder> _tensor_builder;
-};
-
-} // namespace acl_neon
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_COMPILER_ACL_NEON_CONSTANT_INITIALIZER_H__
diff --git a/runtime/neurun/backend/acl_neon/KernelGenerator.cc b/runtime/neurun/backend/acl_neon/KernelGenerator.cc
deleted file mode 100644
index 85c6a0633..000000000
--- a/runtime/neurun/backend/acl_neon/KernelGenerator.cc
+++ /dev/null
@@ -1,2152 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "KernelGenerator.h"
-
-#include <arm_compute/runtime/NEON/NEFunctions.h>   // Include all ARM Compute NEON functions
-#include <arm_compute/runtime/NEON/NEFunctionsEx.h> // Include all ARM Compute EX NEON functions
-
-#include <Convert.h>
-#include <Swizzle.h>
-
-#include "util/Padding.h"
-#include "ir/Index.h"
-#include "ir/DataType.h"
-#include "ir/InternalType.h"
-#include "compiler/IExecutionBuilder.h"
-#include "exec/NopFunction.h"
-#include "util/logging.h"
-#include "util/Utils.h"
-
-using ::neurun::compiler::IExecutionBuilder;
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_neon
-{
-
-using ::neurun::backend::acl_common::asAclFunction;
-
-//
-// ActivationBuilder
-//
-class ActivationBuilder
-{
-public:
-  ActivationBuilder(IExecutionBuilder &builder) : _builder(builder)
-  {
-    // DO NOTHING
-  }
-
-private:
-  void appendReLU(::arm_compute::ITensor *ifm_alloc);
-  void appendReLU1(::arm_compute::ITensor *ifm_alloc);
-  void appendReLU6(::arm_compute::ITensor *ifm_alloc);
-
-public:
-  void append(ir::Activation act, ::arm_compute::ITensor *ifm_alloc);
-
-private:
-  IExecutionBuilder &_builder;
-};
-
-void ActivationBuilder::appendReLU(::arm_compute::ITensor *ifm_alloc)
-{
-  const ::arm_compute::ActivationLayerInfo act_info{
-      ::arm_compute::ActivationLayerInfo::ActivationFunction::RELU};
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEActivationLayer>();
-
-  fn->configure(ifm_alloc, nullptr, act_info);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _builder.append(std::move(acl_fn));
-}
-
-void ActivationBuilder::appendReLU1(::arm_compute::ITensor *ifm_alloc)
-{
-  const ::arm_compute::ActivationLayerInfo act_info{
-      ::arm_compute::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 1.0f, -1.0f};
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEActivationLayer>();
-
-  fn->configure(ifm_alloc, nullptr, act_info);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _builder.append(std::move(acl_fn));
-}
-
-void ActivationBuilder::appendReLU6(::arm_compute::ITensor *ifm_alloc)
-{
-  const ::arm_compute::ActivationLayerInfo act_info{
-      ::arm_compute::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.0f, 0.0f};
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEActivationLayer>();
-
-  fn->configure(ifm_alloc, nullptr, act_info);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _builder.append(std::move(acl_fn));
-}
-
-void ActivationBuilder::append(ir::Activation act, ::arm_compute::ITensor *ifm_alloc)
-{
-  switch (act)
-  {
-    case ir::Activation::NONE:
-    {
-      // DO NOTHING
-      break;
-    }
-    case ir::Activation::RELU:
-    {
-      appendReLU(ifm_alloc);
-      break;
-    }
-    case ir::Activation::RELU1:
-    {
-      appendReLU1(ifm_alloc);
-      break;
-    }
-    case ir::Activation::RELU6:
-    {
-      appendReLU6(ifm_alloc);
-      break;
-    }
-    default:
-    {
-      throw std::runtime_error("Not supported, yet");
-    }
-  }
-}
-
-//
-// KernelGenerator
-//
-KernelGenerator::KernelGenerator(const ir::Operands &ctx,
-                                 const std::shared_ptr<TensorBuilder> &tensor_builder)
-    : _ctx(ctx), _tensor_builder(tensor_builder), _current_subg_layout(ir::Layout::UNKNOWN)
-{
-  // DO NOTHING
-}
-
-void KernelGenerator::visit(const ir::OpSequence &op_seq)
-{
-  _current_subg_layout = op_seq.getLayout();
-  for (const auto &e : op_seq.operations())
-  {
-    const auto &node = *(e.node);
-    _tensor_builder->preVisit(node);
-    node.accept(*this);
-    _tensor_builder->postVisit(node);
-  }
-}
-
-void KernelGenerator::visit(const ir::operation::Abs &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::Abs::Input::INPUT)};
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input_alloc = _tensor_builder->at(input_index).get();
-
-  const ::arm_compute::ActivationLayerInfo act_info{
-      ::arm_compute::ActivationLayerInfo::ActivationFunction::ABS};
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEActivationLayer>();
-
-  fn->configure(input_alloc->handle(), output_alloc->handle(), act_info);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::ArgMax &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::ArgMax::Input::INPUT)};
-
-  const auto ifm_rank = node.param().rank;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-  auto frontend_layout = _current_subg_layout;
-  auto backend_layout = ifm_alloc->layout();
-
-  int axis_value = node.param().axis;
-  if (axis_value < 0)
-  {
-    axis_value += ifm_rank;
-  }
-  assert(axis_value >= 0 && axis_value < ifm_rank);
-  const auto fixed_axis =
-      acl_common::ToARMComputeAxis(ifm_rank, axis_value, frontend_layout, backend_layout).value();
-
-  // auto fn = nnfw::cpp14::make_unique<::arm_compute::NEArgMinMaxLayer>();
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEArgMax>();
-
-  // NOTE
-  // if (ofm_alloc->info()->data_type() == arm_compute::DataType::S32)
-  //{
-  ofm_alloc->info()->set_data_type(arm_compute::DataType::U32);
-  //}
-  fn->configure(ifm_alloc->handle(), fixed_axis, ofm_alloc->handle());
-  // fn->configure(ifm_alloc->handle(), fixed_axis, ofm_alloc->handle(),
-  // arm_compute::ReductionOperation::ARG_IDX_MAX);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::BatchToSpaceND &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::BatchToSpaceND::Input::INPUT)};
-  const auto block_size_index{
-      node.getInputs().at(ir::operation::BatchToSpaceND::Input::BLOCK_SIZE)};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-  auto block_size_alloc = _tensor_builder->at(block_size_index).get();
-
-  assert(_ctx.at(block_size_index).isConstant());
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEBatchToSpaceLayer>();
-
-  fn->configure(ifm_alloc->handle(), block_size_alloc->handle(), ofm_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Cast &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::Cast::Input::INPUT)};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NECast>();
-
-  auto input_sub_type = _ctx.at(ifm_index).typeInfo().type() == ir::DataType::BOOL8
-                            ? arm_compute::SubDataType::BOOL
-                            : arm_compute::SubDataType::NONE;
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle(), input_sub_type);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Conv2D &node)
-{
-  using ir::operation::Conv2D;
-
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(Conv2D::Input::INPUT)};
-  const auto ker_index{node.getInputs().at(Conv2D::Input::KERNEL)};
-  const auto bias_index{node.getInputs().at(Conv2D::Input::BIAS)};
-
-  const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(_current_subg_layout);
-  const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(_current_subg_layout);
-  // Kernel format is [depth_out, kernel_height, kernel_width, depth_in].
-  const auto &ker_shape = _ctx.at(ker_index).shape();
-  const auto ker_height = ker_shape.dim(1);
-  const auto ker_width = ker_shape.dim(2);
-
-  const auto stride = node.param().stride;
-  const auto padding = neurun::util::calculatePadding(node.param().padding, ifm_shape, ofm_shape,
-                                                      stride, ker_width, ker_height);
-  const auto activation = node.param().activation;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-  auto ker_alloc = _tensor_builder->at(ker_index).get();
-  auto bias_alloc = _tensor_builder->at(bias_index).get();
-
-  const auto conv_info = acl_common::asPadStrideInfo(padding, stride);
-  const auto act_info = acl_common::asActivationLayerInfo(activation);
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEConvolutionLayer>(
-      _tensor_builder->acl_tensor_manager()->internal_buffer_manager());
-
-  fn->configure(ifm_alloc->handle(), ker_alloc->handle(), bias_alloc->handle(), ofm_alloc->handle(),
-                conv_info, ::arm_compute::WeightsInfo(), ::arm_compute::Size2D(1U, 1U), act_info);
-
-  _execution_builder->append(asAclFunction(std::move(fn)));
-}
-
-void KernelGenerator::visit(const ir::operation::DepthToSpace &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::DepthToSpace::Input::INPUT)};
-
-  auto block_size = node.param().block_size;
-  assert(block_size > 0);
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input_alloc = _tensor_builder->at(input_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEDepthToSpaceLayerEx>();
-
-  fn->configure(input_alloc->handle(), output_alloc->handle(), block_size);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::DepthwiseConv2D &node)
-{
-  using ir::operation::DepthwiseConv2D;
-
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(DepthwiseConv2D::Input::INPUT)};
-  const auto ker_index{node.getInputs().at(DepthwiseConv2D::Input::KERNEL)};
-  const auto bias_index{node.getInputs().at(DepthwiseConv2D::Input::BIAS)};
-
-  const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(_current_subg_layout);
-  const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(_current_subg_layout);
-  // Kernel format is [1, kernel_height, kernel_width, depth_out].
-  const auto &ker_shape = _ctx.at(ker_index).shape();
-  const auto ker_height = ker_shape.dim(1);
-  const auto ker_width = ker_shape.dim(2);
-
-  const auto stride = node.param().stride;
-  const auto padding = neurun::util::calculatePadding(node.param().padding, ifm_shape, ofm_shape,
-                                                      stride, ker_width, ker_height);
-  const auto multiplier = node.param().multiplier;
-  const auto activation = node.param().activation;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-  auto ker_alloc = _tensor_builder->at(ker_index).get();
-  auto bias_alloc = _tensor_builder->at(bias_index).get();
-
-  const auto conv_info = acl_common::asPadStrideInfo(padding, stride);
-  const auto act_info = acl_common::asActivationLayerInfo(activation);
-
-  if (ker_height == 3 && ker_width == 3)
-  {
-    auto fn = nnfw::cpp14::make_unique<::arm_compute::NEDepthwiseConvolutionLayer3x3>();
-
-    fn->configure(ifm_alloc->handle(), ker_alloc->handle(), bias_alloc->handle(),
-                  ofm_alloc->handle(), conv_info, multiplier, act_info);
-
-    _execution_builder->append(asAclFunction(std::move(fn)));
-  }
-  else
-  {
-    auto fn = nnfw::cpp14::make_unique<::arm_compute::NEDepthwiseConvolutionLayer>();
-
-    fn->configure(ifm_alloc->handle(), ker_alloc->handle(), bias_alloc->handle(),
-                  ofm_alloc->handle(), conv_info, multiplier, act_info);
-
-    _execution_builder->append(asAclFunction(std::move(fn)));
-  }
-}
-
-void KernelGenerator::visit(const ir::operation::Dequantize &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::Dequantize::Input::INPUT)};
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input_alloc = _tensor_builder->at(input_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEDequantizationLayer>();
-
-  fn->configure(input_alloc->handle(), output_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::MaxPool2D &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::MaxPool2D::Input::INPUT)};
-
-  const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(_current_subg_layout);
-  const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(_current_subg_layout);
-
-  const auto kh = node.param().kh;
-  const auto kw = node.param().kw;
-  const auto stride = node.param().stride;
-  const auto padding =
-      neurun::util::calculatePadding(node.param().padding, ifm_shape, ofm_shape, stride, kw, kh);
-  const auto activation = node.param().activation;
-
-  VERBOSE(MaxPool2D) << "IFM_H: " << ifm_shape.H << std::endl;
-  VERBOSE(MaxPool2D) << "IFM_W: " << ifm_shape.W << std::endl;
-  VERBOSE(MaxPool2D) << "OFM_H: " << ofm_shape.H << std::endl;
-  VERBOSE(MaxPool2D) << "OFM_W: " << ofm_shape.W << std::endl;
-  VERBOSE(MaxPool2D) << "KER_H: " << kh << std::endl;
-  VERBOSE(MaxPool2D) << "KER_W: " << kw << std::endl;
-  VERBOSE(MaxPool2D) << "STRIDE_H: " << stride.vertical << std::endl;
-  VERBOSE(MaxPool2D) << "STRIDE_W: " << stride.horizontal << std::endl;
-  VERBOSE(MaxPool2D) << "PAD(T): " << padding.top << std::endl;
-  VERBOSE(MaxPool2D) << "PAD(B): " << padding.bottom << std::endl;
-  VERBOSE(MaxPool2D) << "PAD(L): " << padding.left << std::endl;
-  VERBOSE(MaxPool2D) << "PAD(R): " << padding.right << std::endl;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-
-  ::arm_compute::PoolingLayerInfo info{::arm_compute::PoolingType::MAX,
-                                       ::arm_compute::Size2D{kw, kh},
-                                       acl_common::asPadStrideInfo(padding, stride)};
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEPoolingLayer>();
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle(), info);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append((std::move(acl_fn)));
-
-  ActivationBuilder{*_execution_builder}.append(activation, ofm_alloc->handle());
-}
-
-void KernelGenerator::visit(const ir::operation::Mean &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::Mean::Input::INPUT)};
-  const auto &axes{node.param().axes};
-  const auto keep_dims{node.param().keep_dims};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-  const auto frontend_layout = _current_subg_layout;
-  const auto backend_layout = ifm_alloc->layout();
-
-  // Convert to ACL axes taking into account negative values and possible duplicates.
-  std::set<std::uint32_t> acl_axes;
-  const int ifm_rank = node.param().rank;
-  for (int axis : axes)
-  {
-    if (axis < 0)
-      axis += ifm_rank;
-    acl_axes.insert(
-        acl_common::ToARMComputeAxis(ifm_rank, axis, frontend_layout, backend_layout).value());
-  }
-
-  arm_compute::Coordinates fixed_axis;
-  for (const auto axis : acl_axes)
-  {
-    fixed_axis.set(fixed_axis.num_dimensions(), axis);
-  }
-
-  // NOTE NEReduceMean has a bug that does not support NHWC layout
-  //      NEReduceMean intermediate tensors are always NCHW layout
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEReduceMeanEx>();
-
-  fn->configure(ifm_alloc->handle(), fixed_axis, keep_dims, ofm_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::AvgPool2D &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::AvgPool2D::Input::INPUT)};
-
-  const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(_current_subg_layout);
-  const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(_current_subg_layout);
-
-  const auto kh = node.param().kh;
-  const auto kw = node.param().kw;
-  const auto stride = node.param().stride;
-  const auto padding =
-      neurun::util::calculatePadding(node.param().padding, ifm_shape, ofm_shape, stride, kw, kh);
-  const auto activation = node.param().activation;
-
-  VERBOSE(AvgPool2D) << "IFM_H: " << ifm_shape.H << std::endl;
-  VERBOSE(AvgPool2D) << "IFM_W: " << ifm_shape.W << std::endl;
-  VERBOSE(AvgPool2D) << "OFM_H: " << ofm_shape.H << std::endl;
-  VERBOSE(AvgPool2D) << "OFM_W: " << ofm_shape.W << std::endl;
-  VERBOSE(AvgPool2D) << "KER_H: " << kh << std::endl;
-  VERBOSE(AvgPool2D) << "KER_W: " << kw << std::endl;
-  VERBOSE(AvgPool2D) << "STRIDE_H: " << stride.vertical << std::endl;
-  VERBOSE(AvgPool2D) << "STRIDE_W: " << stride.horizontal << std::endl;
-  VERBOSE(AvgPool2D) << "PAD(T): " << padding.top << std::endl;
-  VERBOSE(AvgPool2D) << "PAD(B): " << padding.bottom << std::endl;
-  VERBOSE(AvgPool2D) << "PAD(L): " << padding.left << std::endl;
-  VERBOSE(AvgPool2D) << "PAD(R): " << padding.right << std::endl;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-
-  ::arm_compute::PoolingLayerInfo info{
-      ::arm_compute::PoolingType::AVG, ::arm_compute::Size2D{kw, kh},
-      acl_common::asPadStrideInfo(padding, stride), true /* exclude_padding */};
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEPoolingLayer>();
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle(), info);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append((std::move(acl_fn)));
-
-  ActivationBuilder{*_execution_builder}.append(activation, ofm_alloc->handle());
-}
-
-void KernelGenerator::visit(const ir::operation::Concat &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-
-  std::vector<ir::OperandIndex> input_indexes;
-  for (const auto &input : node.getInputs())
-    input_indexes.emplace_back(input);
-
-  const auto axis = node.param().axis;
-
-  // If tensor allocator allocate as subtensor
-  bool canEliminate = true;
-  for (auto ifm_ind : input_indexes)
-  {
-    if (!_tensor_builder->isSubTensorOf(ofm_index, ifm_ind))
-    {
-      canEliminate = false;
-      break;
-    }
-  }
-  if (canEliminate)
-  {
-    // If concat eliminated, return a NOP IFunction
-    _execution_builder->append(nnfw::cpp14::make_unique<exec::NopFunction>());
-    return;
-  }
-
-  auto output_alloc = _tensor_builder->at(ofm_index).get();
-  std::vector<::arm_compute::ITensor *> input_tensors;
-  for (const auto &ifm_ind : input_indexes)
-    input_tensors.emplace_back(_tensor_builder->at(ifm_ind)->handle());
-
-  std::unique_ptr<::arm_compute::IFunction> fn;
-  if (input_indexes.size() < 2)
-  {
-    auto l = nnfw::cpp14::make_unique<::arm_compute::NECopy>();
-    l->configure(input_tensors.at(0), output_alloc->handle());
-    fn = std::move(l);
-  }
-  else
-  {
-    auto l = nnfw::cpp14::make_unique<::arm_compute::NEConcatenateLayer>();
-    const auto rank = node.param().rank;
-    const auto frontend_layout = _current_subg_layout;
-    const auto backend_layout = output_alloc->layout();
-    const auto fixed_axis =
-        acl_common::ToARMComputeAxis(rank, axis, frontend_layout, backend_layout).value();
-    l->configure(input_tensors, output_alloc->handle(), fixed_axis);
-    fn = std::move(l);
-  }
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::EmbeddingLookup &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto lookups_index{node.getInputs().at(ir::operation::EmbeddingLookup::Input::LOOKUPS)};
-  const auto values_index{node.getInputs().at(ir::operation::EmbeddingLookup::Input::VALUES)};
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto lookups_alloc = _tensor_builder->at(lookups_index).get();
-  auto values_alloc = _tensor_builder->at(values_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEEmbeddingLookup>();
-
-  fn->configure(values_alloc->handle(), output_alloc->handle(), lookups_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Floor &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::Floor::Input::INPUT)};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEFloor>();
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::FullyConnected &node)
-{
-  using ir::operation::FullyConnected;
-
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(FullyConnected::Input::INPUT)};
-  const auto weight_index{node.getInputs().at(FullyConnected::Input::WEIGHT)};
-  const auto bias_index{node.getInputs().at(FullyConnected::Input::BIAS)};
-
-  const auto input_rank = _ctx.at(input_index).shape().rank();
-
-  const auto output_size =
-      _ctx.at(output_index).shape().dim(_ctx.at(output_index).shape().rank() - 1);
-  UNUSED_RELEASE(output_size);
-  assert(_ctx.at(bias_index).shape().dim(0) == output_size);
-  assert(_ctx.at(weight_index).shape().dim(0) == output_size);
-  const auto batch_size =
-      _ctx.at(output_index).shape().dim(_ctx.at(output_index).shape().rank() - 2);
-  const auto input_size =
-      _ctx.at(weight_index).shape().dim(_ctx.at(weight_index).shape().rank() - 1);
-
-  // Check for reshaping input's shape into rank-2
-  bool needs_reshape = false;
-  ir::Shape reshape(2);
-  if (input_rank == 3 || input_rank == 4)
-  {
-    const auto &ifm_shape = _ctx.at(input_index).shape();
-    auto feature_size = 1;
-    for (int i = 0; i < ifm_shape.rank(); ++i)
-    {
-      feature_size *= ifm_shape.dim(i);
-    }
-
-    UNUSED_RELEASE(feature_size);
-    assert(feature_size == batch_size * input_size);
-
-    // for reshaping
-    needs_reshape = true;
-    reshape.dim(0) = batch_size; /* H */
-    reshape.dim(1) = input_size; /* W */
-  }
-
-  const auto activation = node.param().activation;
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  const auto input_alloc = _tensor_builder->at(input_index).get();
-  const auto weight_alloc = _tensor_builder->at(weight_index).get();
-  const auto bias_alloc = _tensor_builder->at(bias_index).get();
-  const auto frontend_layout = _current_subg_layout;
-  const auto acl_layout = output_alloc->handle()->info()->data_layout();
-
-  auto fn = nnfw::cpp14::make_unique<arm_compute::NEFullyConnectedReshapingLayer>(
-      _tensor_builder->acl_tensor_manager()->internal_buffer_manager());
-
-  arm_compute::NEFullyConnectedReshapingLayer::KernelType kernel_type =
-      _ctx.at(weight_index).isConstant()
-          ? arm_compute::NEFullyConnectedReshapingLayer::KernelType::PREPROCESSED_WEIGHTS
-          : arm_compute::NEFullyConnectedReshapingLayer::KernelType::GENERAL;
-
-  fn->configure(
-      input_alloc->handle(), weight_alloc->handle(), bias_alloc->handle(), output_alloc->handle(),
-      needs_reshape,
-      ::neurun::backend::acl_common::asTensorShape(
-          reshape, frontend_layout, ::neurun::backend::acl_common::asRuntimeLayout(acl_layout)),
-      kernel_type);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-
-  ActivationBuilder{*_execution_builder}.append(activation, output_alloc->handle());
-}
-
-void KernelGenerator::visit(const ir::operation::HashtableLookup &node)
-{
-  const auto output_index{node.getOutputs().at(ir::operation::HashtableLookup::Output::OUTPUT)};
-  const auto hits_index{node.getOutputs().at(ir::operation::HashtableLookup::Output::HITS)};
-
-  const auto lookups_index{node.getInputs().at(ir::operation::HashtableLookup::Input::LOOKUPS)};
-  const auto keys_index{node.getInputs().at(ir::operation::HashtableLookup::Input::KEYS)};
-  const auto values_index{node.getInputs().at(ir::operation::HashtableLookup::Input::VALUES)};
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto hits_alloc = _tensor_builder->at(hits_index).get();
-
-  auto lookups_alloc = _tensor_builder->at(lookups_index).get();
-  auto keys_alloc = _tensor_builder->at(keys_index).get();
-  auto values_alloc = _tensor_builder->at(values_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEHashtableLookup>();
-
-  fn->configure(lookups_alloc->handle(), keys_alloc->handle(), values_alloc->handle(),
-                output_alloc->handle(), hits_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Gather &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-
-  const auto ifm_index{node.getInputs().at(ir::operation::Gather::Input::INPUT)};
-  const auto indices_index{node.getInputs().at(ir::operation::Gather::Input::INDICES)};
-
-  const auto ifm_shape = _ctx.at(ifm_index).shape();
-
-  const auto ifm_rank = node.param().rank;
-  const auto axis_raw = node.param().axis;
-  const auto axis_value = (axis_raw < 0 ? (ifm_rank + axis_raw) : axis_raw);
-  // Converting in reverse order
-  const int axis = ::neurun::backend::acl_common::ToARMComputeAxis(ifm_rank, axis_value).value();
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-  auto indices_alloc = _tensor_builder->at(indices_index).get();
-  const auto backend_layout = ofm_alloc->layout();
-  UNUSED_RELEASE(backend_layout);
-
-  // NOTE The frontend layout and backend layout must be the same for this operation.
-  //      If not the same, we have to add a stage(?) to perform permutation of output tensor. It
-  //      is not not efficient even if it works well. If so, it would be better to set the
-  //      layout of these backend tensors to the same layout.
-  //      There is also one thing we have to think about. This operation depends on the layout of
-  //      a model. For example, if a model in NHWC has this operation as output rank == 4, indices
-  //      rank == 2 and axis == 2, this operation should work as the axis W and C, but the axis W
-  //      and C are not sequential in NCHW. So the backend in NCHW cannot handle this case.
-  assert(backend_layout == ifm_alloc->layout());
-  assert(backend_layout == indices_alloc->layout());
-  assert(ifm_rank < 4 || _current_subg_layout == backend_layout);
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEGatherEx>();
-
-  fn->configure(ifm_alloc->handle(), indices_alloc->handle(), ofm_alloc->handle(), axis);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::InstanceNorm &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::InstanceNorm::Input::INPUT)};
-  const auto gamma_index{node.getInputs().at(ir::operation::InstanceNorm::Input::GAMMA)};
-  const auto beta_index{node.getInputs().at(ir::operation::InstanceNorm::Input::BETA)};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-  auto gamma_alloc = _tensor_builder->at(gamma_index).get();
-  auto beta_alloc = _tensor_builder->at(beta_index).get();
-  auto epsilon = node.param().epsilon;
-  auto activation = node.param().activation;
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEInstanceNormalizationLayerEx>();
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle(), gamma_alloc->handle(),
-                beta_alloc->handle(), epsilon);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-
-  ActivationBuilder{*_execution_builder}.append(activation, ofm_alloc->handle());
-}
-
-void KernelGenerator::visit(const ir::operation::L2Normalization &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::L2Normalization::Input::INPUT)};
-
-  // {CL|Neon}L2Normalization performs the reduction only along dimension 0
-  // L2 Normalization always performs the reduction along the depth axis
-  // Thus, we repurpose {CL|Neon}NormalizationLayers to act as depthwise L2 normalizations by
-  // choosing normalization parameters as below
-
-  const auto &ifm_shape = _ctx.at(ifm_index).shape();
-  // TODO Support optional constant dimension that normalization would be performed on
-  const auto normalization_axis = node.param().rank - 1;
-  int32_t radius =
-      2 * ifm_shape.dim(normalization_axis) + 1; // normSize = depth(last dimension) * 2 + 1
-  float alpha = 1.0f;                            // In the implementation to make alpha_ become 1
-  float beta = 0.5f;                             // pow(reduction, -0.5) = 1 / sqrt(reduction)
-  float bias = 0.0f;                             // Don't offset the reduction.
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-
-  const auto norm_info = ::arm_compute::NormalizationLayerInfo(::arm_compute::NormType::CROSS_MAP,
-                                                               radius, alpha, beta, bias, false);
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NENormalizationLayer>();
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle(), norm_info);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::L2Pool2D &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::L2Pool2D::Input::INPUT)};
-
-  const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(_current_subg_layout);
-  const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(_current_subg_layout);
-
-  uint32_t kw = node.param().kw;
-  uint32_t kh = node.param().kh;
-  const auto stride = node.param().stride;
-  const auto padding =
-      neurun::util::calculatePadding(node.param().padding, ifm_shape, ofm_shape, stride, kw, kh);
-  const auto activation = node.param().activation;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-
-  ::arm_compute::PoolingLayerInfo info{
-      ::arm_compute::PoolingType::L2, ::arm_compute::Size2D{kw, kh},
-      ::neurun::backend::acl_common::asPadStrideInfo(padding, stride)};
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEPoolingLayer>();
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle(), info);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-
-  ActivationBuilder{*_execution_builder}.append(activation, ofm_alloc->handle());
-}
-
-void KernelGenerator::visit(const ir::operation::LocalResponseNormalization &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{
-      node.getInputs().at(ir::operation::LocalResponseNormalization::Input::INPUT)};
-
-  auto radius = node.param().radius;
-  auto alpha = node.param().alpha;
-  auto beta = node.param().beta;
-  auto bias = node.param().bias;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-
-  const auto norm_info = ::arm_compute::NormalizationLayerInfo(
-      ::arm_compute::NormType::CROSS_MAP, radius * 2 + 1, alpha, beta, bias, false);
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NENormalizationLayer>();
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle(), norm_info);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::LogicalAnd &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input0_index{node.getInputs().at(ir::operation::LogicalAnd::Input::INPUT0)};
-  const auto input1_index{node.getInputs().at(ir::operation::LogicalAnd::Input::INPUT1)};
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input0_alloc = _tensor_builder->at(input0_index).get();
-  auto input1_alloc = _tensor_builder->at(input1_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NELogicalAnd>();
-
-  fn->configure(input0_alloc->handle(), input1_alloc->handle(), output_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::LogicalNot &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::LogicalNot::Input::INPUT)};
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input_alloc = _tensor_builder->at(input_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEBitwiseNot>();
-
-  fn->configure(input_alloc->handle(), output_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::LogicalOr &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input0_index{node.getInputs().at(ir::operation::LogicalOr::Input::INPUT0)};
-  const auto input1_index{node.getInputs().at(ir::operation::LogicalOr::Input::INPUT1)};
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input0_alloc = _tensor_builder->at(input0_index).get();
-  auto input1_alloc = _tensor_builder->at(input1_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NELogicalOr>();
-
-  fn->configure(input0_alloc->handle(), input1_alloc->handle(), output_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Logistic &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::Logistic::Input::INPUT)};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-
-  const ::arm_compute::ActivationLayerInfo act_info{
-      ::arm_compute::ActivationLayerInfo::ActivationFunction::LOGISTIC};
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEActivationLayer>();
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle(), act_info);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::LSTM &node)
-{
-  // TODO Support dynamic rnn
-  // TODO Fix subtle error in the case of non-CIFG, non-peephole and No Projection.
-  const auto scratch_buffer_index{
-      node.getOutputs().at(ir::operation::LSTM::Output::SCRATCH_BUFFER)};
-  const auto output_state_out_index{
-      node.getOutputs().at(ir::operation::LSTM::Output::OUTPUT_STATE_OUT)};
-  const auto cell_state_out_index{
-      node.getOutputs().at(ir::operation::LSTM::Output::CELL_STATE_OUT)};
-  const auto output_index{node.getOutputs().at(ir::operation::LSTM::Output::OUTPUT)};
-
-  const auto input_index{node.getInputs().at(ir::operation::LSTM::Input::INPUT)};
-  const auto input_to_input_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::INPUT_TO_INPUT_WEIGHTS)}; // optional
-  const auto input_to_forget_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::INPUT_TO_FORGET_WEIGHTS)};
-  const auto input_to_cell_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::INPUT_TO_CELL_WEIGHTS)};
-  const auto input_to_output_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::INPUT_TO_OUTPUT_WEIGHTS)};
-  const auto recurrent_to_input_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::RECURRENT_TO_INPUT_WEIGHTS)}; // optional
-  const auto recurrent_to_forget_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::RECURRENT_TO_FORGET_WEIGHTS)};
-  const auto recurrent_to_cell_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::RECURRENT_TO_CELL_WEIGHTS)};
-  const auto recurrent_to_output_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::RECURRENT_TO_OUTPUT_WEIGHTS)};
-  const auto cell_to_input_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::CELL_TO_INPUT_WEIGHTS)}; // optional
-  const auto cell_to_forget_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::CELL_TO_FORGET_WEIGHTS)}; // optional
-  const auto cell_to_output_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::CELL_TO_OUTPUT_WEIGHTS)}; // optional
-  const auto input_gate_bias_index{
-      node.getInputs().at(ir::operation::LSTM::Input::INPUT_GATE_BIAS)};
-  const auto forget_gate_bias_index{
-      node.getInputs().at(ir::operation::LSTM::Input::FORGET_GATE_BIAS)};
-  const auto cell_bias_index{node.getInputs().at(ir::operation::LSTM::Input::CELL_BIAS)};
-  const auto output_gate_bias_index{
-      node.getInputs().at(ir::operation::LSTM::Input::OUTPUT_GATE_BIAS)};
-  const auto projection_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::PROJECTION_WEIGHTS)}; // optional
-  const auto projection_bias_index{
-      node.getInputs().at(ir::operation::LSTM::Input::PROJECTION_BIAS)}; // optional
-  const auto output_state_in_index{
-      node.getInputs().at(ir::operation::LSTM::Input::OUTPUT_STATE_IN)};
-  const auto cell_state_in_index{node.getInputs().at(ir::operation::LSTM::Input::CELL_STATE_IN)};
-  const auto cell_threshold = node.param().cell_threshold;
-  const auto projection_threshold = node.param().projection_threshold;
-
-  bool has_input_to_input_weights = _ctx.at(input_to_input_weights_index).shape().dim(0) != 0 &&
-                                    _ctx.at(input_to_input_weights_index).shape().dim(1) != 0;
-  bool has_recurrent_to_input_weights =
-      _ctx.at(recurrent_to_input_weights_index).shape().dim(0) != 0 &&
-      _ctx.at(recurrent_to_input_weights_index).shape().dim(1) != 0;
-  bool has_cell_to_forget_weights = _ctx.at(cell_to_forget_weights_index).shape().dim(0) != 0;
-  bool has_cell_to_output_weights = _ctx.at(cell_to_output_weights_index).shape().dim(0) != 0;
-  bool has_projection_weights = _ctx.at(projection_weights_index).shape().dim(0) != 0 &&
-                                _ctx.at(projection_weights_index).shape().dim(1) != 0;
-  bool has_projection_bias = _ctx.at(projection_bias_index).shape().dim(0);
-
-  // NOTE The input_to_input_weights and the recurrent_to_input_weights do not exist in CIFG.
-  // true: no CIFG
-  // false: CIFG
-  // NOTE The cell_to_input_weights does not exist in non-peephole although regular LSTM(non-CIFG).
-  bool has_cifg_param = has_input_to_input_weights && has_recurrent_to_input_weights;
-
-  // NOTE The cell_to_forget_weights and the cell_to_output_weights exist in peephole.
-  // But the cell_to_input_weights does not exist in regular CIFG although peephole.
-  // true: peephole
-  // false: no peephole
-  bool has_peephole_param = has_cell_to_forget_weights && has_cell_to_output_weights;
-
-  // NOTE Although the projection weights has data the projection bias may not have data.
-  bool has_projection_param = has_projection_weights;
-
-  const auto activation = node.param().activation;
-  const auto cell_clip = cell_threshold;
-  const auto projection_clip = projection_threshold;
-  assert(cell_clip >= 0.f && projection_clip >= 0.f);
-
-  auto scratch_buffer_alloc = _tensor_builder->at(scratch_buffer_index).get();
-  auto output_state_out_alloc = _tensor_builder->at(output_state_out_index).get();
-  auto cell_state_out_alloc = _tensor_builder->at(cell_state_out_index).get();
-  auto output_alloc = _tensor_builder->at(output_index).get();
-
-  auto input_alloc = _tensor_builder->at(input_index).get();
-
-  auto input_to_forget_weights_alloc = _tensor_builder->at(input_to_forget_weights_index).get();
-  auto input_to_cell_weights_alloc = _tensor_builder->at(input_to_cell_weights_index).get();
-  auto input_to_output_weights_alloc = _tensor_builder->at(input_to_output_weights_index).get();
-  auto recurrent_to_forget_weights_alloc =
-      _tensor_builder->at(recurrent_to_forget_weights_index).get();
-  auto recurrent_to_cell_weights_alloc = _tensor_builder->at(recurrent_to_cell_weights_index).get();
-  auto recurrent_to_output_weights_alloc =
-      _tensor_builder->at(recurrent_to_output_weights_index).get();
-
-  auto forget_gate_bias_alloc = _tensor_builder->at(forget_gate_bias_index).get();
-  auto cell_bias_alloc = _tensor_builder->at(cell_bias_index).get();
-  auto output_gate_bias_alloc = _tensor_builder->at(output_gate_bias_index).get();
-  auto output_state_in_alloc = _tensor_builder->at(output_state_in_index).get();
-  auto cell_state_in_alloc = _tensor_builder->at(cell_state_in_index).get();
-
-  auto act_info = ::neurun::backend::acl_common::asActivationLayerInfo(activation);
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NELSTMLayer>();
-
-  ::arm_compute::LSTMParams<::arm_compute::ITensor> lstm_params{};
-  if (has_cifg_param)
-  {
-    auto input_to_input_weights_alloc =
-        _tensor_builder->at(input_to_input_weights_index).get(); // optional
-    auto recurrent_to_input_weights_alloc =
-        _tensor_builder->at(recurrent_to_input_weights_index).get(); // optional
-    auto cell_to_input_weights_handle =
-        has_peephole_param ? _tensor_builder->at(cell_to_input_weights_index).get()->handle()
-                           : nullptr; // optional (non-cifg && peephole)
-    auto input_gate_bias_alloc = _tensor_builder->at(input_gate_bias_index).get(); // optional
-    lstm_params.set_cifg_params(input_to_input_weights_alloc->handle(),
-                                recurrent_to_input_weights_alloc->handle(),
-                                cell_to_input_weights_handle, input_gate_bias_alloc->handle());
-  }
-  if (has_peephole_param)
-  {
-    auto cell_to_forget_weights_alloc =
-        _tensor_builder->at(cell_to_forget_weights_index).get(); // optional
-    auto cell_to_output_weights_alloc =
-        _tensor_builder->at(cell_to_output_weights_index).get(); // optional
-    lstm_params.set_peephole_params(cell_to_forget_weights_alloc->handle(),
-                                    cell_to_output_weights_alloc->handle());
-  }
-  if (has_projection_param)
-  {
-    auto projection_weights_alloc = _tensor_builder->at(projection_weights_index).get(); // optional
-    auto projection_bias_handle = has_projection_bias
-                                      ? _tensor_builder->at(projection_bias_index).get()->handle()
-                                      : nullptr; // optional
-    lstm_params.set_projection_params(projection_weights_alloc->handle(), projection_bias_handle);
-  }
-
-  fn->configure(
-      input_alloc->handle(), input_to_forget_weights_alloc->handle(),
-      input_to_cell_weights_alloc->handle(), input_to_output_weights_alloc->handle(),
-      recurrent_to_forget_weights_alloc->handle(), recurrent_to_cell_weights_alloc->handle(),
-      recurrent_to_output_weights_alloc->handle(), forget_gate_bias_alloc->handle(),
-      cell_bias_alloc->handle(), output_gate_bias_alloc->handle(), output_state_in_alloc->handle(),
-      cell_state_in_alloc->handle(), scratch_buffer_alloc->handle(),
-      output_state_out_alloc->handle(), cell_state_out_alloc->handle(), output_alloc->handle(),
-      lstm_params, act_info, cell_clip, projection_clip);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Mul &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto lhs_index{node.getInputs().at(ir::operation::Mul::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::Mul::Input::RHS)};
-
-  const auto activation = node.param().activation;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto lhs_alloc = _tensor_builder->at(lhs_index).get();
-  auto rhs_alloc = _tensor_builder->at(rhs_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEPixelWiseMultiplication>();
-
-  // RoundingPolicy for scale:1.0 is only allowed RoundingPolicy::TO_ZERO
-  fn->configure(lhs_alloc->handle(), rhs_alloc->handle(), ofm_alloc->handle(), 1.0, // scale
-                arm_compute::ConvertPolicy::SATURATE, arm_compute::RoundingPolicy::TO_ZERO);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-
-  ActivationBuilder{*_execution_builder}.append(activation, ofm_alloc->handle());
-}
-
-void KernelGenerator::visit(const ir::operation::Neg &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::Neg::Input::INPUT)};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NENegLayer>();
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Pack &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  auto axis{node.param().axis};
-
-  const auto output_rank = node.param().rank;
-
-  std::vector<ir::OperandIndex> input_indexes;
-  for (const auto &input_index : node.getInputs())
-    input_indexes.emplace_back(input_index);
-
-  auto output = _tensor_builder->at(output_index).get()->handle();
-  std::vector<arm_compute::ITensor *> inputs;
-  for (const auto &input_index : input_indexes)
-    inputs.emplace_back(_tensor_builder->at(input_index)->handle());
-
-  const auto frontend_layout = _current_subg_layout;
-  const auto backend_layout = _tensor_builder->at(output_index).get()->layout();
-
-  if (axis < 0)
-    axis += output_rank;
-  axis = acl_common::ToARMComputeAxis(output_rank, axis, frontend_layout, backend_layout).value();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEStackLayer>();
-
-  fn->configure(inputs, axis, output);
-
-  _execution_builder->append(asAclFunction(std::move(fn)));
-}
-
-void KernelGenerator::visit(const ir::operation::Pad &node)
-{
-  const auto input_index{node.getInputs().at(ir::operation::Pad::Input::INPUT)};
-  const auto pad_index{node.getInputs().at(ir::operation::Pad::Input::PAD)};
-  const auto output_index{node.getOutputs().at(0)};
-  assert(_ctx.at(pad_index).isConstant());
-
-  auto rank = node.param().rank;
-  auto pad_base = _ctx.at(pad_index).data().base();
-
-  auto input = _tensor_builder->at(input_index).get()->handle();
-  auto output = _tensor_builder->at(output_index).get()->handle();
-
-  ::arm_compute::PaddingList padding_list;
-  padding_list.resize(rank);
-  for (int32_t n = 0; n < rank; ++n)
-  {
-    const int32_t *from = reinterpret_cast<const int32_t *>(pad_base) + (n * 2);
-
-    const auto frontend_layout = _current_subg_layout;
-    const auto backend_layout = _tensor_builder->at(input_index).get()->layout();
-    const auto axis =
-        acl_common::ToARMComputeAxis(rank, n, frontend_layout, backend_layout).value();
-    padding_list[axis] = ::arm_compute::PaddingInfo{from[0], from[1]};
-  }
-
-  const auto input_type = _ctx.at(input_index).typeInfo();
-  UNUSED_RELEASE(input_type);
-  assert(input->info()->data_type() == acl_common::asDataType(input_type.type()));
-  assert(input->info()->quantization_info() ==
-         ::arm_compute::QuantizationInfo(input_type.scale(), input_type.offset()));
-  const auto pixel_value =
-      ::arm_compute::PixelValue(0, input->info()->data_type(), input->info()->quantization_info());
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEPadLayer>();
-  fn->configure(input, output, padding_list, pixel_value);
-
-  _execution_builder->append(asAclFunction(std::move(fn)));
-}
-
-void KernelGenerator::visit(const ir::operation::Permute &node)
-{
-  const auto ofm_idx{node.getOutputs().at(0)};
-  const auto ifm_idx{node.getInputs().at(0)};
-  const auto permute_type = node.getPermuteType();
-  auto ofm_alloc = _tensor_builder->at(ofm_idx).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_idx).get();
-  const auto rank = _ctx.at(ofm_idx).shape().rank();
-  assert(_ctx.at(ifm_idx).shape().rank() == _ctx.at(ofm_idx).shape().rank());
-
-  std::unique_ptr<::arm_compute::IFunction> fn;
-  arm_compute::PermutationVector pv;
-  if (permute_type == ir::operation::Permute::Type::NCHW_TO_NHWC && rank == 4)
-  {
-    // WHCN -> CWHN
-    pv = arm_compute::PermutationVector{2, 0, 1};
-
-    auto l = nnfw::cpp14::make_unique<::arm_compute::NEPermute>();
-
-    l->configure(ifm_alloc->handle(), ofm_alloc->handle(), pv);
-
-    fn = std::move(l);
-  }
-  else if (permute_type == ir::operation::Permute::Type::NHWC_TO_NCHW && rank == 4)
-  {
-    // CWHN -> WHCN
-    pv = arm_compute::PermutationVector{1, 2, 0};
-
-    auto l = nnfw::cpp14::make_unique<::arm_compute::NEPermute>();
-
-    l->configure(ifm_alloc->handle(), ofm_alloc->handle(), pv);
-
-    fn = std::move(l);
-  }
-  else
-  {
-    auto l = nnfw::cpp14::make_unique<::arm_compute::NECopy>();
-
-    l->configure(ifm_alloc->handle(), ofm_alloc->handle());
-
-    fn = std::move(l);
-  }
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::PReLU &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::PReLU::Input::INPUT)};
-  const auto alpha_index{node.getInputs().at(ir::operation::PReLU::Input::ALPHA)};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-  auto alpha_alloc = _tensor_builder->at(alpha_index).get();
-
-  std::unique_ptr<::arm_compute::IFunction> fn;
-
-  auto l = nnfw::cpp14::make_unique<::arm_compute::NEPReLU>();
-
-  l->configure(ifm_alloc->handle(), alpha_alloc->handle(), ofm_alloc->handle());
-
-  fn = std::move(l);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::ReduceMax &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::ReduceMax::Input::INPUT)};
-  const auto &axes{node.param().axes};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-  const auto frontend_layout = _current_subg_layout;
-  const auto backend_layout = ifm_alloc->layout();
-
-  // Convert to ACL axes taking into account negative values and possible duplicates.
-  std::set<std::uint32_t> acl_axes;
-  const int ifm_rank = node.param().rank;
-  for (int axis : axes)
-  {
-    if (axis < 0)
-      axis += ifm_rank;
-    acl_axes.insert(
-        acl_common::ToARMComputeAxis(ifm_rank, axis, frontend_layout, backend_layout).value());
-  }
-
-  arm_compute::Coordinates reduce_axes;
-  for (const auto axis : acl_axes)
-  {
-    reduce_axes.set(reduce_axes.num_dimensions(), axis);
-  }
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEReduceOperation>();
-
-  fn->configure(ifm_alloc->handle(), reduce_axes, false, ofm_alloc->handle(),
-                ::arm_compute::ReduceOperation::MAX);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::ReduceMin &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::ReduceMin::Input::INPUT)};
-  const auto &axes{node.param().axes};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-  const auto frontend_layout = _current_subg_layout;
-  const auto backend_layout = ifm_alloc->layout();
-
-  // Convert to ACL axes taking into account negative values and possible duplicates.
-  std::set<std::uint32_t> acl_axes;
-  const int ifm_rank = node.param().rank;
-  for (int axis : axes)
-  {
-    if (axis < 0)
-      axis += ifm_rank;
-    acl_axes.insert(
-        acl_common::ToARMComputeAxis(ifm_rank, axis, frontend_layout, backend_layout).value());
-  }
-
-  arm_compute::Coordinates reduce_axes;
-  for (const auto axis : acl_axes)
-  {
-    reduce_axes.set(reduce_axes.num_dimensions(), axis);
-  }
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEReduceOperation>();
-
-  fn->configure(ifm_alloc->handle(), reduce_axes, false, ofm_alloc->handle(),
-                ::arm_compute::ReduceOperation::MIN);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::ReduceSum &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::ReduceSum::Input::INPUT)};
-  const auto &axes{node.param().axes};
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input_alloc = _tensor_builder->at(input_index).get();
-  const auto frontend_layout = _current_subg_layout;
-  const auto backend_layout = input_alloc->layout();
-
-  // Convert to ACL axes taking into account negative values and possible duplicates.
-  std::set<std::uint32_t> acl_axes;
-  const int input_rank = node.param().rank;
-  for (int axis : axes)
-  {
-    if (axis < 0)
-      axis += input_rank;
-    acl_axes.insert(
-        acl_common::ToARMComputeAxis(input_rank, axis, frontend_layout, backend_layout).value());
-  }
-
-  arm_compute::Coordinates fixed_axes;
-  for (const auto axis : acl_axes)
-  {
-    fixed_axes.set(fixed_axes.num_dimensions(), axis);
-  }
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEReduceSum>();
-
-  fn->configure(input_alloc->handle(), fixed_axes, false, output_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::ReLU &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::ReLU::Input::INPUT)};
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input_alloc = _tensor_builder->at(input_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<arm_compute::NEActivationLayer>();
-
-  const ::arm_compute::ActivationLayerInfo act_info{
-      ::arm_compute::ActivationLayerInfo::ActivationFunction::RELU};
-
-  fn->configure(input_alloc->handle(), output_alloc->handle(), act_info);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::ReLU1 &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::ReLU1::Input::INPUT)};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-
-  const ::arm_compute::ActivationLayerInfo act_info{
-      ::arm_compute::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 1.0f, -1.0f};
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEActivationLayer>();
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle(), act_info);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::ReLU6 &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::ReLU6::Input::INPUT)};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-
-  const ::arm_compute::ActivationLayerInfo act_info{
-      ::arm_compute::ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.0f};
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEActivationLayer>();
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle(), act_info);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Reshape &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::Reshape::Input::INPUT)};
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input_alloc = _tensor_builder->at(input_index).get();
-
-  // NOTE This operation must not be changed the layout from frontend to backend
-  //      So, PermutationOperationPass makes layouts of frontend and backend the same.
-  const auto frontend_layout = _current_subg_layout;
-  const auto backend_layout = output_alloc->layout();
-  assert((_ctx.at(input_index).shape().rank() < 4 && _ctx.at(output_index).shape().rank() < 4) ||
-         frontend_layout == backend_layout);
-  UNUSED_RELEASE(frontend_layout);
-  UNUSED_RELEASE(backend_layout);
-
-  auto fn = nnfw::cpp14::make_unique<arm_compute::NEReshapeLayer>();
-
-  fn->configure(input_alloc->handle(), output_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::ResizeBilinear &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-
-  const auto ifm_index{node.getInputs().at(ir::operation::ResizeBilinear::Input::INPUT)};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEScale>();
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle(),
-                ::arm_compute::InterpolationPolicy::BILINEAR, ::arm_compute::BorderMode::REPLICATE,
-                ::arm_compute::PixelValue(0.f), ::arm_compute::SamplingPolicy::TOP_LEFT);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::RNN &node)
-{
-  const auto output_index{node.getOutputs().at(ir::operation::RNN::Output::OUTPUT)};
-  const auto hidden_state_out_index{
-      node.getOutputs().at(ir::operation::RNN::Output::HIDDEN_STATE_OUT)};
-
-  const auto input_index{node.getInputs().at(ir::operation::RNN::Input::INPUT)};
-  const auto weights_index{node.getInputs().at(ir::operation::RNN::Input::WEIGHTS)};
-  const auto recurrent_weights_index{
-      node.getInputs().at(ir::operation::RNN::Input::RECURRENT_WEIGHTS)};
-  const auto bias_index{node.getInputs().at(ir::operation::RNN::Input::BIAS)};
-  const auto hidden_state_in_index{node.getInputs().at(ir::operation::RNN::Input::HIDDEN_STATE_IN)};
-
-  const auto activation = node.param().activation;
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto hidden_state_out_alloc = _tensor_builder->at(hidden_state_out_index).get();
-
-  auto input_alloc = _tensor_builder->at(input_index).get();
-  auto weights_alloc = _tensor_builder->at(weights_index).get();
-  auto recurrent_weights_alloc = _tensor_builder->at(recurrent_weights_index).get();
-  auto bias_alloc = _tensor_builder->at(bias_index).get();
-  auto hidden_state_in_alloc = _tensor_builder->at(hidden_state_in_index).get();
-  auto act_info = ::neurun::backend::acl_common::asActivationLayerInfo(activation);
-
-  auto copy_layer = nnfw::cpp14::make_unique<::arm_compute::NECopy>();
-  copy_layer->configure(hidden_state_in_alloc->handle(), hidden_state_out_alloc->handle());
-  _execution_builder->append(asAclFunction(std::move(copy_layer)));
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NERNNLayerEx>(
-      _tensor_builder->acl_tensor_manager()->internal_buffer_manager());
-  fn->configure(input_alloc->handle(), weights_alloc->handle(), recurrent_weights_alloc->handle(),
-                bias_alloc->handle(), hidden_state_out_alloc->handle(), output_alloc->handle(),
-                act_info);
-  _execution_builder->append(asAclFunction(std::move(fn)));
-}
-
-void KernelGenerator::visit(const ir::operation::RSQRT &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::RSQRT::Input::INPUT)};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NERsqrtLayer>();
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle());
-
-  _execution_builder->append(asAclFunction(std::move(fn)));
-}
-
-void KernelGenerator::visit(const ir::operation::Squeeze &node)
-{
-  // Squeeze is identical to reshape except that it has an optional dimensions input.
-  // In addition, optional dims_index is ignored since output tensor already has squeezed shape
-  // by freezer and toco
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::Squeeze::Input::INPUT)};
-  const auto dims{node.param().dims};
-  const auto ndim{node.param().ndim};
-  (void)dims;
-  (void)ndim;
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input_alloc = _tensor_builder->at(input_index).get();
-  auto fn = nnfw::cpp14::make_unique<arm_compute::NEReshapeLayer>();
-  fn->configure(input_alloc->handle(), output_alloc->handle());
-  auto acl_fn = asAclFunction(std::move(fn));
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Tanh &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::Tanh::Input::INPUT)};
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input_alloc = _tensor_builder->at(input_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<arm_compute::NEActivationLayer>();
-
-  const ::arm_compute::ActivationLayerInfo act_info{
-      ::arm_compute::ActivationLayerInfo::ActivationFunction::TANH, 1.0f, 1.0f};
-
-  fn->configure(input_alloc->handle(), output_alloc->handle(), act_info);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Softmax &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::Softmax::Input::INPUT)};
-  const auto beta = node.param().beta;
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input_alloc = _tensor_builder->at(input_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NESoftmaxLayer>(
-      _tensor_builder->acl_tensor_manager()->internal_buffer_manager());
-
-  fn->configure(input_alloc->handle(), output_alloc->handle(), beta);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::SpaceToBatchND &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::SpaceToBatchND::Input::INPUT)};
-  const auto block_size_index{
-      node.getInputs().at(ir::operation::SpaceToBatchND::Input::BLOCK_SIZE)};
-  const auto paddings_index{node.getInputs().at(ir::operation::SpaceToBatchND::Input::PADDINGS)};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-  auto block_size_alloc = _tensor_builder->at(block_size_index).get();
-  auto paddings_alloc = _tensor_builder->at(paddings_index).get();
-
-  assert(_ctx.at(block_size_index).isConstant());
-  assert(_ctx.at(paddings_index).isConstant());
-
-  // NESpaceToBatchLayer has a bug that padding's values are 0 even when zero point of QASYMM8 is
-  // not 0.
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NESpaceToBatchLayerEx>();
-
-  fn->configure(ifm_alloc->handle(), block_size_alloc->handle(), paddings_alloc->handle(),
-                ofm_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::SpaceToDepth &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::SpaceToDepth::Input::INPUT)};
-
-  auto block_size = node.param().block_size;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NESpaceToDepthLayerEx>();
-
-  fn->configure(ifm_alloc->handle(), ofm_alloc->handle(), block_size);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Split &node)
-{
-  // TODO Support this op by SubTensor
-  const auto ifm_index{node.getInputs().at(ir::operation::Split::Input::INPUT)};
-
-  assert(node.param().num_splits == static_cast<int>(node.getOutputs().size()));
-
-  const auto ifm_rank = node.param().rank;
-  std::vector<ir::OperandIndex> output_indexes;
-  for (const auto &output : node.getOutputs())
-    output_indexes.emplace_back(output);
-
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-  std::vector<arm_compute::ITensor *> output_allocs;
-  for (const auto &ofm_ind : output_indexes)
-    output_allocs.emplace_back(_tensor_builder->at(ofm_ind).get()->handle());
-
-  const auto frontend_layout = _current_subg_layout;
-  const auto backend_layout = ifm_alloc->layout();
-  auto axis = node.param().axis;
-  if (axis < 0)
-    axis += ifm_rank;
-  axis = acl_common::ToARMComputeAxis(ifm_rank, axis, frontend_layout, backend_layout).value();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NESplit>();
-
-  fn->configure(ifm_alloc->handle(), output_allocs, axis);
-
-  _execution_builder->append(asAclFunction(std::move(fn)));
-}
-
-void KernelGenerator::visit(const ir::operation::SQRT &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::SQRT::Input::INPUT)};
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input_alloc = _tensor_builder->at(input_index).get();
-
-  const ::arm_compute::ActivationLayerInfo act_info{
-      ::arm_compute::ActivationLayerInfo::ActivationFunction::SQRT};
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEActivationLayer>();
-
-  fn->configure(input_alloc->handle(), output_alloc->handle(), act_info);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::SquaredDifference &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto lhs_index{node.getInputs().at(ir::operation::SquaredDifference::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::SquaredDifference::Input::RHS)};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto lhs_alloc = _tensor_builder->at(lhs_index).get();
-  auto rhs_alloc = _tensor_builder->at(rhs_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEElementwiseSquaredDiff>();
-
-  fn->configure(lhs_alloc->handle(), rhs_alloc->handle(), ofm_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Sub &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto lhs_index{node.getInputs().at(ir::operation::Sub::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::Sub::Input::RHS)};
-
-  const auto activation = node.param().activation;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto lhs_alloc = _tensor_builder->at(lhs_index).get();
-  auto rhs_alloc = _tensor_builder->at(rhs_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEArithmeticSubtraction>();
-
-  fn->configure(lhs_alloc->handle(), rhs_alloc->handle(), ofm_alloc->handle(),
-                arm_compute::ConvertPolicy::SATURATE);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-
-  ActivationBuilder{*_execution_builder}.append(activation, ofm_alloc->handle());
-}
-
-void KernelGenerator::visit(const ir::operation::Slice &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::Slice::Input::INPUT)};
-  const auto begins_index{node.getInputs().at(ir::operation::Slice::Input::BEGINS)};
-  const auto sizes_index{node.getInputs().at(ir::operation::Slice::Input::SIZES)};
-
-  auto outputData_alloc = _tensor_builder->at(output_index).get();
-  auto inputData_alloc = _tensor_builder->at(input_index).get();
-  const auto frontend_layout = _current_subg_layout;
-  const auto backend_layout = inputData_alloc->layout();
-
-  // Set initializers for indices data such as order of inputData
-  int input_rank = node.param().rank;
-  std::vector<int32_t> starts;
-  std::vector<int32_t> ends;
-  starts.resize(input_rank, 0);
-  ends.resize(input_rank, 0);
-  {
-    auto beginData_base = _ctx.at(begins_index).data().base();
-    auto sizeData_base = _ctx.at(sizes_index).data().base();
-    const int beginData_size = _ctx.at(begins_index).shape().num_elements();
-    const int sizeData_size = _ctx.at(sizes_index).shape().num_elements();
-
-    using ir::DataType;
-
-    UNUSED_RELEASE(beginData_size);
-    UNUSED_RELEASE(sizeData_size);
-
-    assert(_ctx.at(begins_index).typeInfo().type() == DataType::INT32);
-    assert(_ctx.at(sizes_index).typeInfo().type() == DataType::INT32);
-    assert(beginData_size == input_rank);
-    assert(sizeData_size == input_rank);
-
-    assert(beginData_base != nullptr);
-    for (int n = 0; n < input_rank; ++n)
-    {
-      auto axis = ::neurun::backend::acl_common::ToARMComputeAxis(input_rank, n, frontend_layout,
-                                                                  backend_layout)
-                      .value();
-
-      int32_t begin_value = *(reinterpret_cast<const int32_t *>(beginData_base) + n);
-      starts[axis] = begin_value;
-
-      int32_t size_value = *(reinterpret_cast<const int32_t *>(sizeData_base) + n);
-      ends[axis] = begin_value + size_value;
-    }
-  }
-
-  ::arm_compute::Coordinates starts_set;
-  ::arm_compute::Coordinates ends_set;
-
-  for (size_t i = 0; i < starts.size(); ++i)
-  {
-    starts_set.set(i, starts[i]);
-    ends_set.set(i, ends[i]);
-  }
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NESlice>();
-
-  fn->configure(inputData_alloc->handle(), outputData_alloc->handle(), starts_set, ends_set);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::StridedSlice &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::StridedSlice::Input::INPUT)};
-  const auto starts_index{node.getInputs().at(ir::operation::StridedSlice::Input::STARTS)};
-  const auto ends_index{node.getInputs().at(ir::operation::StridedSlice::Input::ENDS)};
-  const auto strides_index{node.getInputs().at(ir::operation::StridedSlice::Input::STRIDES)};
-
-  auto outputData_alloc = _tensor_builder->at(output_index).get();
-  auto inputData_alloc = _tensor_builder->at(input_index).get();
-  const auto frontend_layout = _current_subg_layout;
-  const auto backend_layout = inputData_alloc->layout();
-
-  // Set initializers for indices data such as order of inputData
-  int input_rank = node.param().rank;
-  std::vector<int32_t> starts;
-  std::vector<int32_t> ends;
-  std::vector<int32_t> strides;
-  starts.resize(input_rank, 0);
-  ends.resize(input_rank, 0);
-  strides.resize(input_rank, 0);
-  {
-    auto startData_base = _ctx.at(starts_index).data().base();
-    auto endData_base = _ctx.at(ends_index).data().base();
-    auto stridesData_base = _ctx.at(strides_index).data().base();
-    const int startData_size = _ctx.at(starts_index).shape().num_elements();
-    const int endData_size = _ctx.at(ends_index).shape().num_elements();
-    const int stridesData_size = _ctx.at(strides_index).shape().num_elements();
-
-    using ir::DataType;
-
-    UNUSED_RELEASE(startData_size);
-    UNUSED_RELEASE(endData_size);
-    UNUSED_RELEASE(stridesData_size);
-
-    assert(_ctx.at(starts_index).typeInfo().type() == DataType::INT32);
-    assert(_ctx.at(ends_index).typeInfo().type() == DataType::INT32);
-    assert(_ctx.at(strides_index).typeInfo().type() == DataType::INT32);
-    assert(startData_size == input_rank);
-    assert(endData_size == input_rank);
-    assert(stridesData_size == input_rank);
-
-    assert(startData_base != nullptr);
-    for (int n = 0; n < input_rank; ++n)
-    {
-      auto axis = ::neurun::backend::acl_common::ToARMComputeAxis(input_rank, n, frontend_layout,
-                                                                  backend_layout)
-                      .value();
-
-      int32_t start_value = *(reinterpret_cast<const int32_t *>(startData_base) + n);
-      starts[axis] = start_value;
-
-      int32_t end_value = *(reinterpret_cast<const int32_t *>(endData_base) + n);
-      ends[axis] = end_value;
-
-      int32_t strides_value = *(reinterpret_cast<const int32_t *>(stridesData_base) + n);
-      strides[axis] = strides_value;
-    }
-  }
-
-  // Set mask bits such as order of inputData
-  // FIXME Take the layouts into account.
-  const auto begin_mask = acl_common::ReorderBits<int32_t>(node.param().begin_mask, input_rank);
-  const auto end_mask = acl_common::ReorderBits<int32_t>(node.param().end_mask, input_rank);
-  const auto shrink_axis_mask =
-      acl_common::ReorderBits<int32_t>(node.param().shrink_axis_mask, input_rank);
-
-  ::arm_compute::Coordinates starts_set;
-  ::arm_compute::Coordinates ends_set;
-  ::arm_compute::BiStrides strides_set;
-
-  for (size_t i = 0; i < starts.size(); ++i)
-  {
-    starts_set.set(i, starts[i]);
-    ends_set.set(i, ends[i]);
-    strides_set.set(i, strides[i]);
-  }
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEStridedSlice>();
-
-  fn->configure(inputData_alloc->handle(), outputData_alloc->handle(), starts_set, ends_set,
-                strides_set, begin_mask, end_mask, shrink_axis_mask);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::TransposeConv &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto output_shape_index{
-      node.getInputs().at(ir::operation::TransposeConv::Input::OUTPUT_SHAPE)};
-  const auto ker_index{node.getInputs().at(ir::operation::TransposeConv::Input::KERNEL)};
-  const auto ifm_index{node.getInputs().at(ir::operation::TransposeConv::Input::INPUT)};
-
-  const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(_current_subg_layout);
-  const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(_current_subg_layout);
-  const auto ker_shape = _ctx.at(ker_index).shape().asFeature(_current_subg_layout);
-
-  const auto stride = node.param().stride;
-
-  assert((node.param().padding.type == ir::PaddingType::SAME) ||
-         (node.param().padding.type == ir::PaddingType::VALID));
-  auto padding = neurun::util::calculatePadding(node.param().padding, ofm_shape, ifm_shape, stride,
-                                                ker_shape.W, ker_shape.H);
-
-  uint32_t invalid_horizontal = 0;
-  uint32_t invalid_vertical = 0;
-  if (node.param().padding.type == ir::PaddingType::VALID)
-  {
-    invalid_horizontal =
-        ofm_shape.W - (1 + (ifm_shape.W - 1) * stride.horizontal) - (ker_shape.W - 1);
-    invalid_vertical = ofm_shape.H - (1 + (ifm_shape.H - 1) * stride.vertical) - (ker_shape.H - 1);
-  }
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-  auto ker_alloc = _tensor_builder->at(ker_index).get();
-
-  const auto tconv_info = acl_common::asPadStrideInfo(padding, stride);
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NETransposeConvLayer>();
-
-  fn->configure(ifm_alloc->handle(), ker_alloc->handle(), nullptr, ofm_alloc->handle(), tconv_info,
-                invalid_horizontal, invalid_vertical);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Transpose &node)
-{
-  const auto ofm_idx{node.getOutputs().at(0)};
-  const auto ifm_idx{node.getInputs().at(ir::operation::Transpose::Input::INPUT)};
-  const auto &perm{node.param().perm};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_idx).get();
-  const auto ifm_alloc = _tensor_builder->at(ifm_idx).get();
-  const auto frontend_layout = _current_subg_layout;
-  const auto backend_layout = ifm_alloc->layout();
-
-  const auto rank = node.param().rank;
-  std::vector<std::int32_t> pv(perm.cbegin(), perm.cend());
-  auto backend_pv = ::neurun::backend::acl_common::getARMComputePermutationVector(
-      rank, pv, frontend_layout, backend_layout);
-
-  std::unique_ptr<::arm_compute::IFunction> fn;
-
-  if (ifm_alloc->num_dimensions() <= 2 && ofm_alloc->num_dimensions() <= 2)
-  {
-    auto l = nnfw::cpp14::make_unique<::arm_compute::NETranspose>();
-
-    l->configure(ifm_alloc->handle(), ofm_alloc->handle());
-
-    fn = std::move(l);
-  }
-  else
-  {
-    auto l = nnfw::cpp14::make_unique<::arm_compute::NEPermute>();
-
-    l->configure(ifm_alloc->handle(), ofm_alloc->handle(), backend_pv);
-
-    fn = std::move(l);
-  }
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Unpack &node)
-{
-  const auto input_index{node.getInputs().at(ir::operation::Unpack::Input::INPUT)};
-  auto axis{node.param().axis};
-
-  const auto input_rank = node.param().rank;
-
-  std::vector<ir::OperandIndex> output_indexes;
-  for (const auto &output_index : node.getOutputs())
-    output_indexes.emplace_back(output_index);
-
-  auto input = _tensor_builder->at(input_index).get()->handle();
-  std::vector<arm_compute::ITensor *> outputs;
-  for (const auto &output_index : output_indexes)
-    outputs.emplace_back(_tensor_builder->at(output_index)->handle());
-
-  const auto frontend_layout = _current_subg_layout;
-  const auto backend_layout = _tensor_builder->at(input_index).get()->layout();
-  if (axis < 0)
-    axis += input_rank;
-  axis = acl_common::ToARMComputeAxis(input_rank, axis, frontend_layout, backend_layout).value();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEUnstack>();
-
-  fn->configure(input, outputs, axis);
-
-  _execution_builder->append(asAclFunction(std::move(fn)));
-}
-
-void KernelGenerator::visit(const ir::operation::Add &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto lhs_index{node.getInputs().at(ir::operation::Add::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::Add::Input::RHS)};
-
-  const auto activation = node.param().activation;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto lhs_alloc = _tensor_builder->at(lhs_index).get();
-  auto rhs_alloc = _tensor_builder->at(rhs_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEArithmeticAddition>();
-
-  fn->configure(lhs_alloc->handle(), rhs_alloc->handle(), ofm_alloc->handle(),
-                arm_compute::ConvertPolicy::SATURATE);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-
-  ActivationBuilder{*_execution_builder}.append(activation, ofm_alloc->handle());
-}
-
-void KernelGenerator::visit(const ir::operation::Div &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto lhs_index{node.getInputs().at(ir::operation::Div::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::Div::Input::RHS)};
-
-  const auto activation = node.param().activation;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto lhs_alloc = _tensor_builder->at(lhs_index).get();
-  auto rhs_alloc = _tensor_builder->at(rhs_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEElementwiseDivision>();
-
-  fn->configure(lhs_alloc->handle(), rhs_alloc->handle(), ofm_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-
-  ActivationBuilder{*_execution_builder}.append(activation, ofm_alloc->handle());
-}
-
-void KernelGenerator::visit(const ir::operation::Exp &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::Exp::Input::INPUT)};
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input_alloc = _tensor_builder->at(input_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEExpLayer>();
-
-  fn->configure(input_alloc->handle(), output_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Comparison &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input0_index{node.getInputs().at(ir::operation::Comparison::Input::INPUT0)};
-  const auto input1_index{node.getInputs().at(ir::operation::Comparison::Input::INPUT1)};
-
-  const auto comparison_type = node.param().comparison_type;
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input0_alloc = _tensor_builder->at(input0_index).get();
-  auto input1_alloc = _tensor_builder->at(input1_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEElementwiseComparison>();
-
-  fn->configure(input0_alloc->handle(), input1_alloc->handle(), output_alloc->handle(),
-                (arm_compute::ComparisonOperation)comparison_type);
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Min &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto lhs_index{node.getInputs().at(ir::operation::Min::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::Min::Input::RHS)};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto lhs_alloc = _tensor_builder->at(lhs_index).get();
-  auto rhs_alloc = _tensor_builder->at(rhs_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEElementwiseMin>();
-
-  fn->configure(lhs_alloc->handle(), rhs_alloc->handle(), ofm_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Max &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto lhs_index{node.getInputs().at(ir::operation::Max::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::Max::Input::RHS)};
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto lhs_alloc = _tensor_builder->at(lhs_index).get();
-  auto rhs_alloc = _tensor_builder->at(rhs_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::arm_compute::NEElementwiseMax>();
-
-  fn->configure(lhs_alloc->handle(), rhs_alloc->handle(), ofm_alloc->handle());
-
-  auto acl_fn = asAclFunction(std::move(fn));
-
-  _execution_builder->append(std::move(acl_fn));
-}
-
-} // namespace acl_neon
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/acl_neon/KernelGenerator.h b/runtime/neurun/backend/acl_neon/KernelGenerator.h
deleted file mode 100644
index f041fb725..000000000
--- a/runtime/neurun/backend/acl_neon/KernelGenerator.h
+++ /dev/null
@@ -1,111 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_NEON_KERNEL_GENERATOR_H__
-#define __NEURUN_BACKEND_ACL_NEON_KERNEL_GENERATOR_H__
-
-#include <backend/IKernelGenerator.h>
-
-#include "ir/Operands.h"
-#include "TensorBuilder.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_neon
-{
-
-class KernelGenerator : public IKernelGenerator
-{
-public:
-  KernelGenerator(const ir::Operands &ctx, const std::shared_ptr<TensorBuilder> &tensor_builder);
-
-  void visit(const ir::OpSequence &) override;
-  void visit(const ir::operation::Abs &) override;
-  void visit(const ir::operation::ArgMax &) override;
-  void visit(const ir::operation::BatchToSpaceND &) override;
-  void visit(const ir::operation::Cast &) override;
-  void visit(const ir::operation::Conv2D &) override;
-  void visit(const ir::operation::DepthToSpace &) override;
-  void visit(const ir::operation::DepthwiseConv2D &) override;
-  void visit(const ir::operation::Dequantize &) override;
-  void visit(const ir::operation::MaxPool2D &) override;
-  void visit(const ir::operation::Mean &) override;
-  void visit(const ir::operation::AvgPool2D &) override;
-  void visit(const ir::operation::Concat &) override;
-  void visit(const ir::operation::EmbeddingLookup &) override;
-  void visit(const ir::operation::Floor &) override;
-  void visit(const ir::operation::FullyConnected &) override;
-  void visit(const ir::operation::Gather &) override;
-  void visit(const ir::operation::HashtableLookup &) override;
-  void visit(const ir::operation::InstanceNorm &) override;
-  void visit(const ir::operation::L2Normalization &) override;
-  void visit(const ir::operation::L2Pool2D &) override;
-  void visit(const ir::operation::LocalResponseNormalization &) override;
-  void visit(const ir::operation::LogicalAnd &) override;
-  void visit(const ir::operation::LogicalNot &) override;
-  void visit(const ir::operation::LogicalOr &) override;
-  void visit(const ir::operation::Logistic &) override;
-  void visit(const ir::operation::LSTM &) override;
-  void visit(const ir::operation::Mul &) override;
-  void visit(const ir::operation::Neg &) override;
-  void visit(const ir::operation::Pack &) override;
-  void visit(const ir::operation::Pad &) override;
-  void visit(const ir::operation::Permute &) override;
-  void visit(const ir::operation::PReLU &) override;
-  void visit(const ir::operation::ReduceMax &) override;
-  void visit(const ir::operation::ReduceMin &) override;
-  void visit(const ir::operation::ReduceSum &) override;
-  void visit(const ir::operation::ReLU &) override;
-  void visit(const ir::operation::ReLU1 &) override;
-  void visit(const ir::operation::ReLU6 &) override;
-  void visit(const ir::operation::Reshape &) override;
-  void visit(const ir::operation::ResizeBilinear &) override;
-  void visit(const ir::operation::RNN &) override;
-  void visit(const ir::operation::RSQRT &) override;
-  void visit(const ir::operation::Squeeze &) override;
-  void visit(const ir::operation::Tanh &) override;
-  void visit(const ir::operation::Softmax &) override;
-  void visit(const ir::operation::SpaceToBatchND &) override;
-  void visit(const ir::operation::SpaceToDepth &) override;
-  void visit(const ir::operation::Split &) override;
-  void visit(const ir::operation::SQRT &) override;
-  void visit(const ir::operation::SquaredDifference &) override;
-  void visit(const ir::operation::Sub &) override;
-  void visit(const ir::operation::Slice &) override;
-  void visit(const ir::operation::StridedSlice &) override;
-  void visit(const ir::operation::TransposeConv &) override;
-  void visit(const ir::operation::Transpose &) override;
-  void visit(const ir::operation::Unpack &) override;
-  void visit(const ir::operation::Add &) override;
-  void visit(const ir::operation::Div &) override;
-  void visit(const ir::operation::Exp &) override;
-  void visit(const ir::operation::Comparison &) override;
-  void visit(const ir::operation::Min &) override;
-  void visit(const ir::operation::Max &) override;
-
-private:
-  const ir::Operands &_ctx;
-  std::shared_ptr<TensorBuilder> _tensor_builder;
-  ir::Layout _current_subg_layout;
-};
-
-} // namespace acl_neon
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ACL_NEON_KERNEL_GENERATOR_H__
diff --git a/runtime/neurun/backend/acl_neon/PluginClassesAllocator.cc b/runtime/neurun/backend/acl_neon/PluginClassesAllocator.cc
deleted file mode 100644
index 75f2e9797..000000000
--- a/runtime/neurun/backend/acl_neon/PluginClassesAllocator.cc
+++ /dev/null
@@ -1,33 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <util/logging.h>
-
-#include "Backend.h"
-
-extern "C" {
-neurun::backend::Backend *neurun_backend_create()
-{
-  VERBOSE(neurun_backend_create) << "'acl_neon' loaded\n";
-  return new neurun::backend::acl_neon::Backend;
-}
-
-void neurun_backend_destroy(neurun::backend::Backend *backend)
-{
-  VERBOSE(neurun_backend_create) << "'acl_neon' unloaded\n";
-  delete backend;
-}
-}
diff --git a/runtime/neurun/backend/acl_neon/ShapeFixer.cc b/runtime/neurun/backend/acl_neon/ShapeFixer.cc
deleted file mode 100644
index 1d80e57e9..000000000
--- a/runtime/neurun/backend/acl_neon/ShapeFixer.cc
+++ /dev/null
@@ -1,439 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ShapeFixer.h"
-
-#include <arm_compute/runtime/NEON/functions/NESoftmaxLayer.h>
-#include <arm_compute/runtime/NEON/functions/NEArithmeticAddition.h>
-#include <arm_compute/runtime/NEON/functions/NEArithmeticSubtraction.h>
-#include <arm_compute/runtime/NEON/functions/NEPixelWiseMultiplication.h>
-#include <arm_compute/runtime/NEON/functions/NEPoolingLayer.h>
-#include <arm_compute/runtime/NEON/functions/NEActivationLayer.h>
-#include <arm_compute/runtime/NEON/functions/NEConvolutionLayer.h>
-#include <arm_compute/runtime/NEON/functions/NEDepthwiseConvolutionLayer.h>
-#include <arm_compute/runtime/NEON/functions/NEReshapeLayer.h>
-#include <arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h>
-#include <arm_compute/runtime/NEON/functions/NEFullyConnectedReshapingLayer.h>
-
-#include <Convert.h>
-#include <Swizzle.h>
-
-#include "util/Padding.h"
-#include "ir/Index.h"
-#include "compiler/IExecutionBuilder.h"
-#include "exec/NopFunction.h"
-#include "util/logging.h"
-#include "util/Utils.h"
-
-using ::neurun::compiler::IExecutionBuilder;
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_neon
-{
-
-using ::neurun::backend::acl_common::asAclFunction;
-
-ShapeFixer::ShapeFixer(const ir::Operands &ctx,
-                       const std::shared_ptr<TensorBuilder> &tensor_builder)
-    : _ctx(ctx), _tensor_builder(tensor_builder)
-{
-  assert(tensor_builder);
-}
-
-void ShapeFixer::visit(const ir::operation::Abs &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::ArgMax &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::BatchToSpaceND &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::BatchToSpaceND::Input::INPUT)};
-  _tensor_builder->dimCorrection(ofm_index, false);
-  _tensor_builder->dimCorrection(ifm_index, false);
-}
-
-void ShapeFixer::visit(const ir::operation::Cast &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Conv2D &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::DepthToSpace &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::DepthwiseConv2D &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Dequantize &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::MaxPool2D &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Mean &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::AvgPool2D &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Concat &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  _tensor_builder->dimCorrection(ofm_index, false);
-  for (const auto &inputs : node.getInputs())
-    _tensor_builder->dimCorrection(inputs, false);
-}
-
-void ShapeFixer::visit(const ir::operation::EmbeddingLookup &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto values_index{node.getInputs().at(ir::operation::EmbeddingLookup::Input::VALUES)};
-  _tensor_builder->dimCorrection(values_index, false);
-  _tensor_builder->dimCorrection(output_index, false);
-}
-
-void ShapeFixer::visit(const ir::operation::Exp &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Floor &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::FullyConnected &node)
-{
-  using ir::operation::FullyConnected;
-  const auto input_index{node.getInputs().at(FullyConnected::Input::INPUT)};
-  const auto input_rank = _ctx.at(input_index).shape().rank();
-  // Check for reshaping input's shape into rank-2
-  if (input_rank == 3 || input_rank == 4)
-    _tensor_builder->dimCorrection(input_index, false);
-}
-
-void ShapeFixer::visit(const ir::operation::HashtableLookup &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto values_index{node.getInputs().at(ir::operation::EmbeddingLookup::Input::VALUES)};
-  _tensor_builder->dimCorrection(values_index, false);
-  _tensor_builder->dimCorrection(output_index, false);
-}
-
-void ShapeFixer::visit(const ir::operation::Gather &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::Gather::Input::INPUT)};
-  const auto indices_index{node.getInputs().at(ir::operation::Gather::Input::INDICES)};
-  _tensor_builder->dimCorrection(ofm_index, false);
-  _tensor_builder->dimCorrection(ifm_index, false);
-  _tensor_builder->dimCorrection(indices_index, false);
-}
-
-void ShapeFixer::visit(const ir::operation::InstanceNorm &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::L2Normalization &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::L2Pool2D &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::LocalResponseNormalization &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::LogicalAnd &node)
-{
-  const auto input0_index{node.getInputs().at(ir::operation::LogicalAnd::Input::INPUT0)};
-  const auto input1_index{node.getInputs().at(ir::operation::LogicalAnd::Input::INPUT1)};
-
-  if (!(_ctx.at(input0_index).shape() == _ctx.at(input1_index).shape()))
-  {
-    const auto broadcast_rank =
-        std::max(_ctx.at(input0_index).shape().rank(), _ctx.at(input1_index).shape().rank());
-
-    // TODO remove const_cast later. For example, _ctx may need to be a non const variable or
-    //      a node to extend shape may be inserted in front of this operation
-    const_cast<ir::Shape &>(_ctx.at(input0_index).shape()).extendRank(broadcast_rank);
-    const_cast<ir::Shape &>(_ctx.at(input1_index).shape()).extendRank(broadcast_rank);
-  }
-}
-
-void ShapeFixer::visit(const ir::operation::LogicalNot &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::LogicalOr &node)
-{
-  const auto input0_index{node.getInputs().at(ir::operation::LogicalOr::Input::INPUT0)};
-  const auto input1_index{node.getInputs().at(ir::operation::LogicalOr::Input::INPUT1)};
-
-  if (!(_ctx.at(input0_index).shape() == _ctx.at(input1_index).shape()))
-  {
-    const auto broadcast_rank =
-        std::max(_ctx.at(input0_index).shape().rank(), _ctx.at(input1_index).shape().rank());
-
-    // TODO remove const_cast later. For example, _ctx may need to be a non const variable or
-    //      a node to extend shape may be inserted in front of this operation
-    const_cast<ir::Shape &>(_ctx.at(input0_index).shape()).extendRank(broadcast_rank);
-    const_cast<ir::Shape &>(_ctx.at(input1_index).shape()).extendRank(broadcast_rank);
-  }
-}
-
-void ShapeFixer::visit(const ir::operation::Logistic &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::LSTM &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Pack &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  _tensor_builder->dimCorrection(ofm_index, false);
-  for (const auto &inputs : node.getInputs())
-  {
-    _tensor_builder->dimCorrection(inputs, false);
-    const auto ofm_rank = _ctx.at(ofm_index).shape().rank();
-
-    // TODO remove const_cast later. For example, _ctx may need to be a non const variable or
-    //      a node to extend shape may be inserted in front of this operation
-    const_cast<ir::Shape &>(_ctx.at(inputs).shape()).extendRank(ofm_rank);
-  }
-}
-
-void ShapeFixer::visit(const ir::operation::Pad &node)
-{
-  const auto input_index{node.getInputs().at(ir::operation::Pad::Input::INPUT)};
-  const auto output_index{node.getOutputs().at(0)};
-  _tensor_builder->dimCorrection(input_index, false);
-  _tensor_builder->dimCorrection(output_index, false);
-}
-
-void ShapeFixer::visit(const ir::operation::Mul &node)
-{
-  const auto lhs_index{node.getInputs().at(ir::operation::Mul::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::Mul::Input::RHS)};
-
-  if (!(_ctx.at(lhs_index).shape() == _ctx.at(rhs_index).shape()))
-  {
-    const auto broadcast_rank =
-        std::max(_ctx.at(lhs_index).shape().rank(), _ctx.at(rhs_index).shape().rank());
-
-    // TODO remove const_cast later. For example, _ctx may need to be a non const variable or
-    //      a node to extend shape may be inserted in front of this operation
-    const_cast<ir::Shape &>(_ctx.at(lhs_index).shape()).extendRank(broadcast_rank);
-    const_cast<ir::Shape &>(_ctx.at(rhs_index).shape()).extendRank(broadcast_rank);
-  }
-}
-
-void ShapeFixer::visit(const ir::operation::Neg &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Permute &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::PReLU &node)
-{
-  const auto ifm_index{node.getInputs().at(ir::operation::PReLU::Input::INPUT)};
-  const auto alpha_index{node.getInputs().at(ir::operation::PReLU::Input::ALPHA)};
-
-  if (!(_ctx.at(ifm_index).shape() == _ctx.at(alpha_index).shape()))
-  {
-    const auto broadcast_rank =
-        std::max(_ctx.at(ifm_index).shape().rank(), _ctx.at(alpha_index).shape().rank());
-    const_cast<ir::Shape &>(_ctx.at(ifm_index).shape()).extendRank(broadcast_rank);
-    const_cast<ir::Shape &>(_ctx.at(alpha_index).shape()).extendRank(broadcast_rank);
-  }
-}
-
-void ShapeFixer::visit(const ir::operation::ReduceMax &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::ReduceMin &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::ReduceSum &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::ReLU &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::ReLU1 &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::ReLU6 &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Reshape &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::Reshape::Input::INPUT)};
-
-  _tensor_builder->dimCorrection(input_index, false);
-  _tensor_builder->dimCorrection(output_index, false);
-}
-
-void ShapeFixer::visit(const ir::operation::ResizeBilinear &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::RNN &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Comparison &node)
-{
-  const auto input0_index{node.getInputs().at(ir::operation::Comparison::Input::INPUT0)};
-  const auto input1_index{node.getInputs().at(ir::operation::Comparison::Input::INPUT1)};
-
-  if (!(_ctx.at(input0_index).shape() == _ctx.at(input1_index).shape()))
-  {
-    const auto broadcast_rank =
-        std::max(_ctx.at(input0_index).shape().rank(), _ctx.at(input1_index).shape().rank());
-
-    // TODO remove const_cast later. For example, _ctx may need to be a non const variable or
-    //      a node to extend shape may be inserted in front of this operation
-    const_cast<ir::Shape &>(_ctx.at(input0_index).shape()).extendRank(broadcast_rank);
-    const_cast<ir::Shape &>(_ctx.at(input1_index).shape()).extendRank(broadcast_rank);
-  }
-}
-
-void ShapeFixer::visit(const ir::operation::RSQRT &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Squeeze &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::Squeeze::Input::INPUT)};
-  _tensor_builder->dimCorrection(input_index, false);
-  _tensor_builder->dimCorrection(output_index, false);
-}
-
-void ShapeFixer::visit(const ir::operation::Tanh &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Slice &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::StridedSlice &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::StridedSlice::Input::INPUT)};
-  _tensor_builder->dimCorrection(ofm_index, false);
-  _tensor_builder->dimCorrection(ifm_index, false);
-}
-
-void ShapeFixer::visit(const ir::operation::Softmax &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::SpaceToBatchND &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::SpaceToBatchND::Input::INPUT)};
-  _tensor_builder->dimCorrection(ofm_index, false);
-  _tensor_builder->dimCorrection(ifm_index, false);
-}
-
-void ShapeFixer::visit(const ir::operation::SpaceToDepth &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Split &node)
-{
-  const auto input_index{node.getInputs().at(ir::operation::Split::Input::INPUT)};
-  _tensor_builder->dimCorrection(input_index, false);
-  for (const auto &output : node.getOutputs())
-    _tensor_builder->dimCorrection(output, false);
-}
-
-void ShapeFixer::visit(const ir::operation::SQRT &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::SquaredDifference &node)
-{
-  const auto lhs_index{node.getInputs().at(ir::operation::SquaredDifference::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::SquaredDifference::Input::RHS)};
-
-  if (!(_ctx.at(lhs_index).shape() == _ctx.at(rhs_index).shape()))
-  {
-    const auto broadcast_rank =
-        std::max(_ctx.at(lhs_index).shape().rank(), _ctx.at(rhs_index).shape().rank());
-
-    // TODO remove const_cast later. For example, _ctx may need to be a non const variable or
-    //      a node to extend shape may be inserted in front of this operation
-    const_cast<ir::Shape &>(_ctx.at(lhs_index).shape()).extendRank(broadcast_rank);
-    const_cast<ir::Shape &>(_ctx.at(rhs_index).shape()).extendRank(broadcast_rank);
-  }
-}
-
-void ShapeFixer::visit(const ir::operation::Sub &node)
-{
-  const auto lhs_index{node.getInputs().at(ir::operation::Sub::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::Sub::Input::RHS)};
-
-  if (!(_ctx.at(lhs_index).shape() == _ctx.at(rhs_index).shape()))
-  {
-    const auto broadcast_rank =
-        std::max(_ctx.at(lhs_index).shape().rank(), _ctx.at(rhs_index).shape().rank());
-    // TODO remove const_cast later. For example, _ctx may need to be a non const variable or
-    //      a node to extend shape may be inserted in front of this operation
-    const_cast<ir::Shape &>(_ctx.at(lhs_index).shape()).extendRank(broadcast_rank);
-    const_cast<ir::Shape &>(_ctx.at(rhs_index).shape()).extendRank(broadcast_rank);
-  }
-}
-
-void ShapeFixer::visit(const ir::operation::TransposeConv &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Transpose &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Unpack &node)
-{
-  const auto input_index{node.getInputs().at(ir::operation::Unpack::Input::INPUT)};
-  _tensor_builder->dimCorrection(input_index, false);
-  for (const auto &output_index : node.getOutputs())
-    _tensor_builder->dimCorrection(output_index, false);
-}
-
-void ShapeFixer::visit(const ir::operation::Add &node)
-{
-  const auto lhs_index{node.getInputs().at(ir::operation::Add::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::Add::Input::RHS)};
-
-  if (!(_ctx.at(lhs_index).shape() == _ctx.at(rhs_index).shape()))
-  {
-    const auto broadcast_rank =
-        std::max(_ctx.at(lhs_index).shape().rank(), _ctx.at(rhs_index).shape().rank());
-    const_cast<ir::Shape &>(_ctx.at(lhs_index).shape()).extendRank(broadcast_rank);
-    const_cast<ir::Shape &>(_ctx.at(rhs_index).shape()).extendRank(broadcast_rank);
-  }
-}
-
-void ShapeFixer::visit(const ir::operation::Div &node)
-{
-  const auto lhs_index{node.getInputs().at(ir::operation::Div::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::Div::Input::RHS)};
-
-  if (!(_ctx.at(lhs_index).shape() == _ctx.at(rhs_index).shape()))
-  {
-    const auto broadcast_rank =
-        std::max(_ctx.at(lhs_index).shape().rank(), _ctx.at(rhs_index).shape().rank());
-
-    // TODO remove const_cast later. For example, _ctx may need to be a non const variable or
-    //      a node to extend shape may be inserted in front of this operation
-    const_cast<ir::Shape &>(_ctx.at(lhs_index).shape()).extendRank(broadcast_rank);
-    const_cast<ir::Shape &>(_ctx.at(rhs_index).shape()).extendRank(broadcast_rank);
-  }
-}
-
-void ShapeFixer::visit(const ir::operation::Min &node)
-{
-  const auto lhs_index{node.getInputs().at(ir::operation::Min::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::Min::Input::RHS)};
-
-  if (!(_ctx.at(lhs_index).shape() == _ctx.at(rhs_index).shape()))
-  {
-    const auto broadcast_rank =
-        std::max(_ctx.at(lhs_index).shape().rank(), _ctx.at(rhs_index).shape().rank());
-
-    // TODO remove const_cast later. For example, _ctx may need to be a non const variable or
-    //      a node to extend shape may be inserted in front of this operation
-    const_cast<ir::Shape &>(_ctx.at(lhs_index).shape()).extendRank(broadcast_rank);
-    const_cast<ir::Shape &>(_ctx.at(rhs_index).shape()).extendRank(broadcast_rank);
-  }
-}
-
-void ShapeFixer::visit(const ir::operation::Max &node)
-{
-  const auto lhs_index{node.getInputs().at(ir::operation::Max::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::Max::Input::RHS)};
-
-  if (!(_ctx.at(lhs_index).shape() == _ctx.at(rhs_index).shape()))
-  {
-    const auto broadcast_rank =
-        std::max(_ctx.at(lhs_index).shape().rank(), _ctx.at(rhs_index).shape().rank());
-
-    // TODO remove const_cast later. For example, _ctx may need to be a non const variable or
-    //      a node to extend shape may be inserted in front of this operation
-    const_cast<ir::Shape &>(_ctx.at(lhs_index).shape()).extendRank(broadcast_rank);
-    const_cast<ir::Shape &>(_ctx.at(rhs_index).shape()).extendRank(broadcast_rank);
-  }
-}
-
-} // namespace acl_neon
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/acl_neon/ShapeFixer.h b/runtime/neurun/backend/acl_neon/ShapeFixer.h
deleted file mode 100644
index aa1f8f75a..000000000
--- a/runtime/neurun/backend/acl_neon/ShapeFixer.h
+++ /dev/null
@@ -1,109 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_NEON_SHAPE_FIXER_H__
-#define __NEURUN_BACKEND_ACL_NEON_SHAPE_FIXER_H__
-
-#include <backend/IShapeFixer.h>
-
-#include "ir/Operands.h"
-#include "TensorBuilder.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_neon
-{
-
-class ShapeFixer : public IShapeFixer
-{
-public:
-  ShapeFixer(const ir::Operands &ctx, const std::shared_ptr<TensorBuilder> &tensor_builder);
-
-  void visit(const ir::operation::Abs &) override;
-  void visit(const ir::operation::ArgMax &) override;
-  void visit(const ir::operation::BatchToSpaceND &) override;
-  void visit(const ir::operation::Cast &) override;
-  void visit(const ir::operation::Conv2D &) override;
-  void visit(const ir::operation::DepthToSpace &) override;
-  void visit(const ir::operation::DepthwiseConv2D &) override;
-  void visit(const ir::operation::Dequantize &) override;
-  void visit(const ir::operation::MaxPool2D &) override;
-  void visit(const ir::operation::Mean &) override;
-  void visit(const ir::operation::AvgPool2D &) override;
-  void visit(const ir::operation::Concat &) override;
-  void visit(const ir::operation::EmbeddingLookup &) override;
-  void visit(const ir::operation::Exp &) override;
-  void visit(const ir::operation::Floor &) override;
-  void visit(const ir::operation::FullyConnected &) override;
-  void visit(const ir::operation::Gather &) override;
-  void visit(const ir::operation::HashtableLookup &) override;
-  void visit(const ir::operation::InstanceNorm &) override;
-  void visit(const ir::operation::L2Normalization &) override;
-  void visit(const ir::operation::L2Pool2D &) override;
-  void visit(const ir::operation::LocalResponseNormalization &) override;
-  void visit(const ir::operation::LogicalAnd &) override;
-  void visit(const ir::operation::LogicalNot &) override;
-  void visit(const ir::operation::LogicalOr &) override;
-  void visit(const ir::operation::Logistic &) override;
-  void visit(const ir::operation::LSTM &) override;
-  void visit(const ir::operation::Mul &) override;
-  void visit(const ir::operation::Neg &) override;
-  void visit(const ir::operation::Pack &) override;
-  void visit(const ir::operation::Pad &) override;
-  void visit(const ir::operation::Permute &) override;
-  void visit(const ir::operation::PReLU &) override;
-  void visit(const ir::operation::ReduceMax &) override;
-  void visit(const ir::operation::ReduceMin &) override;
-  void visit(const ir::operation::ReduceSum &) override;
-  void visit(const ir::operation::ReLU &) override;
-  void visit(const ir::operation::ReLU1 &) override;
-  void visit(const ir::operation::ReLU6 &) override;
-  void visit(const ir::operation::Reshape &) override;
-  void visit(const ir::operation::ResizeBilinear &) override;
-  void visit(const ir::operation::RNN &) override;
-  void visit(const ir::operation::RSQRT &) override;
-  void visit(const ir::operation::Squeeze &) override;
-  void visit(const ir::operation::Tanh &) override;
-  void visit(const ir::operation::Softmax &) override;
-  void visit(const ir::operation::SpaceToBatchND &) override;
-  void visit(const ir::operation::SpaceToDepth &) override;
-  void visit(const ir::operation::Split &) override;
-  void visit(const ir::operation::SQRT &) override;
-  void visit(const ir::operation::SquaredDifference &) override;
-  void visit(const ir::operation::Sub &) override;
-  void visit(const ir::operation::Slice &) override;
-  void visit(const ir::operation::StridedSlice &) override;
-  void visit(const ir::operation::TransposeConv &) override;
-  void visit(const ir::operation::Transpose &) override;
-  void visit(const ir::operation::Unpack &) override;
-  void visit(const ir::operation::Add &) override;
-  void visit(const ir::operation::Div &) override;
-  void visit(const ir::operation::Comparison &) override;
-  void visit(const ir::operation::Min &) override;
-  void visit(const ir::operation::Max &) override;
-
-private:
-  const ir::Operands &_ctx;
-  std::shared_ptr<TensorBuilder> _tensor_builder;
-};
-
-} // namespace acl_neon
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ACL_NEON_SHAPE_FIXER_H__
diff --git a/runtime/neurun/backend/acl_neon/TensorBuilder.h b/runtime/neurun/backend/acl_neon/TensorBuilder.h
deleted file mode 100644
index 0a6b4921d..000000000
--- a/runtime/neurun/backend/acl_neon/TensorBuilder.h
+++ /dev/null
@@ -1,39 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_NEON_TENSOR_BUILDER_H__
-#define __NEURUN_BACKEND_ACL_NEON_TENSOR_BUILDER_H__
-
-#include <TemplTensorBuilder.h>
-
-#include "operand/NETensor.h"
-#include "operand/NESubTensor.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_neon
-{
-
-using TensorBuilder =
-    acl_common::TemplTensorBuilder<operand::INETensor, operand::NETensor, operand::NESubTensor>;
-
-} // namespace acl_neon
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ACL_NEON_TENSOR_BUILDER_H__
diff --git a/runtime/neurun/backend/acl_neon/TensorManager.h b/runtime/neurun/backend/acl_neon/TensorManager.h
deleted file mode 100644
index 725275cef..000000000
--- a/runtime/neurun/backend/acl_neon/TensorManager.h
+++ /dev/null
@@ -1,78 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_NEON_TENSOR_MANAGER_H__
-#define __NEURUN_BACKEND_ACL_NEON_TENSOR_MANAGER_H__
-
-#include <arm_compute/runtime/Allocator.h>
-#include <arm_compute/runtime/PoolManager.h>
-#include <arm_compute/runtime/OffsetLifetimeManager.h>
-#include <arm_compute/runtime/MemoryManagerOnDemand.h>
-#include <arm_compute/runtime/MemoryGroup.h>
-
-#include <AclMemoryManager.h>
-#include <AclLinearMemoryManager.h>
-#include <AclInternalBufferManager.h>
-#include <AclTensorManager.h>
-
-#include "operand/NETensor.h"
-#include "operand/NESubTensor.h"
-
-#include "util/logging.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_neon
-{
-
-using MemoryManager =
-    acl_common::AclMemoryManager<operand::INETensor, operand::NETensor, operand::NESubTensor>;
-
-using LinearMemoryManager = acl_common::AclLinearMemoryManager<
-    operand::INETensor, operand::NETensor, operand::NESubTensor,
-    ::arm_compute::MemoryManagerOnDemand, ::arm_compute::PoolManager,
-    ::arm_compute::OffsetLifetimeManager, ::arm_compute::Allocator, ::arm_compute::MemoryGroup>;
-
-using InternalBufferManager = acl_common::AclInternalBufferManager<
-    ::arm_compute::MemoryManagerOnDemand, ::arm_compute::PoolManager,
-    ::arm_compute::OffsetLifetimeManager, ::arm_compute::Allocator>;
-
-using TensorManager = acl_common::AclTensorManager<acl_neon::operand::INETensor, operand::NETensor,
-                                                   operand::NESubTensor>;
-
-TensorManager *createTensorManager()
-{
-  const std::string executor_str = util::getConfigString(util::config::EXECUTOR);
-  if (executor_str == "Linear")
-  {
-    VERBOSE(acl_neon_createTensorManager) << "AclTensorManager as Linear" << std::endl;
-    return new TensorManager(new MemoryManager(), new LinearMemoryManager(),
-                             new InternalBufferManager());
-  }
-  else
-  {
-    VERBOSE(acl_neon_createTensorManager) << "AclTensorManager" << std::endl;
-    return new TensorManager(new MemoryManager(), new MemoryManager(), new InternalBufferManager());
-  }
-}
-
-} // namespace acl_neon
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ACL_NEON_TENSOR_MANAGER_H__
diff --git a/runtime/neurun/backend/acl_neon/TensorRegister.cc b/runtime/neurun/backend/acl_neon/TensorRegister.cc
deleted file mode 100644
index fe766cdf9..000000000
--- a/runtime/neurun/backend/acl_neon/TensorRegister.cc
+++ /dev/null
@@ -1,30 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "TensorRegister.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_neon
-{
-
-// NOTHING
-
-} // srcn
-} // backend
-} // neurun
diff --git a/runtime/neurun/backend/acl_neon/TensorRegister.h b/runtime/neurun/backend/acl_neon/TensorRegister.h
deleted file mode 100644
index 115e05dee..000000000
--- a/runtime/neurun/backend/acl_neon/TensorRegister.h
+++ /dev/null
@@ -1,51 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_NEON_TENSOR_REGISTER_H__
-#define __NEURUN_BACKEND_ACL_NEON_TENSOR_REGISTER_H__
-
-#include <AclTensorRegister.h>
-#include <misc/polymorphic_downcast.h>
-#include "TensorBuilder.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_neon
-{
-
-class TensorRegister : public acl_common::AclTensorRegister
-{
-public:
-  TensorRegister(const ir::Operands &operands, const std::shared_ptr<TensorBuilder> &tensor_builder)
-      : acl_common::AclTensorRegister{operands, tensor_builder}
-  {
-    // DO NOTHING
-  }
-
-  void setUsesCount(const ir::OperandIndex &ind, size_t num_uses) const override
-  {
-    nnfw::misc::polymorphic_downcast<TensorBuilder *>(tensor_builder().get())
-        ->setUsesCount(ind, num_uses);
-  }
-};
-
-} // namespace acl_neon
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ACL_NEON_TENSOR_REGISTER_H__
diff --git a/runtime/neurun/backend/acl_neon/operand/INETensor.cc b/runtime/neurun/backend/acl_neon/operand/INETensor.cc
deleted file mode 100644
index fdb20970d..000000000
--- a/runtime/neurun/backend/acl_neon/operand/INETensor.cc
+++ /dev/null
@@ -1,33 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "INETensor.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_neon
-{
-namespace operand
-{
-
-void INETensor::access(const std::function<void(ITensor &tensor)> &fn) { fn(*this); }
-
-} // namespace operand
-} // namespace acl_neon
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/acl_neon/operand/INETensor.h b/runtime/neurun/backend/acl_neon/operand/INETensor.h
deleted file mode 100644
index 22b1140cf..000000000
--- a/runtime/neurun/backend/acl_neon/operand/INETensor.h
+++ /dev/null
@@ -1,46 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_NEON_OPERAND_I_NE_TENSOR_H__
-#define __NEURUN_BACKEND_ACL_NEON_OPERAND_I_NE_TENSOR_H__
-
-#include <arm_compute/core/ITensor.h>
-
-#include <IACLTensor.h>
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_neon
-{
-namespace operand
-{
-
-class INETensor : public acl_common::IACLTensor
-{
-public:
-  const arm_compute::ITensor *handle() const override = 0;
-  arm_compute::ITensor *handle() override = 0;
-  void access(const std::function<void(ITensor &tensor)> &fn) final;
-};
-
-} // namespace operand
-} // namespace acl_neon
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ACL_NEON_OPERAND_I_NE_TENSOR_H__
diff --git a/runtime/neurun/backend/acl_neon/operand/NESubTensor.cc b/runtime/neurun/backend/acl_neon/operand/NESubTensor.cc
deleted file mode 100644
index a36af609c..000000000
--- a/runtime/neurun/backend/acl_neon/operand/NESubTensor.cc
+++ /dev/null
@@ -1,44 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "NESubTensor.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_neon
-{
-namespace operand
-{
-
-NESubTensor::NESubTensor(INETensor *parent, const arm_compute::TensorShape &tensor_shape,
-                         const arm_compute::Coordinates &coords, size_t rank, bool extend_parent)
-    : _ne_sub_tensor(std::make_shared<arm_compute::SubTensor>(parent->handle(), tensor_shape,
-                                                              coords, extend_parent)),
-      _rank{rank}
-{
-  // DO NOTHING
-}
-
-const arm_compute::SubTensor *NESubTensor::handle() const { return _ne_sub_tensor.get(); }
-
-arm_compute::SubTensor *NESubTensor::handle() { return _ne_sub_tensor.get(); }
-
-} // namespace operand
-} // namespace acl_neon
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/acl_neon/operand/NESubTensor.h b/runtime/neurun/backend/acl_neon/operand/NESubTensor.h
deleted file mode 100644
index 010e4deda..000000000
--- a/runtime/neurun/backend/acl_neon/operand/NESubTensor.h
+++ /dev/null
@@ -1,63 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_NEON_OPERAND_NE_SUB_TENSOR_H__
-#define __NEURUN_BACKEND_ACL_NEON_OPERAND_NE_SUB_TENSOR_H__
-
-#include <arm_compute/runtime/SubTensor.h>
-#include "INETensor.h"
-#include "compiler/SubTensorInfo.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_neon
-{
-namespace operand
-{
-
-class NESubTensor : public INETensor
-{
-public:
-  NESubTensor() = delete;
-
-public:
-  NESubTensor(INETensor *parent, const arm_compute::TensorShape &tensor_shape,
-              const arm_compute::Coordinates &coords, size_t rank, bool extend_parent = false);
-
-public:
-  size_t num_dimensions() const final { return _rank; }
-
-public:
-  const arm_compute::SubTensor *handle() const override;
-  arm_compute::SubTensor *handle() override;
-
-public:
-  // This method is used to prevent the use of memcpy for SubTensor
-  bool has_padding() const override { return true; }
-
-private:
-  std::shared_ptr<arm_compute::SubTensor> _ne_sub_tensor;
-  size_t _rank;
-};
-
-} // namespace operand
-} // namespace acl_neon
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ACL_NEON_OPERAND_NE_SUB_TENSOR_H__
diff --git a/runtime/neurun/backend/acl_neon/operand/NETensor.cc b/runtime/neurun/backend/acl_neon/operand/NETensor.cc
deleted file mode 100644
index 8a9ece88f..000000000
--- a/runtime/neurun/backend/acl_neon/operand/NETensor.cc
+++ /dev/null
@@ -1,45 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <arm_compute/runtime/Memory.h>
-#include <arm_compute/runtime/MemoryRegion.h>
-#include "NETensor.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_neon
-{
-namespace operand
-{
-
-NETensor::NETensor(const arm_compute::TensorInfo &info, size_t rank, size_t num_uses)
-    : _ne_tensor(std::make_shared<arm_compute::Tensor>()), _rank{rank}, _num_uses{num_uses}
-{
-  allocator()->init(info);
-}
-
-const arm_compute::Tensor *NETensor::handle() const { return _ne_tensor.get(); }
-
-arm_compute::Tensor *NETensor::handle() { return _ne_tensor.get(); }
-
-arm_compute::TensorAllocator *NETensor::allocator() { return _ne_tensor->allocator(); }
-
-} // namespace operand
-} // namespace acl_neon
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/acl_neon/operand/NETensor.h b/runtime/neurun/backend/acl_neon/operand/NETensor.h
deleted file mode 100644
index 3de4695e9..000000000
--- a/runtime/neurun/backend/acl_neon/operand/NETensor.h
+++ /dev/null
@@ -1,64 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_NEON_OPERAND_NE_TENSOR_H__
-#define __NEURUN_BACKEND_ACL_NEON_OPERAND_NE_TENSOR_H__
-
-#include <arm_compute/core/TensorInfo.h>
-#include <arm_compute/runtime/Tensor.h>
-#include "arm_compute/runtime/TensorAllocator.h"
-#include "INETensor.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_neon
-{
-namespace operand
-{
-
-class NETensor : public INETensor
-{
-public:
-  NETensor() = delete;
-
-public:
-  NETensor(const arm_compute::TensorInfo &info, size_t rank, size_t num_uses);
-
-public:
-  size_t num_dimensions() const final { return _rank; }
-
-public:
-  const arm_compute::Tensor *handle() const override;
-  arm_compute::Tensor *handle() override;
-  size_t num_uses() const { return _num_uses; }
-
-public:
-  arm_compute::TensorAllocator *allocator();
-
-private:
-  std::shared_ptr<arm_compute::Tensor> _ne_tensor;
-  size_t _rank;
-  size_t _num_uses;
-};
-
-} // namespace operand
-} // namespace acl_neon
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ACL_NEON_OPERAND_NE_TENSOR_H__
diff --git a/runtime/neurun/backend/cpu/Backend.h b/runtime/neurun/backend/cpu/Backend.h
deleted file mode 100644
index e52a776b9..000000000
--- a/runtime/neurun/backend/cpu/Backend.h
+++ /dev/null
@@ -1,64 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_CPU_BACKEND_H__
-#define __NEURUN_BACKEND_CPU_BACKEND_H__
-
-#include <memory>
-#include <backend/Backend.h>
-#include <ir/Operands.h>
-
-#include "Config.h"
-#include "ConstantInitializer.h"
-#include "KernelGenerator.h"
-#include "ShapeFixer.h"
-#include "TensorRegister.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-
-class Backend : public ::neurun::backend::Backend
-{
-public:
-  Backend() : _config{std::make_shared<Config>()} {}
-
-  std::shared_ptr<IConfig> config() const override { return _config; }
-
-  std::unique_ptr<BackendContext>
-  newContext(const ir::Operands &operands,
-             const std::shared_ptr<custom::IKernelBuilder> &kb) const override
-  {
-    auto tensor_builder = std::make_shared<TensorBuilder>();
-    return std::unique_ptr<BackendContext>{new BackendContext{
-        this, tensor_builder, std::make_shared<ConstantInitializer>(operands, tensor_builder),
-        std::make_shared<KernelGenerator>(operands, tensor_builder, kb),
-        std::make_shared<ShapeFixer>(operands, tensor_builder),
-        std::make_shared<TensorRegister>(operands, tensor_builder)}};
-  }
-
-private:
-  std::shared_ptr<IConfig> _config;
-};
-
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_CPU_BACKEND_H__
diff --git a/runtime/neurun/backend/cpu/CMakeLists.txt b/runtime/neurun/backend/cpu/CMakeLists.txt
deleted file mode 100644
index 82c838b15..000000000
--- a/runtime/neurun/backend/cpu/CMakeLists.txt
+++ /dev/null
@@ -1,16 +0,0 @@
-set(LIB_NEURUN_BACKEND_CPU neurun_backend_cpu)
-
-file(GLOB_RECURSE SOURCES "*.cc")
-
-add_library(${LIB_NEURUN_BACKEND_CPU} SHARED ${SOURCES})
-
-target_link_libraries(${LIB_NEURUN_BACKEND_CPU} PUBLIC nnfw_lib_cpp14)
-target_link_libraries(${LIB_NEURUN_BACKEND_CPU} PRIVATE nnfw_lib_misc nnfw_lib_cker)
-target_link_libraries(${LIB_NEURUN_BACKEND_CPU} PRIVATE neurun_core)
-target_link_libraries(${LIB_NEURUN_BACKEND_CPU} PRIVATE ${LIB_NEURUN_BACKEND_CPU_COMMON})
-target_link_libraries(${LIB_NEURUN_BACKEND_CPU} PRIVATE nnfw_common)
-target_link_libraries(${LIB_NEURUN_BACKEND_CPU} PRIVATE nnfw_coverage)
-
-set_target_properties(${LIB_NEURUN_BACKEND_CPU} PROPERTIES OUTPUT_NAME backend_cpu)
-
-install(TARGETS ${LIB_NEURUN_BACKEND_CPU} DESTINATION lib)
diff --git a/runtime/neurun/backend/cpu/Config.cc b/runtime/neurun/backend/cpu/Config.cc
deleted file mode 100644
index 39127406f..000000000
--- a/runtime/neurun/backend/cpu/Config.cc
+++ /dev/null
@@ -1,30 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "Config.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-
-bool Config::initialize() { return true; }
-
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/cpu/Config.h b/runtime/neurun/backend/cpu/Config.h
deleted file mode 100644
index be303b556..000000000
--- a/runtime/neurun/backend/cpu/Config.h
+++ /dev/null
@@ -1,53 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_CPU_CONFIG_H__
-#define __NEURUN_BACKEND_CPU_CONFIG_H__
-
-#include <backend/IConfig.h>
-#include <cpp14/memory.h>
-#include <util/ITimer.h>
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-
-class Config : public IConfig
-{
-public:
-  std::string id() override { return "cpu"; }
-  bool initialize() override;
-  bool SupportPermutation() override { return true; }
-  bool SupportSubTensorAlloc() override
-  {
-    // NOTE CPU allocator cannot support subtensor allocation yet
-    return false;
-  }
-
-  std::unique_ptr<util::ITimer> timer() override
-  {
-    return nnfw::cpp14::make_unique<util::CPUTimer>();
-  }
-};
-
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_CPU_CONFIG_H__
diff --git a/runtime/neurun/backend/cpu/ConstantInitializer.cc b/runtime/neurun/backend/cpu/ConstantInitializer.cc
deleted file mode 100644
index e6e7d8deb..000000000
--- a/runtime/neurun/backend/cpu/ConstantInitializer.cc
+++ /dev/null
@@ -1,68 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ConstantInitializer.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-
-ConstantInitializer::ConstantInitializer(const ir::Operands &operands,
-                                         const std::shared_ptr<TensorBuilder> &tensor_builder)
-    : _operands{operands}, _tensor_builder{tensor_builder}
-{
-  // DO NOTHING
-}
-
-void ConstantInitializer::visit(const ir::operation::Conv2D &node)
-{
-  const auto &kernel_index = node.getInputs().at(ir::operation::Conv2D::KERNEL);
-  const auto &kernel_obj = _operands.at(kernel_index);
-  registerCopyInitializer(kernel_index, kernel_obj);
-
-  const auto &bias_index = node.getInputs().at(ir::operation::Conv2D::BIAS);
-  const auto &bias_obj = _operands.at(bias_index);
-  registerCopyInitializer(bias_index, bias_obj);
-}
-
-void ConstantInitializer::visit(const ir::operation::DepthwiseConv2D &node)
-{
-  const auto &kernel_index = node.getInputs().at(ir::operation::DepthwiseConv2D::KERNEL);
-  const auto &kernel_obj = _operands.at(kernel_index);
-  registerCopyInitializer(kernel_index, kernel_obj);
-
-  const auto &bias_index = node.getInputs().at(ir::operation::DepthwiseConv2D::BIAS);
-  const auto &bias_obj = _operands.at(bias_index);
-  registerCopyInitializer(bias_index, bias_obj);
-}
-
-void ConstantInitializer::visit(const ir::operation::FullyConnected &node)
-{
-  const auto &weight_index = node.getInputs().at(ir::operation::FullyConnected::WEIGHT);
-  const auto &weight_obj = _operands.at(weight_index);
-  registerCopyInitializer(weight_index, weight_obj);
-
-  const auto &bias_index = node.getInputs().at(ir::operation::FullyConnected::BIAS);
-  const auto &bias_obj = _operands.at(bias_index);
-  registerCopyInitializer(bias_index, bias_obj);
-}
-
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/cpu/ConstantInitializer.h b/runtime/neurun/backend/cpu/ConstantInitializer.h
deleted file mode 100644
index a53321997..000000000
--- a/runtime/neurun/backend/cpu/ConstantInitializer.h
+++ /dev/null
@@ -1,55 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_COMPILER_CPU_CONSTANT_INITIALIZER_H__
-#define __NEURUN_COMPILER_CPU_CONSTANT_INITIALIZER_H__
-
-#include <backend/IConstantInitializer.h>
-#include <ir/Operands.h>
-#include "TensorBuilder.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-
-class ConstantInitializer : public IConstantInitializer
-{
-public:
-  ConstantInitializer(const ir::Operands &operands,
-                      const std::shared_ptr<TensorBuilder> &tensor_builder);
-
-public:
-  void visit(const ir::operation::Conv2D &) override;
-  void visit(const ir::operation::DepthwiseConv2D &) override;
-  void visit(const ir::operation::FullyConnected &) override;
-
-private:
-  const ir::Operands &operands() const override { return _operands; }
-  std::shared_ptr<ITensorBuilder> tensor_builder() const override { return _tensor_builder; }
-
-private:
-  const ir::Operands &_operands;
-  std::shared_ptr<TensorBuilder> _tensor_builder;
-};
-
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_COMPILER_CPU_CONSTANT_INITIALIZER_H__
diff --git a/runtime/neurun/backend/cpu/KernelGenerator.cc b/runtime/neurun/backend/cpu/KernelGenerator.cc
deleted file mode 100644
index 09bd1367d..000000000
--- a/runtime/neurun/backend/cpu/KernelGenerator.cc
+++ /dev/null
@@ -1,624 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "KernelGenerator.h"
-
-#include <stdexcept>
-
-#include "cpp14/memory.h"
-#include "util/Padding.h"
-#include "kernel/OperationUtils.h"
-#include "kernel/ConvolutionLayer.h"
-#include "kernel/AvgPoolLayer.h"
-#include "kernel/MaxPoolLayer.h"
-#include "kernel/ConcatLayer.h"
-#include "kernel/FullyConnectedLayer.h"
-#include "kernel/ReshapeLayer.h"
-#include "kernel/SoftMaxLayer.h"
-#include "kernel/PermuteLayer.h"
-#include "kernel/DepthwiseConvolutionLayer.h"
-#include "kernel/AddLayer.h"
-#include "kernel/SubLayer.h"
-#include "kernel/MulLayer.h"
-#include "kernel/GatherLayer.h"
-#include "kernel/LogisticLayer.h"
-#include "kernel/PadLayer.h"
-
-#include <backend/Backend.h>
-#include <backend/IConfig.h>
-
-#include "util/logging.h"
-
-#include "util/Utils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-
-KernelGenerator::KernelGenerator(
-    const ir::Operands &operand_ctx, const std::shared_ptr<TensorBuilder> &tensor_builder,
-    const std::shared_ptr<backend::custom::IKernelBuilder> &kernel_builer)
-    : _ctx(operand_ctx), _tensor_builder(tensor_builder), _kernel_builder(kernel_builer),
-      _current_subg_layout(ir::Layout::UNKNOWN)
-{
-  // DO NOTHING
-}
-
-void KernelGenerator::visit(const ir::OpSequence &op_seq)
-{
-  _current_subg_layout = op_seq.getLayout();
-  for (const auto &e : op_seq.operations())
-  {
-    const auto &node = *(e.node);
-    _tensor_builder->preVisit(node);
-    node.accept(*this);
-    _tensor_builder->postVisit(node);
-  }
-}
-
-void KernelGenerator::visit(const ir::operation::Conv2D &node)
-{
-  using ir::operation::Conv2D;
-
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(Conv2D::Input::INPUT)};
-  const auto ker_index{node.getInputs().at(Conv2D::Input::KERNEL)};
-  const auto bias_index{node.getInputs().at(Conv2D::Input::BIAS)};
-
-  const auto stride = node.param().stride;
-  const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(_current_subg_layout);
-  const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(_current_subg_layout);
-  // Kernel format is [depth_out, kernel_height, kernel_width, depth_in].
-  const auto &ker_shape = _ctx.at(ker_index).shape();
-  const auto ker_height = ker_shape.dim(1);
-  const auto ker_width = ker_shape.dim(2);
-  const auto padding = neurun::util::calculatePadding(node.param().padding, ifm_shape, ofm_shape,
-                                                      stride, ker_width, ker_height);
-  const auto activation = node.param().activation;
-
-  const auto ofm_backend_descr =
-      ::neurun::backend::cpu::kernel::getTensorDescriptor(_ctx.at(ofm_index), _current_subg_layout);
-  const auto ifm_backend_descr =
-      ::neurun::backend::cpu::kernel::getTensorDescriptor(_ctx.at(ifm_index), _current_subg_layout);
-  const auto ker_backend_descr =
-      ::neurun::backend::cpu::kernel::getTensorDescriptor(_ctx.at(ker_index), ir::Layout::UNKNOWN);
-  const auto bias_backend_descr =
-      ::neurun::backend::cpu::kernel::getTensorDescriptor(_ctx.at(bias_index), ir::Layout::UNKNOWN);
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index);
-  auto ifm_alloc = _tensor_builder->at(ifm_index);
-  auto ker_alloc = _tensor_builder->at(ker_index);
-  auto bias_alloc = _tensor_builder->at(bias_index);
-
-  auto fn = nnfw::cpp14::make_unique<::neurun::backend::cpu::kernel::ConvolutionLayer>();
-
-  fn->configure(ifm_alloc->buffer(), ifm_backend_descr, ker_alloc->buffer(), ker_backend_descr,
-                bias_alloc->buffer(), bias_backend_descr, padding.left, padding.right, padding.top,
-                padding.bottom, stride.horizontal, stride.vertical, activation, ofm_alloc->buffer(),
-                ofm_backend_descr);
-
-  _execution_builder->append(std::move(fn));
-}
-
-void KernelGenerator::visit(const ir::operation::DepthwiseConv2D &node)
-{
-  using ir::operation::DepthwiseConv2D;
-
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(DepthwiseConv2D::Input::INPUT)};
-  const auto ker_index{node.getInputs().at(DepthwiseConv2D::Input::KERNEL)};
-  const auto bias_index{node.getInputs().at(DepthwiseConv2D::Input::BIAS)};
-
-  const auto stride = node.param().stride;
-  const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(_current_subg_layout);
-  const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(_current_subg_layout);
-  // Kernel format is [1, kernel_height, kernel_width, depth_out].
-  const auto &ker_shape = _ctx.at(ker_index).shape();
-  const auto ker_height = ker_shape.dim(1);
-  const auto ker_width = ker_shape.dim(2);
-  const auto padding = neurun::util::calculatePadding(node.param().padding, ifm_shape, ofm_shape,
-                                                      stride, ker_width, ker_height);
-
-  const auto ofm_backend_descr =
-      ::neurun::backend::cpu::kernel::getTensorDescriptor(_ctx.at(ofm_index), _current_subg_layout);
-  const auto ifm_backend_descr =
-      ::neurun::backend::cpu::kernel::getTensorDescriptor(_ctx.at(ifm_index), _current_subg_layout);
-  const auto ker_backend_descr =
-      ::neurun::backend::cpu::kernel::getTensorDescriptor(_ctx.at(ker_index), ir::Layout::UNKNOWN);
-  const auto bias_backend_descr =
-      ::neurun::backend::cpu::kernel::getTensorDescriptor(_ctx.at(bias_index), ir::Layout::UNKNOWN);
-
-  const auto multiplier = node.param().multiplier;
-  const auto activation = node.param().activation;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index);
-  auto ifm_alloc = _tensor_builder->at(ifm_index);
-  auto ker_alloc = _tensor_builder->at(ker_index);
-  auto bias_alloc = _tensor_builder->at(bias_index);
-
-  auto fn = nnfw::cpp14::make_unique<::neurun::backend::cpu::kernel::DepthwiseConvolutionLayer>();
-
-  fn->configure(ifm_alloc->buffer(), ifm_backend_descr, ker_alloc->buffer(), ker_backend_descr,
-                bias_alloc->buffer(), bias_backend_descr, padding.left, padding.right, padding.top,
-                padding.bottom, stride.horizontal, stride.vertical, multiplier, activation,
-                ofm_alloc->buffer(), ofm_backend_descr);
-
-  _execution_builder->append(std::move(fn));
-}
-
-void KernelGenerator::visit(const ir::operation::MaxPool2D &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::MaxPool2D::Input::INPUT)};
-
-  const auto kh = node.param().kh;
-  const auto kw = node.param().kw;
-
-  const auto stride = node.param().stride;
-  const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(_current_subg_layout);
-  const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(_current_subg_layout);
-  const auto padding =
-      neurun::util::calculatePadding(node.param().padding, ifm_shape, ofm_shape, stride, kw, kh);
-  const auto activation = node.param().activation;
-
-  const auto ofm_backend_descr =
-      ::neurun::backend::cpu::kernel::getTensorDescriptor(_ctx.at(ofm_index), _current_subg_layout);
-  const auto ifm_backend_descr =
-      ::neurun::backend::cpu::kernel::getTensorDescriptor(_ctx.at(ifm_index), _current_subg_layout);
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::neurun::backend::cpu::kernel::MaxPoolLayer>();
-
-  fn->configure(ifm_alloc->buffer(), ifm_backend_descr, padding.left, padding.right, padding.top,
-                padding.bottom, stride.horizontal, stride.vertical, kw, kh, activation,
-                ofm_alloc->buffer(), ofm_backend_descr);
-
-  _execution_builder->append(std::move(fn));
-}
-
-void KernelGenerator::visit(const ir::operation::AvgPool2D &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::AvgPool2D::Input::INPUT)};
-
-  const auto kh = node.param().kh;
-  const auto kw = node.param().kw;
-  const auto stride = node.param().stride;
-  const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(_current_subg_layout);
-  const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(_current_subg_layout);
-  const auto padding =
-      neurun::util::calculatePadding(node.param().padding, ifm_shape, ofm_shape, stride, kw, kh);
-  const auto activation = node.param().activation;
-
-  const auto ofm_backend_descr =
-      ::neurun::backend::cpu::kernel::getTensorDescriptor(_ctx.at(ofm_index), _current_subg_layout);
-  const auto ifm_backend_descr =
-      ::neurun::backend::cpu::kernel::getTensorDescriptor(_ctx.at(ifm_index), _current_subg_layout);
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto ifm_alloc = _tensor_builder->at(ifm_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::neurun::backend::cpu::kernel::AvgPoolLayer>();
-
-  fn->configure(ifm_alloc->buffer(), ifm_backend_descr, padding.left, padding.right, padding.top,
-                padding.bottom, stride.horizontal, stride.vertical, kw, kh, activation,
-                ofm_alloc->buffer(), ofm_backend_descr);
-
-  _execution_builder->append(std::move(fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Concat &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-
-  const auto rank = _ctx.at(ofm_index).shape().rank();
-  const auto axis =
-      ::neurun::backend::cpu::kernel::getAxis(rank, node.param().axis, _current_subg_layout);
-
-  const auto ofm_backend_descr =
-      ::neurun::backend::cpu::kernel::getTensorDescriptor(_ctx.at(ofm_index), _current_subg_layout);
-  std::vector<::neurun::backend::cpu::kernel::TensorDescriptor> ifm_backend_descrs;
-  for (auto &in_idx : node.getInputs())
-    ifm_backend_descrs.emplace_back(
-        ::neurun::backend::cpu::kernel::getTensorDescriptor(_ctx.at(in_idx), _current_subg_layout));
-
-  auto output_alloc = _tensor_builder->at(ofm_index).get();
-
-  std::vector<const uint8_t *> input_buffers;
-  for (auto &ifm_idx : node.getInputs())
-    input_buffers.emplace_back(_tensor_builder->at(ifm_idx).get()->buffer());
-
-  auto fn = nnfw::cpp14::make_unique<::neurun::backend::cpu::kernel::ConcatLayer>();
-
-  fn->configure(input_buffers, ifm_backend_descrs, axis, output_alloc->buffer(), ofm_backend_descr);
-
-  _execution_builder->append(std::move(fn));
-}
-
-void KernelGenerator::visit(const ir::operation::FullyConnected &node)
-{
-  using ir::operation::FullyConnected;
-
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(FullyConnected::Input::INPUT)};
-  const auto weight_index{node.getInputs().at(FullyConnected::Input::WEIGHT)};
-  const auto bias_index{node.getInputs().at(FullyConnected::Input::BIAS)};
-
-  const auto ofm_backend_descr = ::neurun::backend::cpu::kernel::getTensorDescriptor(
-      _ctx.at(output_index), _current_subg_layout);
-  const auto ifm_backend_descr = ::neurun::backend::cpu::kernel::getTensorDescriptor(
-      _ctx.at(input_index), _current_subg_layout);
-  const auto weight_backend_descr = ::neurun::backend::cpu::kernel::getTensorDescriptor(
-      _ctx.at(weight_index), ir::Layout::UNKNOWN);
-  const auto bias_backend_descr =
-      ::neurun::backend::cpu::kernel::getTensorDescriptor(_ctx.at(bias_index), ir::Layout::UNKNOWN);
-
-  const auto activation = node.param().activation;
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input_alloc = _tensor_builder->at(input_index).get();
-  auto weight_alloc = _tensor_builder->at(weight_index).get();
-  auto bias_alloc = _tensor_builder->at(bias_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::neurun::backend::cpu::kernel::FullyConnectedLayer>();
-
-  fn->configure(input_alloc->buffer(), ifm_backend_descr, weight_alloc->buffer(),
-                weight_backend_descr, bias_alloc->buffer(), bias_backend_descr, activation,
-                output_alloc->buffer(), ofm_backend_descr);
-
-  _execution_builder->append(std::move(fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Reshape &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::Reshape::Input::INPUT)};
-
-  const auto ofm_backend_descr = ::neurun::backend::cpu::kernel::getTensorDescriptor(
-      _ctx.at(output_index), _current_subg_layout);
-  const auto ifm_backend_descr = ::neurun::backend::cpu::kernel::getTensorDescriptor(
-      _ctx.at(input_index), _current_subg_layout);
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input_alloc = _tensor_builder->at(input_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::neurun::backend::cpu::kernel::ReshapeLayer>();
-
-  fn->configure(input_alloc->buffer(), ifm_backend_descr, output_alloc->buffer(),
-                ofm_backend_descr);
-
-  _execution_builder->append(std::move(fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Squeeze &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::Squeeze::Input::INPUT)};
-
-  const auto ofm_backend_descr = ::neurun::backend::cpu::kernel::getTensorDescriptor(
-      _ctx.at(output_index), _current_subg_layout);
-  const auto ifm_backend_descr = ::neurun::backend::cpu::kernel::getTensorDescriptor(
-      _ctx.at(input_index), _current_subg_layout);
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input_alloc = _tensor_builder->at(input_index).get();
-
-  // Squeeze can share same kernel with reshape
-  auto fn = nnfw::cpp14::make_unique<::neurun::backend::cpu::kernel::ReshapeLayer>();
-
-  fn->configure(input_alloc->buffer(), ifm_backend_descr, output_alloc->buffer(),
-                ofm_backend_descr);
-
-  _execution_builder->append(std::move(fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Softmax &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::Softmax::Input::INPUT)};
-
-  const auto ofm_backend_descr = ::neurun::backend::cpu::kernel::getTensorDescriptor(
-      _ctx.at(output_index), _current_subg_layout);
-  const auto ifm_backend_descr = ::neurun::backend::cpu::kernel::getTensorDescriptor(
-      _ctx.at(input_index), _current_subg_layout);
-
-  const auto beta = node.param().beta;
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input_alloc = _tensor_builder->at(input_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::neurun::backend::cpu::kernel::SoftMaxLayer>();
-
-  fn->configure(input_alloc->buffer(), ifm_backend_descr, beta, output_alloc->buffer(),
-                ofm_backend_descr);
-
-  _execution_builder->append(std::move(fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Add &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto lhs_index{node.getInputs().at(ir::operation::Add::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::Add::Input::RHS)};
-
-  const auto ofm_backend_descr =
-      ::neurun::backend::cpu::kernel::getTensorDescriptor(_ctx.at(ofm_index), _current_subg_layout);
-  const auto lhs_backend_descr =
-      ::neurun::backend::cpu::kernel::getTensorDescriptor(_ctx.at(lhs_index), _current_subg_layout);
-  const auto rhs_backend_descr =
-      ::neurun::backend::cpu::kernel::getTensorDescriptor(_ctx.at(rhs_index), _current_subg_layout);
-
-  const auto activation = node.param().activation;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto lhs_alloc = _tensor_builder->at(lhs_index).get();
-  auto rhs_alloc = _tensor_builder->at(rhs_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::neurun::backend::cpu::kernel::AddLayer>();
-
-  fn->configure(lhs_alloc->buffer(), lhs_backend_descr, rhs_alloc->buffer(), rhs_backend_descr,
-                activation, ofm_alloc->buffer(), ofm_backend_descr);
-
-  _execution_builder->append(std::move(fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Gather &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::Gather::Input::INPUT)};
-  const auto indices_index{node.getInputs().at(ir::operation::Gather::Input::INDICES)};
-
-  const auto output_backend_descr = ::neurun::backend::cpu::kernel::getTensorDescriptor(
-      _ctx.at(output_index), _current_subg_layout);
-  const auto input_backend_descr = ::neurun::backend::cpu::kernel::getTensorDescriptor(
-      _ctx.at(input_index), _current_subg_layout);
-  const auto indices_backend_descr = ::neurun::backend::cpu::kernel::getTensorDescriptor(
-      _ctx.at(indices_index), _current_subg_layout);
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input_alloc = _tensor_builder->at(input_index).get();
-  auto indices_alloc = _tensor_builder->at(indices_index).get();
-
-  const auto backend_layout = output_alloc->layout();
-  UNUSED_RELEASE(backend_layout);
-
-  // NOTE The frontend layout and backend layout must be the same for this operation.
-  //      If not the same, we have to add a stage(?) to perform permutation of output tensor. It
-  //      is not not efficient even if it works well. If so, it would be better to set the
-  //      layout of these backend tensors to the same layout.
-  //      There is also one thing we have to think about. This operation depends on the layout of
-  //      a model. For example, if a model in NHWC has this operation as output rank == 4, indices
-  //      rank == 2 and axis == 2, this operation should work as the axis W and C, but the axis W
-  //      and C are not sequential in NCHW. So the backend in NCHW cannot handle this case.
-  assert(backend_layout == input_alloc->layout());
-  assert(backend_layout == indices_alloc->layout());
-  const auto &input_shape = _ctx.at(input_index).shape();
-  UNUSED_RELEASE(input_shape);
-  assert(input_shape.rank() < 4 || _current_subg_layout == backend_layout);
-
-  const auto axis_raw = node.param().axis;
-  const auto axis_value = (axis_raw < 0 ? (input_shape.rank() + axis_raw) : axis_raw);
-
-  auto fn = nnfw::cpp14::make_unique<::neurun::backend::cpu::kernel::GatherLayer>();
-
-  fn->configure(input_alloc->buffer(), input_backend_descr, indices_alloc->buffer(),
-                indices_backend_descr, output_alloc->buffer(), output_backend_descr, axis_value);
-
-  _execution_builder->append(std::move(fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Sub &node)
-{
-  // The same as Add
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto lhs_index{node.getInputs().at(ir::operation::Sub::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::Sub::Input::RHS)};
-
-  const auto ofm_backend_descr =
-      ::neurun::backend::cpu::kernel::getTensorDescriptor(_ctx.at(ofm_index), _current_subg_layout);
-  const auto lhs_backend_descr =
-      ::neurun::backend::cpu::kernel::getTensorDescriptor(_ctx.at(lhs_index), _current_subg_layout);
-  const auto rhs_backend_descr =
-      ::neurun::backend::cpu::kernel::getTensorDescriptor(_ctx.at(rhs_index), _current_subg_layout);
-
-  const auto activation = node.param().activation;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto lhs_alloc = _tensor_builder->at(lhs_index).get();
-  auto rhs_alloc = _tensor_builder->at(rhs_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::neurun::backend::cpu::kernel::SubLayer>();
-
-  fn->configure(lhs_alloc->buffer(), lhs_backend_descr, rhs_alloc->buffer(), rhs_backend_descr,
-                activation, ofm_alloc->buffer(), ofm_backend_descr);
-
-  _execution_builder->append(std::move(fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Mul &node)
-{
-  // The same as Add
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto lhs_index{node.getInputs().at(ir::operation::Sub::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::Sub::Input::RHS)};
-
-  const auto ofm_backend_descr =
-      ::neurun::backend::cpu::kernel::getTensorDescriptor(_ctx.at(ofm_index), _current_subg_layout);
-  const auto lhs_backend_descr =
-      ::neurun::backend::cpu::kernel::getTensorDescriptor(_ctx.at(lhs_index), _current_subg_layout);
-  const auto rhs_backend_descr =
-      ::neurun::backend::cpu::kernel::getTensorDescriptor(_ctx.at(rhs_index), _current_subg_layout);
-
-  const auto activation = node.param().activation;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto lhs_alloc = _tensor_builder->at(lhs_index).get();
-  auto rhs_alloc = _tensor_builder->at(rhs_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::neurun::backend::cpu::kernel::MulLayer>();
-
-  fn->configure(lhs_alloc->buffer(), lhs_backend_descr, rhs_alloc->buffer(), rhs_backend_descr,
-                activation, ofm_alloc->buffer(), ofm_backend_descr);
-
-  _execution_builder->append(std::move(fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Permute &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(0)};
-
-  const auto &shape = _ctx.at(output_index).shape();
-  const auto input_backend_ctx = node.param().input_backend_ctx;
-  const auto output_backend_ctx = node.param().output_backend_ctx;
-  const auto data_type = node.getDataType();
-
-  output_backend_ctx->tensor_builder->preVisit(node);
-
-  auto output_object = output_backend_ctx->tensor_builder->tensorAt(output_index);
-  auto input_object = input_backend_ctx->tensor_builder->tensorAt(input_index);
-
-  auto fn = nnfw::cpp14::make_unique<::neurun::backend::cpu::kernel::PermuteLayer>();
-
-  // TODO Support NCHW frontend
-  auto out_shape = shape;
-  if (shape.rank() == 4 && output_object->layout() == ir::Layout::NCHW)
-  {
-    out_shape.dim(1) = shape.dim(3);
-    out_shape.dim(2) = shape.dim(1);
-    out_shape.dim(3) = shape.dim(2);
-  }
-
-  const auto permute_type = node.getPermuteType();
-  // Check Permutation Type
-  const auto inferPermuteType = [&]() {
-    if (input_object->layout() == ir::Layout::NHWC && output_object->layout() == ir::Layout::NCHW)
-    {
-      return ir::operation::Permute::Type::NHWC_TO_NCHW;
-    }
-    else if (input_object->layout() == ir::Layout::NCHW &&
-             output_object->layout() == ir::Layout::NHWC)
-    {
-      return ir::operation::Permute::Type::NCHW_TO_NHWC;
-    }
-    else
-    {
-      return ir::operation::Permute::Type::COPY;
-    }
-  }();
-  UNUSED_RELEASE(inferPermuteType);
-  assert(permute_type == inferPermuteType);
-
-  fn->configure(input_object, output_object, out_shape, permute_type, data_type);
-
-  input_backend_ctx->tensor_builder->postVisit(node);
-
-  _execution_builder->append(std::move(fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Custom &node)
-{
-  auto get_type_info = [this](const ir::Operand &operand) -> custom::TypeInfo {
-    auto backendDescr =
-        ::neurun::backend::cpu::kernel::getTensorDescriptor(operand, _current_subg_layout);
-
-    custom::Shape shape(backendDescr.dimensions.size());
-    for (size_t d = 0; d < backendDescr.dimensions.size(); ++d)
-    {
-      shape.dim(d) = backendDescr.dimensions[d];
-    }
-
-    return {shape, backendDescr.type};
-  };
-
-  auto fill_op_info = [&](const ir::OperandIndexSequence &opSeq,
-                          std::vector<custom::TypeInfo> &types, std::vector<void *> &allocs) {
-    for (auto &idx : opSeq)
-    {
-      const auto &operand = _ctx.at(idx);
-      // TODO make sure using `_current_subg_layout` is correct for custom operations
-      types.emplace_back(get_type_info(operand));
-      auto in_alloc = _tensor_builder->at(idx)->buffer();
-      allocs.emplace_back(in_alloc);
-    }
-  };
-
-  backend::custom::CustomKernelConfigParams params{};
-
-  fill_op_info(node.getInputs(), params.input_types, params.input_allocations);
-  fill_op_info(node.getOutputs(), params.output_types, params.output_allocations);
-
-  params.userdata = node.userdata().data;
-  params.userdata_size = node.userdata().size;
-
-  auto fn = _kernel_builder->buildKernel(node.id(), std::move(params));
-
-  _execution_builder->append(std::move(fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Logistic &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::Logistic::Input::INPUT)};
-
-  const auto ofm_backend_descr = ::neurun::backend::cpu::kernel::getTensorDescriptor(
-      _ctx.at(output_index), _current_subg_layout);
-  const auto ifm_backend_descr = ::neurun::backend::cpu::kernel::getTensorDescriptor(
-      _ctx.at(input_index), _current_subg_layout);
-
-  auto output_alloc = _tensor_builder->at(output_index).get();
-  auto input_alloc = _tensor_builder->at(input_index).get();
-
-  auto fn = nnfw::cpp14::make_unique<::neurun::backend::cpu::kernel::LogisticLayer>();
-
-  fn->configure(input_alloc->buffer(), ifm_backend_descr, output_alloc->buffer(),
-                ofm_backend_descr);
-
-  _execution_builder->append(std::move(fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Pad &node)
-{
-  const auto input_index{node.getInputs().at(ir::operation::Pad::Input::INPUT)};
-  const auto pad_index{node.getInputs().at(ir::operation::Pad::Input::PAD)};
-  const auto output_index{node.getOutputs().at(0)};
-  assert(_ctx.at(pad_index).isConstant());
-
-  auto input = _tensor_builder->at(input_index).get();
-  auto output = _tensor_builder->at(output_index).get();
-  auto pad_rank = _ctx.at(pad_index).shape().dim(0);
-  auto pad_base = reinterpret_cast<const int32_t *>(_ctx.at(pad_index).data().base());
-  const auto ofm_backend_descr = ::neurun::backend::cpu::kernel::getTensorDescriptor(
-      _ctx.at(output_index), _current_subg_layout);
-  const auto ifm_backend_descr = ::neurun::backend::cpu::kernel::getTensorDescriptor(
-      _ctx.at(input_index), _current_subg_layout);
-
-  auto fn = nnfw::cpp14::make_unique<::neurun::backend::cpu::kernel::PadLayer>();
-
-  fn->configure(input->buffer(), ifm_backend_descr, output->buffer(), ofm_backend_descr, pad_base,
-                pad_rank);
-
-  _execution_builder->append(std::move(fn));
-}
-
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/cpu/KernelGenerator.h b/runtime/neurun/backend/cpu/KernelGenerator.h
deleted file mode 100644
index 0083d6791..000000000
--- a/runtime/neurun/backend/cpu/KernelGenerator.h
+++ /dev/null
@@ -1,71 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_CPU_KERNEL_GENERATOR_H__
-#define __NEURUN_BACKEND_CPU_KERNEL_GENERATOR_H__
-
-#include "backend/IKernelGenerator.h"
-#include "ir/Operands.h"
-#include "operand/Tensor.h"
-#include "backend/CustomKernelBuilder.h"
-#include "TensorBuilder.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-
-class KernelGenerator : public IKernelGenerator
-{
-public:
-  KernelGenerator(const ir::Operands &ctx, const std::shared_ptr<TensorBuilder> &tensor_builder,
-                  const std::shared_ptr<custom::IKernelBuilder> &kernel_builder);
-
-  using IKernelGenerator::visit;
-
-  void visit(const ir::OpSequence &) override;
-  void visit(const ir::operation::Conv2D &) override;
-  void visit(const ir::operation::DepthwiseConv2D &) override;
-  void visit(const ir::operation::MaxPool2D &) override;
-  void visit(const ir::operation::AvgPool2D &) override;
-  void visit(const ir::operation::Concat &) override;
-  void visit(const ir::operation::FullyConnected &) override;
-  void visit(const ir::operation::Reshape &) override;
-  void visit(const ir::operation::Squeeze &) override;
-  void visit(const ir::operation::Softmax &) override;
-  void visit(const ir::operation::Add &) override;
-  void visit(const ir::operation::Sub &) override;
-  void visit(const ir::operation::Mul &) override;
-  void visit(const ir::operation::Permute &) override;
-  void visit(const ir::operation::Gather &) override;
-  void visit(const ir::operation::Custom &node) override;
-  void visit(const ir::operation::Logistic &) override;
-  void visit(const ir::operation::Pad &) override;
-
-private:
-  const ir::Operands &_ctx;
-  std::shared_ptr<TensorBuilder> _tensor_builder;
-  std::shared_ptr<backend::custom::IKernelBuilder> _kernel_builder;
-  ir::Layout _current_subg_layout;
-};
-
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_CPU_KERNEL_GENERATOR_H__
diff --git a/runtime/neurun/backend/cpu/MemoryManager.cc b/runtime/neurun/backend/cpu/MemoryManager.cc
deleted file mode 100644
index 926d8fb4c..000000000
--- a/runtime/neurun/backend/cpu/MemoryManager.cc
+++ /dev/null
@@ -1,91 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "MemoryManager.h"
-
-#include <cassert>
-
-#include <MemoryPlannerFactory.h>
-#include "util/logging.h"
-#include "util/ConfigSource.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-
-MemoryManager::MemoryManager() : _mem_planner{createMemoryPlanner()}
-{
-  // DO NOTHING
-}
-
-MemoryManager::MemoryManager(const std::string planner_id)
-    : _mem_planner{createMemoryPlanner(planner_id)}
-{
-  // DO NOTHING
-}
-
-cpu_common::IMemoryPlanner *MemoryManager::createMemoryPlanner()
-{
-  auto planner_id = util::getConfigString(util::config::CPU_MEMORY_PLANNER);
-  return cpu_common::MemoryPlannerFactory::get().create(planner_id);
-}
-
-cpu_common::IMemoryPlanner *MemoryManager::createMemoryPlanner(const std::string planner_id)
-{
-  return cpu_common::MemoryPlannerFactory::get().create(planner_id);
-}
-
-void MemoryManager::buildTensor(const ir::OperandIndex &ind, const ir::OperandInfo &info)
-{
-  auto tensor = std::make_shared<operand::Tensor>(info);
-  _tensors[ind] = tensor;
-}
-
-void MemoryManager::claimPlan(const ir::OperandIndex &ind, uint32_t size)
-{
-  _mem_planner->claim(ind, size);
-}
-
-void MemoryManager::releasePlan(const ir::OperandIndex &ind) { _mem_planner->release(ind); }
-
-void MemoryManager::allocate(void)
-{
-  _mem_alloc = std::make_shared<cpu_common::Allocator>(_mem_planner->capacity());
-  assert(_mem_alloc->base());
-
-  for (auto &mem_plan : _mem_planner->memory_plans())
-  {
-    auto ind = mem_plan.first;
-    auto mem_blk = mem_plan.second;
-
-    uint8_t *buffer = _mem_alloc->base() + mem_blk.offset;
-    auto tensor = _tensors[ind];
-    tensor->setBuffer(buffer);
-
-    VERBOSE(CPU_MEMORYMANAGER) << "TENSOR(#" << ind.value() << "): " << static_cast<void *>(buffer)
-                               << std::endl;
-
-    // If we do not make tensor here currently, kernel generation would cause segmentation fault.
-    // See also : Comments in `allocate` method.
-  }
-}
-
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/cpu/MemoryManager.h b/runtime/neurun/backend/cpu/MemoryManager.h
deleted file mode 100644
index 267e8001b..000000000
--- a/runtime/neurun/backend/cpu/MemoryManager.h
+++ /dev/null
@@ -1,63 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_CPU_MEMORY_MANAGER_H__
-#define __NEURUN_BACKEND_CPU_MEMORY_MANAGER_H__
-
-#include "backend/IMemoryManager.h"
-#include <MemoryPlanner.h>
-#include "operand/Tensor.h"
-#include "ir/OperandIndexMap.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-
-class MemoryManager : public backend::IMemoryManager
-{
-public:
-  MemoryManager();
-  MemoryManager(const std::string);
-  virtual ~MemoryManager() = default;
-
-  void allocate(void) override;
-  void deallocate(void) override { _mem_alloc->release(); }
-
-  void buildTensor(const ir::OperandIndex &ind, const ir::OperandInfo &info);
-  void claimPlan(const ir::OperandIndex &ind, uint32_t size);
-  void releasePlan(const ir::OperandIndex &ind);
-
-  ir::OperandIndexMap<std::shared_ptr<operand::Tensor>> &tensors(void) { return _tensors; }
-
-private:
-  cpu_common::IMemoryPlanner *createMemoryPlanner();
-  cpu_common::IMemoryPlanner *createMemoryPlanner(const std::string);
-
-private:
-  ir::OperandIndexMap<std::shared_ptr<operand::Tensor>> _tensors;
-  ir::OperandIndexMap<cpu_common::Block> _tensor_mem_map;
-  std::shared_ptr<cpu_common::IMemoryPlanner> _mem_planner;
-  std::shared_ptr<cpu_common::Allocator> _mem_alloc;
-};
-
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_CPU_MEMORY_MANAGER_H__
diff --git a/runtime/neurun/backend/cpu/PluginClassesAllocator.cc b/runtime/neurun/backend/cpu/PluginClassesAllocator.cc
deleted file mode 100644
index caba78447..000000000
--- a/runtime/neurun/backend/cpu/PluginClassesAllocator.cc
+++ /dev/null
@@ -1,33 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <util/logging.h>
-
-#include "Backend.h"
-
-extern "C" {
-neurun::backend::Backend *neurun_backend_create()
-{
-  VERBOSE(neurun_backend_create) << "'cpu' loaded\n";
-  return new neurun::backend::cpu::Backend;
-}
-
-void neurun_backend_destroy(neurun::backend::Backend *backend)
-{
-  VERBOSE(neurun_backend_create) << "'cpu' unloaded\n";
-  delete backend;
-}
-}
diff --git a/runtime/neurun/backend/cpu/ShapeFixer.cc b/runtime/neurun/backend/cpu/ShapeFixer.cc
deleted file mode 100644
index 835592b30..000000000
--- a/runtime/neurun/backend/cpu/ShapeFixer.cc
+++ /dev/null
@@ -1,135 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ShapeFixer.h"
-
-#include <stdexcept>
-
-#include "cpp14/memory.h"
-#include "util/Padding.h"
-#include "kernel/OperationUtils.h"
-#include "kernel/ConvolutionLayer.h"
-#include "kernel/AvgPoolLayer.h"
-#include "kernel/MaxPoolLayer.h"
-#include "kernel/ConcatLayer.h"
-#include "kernel/FullyConnectedLayer.h"
-#include "kernel/ReshapeLayer.h"
-#include "kernel/SoftMaxLayer.h"
-#include "kernel/PermuteLayer.h"
-#include "kernel/DepthwiseConvolutionLayer.h"
-#include "kernel/AddLayer.h"
-#include "kernel/SubLayer.h"
-#include "kernel/MulLayer.h"
-#include "kernel/GatherLayer.h"
-
-#include <backend/Backend.h>
-#include <backend/IConfig.h>
-#include "compiler/IExecutionBuilder.h"
-
-#include "util/logging.h"
-
-#include "util/Utils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-
-ShapeFixer::ShapeFixer(const ir::Operands &operand_ctx,
-                       const std::shared_ptr<TensorBuilder> &tensor_builder)
-    : _ctx(operand_ctx), _tensor_builder(tensor_builder)
-{
-  assert(tensor_builder);
-}
-
-void ShapeFixer::visit(const ir::operation::Conv2D &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::DepthwiseConv2D &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::MaxPool2D &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::AvgPool2D &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Concat &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::FullyConnected &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Reshape &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Squeeze &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Softmax &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Gather &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Add &node)
-{
-  const auto lhs_index{node.getInputs().at(ir::operation::Add::Input::LHS)};
-
-  // Quantization : not supported
-  if (_ctx.at(lhs_index).typeInfo().type() == ir::DataType::QUANT8_ASYMM)
-  {
-    throw std::runtime_error{"ShapeFixer: NYI for quantized Add"};
-  }
-}
-
-void ShapeFixer::visit(const ir::operation::Permute &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Sub &node)
-{
-  // The same as Add
-  const auto lhs_index{node.getInputs().at(ir::operation::Sub::Input::LHS)};
-
-  // Quantization : not supported
-  if (_ctx.at(lhs_index).typeInfo().type() == ir::DataType::QUANT8_ASYMM)
-  {
-    throw std::runtime_error{"ShapeFixer: NYI for quantized Sub"};
-  }
-}
-
-void ShapeFixer::visit(const ir::operation::Mul &node)
-{
-  // The same as Add
-  const auto lhs_index{node.getInputs().at(ir::operation::Sub::Input::LHS)};
-
-  // Quantization : not supported
-  if (_ctx.at(lhs_index).typeInfo().type() == ir::DataType::QUANT8_ASYMM)
-  {
-    throw std::runtime_error{"ShapeFixer: NYI for quantized Mul"};
-  }
-}
-
-void ShapeFixer::visit(const ir::operation::Custom &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Logistic &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Pad &node)
-{
-  // TODO: empty this method when quantization is supported
-  const auto lhs_index{node.getInputs().at(ir::operation::Sub::Input::LHS)};
-
-  // Quantization : not supported
-  if (_ctx.at(lhs_index).typeInfo().type() == ir::DataType::QUANT8_ASYMM)
-  {
-    throw std::runtime_error{"ShapeFixer: NYI for quantized Pad"};
-  }
-}
-
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/cpu/ShapeFixer.h b/runtime/neurun/backend/cpu/ShapeFixer.h
deleted file mode 100644
index bbf48498e..000000000
--- a/runtime/neurun/backend/cpu/ShapeFixer.h
+++ /dev/null
@@ -1,65 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_CPU_SHAPE_FIXER_H__
-#define __NEURUN_BACKEND_CPU_SHAPE_FIXER_H__
-
-#include <backend/IShapeFixer.h>
-
-#include "ir/Operands.h"
-#include "operand/Tensor.h"
-#include "TensorBuilder.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-
-class ShapeFixer : public IShapeFixer
-{
-public:
-  ShapeFixer(const ir::Operands &ctx, const std::shared_ptr<TensorBuilder> &tensor_builder);
-
-  void visit(const ir::operation::Conv2D &) override;
-  void visit(const ir::operation::DepthwiseConv2D &) override;
-  void visit(const ir::operation::MaxPool2D &) override;
-  void visit(const ir::operation::AvgPool2D &) override;
-  void visit(const ir::operation::Concat &) override;
-  void visit(const ir::operation::FullyConnected &) override;
-  void visit(const ir::operation::Reshape &) override;
-  void visit(const ir::operation::Squeeze &) override;
-  void visit(const ir::operation::Softmax &) override;
-  void visit(const ir::operation::Add &) override;
-  void visit(const ir::operation::Gather &) override;
-  void visit(const ir::operation::Sub &) override;
-  void visit(const ir::operation::Mul &) override;
-  void visit(const ir::operation::Permute &) override;
-  void visit(const ir::operation::Custom &) override;
-  void visit(const ir::operation::Logistic &) override;
-  void visit(const ir::operation::Pad &) override;
-
-private:
-  const ir::Operands &_ctx;
-  std::shared_ptr<TensorBuilder> _tensor_builder;
-};
-
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_CPU_SHAPE_FIXER_H__
diff --git a/runtime/neurun/backend/cpu/TensorBuilder.cc b/runtime/neurun/backend/cpu/TensorBuilder.cc
deleted file mode 100644
index 2c654c256..000000000
--- a/runtime/neurun/backend/cpu/TensorBuilder.cc
+++ /dev/null
@@ -1,104 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "TensorBuilder.h"
-
-#include <cassert>
-
-#include "util/logging.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-
-TensorBuilder::TensorBuilder() : _tensor_mgr{new TensorManager()}
-{
-  // DO NOTHING
-}
-
-void TensorBuilder::registerTensorInfo(const ir::OperandIndex &ind, const ir::OperandInfo &info,
-                                       ir::Layout, bool as_const)
-{
-  _tensor_info_map.emplace(ind, info);
-
-  if (as_const)
-    _constants.append(ind);
-}
-
-void TensorBuilder::registerSubTensorInfo(const ir::OperandIndex &, const compiler::SubTensorInfo &)
-{
-  // Not supported yet
-  assert(false);
-}
-
-void TensorBuilder::notifyFirstUse(const ir::OperandIndex &ind)
-{
-  assert(_tensor_info_map.find(ind) != _tensor_info_map.end());
-  const auto tensor_info = _tensor_info_map.at(ind);
-  const auto size = tensor_info.total_size();
-  _tensor_mgr->buildTensor(ind, tensor_info, _constants.contains(ind));
-  _tensor_mgr->claimPlan(ind, size);
-}
-
-void TensorBuilder::notifyLastUse(const ir::OperandIndex &ind) { _tensor_mgr->releasePlan(ind); }
-
-bool TensorBuilder::isRegistered(const ir::OperandIndex &ind) const
-{
-  return _tensor_info_map.find(ind) != _tensor_info_map.end();
-}
-
-void TensorBuilder::prepare(void)
-{
-  _tensor_mgr->allocateConsts();
-  _tensor_mgr->allocateNonconsts();
-}
-
-void TensorBuilder::allocateConsts()
-{
-  // NOTE For now nothing to do. Allocation is done in prepare stage, which is not appropriate
-  //      This is because CPU kernels require `ITensor`s to be allocated before Kernel Generation.
-}
-
-void TensorBuilder::allocateNonconsts()
-{
-  // NOTE For now nothing to do. Allocation is done in prepare stage, which is not appropriate
-  //      This is because CPU kernels require `ITensor`s to be allocated before Kernel Generation.
-}
-
-std::shared_ptr<::neurun::backend::operand::ITensor>
-TensorBuilder::tensorAt(const ir::OperandIndex &ind)
-{
-  return _tensor_mgr->at(ind);
-}
-
-void TensorBuilder::iterate(const IterateFunction &fn) { _tensor_mgr->iterate(fn); }
-
-std::shared_ptr<operand::Tensor> TensorBuilder::at(const ir::OperandIndex &ind)
-{
-  return _tensor_mgr->at(ind);
-}
-
-std::unique_ptr<ITensorManager> TensorBuilder::releaseTensorManager(void)
-{
-  return std::move(_tensor_mgr);
-}
-
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/cpu/TensorBuilder.h b/runtime/neurun/backend/cpu/TensorBuilder.h
deleted file mode 100644
index a08db8c9a..000000000
--- a/runtime/neurun/backend/cpu/TensorBuilder.h
+++ /dev/null
@@ -1,88 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_CPU_TENSOR_BUILDER_H__
-#define __NEURUN_BACKEND_CPU_TENSOR_BUILDER_H__
-
-#include <unordered_map>
-
-#include <backend/ITensorBuilder.h>
-#include "operand/Tensor.h"
-#include "ir/OperandIndexMap.h"
-#include "TensorManager.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-
-class TensorBuilder : public ITensorBuilder
-{
-public:
-  TensorBuilder();
-
-  /**
-   * @brief     Register tensor information to allocate on CPU backend
-   * @param[in] ind    Operand index
-   * @param[in] info   Operand information
-   * @param[in] layout Operand data layout
-   */
-  void registerTensorInfo(const ir::OperandIndex &ind, const ir::OperandInfo &info,
-                          ir::Layout backend_layout, bool as_const) override;
-  /**
-   * @brief     Register subtensor information to allocate on CPU backend
-   * @param[in] ind   Operand index
-   * @param[in] info  Tensor information
-   */
-  void registerSubTensorInfo(const ir::OperandIndex &ind,
-                             const compiler::SubTensorInfo &info) override;
-
-  void notifyFirstUse(const ir::OperandIndex &) override;
-  void notifyLastUse(const ir::OperandIndex &) override;
-
-  bool isRegistered(const ir::OperandIndex &) const override;
-
-  void prepare(void) override;
-  void allocateConsts() override;
-  void allocateNonconsts() override;
-  void postFunctionPrepare() override { /* DO NOTHING */}
-  void finalize() override { /* DO NOTHING */}
-
-  std::shared_ptr<::neurun::backend::operand::ITensor>
-  tensorAt(const ir::OperandIndex &ind) override;
-
-  void iterate(const IterateFunction &fn) override;
-
-  void preVisit(const ir::Operation &) override { /* DO NOTHING */}
-  void postVisit(const ir::Operation &) override { /* DO NOTHING */}
-
-  std::unique_ptr<ITensorManager> releaseTensorManager(void) override;
-
-  std::shared_ptr<operand::Tensor> at(const ir::OperandIndex &ind);
-
-private:
-  std::unique_ptr<TensorManager> _tensor_mgr;
-  ir::OperandIndexMap<ir::OperandInfo> _tensor_info_map;
-  ir::OperandIndexSequence _constants;
-};
-
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_CPU_TENSOR_BUILDER_H__
diff --git a/runtime/neurun/backend/cpu/TensorManager.cc b/runtime/neurun/backend/cpu/TensorManager.cc
deleted file mode 100644
index a346e2cb4..000000000
--- a/runtime/neurun/backend/cpu/TensorManager.cc
+++ /dev/null
@@ -1,95 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "TensorManager.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-
-TensorManager::TensorManager()
-    : _const_mgr{new MemoryManager("Bump")}, _nonconst_mgr{new MemoryManager()}
-{
-  // DO NOTHING
-}
-
-void TensorManager::allocateConsts(void) { _const_mgr->allocate(); }
-
-void TensorManager::allocateNonconsts(void) { _nonconst_mgr->allocate(); }
-
-void TensorManager::deallocateConsts(void) { _const_mgr->deallocate(); }
-
-void TensorManager::deallocateNonconsts(void) { _nonconst_mgr->deallocate(); }
-
-void TensorManager::buildTensor(const ir::OperandIndex &ind, const ir::OperandInfo &tensor_info,
-                                bool as_const)
-{
-  assert(_ind_to_mgr.find(ind) == _ind_to_mgr.end());
-  if (as_const)
-  {
-    _const_mgr->buildTensor(ind, tensor_info);
-    _ind_to_mgr.insert({ind, *_const_mgr});
-  }
-  else
-  {
-    _nonconst_mgr->buildTensor(ind, tensor_info);
-    _ind_to_mgr.insert({ind, *_nonconst_mgr});
-  }
-}
-
-void TensorManager::claimPlan(const ir::OperandIndex &ind, uint32_t size)
-{
-  assert(_ind_to_mgr.find(ind) != _ind_to_mgr.end());
-  _ind_to_mgr.at(ind).claimPlan(ind, size);
-}
-
-void TensorManager::releasePlan(const ir::OperandIndex &ind)
-{
-  assert(_ind_to_mgr.find(ind) != _ind_to_mgr.end());
-  _ind_to_mgr.at(ind).releasePlan(ind);
-}
-
-std::shared_ptr<operand::Tensor> TensorManager::at(const ir::OperandIndex &ind)
-{
-  assert(_ind_to_mgr.find(ind) != _ind_to_mgr.end());
-  return _ind_to_mgr.at(ind).tensors().at(ind);
-}
-
-ir::OperandIndexMap<std::shared_ptr<operand::Tensor>> &TensorManager::constTensors(void)
-{
-  return _const_mgr->tensors();
-}
-
-ir::OperandIndexMap<std::shared_ptr<operand::Tensor>> &TensorManager::nonconstTensors(void)
-{
-  return _nonconst_mgr->tensors();
-}
-
-void TensorManager::iterate(const std::function<void(const ir::OperandIndex &)> &fn)
-{
-  for (auto it : _nonconst_mgr->tensors())
-    fn(it.first);
-
-  for (auto it : _const_mgr->tensors())
-    fn(it.first);
-}
-
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/cpu/TensorManager.h b/runtime/neurun/backend/cpu/TensorManager.h
deleted file mode 100644
index c3ef70663..000000000
--- a/runtime/neurun/backend/cpu/TensorManager.h
+++ /dev/null
@@ -1,64 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_CPU_TENSOR_MANAGER_H__
-#define __NEURUN_BACKEND_CPU_TENSOR_MANAGER_H__
-
-#include "backend/ITensorManager.h"
-#include "MemoryManager.h"
-#include "ir/OperandIndexMap.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-
-class TensorManager : public backend::ITensorManager
-{
-public:
-  TensorManager();
-  virtual ~TensorManager() = default;
-
-  void allocateConsts(void) override;
-  void allocateNonconsts(void) override;
-  void deallocateConsts(void) override;
-  void deallocateNonconsts(void) override;
-
-  void buildTensor(const ir::OperandIndex &ind, const ir::OperandInfo &tensor_info, bool as_const);
-
-  void claimPlan(const ir::OperandIndex &ind, uint32_t size);
-  void releasePlan(const ir::OperandIndex &ind);
-
-  std::shared_ptr<operand::Tensor> at(const ir::OperandIndex &ind);
-
-  ir::OperandIndexMap<std::shared_ptr<operand::Tensor>> &constTensors(void);
-  ir::OperandIndexMap<std::shared_ptr<operand::Tensor>> &nonconstTensors(void);
-
-  void iterate(const std::function<void(const ir::OperandIndex &)> &fn);
-
-private:
-  std::unique_ptr<MemoryManager> _const_mgr;
-  std::unique_ptr<MemoryManager> _nonconst_mgr;
-  ir::OperandIndexMap<MemoryManager &> _ind_to_mgr;
-};
-
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_CPU_TENSOR_MANAGER_H__
diff --git a/runtime/neurun/backend/cpu/TensorRegister.cc b/runtime/neurun/backend/cpu/TensorRegister.cc
deleted file mode 100644
index 2701503f5..000000000
--- a/runtime/neurun/backend/cpu/TensorRegister.cc
+++ /dev/null
@@ -1,35 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "TensorRegister.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-
-TensorRegister::TensorRegister(const ir::Operands &operands,
-                               const std::shared_ptr<TensorBuilder> &tensor_builder)
-    : _operands{operands}, _tensor_builder{tensor_builder}
-{
-  assert(tensor_builder != nullptr);
-}
-
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/cpu/TensorRegister.h b/runtime/neurun/backend/cpu/TensorRegister.h
deleted file mode 100644
index 1bda9fca3..000000000
--- a/runtime/neurun/backend/cpu/TensorRegister.h
+++ /dev/null
@@ -1,50 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_CPU_TENSOR_REGISTER_H__
-#define __NEURUN_BACKEND_CPU_TENSOR_REGISTER_H__
-
-#include <backend/ITensorRegister.h>
-#include "TensorBuilder.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-
-class TensorRegister : public ITensorRegister
-{
-public:
-  TensorRegister(const ir::Operands &operands,
-                 const std::shared_ptr<TensorBuilder> &tensor_builder);
-
-private:
-  const ir::Operands &operands() const override { return _operands; }
-  std::shared_ptr<ITensorBuilder> tensor_builder() const override { return _tensor_builder; }
-  bool supportSubTensor() const final { return false; }
-
-private:
-  const ir::Operands &_operands;
-  const std::shared_ptr<TensorBuilder> _tensor_builder;
-};
-
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_CPU_TENSOR_REGISTER_H__
diff --git a/runtime/neurun/backend/cpu/kernel/AddLayer.cc b/runtime/neurun/backend/cpu/kernel/AddLayer.cc
deleted file mode 100644
index 8a2d872e5..000000000
--- a/runtime/neurun/backend/cpu/kernel/AddLayer.cc
+++ /dev/null
@@ -1,101 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "AddLayer.h"
-
-#include <cker/operation/BinaryArithmeticOps.h>
-
-#include "OperationUtils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-
-void AddLayer::addFloat32()
-{
-  float output_activation_min, output_activation_max;
-  CalculateActivationRangeFloat(_activation, &output_activation_min, &output_activation_max);
-  nnfw::cker::BinaryArithmeticOpParam op_params;
-  op_params.float_activation_max = output_activation_max;
-  op_params.float_activation_min = output_activation_min;
-
-  const std::function<float(const float &, const float &)> fn = [](const float &a, const float &b) {
-    return a + b;
-  };
-
-  if (!HaveSameShapes(&_lhsDescr, &_rhsDescr))
-  {
-    nnfw::cker::BroadcastBinaryArithmeticOpSlow(
-        op_params, convertToExtendedCkerShape(_lhsDescr), _lhsData.f,
-        convertToExtendedCkerShape(_rhsDescr), _rhsData.f, convertToExtendedCkerShape(_outputDescr),
-        _outputData.f, fn);
-    return;
-  }
-
-  nnfw::cker::BinaryArithmeticOp(op_params, convertTensorDescriptorToCkerShape(_lhsDescr),
-                                 _lhsData.f, convertTensorDescriptorToCkerShape(_rhsDescr),
-                                 _rhsData.f, convertTensorDescriptorToCkerShape(_outputDescr),
-                                 _outputData.f, fn);
-}
-
-void AddLayer::addQuant8()
-{
-  int32_t output_activation_min, output_activation_max;
-  CalculateActivationRangeUint8(_activation, _outputDescr, &output_activation_min,
-                                &output_activation_max);
-  // nnfw::cker::BinaryArithmeticOpParam op_params;
-  // op_params.quantized_activation_max = output_activation_max;
-  // op_params.quantized_activation_min = output_activation_min;
-
-  // cker quant8 add is not implemented yet
-  throw std::runtime_error{"NYI"};
-}
-
-void AddLayer::configure(uint8_t *lhsData, const TensorDescriptor &lhsDescr, uint8_t *rhsData,
-                         const TensorDescriptor &rhsDescr, const ir::Activation activation,
-                         uint8_t *outputData, const TensorDescriptor &outputDescr)
-{
-  _lhsData.u8 = lhsData;
-  _lhsDescr = lhsDescr;
-  _rhsData.u8 = rhsData;
-  _rhsDescr = rhsDescr;
-  _inputType = lhsDescr.type;
-  _activation = activation;
-  _outputData.u8 = outputData;
-  _outputDescr = outputDescr;
-}
-
-void AddLayer::run()
-{
-  if (_inputType == OperandType::FLOAT32)
-  {
-    addFloat32();
-  }
-  else if (_inputType == OperandType::QUANT8_ASYMM)
-  {
-    addQuant8();
-  }
-}
-
-} // namespace kernel
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/cpu/kernel/AddLayer.h b/runtime/neurun/backend/cpu/kernel/AddLayer.h
deleted file mode 100644
index 7018e4c48..000000000
--- a/runtime/neurun/backend/cpu/kernel/AddLayer.h
+++ /dev/null
@@ -1,77 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_CPU_KERNEL_ADDLAYER_H__
-#define __NEURUN_BACKEND_CPU_KERNEL_ADDLAYER_H__
-
-#include <exec/IFunction.h>
-
-#include "OperationUtils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-
-class AddLayer : public ::neurun::exec::IFunction
-{
-public:
-  AddLayer() : _lhsData(), _rhsData(), _outputData(), _lhsDescr(), _rhsDescr(), _outputDescr()
-  {
-    // DO NOTHING
-  }
-
-public:
-  void addFloat32();
-
-  void addQuant8();
-
-  void configure(uint8_t *lhsData, const TensorDescriptor &lhsDescr, uint8_t *rhsData,
-                 const TensorDescriptor &rhsDescr, const ir::Activation activation,
-                 uint8_t *outputData, const TensorDescriptor &outputDescr);
-
-  void run();
-  void runSync()
-  {
-    // this abstract method is used just for profiling and called for
-    // backend::acl_common::AclFunction
-    run();
-  }
-
-private:
-  DataPtr _lhsData;
-  DataPtr _rhsData;
-  DataPtr _outputData;
-
-  TensorDescriptor _lhsDescr;
-  TensorDescriptor _rhsDescr;
-  TensorDescriptor _outputDescr;
-
-  ir::Activation _activation{ir::Activation::NONE};
-
-  OperandType _inputType{OperandType::FLOAT32};
-};
-
-} // namespace kernel
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_CPU_KERNEL_ADDLAYER_H__
diff --git a/runtime/neurun/backend/cpu/kernel/AvgPoolLayer.cc b/runtime/neurun/backend/cpu/kernel/AvgPoolLayer.cc
deleted file mode 100644
index 389955796..000000000
--- a/runtime/neurun/backend/cpu/kernel/AvgPoolLayer.cc
+++ /dev/null
@@ -1,116 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "AvgPoolLayer.h"
-
-#include "OperationUtils.h"
-
-#include <cker/operation/AveragePool.h>
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-
-#define AVGPOOLING_PARAMETERS                            \
-  nnfw::cker::PoolParams op_params;                      \
-  op_params.stride_height = _strideHeight;               \
-  op_params.stride_width = _strideWidth;                 \
-  op_params.filter_height = _kernelHeight;               \
-  op_params.filter_width = _kernelWidth;                 \
-  op_params.padding_values.height = (int8_t)_paddingTop; \
-  op_params.padding_values.width = (int8_t)_paddingLeft;
-
-AvgPoolLayer::AvgPoolLayer()
-    : _inputData(), _outputData(), _inputDescr(), _outputDescr(), _paddingLeft(0), _paddingTop(0),
-      _paddingRight(0), _paddingBottom(0), _strideWidth(0), _strideHeight(0), _kernelWidth(0),
-      _kernelHeight(0), _activation(ir::Activation::NONE), _inputType(OperandType::FLOAT32)
-{
-  // DO NOTHING
-}
-
-void AvgPoolLayer::averagePoolFloat32()
-{
-  AVGPOOLING_PARAMETERS
-  float output_activation_min, output_activation_max;
-  CalculateActivationRangeFloat(_activation, &output_activation_min, &output_activation_max);
-  op_params.float_activation_min = output_activation_min;
-  op_params.float_activation_max = output_activation_max;
-
-  nnfw::cker::AveragePool(op_params, convertTensorDescriptorToCkerShape(_inputDescr), _inputData.f,
-                          convertTensorDescriptorToCkerShape(_outputDescr), _outputData.f);
-}
-void AvgPoolLayer::averagePoolQuant8()
-{
-  AVGPOOLING_PARAMETERS
-  int32_t output_activation_min = 0;
-  int32_t output_activation_max = 0;
-  CalculateActivationRangeUint8(_activation, _outputDescr, &output_activation_min,
-                                &output_activation_max);
-  op_params.quantized_activation_min = output_activation_min;
-  op_params.quantized_activation_max = output_activation_max;
-
-  nnfw::cker::AveragePool(op_params, convertTensorDescriptorToCkerShape(_inputDescr), _inputData.u8,
-                          convertTensorDescriptorToCkerShape(_outputDescr), _outputData.u8);
-}
-
-void AvgPoolLayer::configure(uint8_t *inputData, const TensorDescriptor inputDescr,
-                             const uint32_t paddingLeft, const uint32_t paddingRight,
-                             const uint32_t paddingTop, const uint32_t paddingBottom,
-                             const uint32_t strideWidth, const uint32_t strideHeight,
-                             const uint32_t kernelWidth, const uint32_t kernelHeight,
-                             const ir::Activation activation, uint8_t *outputData,
-                             const TensorDescriptor outputDescr)
-{
-  _inputData.u8 = inputData;
-  _inputDescr = inputDescr;
-  _inputType = inputDescr.type;
-  _paddingLeft = paddingLeft;
-  _paddingRight = paddingRight;
-  _paddingTop = paddingTop;
-  _paddingBottom = paddingBottom;
-  _strideWidth = strideWidth;
-  _strideHeight = strideHeight;
-  _kernelWidth = kernelWidth;
-  _kernelHeight = kernelHeight;
-  _activation = activation;
-  _outputData.u8 = outputData;
-  _outputDescr = outputDescr;
-}
-
-void AvgPoolLayer::run()
-{
-  if (_inputType == OperandType::FLOAT32)
-  {
-    averagePoolFloat32();
-  }
-  else if (_inputType == OperandType::QUANT8_ASYMM)
-  {
-    averagePoolQuant8();
-  }
-}
-
-#undef AVGPOOLING_PARAMETERS
-
-} // namespace kernel
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/cpu/kernel/AvgPoolLayer.h b/runtime/neurun/backend/cpu/kernel/AvgPoolLayer.h
deleted file mode 100644
index 6339efa41..000000000
--- a/runtime/neurun/backend/cpu/kernel/AvgPoolLayer.h
+++ /dev/null
@@ -1,85 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_CPU_KERNEL_AVGPOOLLAYER_H__
-#define __NEURUN_BACKEND_CPU_KERNEL_AVGPOOLLAYER_H__
-
-#include <exec/IFunction.h>
-
-#include "OperationUtils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-
-class AvgPoolLayer : public ::neurun::exec::IFunction
-{
-public:
-  AvgPoolLayer();
-
-public:
-  void averagePoolFloat32();
-
-  void averagePoolQuant8();
-
-  void configure(uint8_t *inputData, const TensorDescriptor inputDescr, const uint32_t paddingLeft,
-                 const uint32_t paddingRight, const uint32_t paddingTop,
-                 const uint32_t paddingBottom, const uint32_t strideWidth,
-                 const uint32_t strideHeight, const uint32_t kernelWidth,
-                 const uint32_t kernelHeight, const ir::Activation activation, uint8_t *outputData,
-                 const TensorDescriptor outputDescr);
-
-  void run();
-  void runSync()
-  {
-    // this abstract method is used just for profiling and called for
-    // backend::acl_common::AclFunction
-    run();
-  }
-
-private:
-  DataPtr _inputData;
-  DataPtr _outputData;
-
-  TensorDescriptor _inputDescr;
-  TensorDescriptor _outputDescr;
-
-  uint32_t _paddingLeft;
-  uint32_t _paddingTop;
-  uint32_t _paddingRight;
-  uint32_t _paddingBottom;
-
-  uint32_t _strideWidth;
-  uint32_t _strideHeight;
-  uint32_t _kernelWidth;
-  uint32_t _kernelHeight;
-
-  ir::Activation _activation;
-
-  OperandType _inputType;
-};
-
-} // namespace kernel
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_CPU_KERNEL_AVGPOOLLAYER_H__
diff --git a/runtime/neurun/backend/cpu/kernel/ConcatLayer.cc b/runtime/neurun/backend/cpu/kernel/ConcatLayer.cc
deleted file mode 100644
index 471c9b3bb..000000000
--- a/runtime/neurun/backend/cpu/kernel/ConcatLayer.cc
+++ /dev/null
@@ -1,137 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ConcatLayer.h"
-
-#include "OperationUtils.h"
-
-#include <cker/operation/Concatenation.h>
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-
-ConcatLayer::ConcatLayer()
-    : _inputDataPtrs(), _outputData(), _axis(0), _inputDescriptors(), _outputDescr(),
-      _inputType(OperandType::FLOAT32)
-{
-  // DO NOTHING
-}
-
-void ConcatLayer::concatenationFloat32()
-{
-  uint32_t num_inputs = _inputDescriptors.size();
-
-  nnfw::cker::ConcatenationParams op_params;
-  op_params.axis = _axis;
-  op_params.inputs_count = num_inputs;
-
-  std::vector<nnfw::cker::Shape *> inputDimsPtr;
-  std::vector<nnfw::cker::Shape> inputDims;
-  inputDimsPtr.reserve(num_inputs);
-  inputDims.reserve(num_inputs);
-
-  for (uint32_t i = 0; i < num_inputs; i++)
-  {
-    inputDims.push_back(convertTensorDescriptorToCkerShape(_inputDescriptors[i]));
-    inputDimsPtr.push_back(&inputDims[i]);
-  }
-
-  std::vector<const float *> inputFloatPtrs;
-
-  for (auto ptr : _inputDataPtrs)
-  {
-    inputFloatPtrs.emplace_back(reinterpret_cast<const float *>(ptr));
-  }
-
-  nnfw::cker::Concatenation<float>(op_params, inputDimsPtr.data(), inputFloatPtrs.data(),
-                                   convertTensorDescriptorToCkerShape(_outputDescr), _outputData.f);
-}
-void ConcatLayer::concatenationQuant8()
-{
-  uint32_t num_inputs = _inputDescriptors.size();
-
-  std::vector<int32_t> input_zeropoints(num_inputs);
-  std::vector<float> input_scales(num_inputs);
-  for (uint32_t i = 0; i < num_inputs; i++)
-  {
-    input_zeropoints[i] = _inputDescriptors[i].offset;
-    input_scales[i] = _inputDescriptors[i].scale;
-  }
-
-  nnfw::cker::ConcatenationParams op_params;
-  op_params.axis = _axis;
-  op_params.inputs_count = num_inputs;
-  op_params.input_zeropoint = input_zeropoints.data();
-  op_params.input_scale = input_scales.data();
-  op_params.output_zeropoint = _outputDescr.offset;
-  op_params.output_scale = _outputDescr.scale;
-
-  std::vector<nnfw::cker::Shape *> inputDimsPtr;
-  std::vector<nnfw::cker::Shape> inputDims;
-  inputDimsPtr.reserve(num_inputs);
-  inputDims.reserve(num_inputs);
-  for (uint32_t i = 0; i < num_inputs; i++)
-  {
-    inputDims.push_back(convertTensorDescriptorToCkerShape(_inputDescriptors[i]));
-    inputDimsPtr.push_back(&inputDims[i]);
-  }
-
-  nnfw::cker::Concatenation<uint8_t>(op_params, inputDimsPtr.data(), _inputDataPtrs.data(),
-                                     convertTensorDescriptorToCkerShape(_outputDescr),
-                                     _outputData.u8);
-}
-
-void ConcatLayer::configure(const std::vector<const uint8_t *> &inputDataPtrs,
-                            const std::vector<TensorDescriptor> &inputDescriptors, int32_t axis,
-                            uint8_t *outputData, const TensorDescriptor outputDescr)
-{
-  _inputDataPtrs = inputDataPtrs;
-
-  for (auto inputDescr : inputDescriptors)
-  {
-    _inputDescriptors.emplace_back(inputDescr);
-    _inputType = inputDescr.type;
-  }
-
-  _axis = axis;
-
-  _outputData.u8 = outputData;
-  _outputDescr = outputDescr;
-}
-
-void ConcatLayer::run()
-{
-  if (_inputType == OperandType::FLOAT32)
-  {
-    concatenationFloat32();
-  }
-  else if (_inputType == OperandType::QUANT8_ASYMM)
-  {
-    concatenationQuant8();
-  }
-}
-
-} // namespace kernel
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/cpu/kernel/ConcatLayer.h b/runtime/neurun/backend/cpu/kernel/ConcatLayer.h
deleted file mode 100644
index 048aa4208..000000000
--- a/runtime/neurun/backend/cpu/kernel/ConcatLayer.h
+++ /dev/null
@@ -1,73 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_CPU_KERNEL_CONCATLAYER_H__
-#define __NEURUN_BACKEND_CPU_KERNEL_CONCATLAYER_H__
-
-#include <exec/IFunction.h>
-
-#include "OperationUtils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-
-class ConcatLayer : public ::neurun::exec::IFunction
-{
-public:
-  ConcatLayer();
-
-public:
-  void concatenationFloat32();
-
-  void concatenationQuant8();
-
-  void configure(const std::vector<const uint8_t *> &inputDataPtrs,
-                 const std::vector<TensorDescriptor> &inputDescriptors, int32_t axis,
-                 uint8_t *outputData, const TensorDescriptor outputDescr);
-
-  void run();
-  void runSync()
-  {
-    // this abstract method is used just for profiling and called for
-    // backend::acl_common::AclFunction
-    run();
-  }
-
-private:
-  std::vector<const uint8_t *> _inputDataPtrs;
-  DataPtr _outputData;
-
-  int32_t _axis;
-
-  std::vector<TensorDescriptor> _inputDescriptors;
-  TensorDescriptor _outputDescr;
-
-  OperandType _inputType;
-};
-
-} // namespace kernel
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_CPU_KERNEL_CONCATLAYER_H__
diff --git a/runtime/neurun/backend/cpu/kernel/ConvolutionLayer.cc b/runtime/neurun/backend/cpu/kernel/ConvolutionLayer.cc
deleted file mode 100644
index 2fdb0baf7..000000000
--- a/runtime/neurun/backend/cpu/kernel/ConvolutionLayer.cc
+++ /dev/null
@@ -1,140 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ConvolutionLayer.h"
-
-#include <cker/operation/Conv.h>
-
-#include "OperationUtils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-ConvolutionLayer::ConvolutionLayer()
-    : _inputData(), _kernelData(), _outputData(), _biasData(), _inputDescr(), _kernelDescr(),
-      _outputDescr(), _biasDescr(), _paddingLeft(0), _paddingTop(0), _paddingRight(0),
-      _paddingBottom(0), _strideWidth(0), _strideHeight(0), _activation(ir::Activation::NONE),
-      _inputType(OperandType::FLOAT32)
-{
-  // DO NOTHING
-}
-
-void ConvolutionLayer::convFloat32()
-{
-  float output_activation_min, output_activation_max;
-  CalculateActivationRangeFloat(_activation, &output_activation_min, &output_activation_max);
-
-  nnfw::cker::ConvParams op_params;
-  op_params.padding_values.width = _paddingLeft;
-  op_params.padding_values.height = _paddingTop;
-  op_params.stride_width = _strideWidth;
-  op_params.stride_height = _strideHeight;
-  op_params.dilation_width_factor = 1;
-  op_params.dilation_height_factor = 1;
-  op_params.float_activation_min = output_activation_min;
-  op_params.float_activation_max = output_activation_max;
-
-  nnfw::cker::Conv(op_params, convertTensorDescriptorToCkerShape(_inputDescr), _inputData.f,
-                   convertTensorDescriptorToCkerShape(_kernelDescr), _kernelData.f,
-                   convertTensorDescriptorToCkerShape(_biasDescr), _biasData.f,
-                   convertTensorDescriptorToCkerShape(_outputDescr), _outputData.f);
-}
-
-void ConvolutionLayer::convQuant8()
-{
-  int32_t output_activation_min = 0;
-  int32_t output_activation_max = 0;
-  CalculateActivationRangeUint8(_activation, _outputDescr, &output_activation_min,
-                                &output_activation_max);
-
-  float real_multiplier = 0.0;
-  int32_t output_multiplier = 0;
-  int32_t output_shift = 0;
-  GetQuantizedConvolutionMultiplier(_inputDescr, _kernelDescr, _biasDescr, _outputDescr,
-                                    &real_multiplier);
-  QuantizeMultiplier(real_multiplier, &output_multiplier, &output_shift);
-
-  nnfw::cker::ConvParams op_params;
-  op_params.stride_width = _strideWidth;
-  op_params.stride_height = _strideHeight;
-  op_params.dilation_width_factor = 1;
-  op_params.dilation_height_factor = 1;
-  op_params.padding_values.width = _paddingLeft;
-  op_params.padding_values.height = _paddingTop;
-  op_params.input_offset = -_inputDescr.offset;
-  op_params.weights_offset = -_kernelDescr.offset;
-  op_params.output_offset = _outputDescr.offset;
-  op_params.output_multiplier = output_multiplier;
-  op_params.output_shift = output_shift;
-  op_params.quantized_activation_min = output_activation_min;
-  op_params.quantized_activation_max = output_activation_max;
-
-  nnfw::cker::Conv(op_params, convertTensorDescriptorToCkerShape(_inputDescr), _inputData.u8,
-                   convertTensorDescriptorToCkerShape(_kernelDescr), _kernelData.u8,
-                   convertTensorDescriptorToCkerShape(_biasDescr), _biasData.i32,
-                   convertTensorDescriptorToCkerShape(_outputDescr), _outputData.u8);
-}
-
-void ConvolutionLayer::configure(uint8_t *inputData, const TensorDescriptor inputDescr,
-                                 uint8_t *kernelData, const TensorDescriptor kernelDescr,
-                                 uint8_t *biasData, const TensorDescriptor biasDescr,
-                                 const uint32_t paddingLeft, const uint32_t paddingRight,
-                                 const uint32_t paddingTop, const uint32_t paddingBottom,
-                                 const uint32_t strideWidth, const uint32_t strideHeight,
-                                 const ir::Activation activation, uint8_t *outputData,
-                                 const TensorDescriptor outputDescr)
-{
-  _inputData.u8 = inputData;
-  _inputDescr = inputDescr;
-  _inputType = inputDescr.type;
-  _kernelData.u8 = kernelData;
-  _kernelDescr = kernelDescr;
-  _biasData.u8 = biasData;
-  _biasDescr = biasDescr;
-  _paddingLeft = paddingLeft;
-  _paddingRight = paddingRight;
-  _paddingTop = paddingTop;
-  _paddingBottom = paddingBottom;
-  _strideWidth = strideWidth;
-  _strideHeight = strideHeight;
-  _activation = activation;
-  _outputData.u8 = outputData;
-  _outputDescr = outputDescr;
-}
-
-void ConvolutionLayer::run()
-{
-  if (_inputType == OperandType::FLOAT32)
-  {
-    convFloat32();
-  }
-  else if (_inputType == OperandType::QUANT8_ASYMM)
-  {
-    convQuant8();
-  }
-}
-
-#undef ANDROID_NN_CONV_PARAMETERS
-
-} // namespace kernel
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/cpu/kernel/ConvolutionLayer.h b/runtime/neurun/backend/cpu/kernel/ConvolutionLayer.h
deleted file mode 100644
index 16669f316..000000000
--- a/runtime/neurun/backend/cpu/kernel/ConvolutionLayer.h
+++ /dev/null
@@ -1,88 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_CPU_KERNEL_CONVOLUTIONLAYER_H__
-#define __NEURUN_BACKEND_CPU_KERNEL_CONVOLUTIONLAYER_H__
-
-#include <exec/IFunction.h>
-
-#include "OperationUtils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-
-class ConvolutionLayer : public ::neurun::exec::IFunction
-{
-public:
-  ConvolutionLayer();
-
-public:
-  void convFloat32();
-
-  void convQuant8();
-
-  void configure(uint8_t *inputData, const TensorDescriptor inputDescr, uint8_t *kernelData,
-                 const TensorDescriptor kernelDescr, uint8_t *biasData,
-                 const TensorDescriptor biasDescr, const uint32_t paddingLeft,
-                 const uint32_t paddingRight, const uint32_t paddingTop,
-                 const uint32_t paddingBottom, const uint32_t strideW, const uint32_t strideH,
-                 const ir::Activation activation, uint8_t *outputData,
-                 const TensorDescriptor outputDescr);
-
-  void run();
-  void runSync()
-  {
-    // this abstract method is used just for profiling and called for
-    // backend::acl_common::AclFunction
-    run();
-  }
-
-private:
-  DataPtr _inputData;
-  DataPtr _kernelData;
-  DataPtr _outputData;
-  DataPtr _biasData;
-
-  TensorDescriptor _inputDescr;
-  TensorDescriptor _kernelDescr;
-  TensorDescriptor _outputDescr;
-  TensorDescriptor _biasDescr;
-
-  uint32_t _paddingLeft;
-  uint32_t _paddingTop;
-  uint32_t _paddingRight;
-  uint32_t _paddingBottom;
-
-  uint32_t _strideWidth;
-  uint32_t _strideHeight;
-
-  ir::Activation _activation;
-
-  OperandType _inputType;
-};
-
-} // namespace kernel
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_CPU_KERNEL_CONVOLUTIONLAYER_H__
diff --git a/runtime/neurun/backend/cpu/kernel/DepthwiseConvolutionLayer.cc b/runtime/neurun/backend/cpu/kernel/DepthwiseConvolutionLayer.cc
deleted file mode 100644
index e33e3465e..000000000
--- a/runtime/neurun/backend/cpu/kernel/DepthwiseConvolutionLayer.cc
+++ /dev/null
@@ -1,143 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "DepthwiseConvolutionLayer.h"
-
-#include <cker/operation/DepthwiseConv.h>
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-
-DepthwiseConvolutionLayer::DepthwiseConvolutionLayer()
-    : _inputData(), _kernelData(), _outputData(), _biasData(), _inputDescr(), _kernelDescr(),
-      _outputDescr(), _biasDescr(), _paddingLeft(0), _paddingTop(0), _paddingRight(0),
-      _paddingBottom(0), _strideWidth(0), _strideHeight(0), _multiplier(0),
-      _activation(ir::Activation::NONE), _inputType(OperandType::FLOAT32)
-{
-  // DO NOTHING
-}
-
-void DepthwiseConvolutionLayer::convFloat32()
-{
-  float output_activation_min, output_activation_max;
-  CalculateActivationRangeFloat(_activation, &output_activation_min, &output_activation_max);
-
-  nnfw::cker::DepthwiseConvParams op_params;
-  op_params.stride_width = _strideWidth;
-  op_params.stride_height = _strideHeight;
-  op_params.dilation_width_factor = 1;
-  op_params.dilation_height_factor = 1;
-  op_params.padding_values.width = _paddingLeft;
-  op_params.padding_values.height = _paddingTop;
-  op_params.depth_multiplier = _multiplier;
-  op_params.float_activation_min = output_activation_min;
-  op_params.float_activation_max = output_activation_max;
-
-  nnfw::cker::DepthwiseConv(op_params, convertTensorDescriptorToCkerShape(_inputDescr),
-                            _inputData.f, convertTensorDescriptorToCkerShape(_kernelDescr),
-                            _kernelData.f, convertTensorDescriptorToCkerShape(_biasDescr),
-                            _biasData.f, convertTensorDescriptorToCkerShape(_outputDescr),
-                            _outputData.f);
-}
-
-void DepthwiseConvolutionLayer::convQuant8()
-{
-  int32_t output_activation_min = 0;
-  int32_t output_activation_max = 0;
-  CalculateActivationRangeUint8(_activation, _outputDescr, &output_activation_min,
-                                &output_activation_max);
-
-  float real_multiplier = 0.0;
-  int32_t output_multiplier = 0;
-  int32_t output_shift = 0;
-  GetQuantizedConvolutionMultiplier(_inputDescr, _kernelDescr, _biasDescr, _outputDescr,
-                                    &real_multiplier);
-  QuantizeMultiplier(real_multiplier, &output_multiplier, &output_shift);
-
-  nnfw::cker::DepthwiseConvParams op_params;
-  op_params.stride_width = _strideWidth;
-  op_params.stride_height = _strideHeight;
-  op_params.dilation_width_factor = 1;
-  op_params.dilation_height_factor = 1;
-  op_params.padding_values.width = _paddingLeft;
-  op_params.padding_values.height = _paddingTop;
-  op_params.depth_multiplier = _multiplier;
-  op_params.input_offset = -_inputDescr.offset;
-  op_params.weights_offset = -_kernelDescr.offset;
-  op_params.output_offset = _outputDescr.offset;
-  op_params.output_multiplier = output_multiplier;
-  op_params.output_shift = output_shift;
-  op_params.quantized_activation_min = output_activation_min;
-  op_params.quantized_activation_max = output_activation_max;
-
-  nnfw::cker::DepthwiseConv(op_params, convertTensorDescriptorToCkerShape(_inputDescr),
-                            _inputData.u8, convertTensorDescriptorToCkerShape(_kernelDescr),
-                            _kernelData.u8, convertTensorDescriptorToCkerShape(_biasDescr),
-                            _biasData.i32, convertTensorDescriptorToCkerShape(_outputDescr),
-                            _outputData.u8);
-}
-
-void DepthwiseConvolutionLayer::configure(uint8_t *inputData, const TensorDescriptor inputDescr,
-                                          uint8_t *kernelData, const TensorDescriptor kernelDescr,
-                                          uint8_t *biasData, const TensorDescriptor biasDescr,
-                                          const uint32_t paddingLeft, const uint32_t paddingRight,
-                                          const uint32_t paddingTop, const uint32_t paddingBottom,
-                                          const uint32_t strideWidth, const uint32_t strideHeight,
-                                          const uint32_t multiplier,
-                                          const ir::Activation activation, uint8_t *outputData,
-                                          const TensorDescriptor outputDescr)
-{
-  _inputData.u8 = inputData;
-  _inputDescr = inputDescr;
-  _inputType = inputDescr.type;
-  _kernelData.u8 = kernelData;
-  _kernelDescr = kernelDescr;
-  _biasData.u8 = biasData;
-  _biasDescr = biasDescr;
-  _paddingLeft = paddingLeft;
-  _paddingRight = paddingRight;
-  _paddingTop = paddingTop;
-  _paddingBottom = paddingBottom;
-  _strideWidth = strideWidth;
-  _strideHeight = strideHeight;
-  _multiplier = multiplier;
-  _activation = activation;
-  _outputData.u8 = outputData;
-  _outputDescr = outputDescr;
-}
-
-void DepthwiseConvolutionLayer::run()
-{
-  if (_inputType == OperandType::FLOAT32)
-  {
-    convFloat32();
-  }
-  else if (_inputType == OperandType::QUANT8_ASYMM)
-  {
-    convQuant8();
-  }
-}
-
-} // namespace kernel
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/cpu/kernel/DepthwiseConvolutionLayer.h b/runtime/neurun/backend/cpu/kernel/DepthwiseConvolutionLayer.h
deleted file mode 100644
index 575cc0ab1..000000000
--- a/runtime/neurun/backend/cpu/kernel/DepthwiseConvolutionLayer.h
+++ /dev/null
@@ -1,90 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_KERNEL_CPU_DEPTHWISECONVOLUTIONLAYER_H__
-#define __NEURUN_KERNEL_CPU_DEPTHWISECONVOLUTIONLAYER_H__
-
-#include <exec/IFunction.h>
-
-#include "OperationUtils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-
-class DepthwiseConvolutionLayer : public ::neurun::exec::IFunction
-{
-public:
-  DepthwiseConvolutionLayer();
-
-public:
-  void convFloat32();
-
-  void convQuant8();
-
-  void configure(uint8_t *inputData, const TensorDescriptor inputDescr, uint8_t *kernelData,
-                 const TensorDescriptor kernelDescr, uint8_t *biasData,
-                 const TensorDescriptor biasDescr, const uint32_t paddingLeft,
-                 const uint32_t paddingRight, const uint32_t paddingTop,
-                 const uint32_t paddingBottom, const uint32_t strideW, const uint32_t strideH,
-                 const uint32_t multiplier, const ir::Activation activation, uint8_t *outputData,
-                 const TensorDescriptor outputDescr);
-
-  void run();
-  void runSync()
-  {
-    // this abstract method is used just for profiling and called for
-    // backend::acl_common::AclFunction
-    run();
-  }
-
-private:
-  DataPtr _inputData;
-  DataPtr _kernelData;
-  DataPtr _outputData;
-  DataPtr _biasData;
-
-  TensorDescriptor _inputDescr;
-  TensorDescriptor _kernelDescr;
-  TensorDescriptor _outputDescr;
-  TensorDescriptor _biasDescr;
-
-  uint32_t _paddingLeft;
-  uint32_t _paddingTop;
-  uint32_t _paddingRight;
-  uint32_t _paddingBottom;
-
-  uint32_t _strideWidth;
-  uint32_t _strideHeight;
-
-  uint32_t _multiplier;
-
-  ir::Activation _activation;
-
-  OperandType _inputType;
-};
-
-} // namespace kernel
-} // namespace cpu
-} // backend
-} // namespace neurun
-
-#endif // __NEURUN_KERNEL_CPU_DEPTHWISECONVOLUTIONLAYER_H__
diff --git a/runtime/neurun/backend/cpu/kernel/FullyConnectedLayer.cc b/runtime/neurun/backend/cpu/kernel/FullyConnectedLayer.cc
deleted file mode 100644
index 055f71590..000000000
--- a/runtime/neurun/backend/cpu/kernel/FullyConnectedLayer.cc
+++ /dev/null
@@ -1,119 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "FullyConnectedLayer.h"
-
-#include <cker/operation/FullyConnected.h>
-
-#include "OperationUtils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-
-FullyConnectedLayer::FullyConnectedLayer()
-    : _inputData(), _weightsData(), _biasData(), _outputData(), _inputDescr(), _weightsDescr(),
-      _biasDescr(), _outputDescr(), _activation(ir::Activation::NONE),
-      _inputType(OperandType::FLOAT32)
-{
-  // DO NOTHING
-}
-
-void FullyConnectedLayer::fullyConnectedFloat32()
-{
-  float output_activation_min, output_activation_max;
-  CalculateActivationRangeFloat(_activation, &output_activation_min, &output_activation_max);
-
-  nnfw::cker::FullyConnectedParams op_params;
-  op_params.float_activation_min = output_activation_min;
-  op_params.float_activation_max = output_activation_max;
-
-  nnfw::cker::FullyConnected(op_params, convertToExtendedCkerShape(_inputDescr), _inputData.f,
-                             convertToExtendedCkerShape(_weightsDescr), _weightsData.f,
-                             convertToExtendedCkerShape(_biasDescr), _biasData.f,
-                             convertToExtendedCkerShape(_outputDescr), _outputData.f);
-}
-
-// executionMutex is used to protect concurrent access of non-threadsafe resources
-// like gemmlowp::GemmContext.
-void FullyConnectedLayer::fullyConnectedQuant8()
-{
-  float real_multiplier = 0.0;
-  int32_t output_multiplier = 0;
-  int32_t output_shift = 0;
-  int32_t output_activation_min = 0;
-  int32_t output_activation_max = 0;
-  GetQuantizedConvolutionMultiplier(_inputDescr, _weightsDescr, _biasDescr, _outputDescr,
-                                    &real_multiplier);
-  QuantizeMultiplier(real_multiplier, &output_multiplier, &output_shift);
-  CalculateActivationRangeUint8(_activation, _outputDescr, &output_activation_min,
-                                &output_activation_max);
-
-  nnfw::cker::FullyConnectedParams op_params;
-  op_params.input_offset = -_inputDescr.offset;
-  op_params.weights_offset = -_weightsDescr.offset;
-  op_params.output_offset = _outputDescr.offset;
-  op_params.output_multiplier = output_multiplier;
-  op_params.output_shift = output_shift;
-  op_params.quantized_activation_min = output_activation_min;
-  op_params.quantized_activation_max = output_activation_max;
-
-  nnfw::cker::FullyConnected(op_params, convertToExtendedCkerShape(_inputDescr), _inputData.u8,
-                             convertToExtendedCkerShape(_weightsDescr), _weightsData.u8,
-                             convertToExtendedCkerShape(_biasDescr), _biasData.i32,
-                             convertToExtendedCkerShape(_outputDescr), _outputData.u8);
-}
-
-void FullyConnectedLayer::configure(uint8_t *inputData, const TensorDescriptor inputDescr,
-                                    uint8_t *weightsData, const TensorDescriptor weightsDescr,
-                                    uint8_t *biasData, const TensorDescriptor biasDescr,
-                                    ir::Activation activation, uint8_t *outputData,
-                                    const TensorDescriptor outputDescr)
-{
-  _inputData.u8 = inputData;
-  _inputDescr = inputDescr;
-  _inputType = inputDescr.type;
-  _weightsData.u8 = weightsData;
-  _weightsDescr = weightsDescr;
-  _biasData.u8 = biasData;
-  _biasDescr = biasDescr;
-  _activation = activation;
-  _outputData.u8 = outputData;
-  _outputDescr = outputDescr;
-}
-
-void FullyConnectedLayer::run()
-{
-  if (_inputType == OperandType::FLOAT32)
-  {
-    fullyConnectedFloat32();
-  }
-  else if (_inputType == OperandType::QUANT8_ASYMM)
-  {
-    fullyConnectedQuant8();
-  }
-}
-
-} // namespace kernel
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/cpu/kernel/FullyConnectedLayer.h b/runtime/neurun/backend/cpu/kernel/FullyConnectedLayer.h
deleted file mode 100644
index 9fdc393a4..000000000
--- a/runtime/neurun/backend/cpu/kernel/FullyConnectedLayer.h
+++ /dev/null
@@ -1,77 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_CPU_KERNEL_FULLYCONNECTEDLAYER_H__
-#define __NEURUN_BACKEND_CPU_KERNEL_FULLYCONNECTEDLAYER_H__
-
-#include <exec/IFunction.h>
-
-#include "OperationUtils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-
-class FullyConnectedLayer : public ::neurun::exec::IFunction
-{
-public:
-  FullyConnectedLayer();
-
-public:
-  void fullyConnectedFloat32();
-
-  void fullyConnectedQuant8();
-
-  void configure(uint8_t *inputData, const TensorDescriptor inputDescr, uint8_t *weightsData,
-                 const TensorDescriptor weightsDescr, uint8_t *biasData,
-                 const TensorDescriptor biasDescr, ir::Activation activation, uint8_t *outputData,
-                 const TensorDescriptor outputDescr);
-
-  void run();
-  void runSync()
-  {
-    // this abstract method is used just for profiling and called for
-    // backend::acl_common::AclFunction
-    run();
-  }
-
-private:
-  DataPtr _inputData;
-  DataPtr _weightsData;
-  DataPtr _biasData;
-  DataPtr _outputData;
-
-  TensorDescriptor _inputDescr;
-  TensorDescriptor _weightsDescr;
-  TensorDescriptor _biasDescr;
-  TensorDescriptor _outputDescr;
-
-  ir::Activation _activation;
-
-  OperandType _inputType;
-};
-
-} // namespace kernel
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_CPU_KERNEL_FULLYCONNECTEDLAYER_H__
diff --git a/runtime/neurun/backend/cpu/kernel/GatherLayer.cc b/runtime/neurun/backend/cpu/kernel/GatherLayer.cc
deleted file mode 100644
index b29acba79..000000000
--- a/runtime/neurun/backend/cpu/kernel/GatherLayer.cc
+++ /dev/null
@@ -1,79 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "GatherLayer.h"
-
-#include <cker/operation/Gather.h>
-
-#include "OperationUtils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-
-void GatherLayer::configure(uint8_t *inputData, const TensorDescriptor &inputDescr,
-                            uint8_t *indicesData, const TensorDescriptor &indicesDescr,
-                            uint8_t *outputData, const TensorDescriptor &outputDescr, int32_t axis)
-{
-  _inputData.u8 = inputData;
-  _inputDescr = inputDescr;
-  _indicesData.u8 = indicesData;
-  _indicesDescr = indicesDescr;
-  _axis = axis;
-  _inputType = inputDescr.type;
-  _outputData.u8 = outputData;
-  _outputDescr = outputDescr;
-}
-
-void GatherLayer::run()
-{
-  nnfw::cker::GatherParams op_params;
-  op_params.axis = _axis;
-
-  switch (_inputType)
-  {
-    case OperandType::FLOAT32:
-      nnfw::cker::Gather<float>(op_params, convertTensorDescriptorToCkerShape(_inputDescr),
-                                _inputData.f, convertTensorDescriptorToCkerShape(_indicesDescr),
-                                _indicesData.i32, convertTensorDescriptorToCkerShape(_outputDescr),
-                                _outputData.f);
-      break;
-    case OperandType::QUANT8_ASYMM:
-      nnfw::cker::Gather<uint8_t>(op_params, convertTensorDescriptorToCkerShape(_inputDescr),
-                                  _inputData.u8, convertTensorDescriptorToCkerShape(_indicesDescr),
-                                  _indicesData.i32,
-                                  convertTensorDescriptorToCkerShape(_outputDescr), _outputData.u8);
-      break;
-    case OperandType::INT32:
-      nnfw::cker::Gather<int32_t>(
-          op_params, convertTensorDescriptorToCkerShape(_inputDescr), _inputData.i32,
-          convertTensorDescriptorToCkerShape(_indicesDescr), _indicesData.i32,
-          convertTensorDescriptorToCkerShape(_outputDescr), _outputData.i32);
-      break;
-    default:
-      throw std::runtime_error("Gather NYI for this operand type!");
-  }
-}
-
-} // namespace kernel
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/cpu/kernel/GatherLayer.h b/runtime/neurun/backend/cpu/kernel/GatherLayer.h
deleted file mode 100644
index af4f8b8f6..000000000
--- a/runtime/neurun/backend/cpu/kernel/GatherLayer.h
+++ /dev/null
@@ -1,74 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_CPU_KERNEL_GATHERLAYER_H__
-#define __NEURUN_BACKEND_CPU_KERNEL_GATHERLAYER_H__
-
-#include <exec/IFunction.h>
-
-#include "OperationUtils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-
-class GatherLayer : public ::neurun::exec::IFunction
-{
-public:
-  GatherLayer()
-      : _inputData{nullptr}, _indicesData{nullptr}, _outputData{nullptr}, _axis{-1},
-        _inputType{OperandType::FLOAT32}
-  {
-    // DO NOTHING
-  }
-
-public:
-  void configure(uint8_t *inputData, const TensorDescriptor &inputDescr, uint8_t *indicesData,
-                 const TensorDescriptor &indicesDescr, uint8_t *outputData,
-                 const TensorDescriptor &outputDescr, int32_t axis);
-
-  void run();
-  void runSync()
-  {
-    // this abstract method is used just for profiling and called for
-    // backend::acl_common::AclFunction
-    run();
-  }
-
-private:
-  DataPtr _inputData;
-  DataPtr _indicesData;
-  DataPtr _outputData;
-
-  TensorDescriptor _inputDescr;
-  TensorDescriptor _indicesDescr;
-  TensorDescriptor _outputDescr;
-
-  int32_t _axis;
-  OperandType _inputType;
-};
-
-} // namespace kernel
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_CPU_KERNEL_GATHERLAYER_H__
diff --git a/runtime/neurun/backend/cpu/kernel/LogisticLayer.cc b/runtime/neurun/backend/cpu/kernel/LogisticLayer.cc
deleted file mode 100644
index d9916964e..000000000
--- a/runtime/neurun/backend/cpu/kernel/LogisticLayer.cc
+++ /dev/null
@@ -1,75 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "LogisticLayer.h"
-
-#include <cker/operation/Logistic.h>
-
-#include "OperationUtils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-
-LogisticLayer::LogisticLayer()
-    : _inputData(), _outputData(), _inputDescr(), _outputDescr(), _inputType(OperandType::FLOAT32)
-{
-  // DO NOTHING
-}
-
-void LogisticLayer::logisticFloat32()
-{
-  nnfw::cker::Logistic(convertTensorDescriptorToCkerShape(_inputDescr), _inputData.f,
-                       convertTensorDescriptorToCkerShape(_outputDescr), _outputData.f);
-}
-
-void LogisticLayer::logisticQuant8()
-{
-  // cker quant8 logistic is not implemented yet
-  throw std::runtime_error{"NYI"};
-}
-
-void LogisticLayer::configure(uint8_t *inputData, const TensorDescriptor &inputDescr,
-                              uint8_t *outputData, const TensorDescriptor &outputDescr)
-{
-  _inputData.u8 = inputData;
-  _inputDescr = inputDescr;
-  _inputType = inputDescr.type;
-  _outputData.u8 = outputData;
-  _outputDescr = outputDescr;
-}
-
-void LogisticLayer::run()
-{
-  if (_inputType == OperandType::FLOAT32)
-  {
-    logisticFloat32();
-  }
-  else if (_inputType == OperandType::QUANT8_ASYMM)
-  {
-    logisticQuant8();
-  }
-}
-
-} // namespace kernel
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/cpu/kernel/LogisticLayer.h b/runtime/neurun/backend/cpu/kernel/LogisticLayer.h
deleted file mode 100644
index 33fcd6fed..000000000
--- a/runtime/neurun/backend/cpu/kernel/LogisticLayer.h
+++ /dev/null
@@ -1,69 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_CPU_KERNEL_LOGISTICLAYER_H__
-#define __NEURUN_BACKEND_CPU_KERNEL_LOGISTICLAYER_H__
-
-#include <exec/IFunction.h>
-
-#include "OperationUtils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-
-class LogisticLayer : public ::neurun::exec::IFunction
-{
-public:
-  LogisticLayer();
-
-public:
-  void logisticFloat32();
-
-  void logisticQuant8();
-
-  void configure(uint8_t *inputData, const TensorDescriptor &inputDescr, uint8_t *outputData,
-                 const TensorDescriptor &outputDescr);
-
-  void run();
-  void runSync()
-  {
-    // this abstract method is used just for profiling and called for
-    // backend::acl_common::AclFunction
-    run();
-  }
-
-private:
-  DataPtr _inputData;
-  DataPtr _outputData;
-
-  TensorDescriptor _inputDescr;
-  TensorDescriptor _outputDescr;
-
-  OperandType _inputType;
-};
-
-} // namespace kernel
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_CPU_KERNEL_LOGISTICLAYER_H__
diff --git a/runtime/neurun/backend/cpu/kernel/MaxPoolLayer.cc b/runtime/neurun/backend/cpu/kernel/MaxPoolLayer.cc
deleted file mode 100644
index 095cd6d1d..000000000
--- a/runtime/neurun/backend/cpu/kernel/MaxPoolLayer.cc
+++ /dev/null
@@ -1,116 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "MaxPoolLayer.h"
-
-#include <cker/operation/MaxPool.h>
-
-#include "OperationUtils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-
-#define MAXPOOLING_PARAMETERS                            \
-  nnfw::cker::PoolParams op_params;                      \
-  op_params.stride_height = _strideHeight;               \
-  op_params.stride_width = _strideWidth;                 \
-  op_params.filter_height = _kernelHeight;               \
-  op_params.filter_width = _kernelWidth;                 \
-  op_params.padding_values.height = (int8_t)_paddingTop; \
-  op_params.padding_values.width = (int8_t)_paddingLeft;
-
-MaxPoolLayer::MaxPoolLayer()
-    : _inputData(), _outputData(), _inputDescr(), _outputDescr(), _paddingLeft(0), _paddingTop(0),
-      _paddingRight(0), _paddingBottom(0), _strideWidth(0), _strideHeight(0), _kernelWidth(0),
-      _kernelHeight(0), _activation(ir::Activation::NONE), _inputType(OperandType::FLOAT32)
-{
-  // DO NOTHING
-}
-
-void MaxPoolLayer::maxPoolFloat32()
-{
-  MAXPOOLING_PARAMETERS
-  float output_activation_min, output_activation_max;
-  CalculateActivationRangeFloat(_activation, &output_activation_min, &output_activation_max);
-  op_params.float_activation_min = output_activation_min;
-  op_params.float_activation_max = output_activation_max;
-
-  nnfw::cker::MaxPool(op_params, convertTensorDescriptorToCkerShape(_inputDescr), _inputData.f,
-                      convertTensorDescriptorToCkerShape(_outputDescr), _outputData.f);
-}
-void MaxPoolLayer::maxPoolQuant8()
-{
-  MAXPOOLING_PARAMETERS
-  int32_t output_activation_min = 0;
-  int32_t output_activation_max = 0;
-  CalculateActivationRangeUint8(_activation, _outputDescr, &output_activation_min,
-                                &output_activation_max);
-  op_params.quantized_activation_min = output_activation_min;
-  op_params.quantized_activation_max = output_activation_max;
-
-  nnfw::cker::MaxPool(op_params, convertTensorDescriptorToCkerShape(_inputDescr), _inputData.u8,
-                      convertTensorDescriptorToCkerShape(_outputDescr), _outputData.u8);
-}
-
-void MaxPoolLayer::configure(uint8_t *inputData, const TensorDescriptor inputDescr,
-                             const uint32_t paddingLeft, const uint32_t paddingRight,
-                             const uint32_t paddingTop, const uint32_t paddingBottom,
-                             const uint32_t strideWidth, const uint32_t strideHeight,
-                             const uint32_t kernelWidth, const uint32_t kernelHeight,
-                             const ir::Activation activation, uint8_t *outputData,
-                             const TensorDescriptor outputDescr)
-{
-  _inputData.u8 = inputData;
-
-  _inputDescr = inputDescr;
-  _inputType = inputDescr.type;
-  _paddingLeft = paddingLeft;
-  _paddingRight = paddingRight;
-  _paddingTop = paddingTop;
-  _paddingBottom = paddingBottom;
-  _strideWidth = strideWidth;
-  _strideHeight = strideHeight;
-  _kernelWidth = kernelWidth;
-  _kernelHeight = kernelHeight;
-  _activation = activation;
-  _outputData.u8 = outputData;
-  _outputDescr = outputDescr;
-}
-
-void MaxPoolLayer::run()
-{
-  if (_inputType == OperandType::FLOAT32)
-  {
-    maxPoolFloat32();
-  }
-  else if (_inputType == OperandType::QUANT8_ASYMM)
-  {
-    maxPoolQuant8();
-  }
-}
-
-#undef MAXPOOLING_PARAMETERS
-
-} // namespace kernel
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/cpu/kernel/MaxPoolLayer.h b/runtime/neurun/backend/cpu/kernel/MaxPoolLayer.h
deleted file mode 100644
index 88a574c42..000000000
--- a/runtime/neurun/backend/cpu/kernel/MaxPoolLayer.h
+++ /dev/null
@@ -1,85 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_CPU_KERNEL_MAXPOOLLAYER_H__
-#define __NEURUN_BACKEND_CPU_KERNEL_MAXPOOLLAYER_H__
-
-#include <exec/IFunction.h>
-
-#include "OperationUtils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-
-class MaxPoolLayer : public ::neurun::exec::IFunction
-{
-public:
-  MaxPoolLayer();
-
-public:
-  void maxPoolFloat32();
-
-  void maxPoolQuant8();
-
-  void configure(uint8_t *inputData, const TensorDescriptor inputDescr, const uint32_t paddingLeft,
-                 const uint32_t paddingRight, const uint32_t paddingTop,
-                 const uint32_t paddingBottom, const uint32_t strideWidth,
-                 const uint32_t strideHeight, const uint32_t kernelWidth,
-                 const uint32_t kernelHeight, const ir::Activation activation, uint8_t *outputData,
-                 const TensorDescriptor outputDescr);
-
-  void run();
-  void runSync()
-  {
-    // this abstract method is used just for profiling and called for
-    // backend::acl_common::AclFunction
-    run();
-  }
-
-private:
-  DataPtr _inputData;
-  DataPtr _outputData;
-
-  TensorDescriptor _inputDescr;
-  TensorDescriptor _outputDescr;
-
-  uint32_t _paddingLeft;
-  uint32_t _paddingTop;
-  uint32_t _paddingRight;
-  uint32_t _paddingBottom;
-
-  uint32_t _strideWidth;
-  uint32_t _strideHeight;
-  uint32_t _kernelWidth;
-  uint32_t _kernelHeight;
-
-  ir::Activation _activation;
-
-  OperandType _inputType;
-};
-
-} // namespace kernel
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_CPU_KERNEL_MAXPOOLLAYER_H__
diff --git a/runtime/neurun/backend/cpu/kernel/MulLayer.cc b/runtime/neurun/backend/cpu/kernel/MulLayer.cc
deleted file mode 100644
index d6ce2cfad..000000000
--- a/runtime/neurun/backend/cpu/kernel/MulLayer.cc
+++ /dev/null
@@ -1,101 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "MulLayer.h"
-
-#include <cker/operation/BinaryArithmeticOps.h>
-
-#include "OperationUtils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-
-void MulLayer::mulFloat32()
-{
-  float output_activation_min, output_activation_max;
-  CalculateActivationRangeFloat(_activation, &output_activation_min, &output_activation_max);
-  nnfw::cker::BinaryArithmeticOpParam op_params;
-  op_params.float_activation_max = output_activation_max;
-  op_params.float_activation_min = output_activation_min;
-
-  const std::function<float(const float &, const float &)> fn = [](const float &a, const float &b) {
-    return a * b;
-  };
-
-  if (!HaveSameShapes(&_lhsDescr, &_rhsDescr))
-  {
-    nnfw::cker::BroadcastBinaryArithmeticOpSlow(
-        op_params, convertToExtendedCkerShape(_lhsDescr), _lhsData.f,
-        convertToExtendedCkerShape(_rhsDescr), _rhsData.f, convertToExtendedCkerShape(_outputDescr),
-        _outputData.f, fn);
-    return;
-  }
-
-  nnfw::cker::BinaryArithmeticOp(op_params, convertTensorDescriptorToCkerShape(_lhsDescr),
-                                 _lhsData.f, convertTensorDescriptorToCkerShape(_rhsDescr),
-                                 _rhsData.f, convertTensorDescriptorToCkerShape(_outputDescr),
-                                 _outputData.f, fn);
-}
-
-void MulLayer::mulQuant8()
-{
-  int32_t output_activation_min, output_activation_max;
-  CalculateActivationRangeUint8(_activation, _outputDescr, &output_activation_min,
-                                &output_activation_max);
-  // nnfw::cker::BinaryArithmeticOpParam op_params;
-  // op_params.quantized_activation_max = output_activation_max;
-  // op_params.quantized_activation_min = output_activation_min;
-
-  // cker quant8 mul is not implemented yet
-  throw std::runtime_error{"Mull NYI for quantized"};
-}
-
-void MulLayer::configure(uint8_t *lhsData, const TensorDescriptor &lhsDescr, uint8_t *rhsData,
-                         const TensorDescriptor &rhsDescr, const ir::Activation activation,
-                         uint8_t *outputData, const TensorDescriptor &outputDescr)
-{
-  _lhsData.u8 = lhsData;
-  _lhsDescr = lhsDescr;
-  _rhsData.u8 = rhsData;
-  _rhsDescr = rhsDescr;
-  _inputType = lhsDescr.type;
-  _activation = activation;
-  _outputData.u8 = outputData;
-  _outputDescr = outputDescr;
-}
-
-void MulLayer::run()
-{
-  if (_inputType == OperandType::FLOAT32)
-  {
-    mulFloat32();
-  }
-  else if (_inputType == OperandType::QUANT8_ASYMM)
-  {
-    mulQuant8();
-  }
-}
-
-} // namespace kernel
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/cpu/kernel/MulLayer.h b/runtime/neurun/backend/cpu/kernel/MulLayer.h
deleted file mode 100644
index 05fc3052f..000000000
--- a/runtime/neurun/backend/cpu/kernel/MulLayer.h
+++ /dev/null
@@ -1,77 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_CPU_KERNEL_MULLAYER_H__
-#define __NEURUN_BACKEND_CPU_KERNEL_MULLAYER_H__
-
-#include <exec/IFunction.h>
-
-#include "OperationUtils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-
-class MulLayer : public ::neurun::exec::IFunction
-{
-public:
-  MulLayer() : _lhsData(), _rhsData(), _outputData(), _lhsDescr(), _rhsDescr(), _outputDescr()
-  {
-    // DO NOTHING
-  }
-
-public:
-  void mulFloat32();
-
-  void mulQuant8();
-
-  void configure(uint8_t *lhsData, const TensorDescriptor &lhsDescr, uint8_t *rhsData,
-                 const TensorDescriptor &rhsDescr, const ir::Activation activation,
-                 uint8_t *outputData, const TensorDescriptor &outputDescr);
-
-  void run();
-  void runSync()
-  {
-    // this abstract method is used just for profiling and called for
-    // backend::acl_common::AclFunction
-    run();
-  }
-
-private:
-  DataPtr _lhsData;
-  DataPtr _rhsData;
-  DataPtr _outputData;
-
-  TensorDescriptor _lhsDescr;
-  TensorDescriptor _rhsDescr;
-  TensorDescriptor _outputDescr;
-
-  ir::Activation _activation{ir::Activation::NONE};
-
-  OperandType _inputType{OperandType::FLOAT32};
-};
-
-} // namespace kernel
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_CPU_KERNEL_MULLAYER_H__
diff --git a/runtime/neurun/backend/cpu/kernel/OperationUtils.cc b/runtime/neurun/backend/cpu/kernel/OperationUtils.cc
deleted file mode 100644
index 8aa15dcbd..000000000
--- a/runtime/neurun/backend/cpu/kernel/OperationUtils.cc
+++ /dev/null
@@ -1,273 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "OperationUtils.h"
-
-#include <cmath>
-#include <algorithm>
-#include <cassert>
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-
-uint32_t getNumberOfDimensions(const TensorDescriptor &descr) { return descr.dimensions.size(); }
-
-uint32_t getNumberOfElements(const TensorDescriptor &descr)
-{
-  uint32_t count = 1;
-  for (size_t i = 0; i < descr.dimensions.size(); i++)
-  {
-    count *= descr.dimensions[i];
-  }
-  return count;
-}
-
-uint32_t getSizeOfDimension(const TensorDescriptor &descr, uint32_t dimensionIdx)
-{
-  if (dimensionIdx >= descr.dimensions.size())
-  {
-    // TODO, log the error
-    return 0;
-  }
-  return descr.dimensions[dimensionIdx];
-}
-
-void QuantizeMultiplier(double double_multiplier, int32_t *quantized_multiplier, int *shift)
-{
-  if (double_multiplier == 0.)
-  {
-    *quantized_multiplier = 0;
-    *shift = 0;
-    return;
-  }
-  const double q = std::frexp(double_multiplier, shift);
-  auto q_fixed = static_cast<int64_t>(std::round(q * (1ll << 31)));
-
-  assert(q_fixed <= (1ll << 31));
-  if (q_fixed == (1ll << 31))
-  {
-    q_fixed /= 2;
-    ++*shift;
-  }
-  assert(q_fixed <= std::numeric_limits<int32_t>::max());
-  *quantized_multiplier = static_cast<int32_t>(q_fixed);
-}
-
-void GetQuantizedConvolutionMultiplier(const TensorDescriptor &inputDescr,
-                                       const TensorDescriptor &filterDescr,
-                                       const TensorDescriptor &biasDescr,
-                                       const TensorDescriptor &outputDescr, float *multiplier)
-{
-  const float input_product_scale = inputDescr.scale * filterDescr.scale;
-  const float bias_scale = biasDescr.scale;
-  const float output_scale = outputDescr.scale;
-  // The following conditions must be guaranteed by the training pipeline.
-  UNUSED_RELEASE(bias_scale);
-  assert(std::abs(input_product_scale - bias_scale) <=
-         1e-6 * std::min(input_product_scale, bias_scale));
-  assert(input_product_scale >= 0);
-  assert(input_product_scale < output_scale);
-  *multiplier = input_product_scale / output_scale;
-}
-
-void QuantizeMultiplierGreaterThanOne(double double_multiplier, int32_t *quantized_multiplier,
-                                      int *left_shift)
-{
-  assert(double_multiplier > 1.);
-  const double q = std::frexp(double_multiplier, left_shift);
-  int64_t q_fixed = static_cast<int64_t>(std::round(q * (1ll << 31)));
-  assert(q_fixed <= (1ll << 31));
-  if (q_fixed == (1ll << 31))
-  {
-    q_fixed /= 2;
-    ++*left_shift;
-  }
-  assert(*left_shift >= 0);
-  assert(q_fixed <= std::numeric_limits<int32_t>::max());
-  *quantized_multiplier = static_cast<int32_t>(q_fixed);
-}
-
-void CalculateActivationRangeFloat(ir::Activation activation, float *activation_min,
-                                   float *activation_max)
-{
-  if (activation == ir::Activation::RELU)
-  {
-    *activation_min = 0.f;
-    *activation_max = std::numeric_limits<float>::max();
-  }
-  else if (activation == ir::Activation::RELU6)
-  {
-    *activation_min = 0.f;
-    *activation_max = 6.f;
-  }
-  else if (activation == ir::Activation::RELU1)
-  {
-    *activation_min = -1.f;
-    *activation_max = 1.f;
-  }
-  else if (activation == ir::Activation::SIGMOID)
-  {
-    *activation_min = 0.f;
-    *activation_max = 1.f;
-  }
-  else if (activation == ir::Activation::NONE)
-  {
-    *activation_min = std::numeric_limits<float>::lowest();
-    *activation_max = std::numeric_limits<float>::max();
-  }
-  else
-  {
-    std::cout << "Unsupported fused activation function." << std::endl;
-  }
-}
-
-void CalculateActivationRangeUint8(ir::Activation activation, const TensorDescriptor &outputDescr,
-                                   int32_t *act_min, int32_t *act_max)
-{
-  const int32_t qmin = std::numeric_limits<uint8_t>::min();
-  const int32_t qmax = std::numeric_limits<uint8_t>::max();
-  const auto scale = outputDescr.scale;
-  const auto zero_point = outputDescr.offset;
-  auto quantize = [scale, zero_point](float f) {
-    return zero_point + static_cast<int32_t>(std::round(f / scale));
-  };
-  if (activation == ir::Activation::RELU)
-  {
-    *act_min = std::max(qmin, quantize(0.0));
-    *act_max = qmax;
-  }
-  else if (activation == ir::Activation::RELU6)
-  {
-    *act_min = std::max(qmin, quantize(0.0));
-    *act_max = std::min(qmax, quantize(6.0));
-  }
-  else if (activation == ir::Activation::RELU1)
-  {
-    *act_min = std::max(qmin, quantize(-1.0));
-    *act_max = std::min(qmax, quantize(1.0));
-  }
-  else if (activation == ir::Activation::SIGMOID)
-  {
-    *act_min = std::max(qmin, quantize(0.0));
-    *act_max = std::min(qmax, quantize(1.0));
-  }
-  else if (activation == ir::Activation::NONE)
-  {
-    *act_min = qmin;
-    *act_max = qmax;
-  }
-  else
-  {
-    std::cout << "Unsupported fused activation function." << std::endl;
-  }
-}
-
-bool HaveSameShapes(const TensorDescriptor *input1, const TensorDescriptor *input2)
-{
-  if (input1 == input2)
-    return true;
-  if (input2 == NULL || input2 == NULL)
-    return false;
-
-  if (input1 == NULL)
-  {
-    return (getNumberOfDimensions(*input2) == 0);
-  }
-
-  if (getNumberOfDimensions(*input1) != getNumberOfDimensions(*input2))
-    return false;
-
-  for (uint32_t i = 0; i < getNumberOfDimensions(*input1); i++)
-    if (input1->dimensions[i] != input2->dimensions[i])
-      return false;
-
-  return true;
-}
-
-int32_t CalculateInputRadius(int input_integer_bits, int input_left_shift)
-{
-  const double max_input_rescaled = 1.0 * ((1 << input_integer_bits) - 1) *
-                                    (1ll << (31 - input_integer_bits)) / (1ll << input_left_shift);
-  // Tighten bound using floor.  Suppose that we could use the exact value.
-  // After scaling the difference, the result would be at the maximum.  Thus we
-  // must ensure that our value has lower magnitude.
-  return static_cast<int32_t>(std::floor(max_input_rescaled));
-}
-
-TensorDescriptor getTensorDescriptor(const ir::Operand &o, ir::Layout frontend_layout)
-{
-  TensorDescriptor descriptor;
-
-  auto dims = o.shape().dims();
-  if (frontend_layout == ir::Layout::NCHW && o.shape().rank() == 4)
-  {
-    // NCHW -> NHWC
-    uint32_t permutation[4] = {0, 2, 3, 1};
-    for (int i = 0; i < o.shape().rank(); ++i)
-    {
-      dims.at(i) = o.shape().dim(permutation[i]);
-    }
-  }
-  descriptor.dimensions = std::vector<uint32_t>(dims.begin(), dims.end());
-  descriptor.type = static_cast<OperandType>(static_cast<int32_t>(o.typeInfo().type()));
-  descriptor.scale = o.typeInfo().scale();
-  descriptor.offset = o.typeInfo().offset();
-
-  // CPU backend assume that neurun internal shape's rank is always same or less than 4
-  assert(descriptor.dimensions.size() <= 4);
-
-  return descriptor;
-}
-
-uint32_t sizeOfData(OperandType type, const std::vector<uint32_t> &dimensions)
-{
-  uint32_t size = 4;
-
-  switch (type)
-  {
-    case OperandType::FLOAT32:
-    case OperandType::INT32:
-    case OperandType::UINT32:
-      size = 4;
-      break;
-    case OperandType::BOOL8:
-    case OperandType::QUANT8_ASYMM:
-    case OperandType::QUANT8_SYMM:
-      size = 1;
-      break;
-    default:
-      throw std::runtime_error("Not supported operand type.");
-      break;
-  }
-
-  for (auto d : dimensions)
-  {
-    size *= d;
-  }
-
-  return size;
-}
-
-} // namespace kernel
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/cpu/kernel/OperationUtils.h b/runtime/neurun/backend/cpu/kernel/OperationUtils.h
deleted file mode 100644
index b9e8c8974..000000000
--- a/runtime/neurun/backend/cpu/kernel/OperationUtils.h
+++ /dev/null
@@ -1,152 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NNFW_SUPPORT_NNAPI_OPERATION_UTILS_H__
-#define __NNFW_SUPPORT_NNAPI_OPERATION_UTILS_H__
-
-#include <iostream>
-#include <limits>
-#include <vector>
-
-#include <cker/Shape.h>
-
-#include "ir/Operand.h"
-#include "ir/DataType.h"
-#include <ir/InternalType.h>
-
-using OperandType = neurun::ir::DataType;
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-
-struct TensorDescriptor
-{
-  OperandType type;
-  std::vector<uint32_t> dimensions;
-  float scale;
-  int32_t offset;
-};
-
-union DataPtr {
-  uint8_t *u8;
-  int8_t *i8;
-  int32_t *i32;
-  float *f;
-  void *v;
-};
-
-uint32_t getNumberOfDimensions(const TensorDescriptor &descr);
-
-uint32_t getNumberOfElements(const TensorDescriptor &descr);
-
-uint32_t getSizeOfDimension(const TensorDescriptor &descr, uint32_t dimensionIdx);
-
-inline nnfw::cker::Shape convertToExtendedCkerShape(const TensorDescriptor &descr)
-{
-  std::vector<int32_t> raw_shape;
-  raw_shape.resize(4);
-
-  uint32_t src = 4 - descr.dimensions.size();
-  for (uint32_t i = 0; i < 4; ++i)
-  {
-    if (i < src)
-    {
-      raw_shape[i] = 1;
-    }
-    else
-    {
-      raw_shape[i] = descr.dimensions[i - src];
-    }
-  }
-
-  return nnfw::cker::GetShape(raw_shape);
-}
-
-inline nnfw::cker::Shape convertTensorDescriptorToCkerShape(const TensorDescriptor &descr)
-{
-  std::vector<int32_t> raw_shape;
-  raw_shape.resize(4);
-
-  for (uint32_t i = 0; i < 4; ++i)
-  {
-    if (i >= descr.dimensions.size())
-    {
-      raw_shape[i] = 1;
-    }
-    else
-    {
-      raw_shape[i] = descr.dimensions[i];
-    }
-  }
-
-  return nnfw::cker::GetShape(raw_shape);
-}
-
-inline int32_t getAxis(uint32_t rank, int32_t axis, ir::Layout frontend_layout)
-{
-  auto ret = axis;
-
-  if (axis < 0)
-  {
-    ret += rank;
-  }
-
-  // NCHW -> NHWC
-  if (frontend_layout == ir::Layout::NCHW)
-  {
-    int32_t permutation[4] = {0, 3, 1, 2};
-    ret = permutation[ret];
-  }
-
-  return ret;
-}
-
-void QuantizeMultiplier(double double_multiplier, int32_t *quantized_multiplier, int *shift);
-
-void GetQuantizedConvolutionMultiplier(const TensorDescriptor &inputDescr,
-                                       const TensorDescriptor &filterDescr,
-                                       const TensorDescriptor &biasDescr,
-                                       const TensorDescriptor &outputDescr, float *multiplier);
-
-void QuantizeMultiplierGreaterThanOne(double double_multiplier, int32_t *quantized_multiplier,
-                                      int *left_shift);
-
-void CalculateActivationRangeFloat(ir::Activation activation, float *activation_min,
-                                   float *activation_max);
-
-void CalculateActivationRangeUint8(ir::Activation activation, const TensorDescriptor &outputDescr,
-                                   int32_t *act_min, int32_t *act_max);
-
-bool HaveSameShapes(const TensorDescriptor *input1, const TensorDescriptor *input2);
-
-int32_t CalculateInputRadius(int input_integer_bits, int input_left_shift);
-
-TensorDescriptor getTensorDescriptor(const ir::Operand &o, ir::Layout frontend_layout);
-
-uint32_t sizeOfData(OperandType type, const std::vector<uint32_t> &dimensions);
-
-} // namespace kernel
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
-
-#endif // __NNFW_SUPPORT_NNAPI_OPERATION_UTILS_H__
diff --git a/runtime/neurun/backend/cpu/kernel/PadLayer.cc b/runtime/neurun/backend/cpu/kernel/PadLayer.cc
deleted file mode 100644
index 1fd9429b5..000000000
--- a/runtime/neurun/backend/cpu/kernel/PadLayer.cc
+++ /dev/null
@@ -1,76 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "PadLayer.h"
-
-#include "OperationUtils.h"
-
-#include <cker/operation/Pad.h>
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-
-PadLayer::PadLayer()
-    : _inputData(), _outputData(), _inputDescr(), _outputDescr(), _padData(), _padRank(),
-      _constantValueData(), _inputType(OperandType::FLOAT32)
-{
-  // DO NOTHING
-}
-
-void PadLayer::padFloat32()
-{
-  nnfw::cker::Pad(_padData, _padRank, convertTensorDescriptorToCkerShape(_inputDescr), _inputData.f,
-                  convertTensorDescriptorToCkerShape(_outputDescr), _outputData.f,
-                  _constantValueData.f);
-}
-void PadLayer::padQuant8() { throw std::runtime_error("Quantized Pad isn't supported NYI"); }
-
-void PadLayer::configure(uint8_t *inputData, const TensorDescriptor inputDescr, uint8_t *outputData,
-                         const TensorDescriptor outputDescr, const int32_t *padData,
-                         int32_t padRank, uint8_t *constantValueData)
-{
-  _inputData.u8 = inputData;
-  _inputDescr = inputDescr;
-  _inputType = inputDescr.type;
-  _outputData.u8 = outputData;
-  _outputDescr = outputDescr;
-  _padData = padData;
-  _padRank = padRank;
-  _constantValueData.u8 = constantValueData;
-}
-
-void PadLayer::run()
-{
-  if (_inputType == OperandType::FLOAT32)
-  {
-    padFloat32();
-  }
-  else if (_inputType == OperandType::QUANT8_ASYMM)
-  {
-    padQuant8();
-  }
-}
-
-} // namespace kernel
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/cpu/kernel/PadLayer.h b/runtime/neurun/backend/cpu/kernel/PadLayer.h
deleted file mode 100644
index f4413a8ed..000000000
--- a/runtime/neurun/backend/cpu/kernel/PadLayer.h
+++ /dev/null
@@ -1,75 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_CPU_KERNEL_PADLAYER_H__
-#define __NEURUN_BACKEND_CPU_KERNEL_PADLAYER_H__
-
-#include <exec/IFunction.h>
-
-#include "OperationUtils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-
-// Note, this is pad with mode=`CONSTANT`: it doesn't support `REFLECT` and `SYMMETRIC`
-class PadLayer : public ::neurun::exec::IFunction
-{
-public:
-  PadLayer();
-
-public:
-  void padFloat32();
-
-  void padQuant8();
-
-  void configure(uint8_t *inputData, const TensorDescriptor inputDescr, uint8_t *outputData,
-                 const TensorDescriptor outputDescr, const int32_t *padData, int32_t padRank,
-                 uint8_t *constantValueData = nullptr);
-
-  void run();
-  void runSync()
-  {
-    // this abstract method is used just for profiling and called for
-    // backend::acl_common::AclFunction
-    run();
-  }
-
-private:
-  DataPtr _inputData;
-  DataPtr _outputData;
-
-  TensorDescriptor _inputDescr;
-  TensorDescriptor _outputDescr;
-
-  const int32_t *_padData;
-  int32_t _padRank;
-  DataPtr _constantValueData;
-
-  OperandType _inputType;
-};
-
-} // namespace kernel
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_CPU_KERNEL_PADLAYER_H__
diff --git a/runtime/neurun/backend/cpu/kernel/PermuteLayer.cc b/runtime/neurun/backend/cpu/kernel/PermuteLayer.cc
deleted file mode 100644
index 6f28d8436..000000000
--- a/runtime/neurun/backend/cpu/kernel/PermuteLayer.cc
+++ /dev/null
@@ -1,71 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "PermuteLayer.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-
-using Type = ir::operation::Permute::Type;
-
-void PermuteLayer::configure(std::shared_ptr<backend::operand::ITensor> input,
-                             std::shared_ptr<backend::operand::ITensor> output,
-                             const ir::Shape &output_shape, Type type, ir::DataType dataType)
-{
-  _input = input;
-  _output = output;
-  _output_shape = output_shape;
-  _type = type;
-  _dataType = dataType;
-}
-
-void PermuteLayer::run()
-{
-  using ir::DataType;
-  switch (_dataType)
-  {
-    case DataType::FLOAT32:
-      runTempl<float>();
-      break;
-    case DataType::INT32:
-      runTempl<int32_t>();
-      break;
-    case DataType::UINT32:
-      runTempl<uint32_t>();
-      break;
-    case DataType::BOOL8:
-    case DataType::QUANT8_ASYMM:
-      runTempl<uint8_t>();
-      break;
-    case DataType::QUANT8_SYMM:
-      runTempl<int8_t>();
-      break;
-    default:
-      throw std::runtime_error("NYI");
-      break;
-  }
-}
-
-} // namespace kernel
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/cpu/kernel/PermuteLayer.h b/runtime/neurun/backend/cpu/kernel/PermuteLayer.h
deleted file mode 100644
index 1f9110807..000000000
--- a/runtime/neurun/backend/cpu/kernel/PermuteLayer.h
+++ /dev/null
@@ -1,209 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_CPU_KERNEL_PERMUTE_LAYER_H__
-#define __NEURUN_BACKEND_CPU_KERNEL_PERMUTE_LAYER_H__
-
-#include <exec/IFunction.h>
-
-#include "util/feature/nhwc/View.h"
-#include "OperationUtils.h"
-#include "ir/operation/Permute.h"
-#include "util/feature/nhwc/Reader.h"
-#include "util/feature/nchw/View.h"
-#include "util/Coordinates.h"
-
-#include <misc/feature/IndexIterator.h>
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-
-class PermuteLayer : public ::neurun::exec::IFunction
-{
-public:
-  PermuteLayer() = default;
-
-public:
-  void configure(std::shared_ptr<backend::operand::ITensor> input,
-                 std::shared_ptr<backend::operand::ITensor> output, const ir::Shape &output_shape,
-                 ir::operation::Permute::Type type, ir::DataType dataType);
-  void run();
-  void runSync()
-  {
-    // this abstract method is used just for profiling and called for
-    // backend::acl_common::AclFunction
-    run();
-  }
-
-private:
-  template <class T> void runTempl()
-  {
-    auto rank = _output_shape.rank();
-    auto fn = [&](::neurun::backend::operand::ITensor &in_tensor) {
-      _output->access([&](::neurun::backend::operand::ITensor &out_tensor) {
-        auto input_buffer = in_tensor.buffer();
-        auto input_size = in_tensor.total_size();
-        auto output_buffer = out_tensor.buffer();
-        if (_type == ir::operation::Permute::Type::COPY)
-        {
-          assert(in_tensor.layout() == out_tensor.layout());
-          if (!in_tensor.has_padding() && !out_tensor.has_padding())
-          {
-            assert(input_size == out_tensor.total_size());
-            memcpy(output_buffer, input_buffer, input_size);
-            return;
-          }
-        }
-        switch (rank)
-        {
-          case 0:
-          case 1:
-          {
-            const int32_t copy_len = _output_shape.dim(0);
-
-            memcpy(output_buffer, input_buffer, copy_len);
-            break;
-          }
-          case 2:
-          {
-            const int32_t copy_len = _output_shape.dim(1);
-
-            for (auto i = 0; i < _output_shape.dim(0); ++i)
-            {
-              neurun::util::Coordinates coords{i, 0};
-              memcpy(output_buffer + out_tensor.calcOffset(coords),
-                     input_buffer + in_tensor.calcOffset(coords), copy_len * sizeof(T));
-            }
-            break;
-          }
-          case 3:
-          {
-            const int32_t copy_len = _output_shape.dim(2);
-
-            for (auto i = 0; i < _output_shape.dim(0); ++i)
-            {
-              for (auto j = 0; j < _output_shape.dim(1); ++j)
-              {
-                neurun::util::Coordinates coords{i, j, 0};
-                memcpy(output_buffer + out_tensor.calcOffset(coords),
-                       input_buffer + in_tensor.calcOffset(coords), copy_len * sizeof(T));
-              }
-            }
-            break;
-          }
-          case 4:
-          {
-            // TODO Unify permute type and remove switch case
-            switch (_type)
-            {
-              case ir::operation::Permute::Type::NHWC_TO_NCHW:
-              {
-                for (auto n = 0; n < _output_shape.dim(0); ++n)
-                {
-                  for (auto c = 0; c < _output_shape.dim(1); ++c)
-                  {
-                    for (auto h = 0; h < _output_shape.dim(2); ++h)
-                    {
-                      for (auto w = 0; w < _output_shape.dim(3); ++w)
-                      {
-                        const neurun::util::Coordinates in_coords{n, h, w, c};
-                        const auto out_coords =
-                            convertCoordinates(in_coords, in_tensor.layout(), out_tensor.layout());
-                        const auto value =
-                            *reinterpret_cast<T *>(input_buffer + in_tensor.calcOffset(in_coords));
-                        *reinterpret_cast<T *>(output_buffer + out_tensor.calcOffset(out_coords)) =
-                            value;
-                      }
-                    }
-                  }
-                }
-                break;
-              }
-              case ir::operation::Permute::Type::NCHW_TO_NHWC:
-              {
-                for (auto n = 0; n < _output_shape.dim(0); ++n)
-                {
-                  for (auto h = 0; h < _output_shape.dim(1); ++h)
-                  {
-                    for (auto w = 0; w < _output_shape.dim(2); ++w)
-                    {
-                      for (auto c = 0; c < _output_shape.dim(3); ++c)
-                      {
-                        const neurun::util::Coordinates in_coords{n, c, h, w};
-                        const auto out_coords =
-                            convertCoordinates(in_coords, in_tensor.layout(), out_tensor.layout());
-                        const auto value =
-                            *reinterpret_cast<T *>(input_buffer + in_tensor.calcOffset(in_coords));
-                        *reinterpret_cast<T *>(output_buffer + out_tensor.calcOffset(out_coords)) =
-                            value;
-                      }
-                    }
-                  }
-                }
-                break;
-              }
-              case ir::operation::Permute::Type::COPY:
-              {
-                const int32_t copy_len = _output_shape.dim(3);
-
-                for (auto i = 0; i < _output_shape.dim(0); ++i)
-                {
-                  for (auto j = 0; j < _output_shape.dim(1); ++j)
-                  {
-                    for (auto k = 0; k < _output_shape.dim(2); ++k)
-                    {
-                      neurun::util::Coordinates coords{i, j, k, 0};
-                      memcpy(output_buffer + out_tensor.calcOffset(coords),
-                             input_buffer + in_tensor.calcOffset(coords), copy_len * sizeof(T));
-                    }
-                  }
-                }
-                break;
-              }
-              default:
-                throw std::runtime_error("NYI");
-                break;
-            }
-            break;
-          }
-          default:
-            throw std::runtime_error("NYI");
-            break;
-        }
-      });
-    };
-    _input->access(fn);
-  }
-
-private:
-  std::shared_ptr<backend::operand::ITensor> _input{nullptr};
-  std::shared_ptr<backend::operand::ITensor> _output{nullptr};
-  ir::Shape _output_shape{};
-  ir::operation::Permute::Type _type{ir::operation::Permute::Type::COPY};
-  ir::DataType _dataType{ir::DataType::FLOAT32};
-};
-
-} // namespace kernel
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_CPU_KERNEL_PERMUTE_LAYER_H__
diff --git a/runtime/neurun/backend/cpu/kernel/ReshapeLayer.cc b/runtime/neurun/backend/cpu/kernel/ReshapeLayer.cc
deleted file mode 100644
index caeee9f12..000000000
--- a/runtime/neurun/backend/cpu/kernel/ReshapeLayer.cc
+++ /dev/null
@@ -1,54 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ReshapeLayer.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-
-ReshapeLayer::ReshapeLayer() : _inputData(), _outputData(), _inputDescr(), _outputDescr()
-{
-  // DO NOTHING
-}
-
-void ReshapeLayer::reshapeGeneric()
-{
-  size_t count = sizeOfData(_inputDescr.type, _inputDescr.dimensions);
-  memcpy(_outputData.v, _inputData.v, count);
-}
-
-void ReshapeLayer::configure(uint8_t *inputData, const TensorDescriptor &inputDescr,
-                             uint8_t *outputData, const TensorDescriptor &outputDescr)
-{
-  _inputData.u8 = inputData;
-  _inputDescr = inputDescr;
-  _outputData.u8 = outputData;
-  _outputDescr = outputDescr;
-}
-
-void ReshapeLayer::run() { reshapeGeneric(); }
-
-} // namespace kernel
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/cpu/kernel/ReshapeLayer.h b/runtime/neurun/backend/cpu/kernel/ReshapeLayer.h
deleted file mode 100644
index 25dd851b2..000000000
--- a/runtime/neurun/backend/cpu/kernel/ReshapeLayer.h
+++ /dev/null
@@ -1,65 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_CPU_KERNEL_RESHAPELAYER_H__
-#define __NEURUN_BACKEND_CPU_KERNEL_RESHAPELAYER_H__
-
-#include <exec/IFunction.h>
-
-#include "OperationUtils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-
-class ReshapeLayer : public ::neurun::exec::IFunction
-{
-public:
-  ReshapeLayer();
-
-public:
-  void reshapeGeneric();
-
-  void configure(uint8_t *inputData, const TensorDescriptor &inputDescr, uint8_t *outputData,
-                 const TensorDescriptor &outputDescr);
-
-  void run();
-  void runSync()
-  {
-    // this abstract method is used just for profiling and called for
-    // backend::acl_common::AclFunction
-    run();
-  }
-
-private:
-  DataPtr _inputData;
-  DataPtr _outputData;
-
-  TensorDescriptor _inputDescr;
-  TensorDescriptor _outputDescr;
-};
-
-} // namespace kernel
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_CPU_KERNEL_RESHAPELAYER_H__
diff --git a/runtime/neurun/backend/cpu/kernel/SoftMaxLayer.cc b/runtime/neurun/backend/cpu/kernel/SoftMaxLayer.cc
deleted file mode 100644
index 58ba109b4..000000000
--- a/runtime/neurun/backend/cpu/kernel/SoftMaxLayer.cc
+++ /dev/null
@@ -1,172 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "SoftMaxLayer.h"
-
-#include <cker/operation/SoftMax.h>
-
-#include "OperationUtils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-
-SoftMaxLayer::SoftMaxLayer()
-    : _inputData(), _outputData(), _beta(0.0), _inputDescr(), _outputDescr(),
-      _inputType(OperandType::FLOAT32)
-{
-  // DO NOTHING
-}
-
-// Performs softmax along the input of size (input_size * batch_size).
-void Softmax(const float *in, const int input_size, const int batch_size, const float beta,
-             float *out)
-{
-  assert(input_size > 0);
-
-  // For each batch
-  for (int b = 0; b < batch_size; b++)
-  {
-    // Find the max coeff.
-    float max_coeff = in[0];
-    for (int i = 1; i < input_size; i++)
-    {
-      if (in[i] > max_coeff)
-        max_coeff = in[i];
-    }
-
-    // Compute the normalized sum of exps.
-    float exp_sum = 0.0;
-    for (int i = 0; i < input_size; i++)
-    {
-      out[i] = std::exp((in[i] - max_coeff) * beta);
-      exp_sum += out[i];
-    }
-
-    // Divide by the sum of exps.
-    float reciprocal_sum_exp = 1.f / exp_sum;
-    for (int i = 0; i < input_size; i++)
-    {
-      out[i] *= reciprocal_sum_exp;
-    }
-
-    // Advance in and out pointers for the next batch.
-    in += input_size;
-    out += input_size;
-  }
-}
-
-void SoftMaxLayer::softmaxFloat32()
-{
-  TensorDescriptor descrIn4D;
-
-  if (getNumberOfDimensions(_inputDescr) == 2)
-  {
-    uint32_t batch_size = getSizeOfDimension(_inputDescr, 0);
-    if (batch_size == 0)
-      throw std::runtime_error("batch_size should not be 0");
-
-    uint32_t input_size = getNumberOfElements(_inputDescr) / batch_size;
-    Softmax(_inputData.f, input_size, batch_size, _beta, _outputData.f);
-  }
-  else if (getNumberOfDimensions(_inputDescr) == 4)
-  {
-    nnfw::cker::SoftmaxParams op_params;
-    op_params.beta = _beta;
-    nnfw::cker::Softmax(op_params, convertTensorDescriptorToCkerShape(_inputDescr), _inputData.f,
-                        convertTensorDescriptorToCkerShape(_outputDescr), _outputData.f);
-  }
-  else
-  {
-    throw std::runtime_error{"only 2D and 4D tensors supported"};
-  }
-}
-
-void SoftMaxLayer::softmaxQuant8()
-{
-  TensorDescriptor descrIn4D = _inputDescr;
-
-  if (getNumberOfDimensions(_inputDescr) == 2)
-  {
-    uint32_t batch_size = getSizeOfDimension(_inputDescr, 0);
-    if (batch_size == 0)
-      throw std::runtime_error("batch_size should not be 0");
-
-    uint32_t input_size = getNumberOfElements(_inputDescr) / batch_size;
-    descrIn4D.dimensions = {batch_size, 1, 1, input_size};
-  }
-  else if (getNumberOfDimensions(_inputDescr) == 4)
-  {
-    descrIn4D = _inputDescr;
-  }
-  else
-  {
-    throw std::runtime_error{"only 2D and 4D tensors supported"};
-  }
-  if (_outputDescr.offset != 0 || _outputDescr.scale != 1.f / 256)
-  {
-    throw std::runtime_error{"incorrect scale / offset for output"};
-  }
-  static const int32_t kScaledDiffIntegerBits = 5;
-  const double input_beta_real_multiplier = std::min(
-      1.0 * _beta * _inputDescr.scale * (1 << (31 - kScaledDiffIntegerBits)), (1ll << 31) - 1.0);
-  int32_t input_multiplier = 0;
-  int32_t input_left_shift = 0;
-  QuantizeMultiplierGreaterThanOne(input_beta_real_multiplier, &input_multiplier,
-                                   &input_left_shift);
-  float diff_min = -1.0f * CalculateInputRadius(kScaledDiffIntegerBits, input_left_shift);
-
-  nnfw::cker::SoftmaxParams op_params;
-  op_params.input_multiplier = input_multiplier;
-  op_params.input_left_shift = input_left_shift;
-  op_params.diff_min = diff_min;
-  nnfw::cker::Softmax(op_params, convertTensorDescriptorToCkerShape(descrIn4D), _inputData.u8,
-                      convertTensorDescriptorToCkerShape(descrIn4D), _outputData.u8);
-}
-
-void SoftMaxLayer::configure(uint8_t *inputData, const TensorDescriptor &inputDescr,
-                             const float beta, uint8_t *outputData,
-                             const TensorDescriptor &outputDescr)
-{
-  _inputData.u8 = inputData;
-  _inputDescr = inputDescr;
-  _inputType = inputDescr.type;
-  _outputData.u8 = outputData;
-  _outputDescr = outputDescr;
-  _beta = beta;
-}
-
-void SoftMaxLayer::run()
-{
-  if (_inputType == OperandType::FLOAT32)
-  {
-    softmaxFloat32();
-  }
-  else if (_inputType == OperandType::QUANT8_ASYMM)
-  {
-    softmaxQuant8();
-  }
-}
-
-} // namespace kernel
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/cpu/kernel/SoftMaxLayer.h b/runtime/neurun/backend/cpu/kernel/SoftMaxLayer.h
deleted file mode 100644
index 4723afb72..000000000
--- a/runtime/neurun/backend/cpu/kernel/SoftMaxLayer.h
+++ /dev/null
@@ -1,71 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_CPU_KERNEL_SOFTMAXLAYER_H__
-#define __NEURUN_BACKEND_CPU_KERNEL_SOFTMAXLAYER_H__
-
-#include <exec/IFunction.h>
-
-#include "OperationUtils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-
-class SoftMaxLayer : public ::neurun::exec::IFunction
-{
-public:
-  SoftMaxLayer();
-
-public:
-  void softmaxFloat32();
-
-  void softmaxQuant8();
-
-  void configure(uint8_t *inputData, const TensorDescriptor &inputDescr, const float beta,
-                 uint8_t *outputData, const TensorDescriptor &outputDescr);
-
-  void run();
-  void runSync()
-  {
-    // this abstract method is used just for profiling and called for
-    // backend::acl_common::AclFunction
-    run();
-  }
-
-private:
-  DataPtr _inputData;
-  DataPtr _outputData;
-
-  float _beta;
-
-  TensorDescriptor _inputDescr;
-  TensorDescriptor _outputDescr;
-
-  OperandType _inputType;
-};
-
-} // namespace kernel
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_CPU_KERNEL_SOFTMAXLAYER_H__
diff --git a/runtime/neurun/backend/cpu/kernel/SubLayer.cc b/runtime/neurun/backend/cpu/kernel/SubLayer.cc
deleted file mode 100644
index c6f7188e0..000000000
--- a/runtime/neurun/backend/cpu/kernel/SubLayer.cc
+++ /dev/null
@@ -1,100 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "SubLayer.h"
-
-#include <cker/operation/BinaryArithmeticOps.h>
-
-#include "OperationUtils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-
-void SubLayer::subFloat32()
-{
-  float output_activation_min, output_activation_max;
-  CalculateActivationRangeFloat(_activation, &output_activation_min, &output_activation_max);
-  nnfw::cker::BinaryArithmeticOpParam op_params;
-  op_params.float_activation_max = output_activation_max;
-  op_params.float_activation_min = output_activation_min;
-  const std::function<float(const float &, const float &)> fn = [](const float &a, const float &b) {
-    return a - b;
-  };
-
-  if (!HaveSameShapes(&_lhsDescr, &_rhsDescr))
-  {
-    nnfw::cker::BroadcastBinaryArithmeticOpSlow(
-        op_params, convertToExtendedCkerShape(_lhsDescr), _lhsData.f,
-        convertToExtendedCkerShape(_rhsDescr), _rhsData.f, convertToExtendedCkerShape(_outputDescr),
-        _outputData.f, fn);
-    return;
-  }
-
-  nnfw::cker::BinaryArithmeticOp(op_params, convertTensorDescriptorToCkerShape(_lhsDescr),
-                                 _lhsData.f, convertTensorDescriptorToCkerShape(_rhsDescr),
-                                 _rhsData.f, convertTensorDescriptorToCkerShape(_outputDescr),
-                                 _outputData.f, fn);
-}
-
-void SubLayer::subQuant8()
-{
-  int32_t output_activation_min, output_activation_max;
-  CalculateActivationRangeUint8(_activation, _outputDescr, &output_activation_min,
-                                &output_activation_max);
-  // nnfw::cker::SubParam op_params;
-  // op_params.quantized_activation_max = output_activation_max;
-  // op_params.quantized_activation_min = output_activation_min;
-
-  // cker quant8 sub is not implemented yet
-  throw std::runtime_error{"NYI"};
-}
-
-void SubLayer::configure(uint8_t *lhsData, const TensorDescriptor &lhsDescr, uint8_t *rhsData,
-                         const TensorDescriptor &rhsDescr, const ir::Activation activation,
-                         uint8_t *outputData, const TensorDescriptor &outputDescr)
-{
-  _lhsData.u8 = lhsData;
-  _lhsDescr = lhsDescr;
-  _rhsData.u8 = rhsData;
-  _rhsDescr = rhsDescr;
-  _inputType = lhsDescr.type;
-  _activation = activation;
-  _outputData.u8 = outputData;
-  _outputDescr = outputDescr;
-}
-
-void SubLayer::run()
-{
-  if (_inputType == OperandType::FLOAT32)
-  {
-    subFloat32();
-  }
-  else if (_inputType == OperandType::QUANT8_ASYMM)
-  {
-    subQuant8();
-  }
-}
-
-} // namespace kernel
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/cpu/kernel/SubLayer.h b/runtime/neurun/backend/cpu/kernel/SubLayer.h
deleted file mode 100644
index c9abdb48c..000000000
--- a/runtime/neurun/backend/cpu/kernel/SubLayer.h
+++ /dev/null
@@ -1,77 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_CPU_KERNEL_SUBLAYER_H__
-#define __NEURUN_BACKEND_CPU_KERNEL_SUBLAYER_H__
-
-#include <exec/IFunction.h>
-
-#include "OperationUtils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace kernel
-{
-
-class SubLayer : public ::neurun::exec::IFunction
-{
-public:
-  SubLayer() : _lhsData(), _rhsData(), _outputData(), _lhsDescr(), _rhsDescr(), _outputDescr()
-  {
-    // DO NOTHING
-  }
-
-public:
-  void subFloat32();
-
-  void subQuant8();
-
-  void configure(uint8_t *lhsData, const TensorDescriptor &lhsDescr, uint8_t *rhsData,
-                 const TensorDescriptor &rhsDescr, const ir::Activation activation,
-                 uint8_t *outputData, const TensorDescriptor &outputDescr);
-
-  void run();
-  void runSync()
-  {
-    // this abstract method is used just for profiling and called for
-    // backend::acl_common::AclFunction
-    run();
-  }
-
-private:
-  DataPtr _lhsData;
-  DataPtr _rhsData;
-  DataPtr _outputData;
-
-  TensorDescriptor _lhsDescr;
-  TensorDescriptor _rhsDescr;
-  TensorDescriptor _outputDescr;
-
-  ir::Activation _activation{ir::Activation::NONE};
-
-  OperandType _inputType{OperandType::FLOAT32};
-};
-
-} // namespace kernel
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_CPU_KERNEL_SUBLAYER_H__
diff --git a/runtime/neurun/backend/cpu/operand/Tensor.cc b/runtime/neurun/backend/cpu/operand/Tensor.cc
deleted file mode 100644
index 21d4a9dce..000000000
--- a/runtime/neurun/backend/cpu/operand/Tensor.cc
+++ /dev/null
@@ -1,45 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "Tensor.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace operand
-{
-
-size_t Tensor::calcOffset(const neurun::util::Coordinates &coords) const
-{
-  size_t rank = num_dimensions();
-  size_t offset = 0;
-  for (size_t i = 0; i < rank; ++i)
-  {
-    offset = offset * dimension(i) + coords[i];
-  }
-  offset *= sizeOfDataType(data_type());
-  return offset;
-}
-
-void Tensor::access(const std::function<void(ITensor &)> &fn) { fn(*this); }
-
-} // namespace operand
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/cpu/operand/Tensor.h b/runtime/neurun/backend/cpu/operand/Tensor.h
deleted file mode 100644
index dec680873..000000000
--- a/runtime/neurun/backend/cpu/operand/Tensor.h
+++ /dev/null
@@ -1,77 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_CPU_OPERAND_TENSOR_H__
-#define __NEURUN_BACKEND_CPU_OPERAND_TENSOR_H__
-
-#include <backend/operand/ITensor.h>
-#include "ir/OperandInfo.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-namespace operand
-{
-
-class Tensor : public ::neurun::backend::operand::ITensor
-{
-public:
-  Tensor() = delete;
-
-public:
-  Tensor(const ir::OperandInfo &info) : _info(info)
-  {
-    // DO NOTHING
-  }
-
-public:
-  void setBuffer(uint8_t *buffer) { _buffer = buffer; }
-  ir::DataType data_type() const { return _info.typeInfo().type(); }
-
-public:
-  uint8_t *buffer() const override { return _buffer; }
-  /**
-   * @brief Get dimension by index
-   *
-   * @param index Index to get diemension
-   * @return size_t Dimension at index
-   * @note N : dimension(0)
-   *       H : dimension(1)
-   *       W : dimension(2)
-   *       C : dimension(3)
-   */
-  size_t dimension(size_t index) const override { return _info.shape().dim(index); }
-  size_t num_dimensions() const override { return _info.shape().rank(); }
-  size_t total_size() const override { return _info.total_size(); }
-  size_t calcOffset(const neurun::util::Coordinates &coords) const override;
-  ir::Layout layout() const override { return ir::Layout::NHWC; }
-  bool has_padding() const override { return false; }
-  void access(const std::function<void(ITensor &tensor)> &fn) final;
-
-private:
-  ir::OperandInfo _info;
-  uint8_t *_buffer = nullptr;
-};
-
-} // namespace operand
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_CPU_OPERAND_TENSOR_H__
diff --git a/runtime/neurun/backend/cpu_common/CMakeLists.txt b/runtime/neurun/backend/cpu_common/CMakeLists.txt
deleted file mode 100644
index 5b6161ba7..000000000
--- a/runtime/neurun/backend/cpu_common/CMakeLists.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-file(GLOB SOURCES "*.cc")
-file(GLOB_RECURSE TESTS "*.test.cc")
-list(REMOVE_ITEM SOURCES ${TESTS})
-
-add_library(${LIB_NEURUN_BACKEND_CPU_COMMON} STATIC ${SOURCES})
-
-target_include_directories(${LIB_NEURUN_BACKEND_CPU_COMMON} PUBLIC ${NEURUN_INCLUDE_DIR})
-target_include_directories(${LIB_NEURUN_BACKEND_CPU_COMMON} PUBLIC ${CMAKE_CURRENT_SOURCE_DIR})
-target_link_libraries(${LIB_NEURUN_BACKEND_CPU_COMMON} PUBLIC nnfw_lib_cpp14)
-target_link_libraries(${LIB_NEURUN_BACKEND_CPU_COMMON} PRIVATE nnfw_lib_misc)
-target_link_libraries(${LIB_NEURUN_BACKEND_CPU_COMMON} PRIVATE neurun_core)
-target_link_libraries(${LIB_NEURUN_BACKEND_CPU_COMMON} PRIVATE nnfw_common)
-target_link_libraries(${LIB_NEURUN_BACKEND_CPU_COMMON} PRIVATE nnfw_coverage)
-
-set_target_properties(${LIB_NEURUN_BACKEND_CPU_COMMON} PROPERTIES POSITION_INDEPENDENT_CODE ON)
-set_target_properties(${LIB_NEURUN_BACKEND_CPU_COMMON} PROPERTIES OUTPUT_NAME backend_cpu_common)
-
-# Unit Tests
-set(TEST_NEURUN_BACKEND_CPU_COMMON test_neurun_backend_cpu_common)
-
-add_executable(${TEST_NEURUN_BACKEND_CPU_COMMON} ${TESTS})
-
-target_link_libraries(${TEST_NEURUN_BACKEND_CPU_COMMON} ${LIB_NEURUN_BACKEND_CPU_COMMON})
-target_link_libraries(${TEST_NEURUN_BACKEND_CPU_COMMON} gtest gtest_main dl ${LIB_PTHREAD})
-target_include_directories(${TEST_NEURUN_BACKEND_CPU_COMMON} PRIVATE ${NEURUN_INCLUDE_DIR})
-
-add_test(${TEST_NEURUN_BACKEND_CPU_COMMON} ${TEST_NEURUN_BACKEND_CPU_COMMON})
-install(TARGETS ${TEST_NEURUN_BACKEND_CPU_COMMON} DESTINATION unittest)
diff --git a/runtime/neurun/backend/cpu_common/MemoryPlanner.cc b/runtime/neurun/backend/cpu_common/MemoryPlanner.cc
deleted file mode 100644
index 19961362e..000000000
--- a/runtime/neurun/backend/cpu_common/MemoryPlanner.cc
+++ /dev/null
@@ -1,220 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "MemoryPlanner.h"
-#include "util/logging.h"
-#include <cassert>
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu_common
-{
-
-Allocator::Allocator(uint32_t capacity)
-{
-  _base = nnfw::cpp14::make_unique<uint8_t[]>(capacity);
-
-  VERBOSE(ALLOC) << "allocation capacity: " << capacity << std::endl;
-  VERBOSE(ALLOC) << "base pointer: " << static_cast<void *>(_base.get()) << std::endl;
-}
-
-void BumpPlanner::claim(const ir::OperandIndex &ind, size_t size)
-{
-  assert(size != 0);
-
-  Block blk{_capacity, size};
-  _mem_plans[ind] = blk;
-  _capacity += size;
-
-  VERBOSE(BP_PLANNER) << "CLAIM(#" << ind.value() << "): " << blk.offset << ", " << blk.size
-                      << std::endl;
-}
-
-void BumpPlanner::release(const ir::OperandIndex &ind)
-{
-  VERBOSE(BP_PLANNER) << "RELEASE(#" << ind.value() << "): "
-                      << "NOTHING does" << std::endl;
-}
-
-// There are some assumptions for claiming memory(== making a reservation for memory).
-// 1. About _claim_table(std::map).
-//   - The table's data structure is std::map so that it always sorts
-//     value(OperandIndex) by key(base_offset).
-//   - This claim() inserts key/value into _claim_table and the release() removes the key/value from
-//     _claim_table.
-//   - _claim_table shows the memory status at a certain point in time. Therefore,
-//     - If _claim_table has an offset and a certain size at a certain point in time,
-//       it means the place at the offset has been already claimed(== can't claim now. need to find
-//       someplace new).
-//     - If _claim_table doesn't have any element for an offset and a certain size at a certain
-//       point in time, it means the place at the offset can be claimed.
-// 2. In the loop for _claim_table, we can assume the current claim_base_offset value is bigger than
-//    the previous claim_base_offset.
-void FirstFitPlanner::claim(const ir::OperandIndex &ind, size_t size)
-{
-  assert(size != 0);
-
-  // Find the right position for claiming
-  uint32_t next_offset = 0;
-  for (auto &mem_claim : _claim_table)
-  {
-    auto claimed_base_offset = mem_claim.first;
-    auto claimed_size = _mem_plans[mem_claim.second].size;
-    if (next_offset + size <= claimed_base_offset)
-    {
-      break;
-    }
-    else
-    {
-      next_offset = claimed_base_offset + claimed_size;
-    }
-  }
-
-  // Now next_offset is set to the proper offset
-  _claim_table[next_offset] = ind;
-  _mem_plans[ind] = {next_offset, size};
-
-  VERBOSE(FF_PLANNER) << "claim(#" << ind.value() << "): [+" << next_offset << ", " << size << "sz]"
-                      << std::endl;
-
-  if (_capacity < next_offset + size)
-  {
-    _capacity = next_offset + size;
-  }
-}
-
-void FirstFitPlanner::release(const ir::OperandIndex &ind)
-{
-  for (auto it = _claim_table.cbegin(); it != _claim_table.cend(); ++it)
-  {
-    if (it->second == ind)
-    {
-      uint32_t offset = it->first;
-      uint32_t index = ind.value();
-      uint32_t size = _mem_plans[ind].size;
-
-      _claim_table.erase(it);
-
-      VERBOSE(FF_PLANNER) << "release(#" << index << "): [+" << offset << ", " << size << "sz]"
-                          << std::endl;
-      return;
-    }
-  }
-  assert(!"Cannot release for given index. It has been not claimed or released already.");
-}
-
-WICPlanner::WICPlanner()
-    : _initialized(false), _capacity(0), _mem_plans(), _live_operands(), _interference_graph(),
-      _map_size_to_operands(), _claim_table()
-{
-  // DO NOTHING
-}
-
-void WICPlanner::claim(const ir::OperandIndex &ind, size_t size)
-{
-  assert(size != 0);
-
-  _map_size_to_operands.insert({size, ind});
-  for (auto &live_operand : _live_operands)
-  {
-    _interference_graph[live_operand].insert(ind);
-    _interference_graph[ind].insert(live_operand);
-  }
-  _live_operands.insert(ind);
-
-  VERBOSE(WIC_PLANNER) << "claim(#" << ind.value() << "): [" << size << "sz]" << std::endl;
-}
-
-void WICPlanner::release(const ir::OperandIndex &ind)
-{
-  _live_operands.erase(ind);
-  VERBOSE(WIC_PLANNER) << "release(#" << ind.value() << ")" << std::endl;
-}
-
-/*
- * Build memory plans using liveness and size of operands
- * 1. Build inference graph at claim
- *   - Two operands interfere if they have overlapped live range
- * 2. Sort operands descending order of size
- *   - Use std::multimap to sort operands
- * 3. Allocate memory block for sorted operands
- *   - Find free memory block which does not overlap with interfered operands
- */
-void WICPlanner::buildMemoryPlans()
-{
-  for (auto &size_to_operand : _map_size_to_operands)
-  {
-    uint32_t size = size_to_operand.first;
-    ir::OperandIndex ind = size_to_operand.second;
-    VERBOSE(WIC_PLANNER) << "build_plan(#" << ind.value() << "): [" << size << "sz]" << std::endl;
-
-    // Find firstfit which does not interfere with live operands
-    uint32_t next_offset = 0;
-    if (_interference_graph.find(ind) != _interference_graph.end())
-    {
-      std::unordered_set<ir::OperandIndex> &interferences = _interference_graph.find(ind)->second;
-      for (auto &mem_claim : _claim_table)
-      {
-        if (interferences.find(mem_claim.second) != interferences.end())
-        {
-          auto claimed_base_offset = mem_claim.first;
-          auto claimed_size = _mem_plans[mem_claim.second].size;
-          VERBOSE(WIC_PLANNER) << "interfere (#" << mem_claim.second.value() << "): [+"
-                               << claimed_base_offset << ", " << claimed_size << "sz]" << std::endl;
-          if (next_offset + size <= claimed_base_offset)
-          {
-            break;
-          }
-          else if (next_offset < claimed_base_offset + claimed_size)
-          {
-            next_offset = claimed_base_offset + claimed_size;
-          }
-        }
-      }
-    }
-    else
-    {
-      VERBOSE(WIC_PLANNER) << "No interference" << std::endl;
-    }
-
-    _claim_table.insert({next_offset, ind});
-    _mem_plans[ind] = {next_offset, size};
-    VERBOSE(WIC_PLANNER) << "alloc(#" << ind.value() << "): [+" << next_offset << ", " << size
-                         << "sz]" << std::endl;
-
-    if (_capacity < next_offset + size)
-    {
-      _capacity = next_offset + size;
-    }
-  }
-  _initialized = true;
-  _interference_graph.clear();
-  _map_size_to_operands.clear();
-  _claim_table.clear();
-}
-
-WICPlanner::MemoryPlans &WICPlanner::memory_plans()
-{
-  if (!_initialized)
-    buildMemoryPlans();
-  return _mem_plans;
-}
-
-} // namespace cpu_common
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/cpu_common/MemoryPlanner.h b/runtime/neurun/backend/cpu_common/MemoryPlanner.h
deleted file mode 100644
index c4f5e6a9e..000000000
--- a/runtime/neurun/backend/cpu_common/MemoryPlanner.h
+++ /dev/null
@@ -1,217 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/**
- * @file        MemoryPlanner.h
- * @brief       This file contains Memory Planning related classes
- */
-
-#ifndef __NEURUN_BACKEND_CPU_COMMON_MEMORY_PLANNER_H__
-#define __NEURUN_BACKEND_CPU_COMMON_MEMORY_PLANNER_H__
-
-#include <map>
-#include <unordered_set>
-#include <cpp14/memory.h>
-
-#include "ir/OperandIndexMap.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu_common
-{
-
-/**
- * @brief Structure to have memory offset and size
- */
-struct Block
-{
-  uint32_t offset;
-  size_t size;
-};
-
-/**
- * @brief Class to allocate memory
- */
-class Allocator
-{
-public:
-  Allocator(uint32_t capacity);
-  /**
-   * @brief Get memory base pointer
-   * @return base pointer
-   */
-  uint8_t *base() const { return _base.get(); }
-  void release() { _base.reset(); }
-
-private:
-  std::unique_ptr<uint8_t[]> _base;
-};
-
-/**
- * @brief Interface to plan memory
- */
-struct IMemoryPlanner
-{
-  using MemoryPlans = ir::OperandIndexMap<Block>;
-
-  /**
-   * @brief Claim memory for operand
-   * @param[in] index The operand index
-   * @param[in] size The size of the memory
-   */
-  virtual void claim(const ir::OperandIndex &, size_t) = 0;
-  /**
-   * @brief Release memory for operand
-   * @param[in] index The operand index
-   */
-  virtual void release(const ir::OperandIndex &) = 0;
-  /**
-   * @brief Get capacity for memory planning
-   * @return The value of capacity
-   */
-  virtual uint32_t capacity() = 0;
-  /**
-   * @brief Get MemoryPlans
-   * @return MemoryPlans
-   */
-  virtual MemoryPlans &memory_plans() = 0;
-
-  virtual ~IMemoryPlanner() = default;
-};
-
-/**
- * @brief Class to plan memory by bump way
- */
-class BumpPlanner : public IMemoryPlanner
-{
-public:
-  /**
-   * @brief Claim memory for operand by bump way
-   * @param[in] index The operand index
-   * @param[in] size The size of the memory
-   */
-  void claim(const ir::OperandIndex &, size_t) override;
-  /**
-   * @brief Release memory for operand by bump way
-   * @param[in] index The operand index
-   */
-  void release(const ir::OperandIndex &) override;
-  /**
-   * @brief Get capacity for memory planning
-   * @return The value of capacity
-   */
-  uint32_t capacity() override { return _capacity; }
-  /**
-   * @brief Get MemoryPlans
-   * @return MemoryPlans
-   */
-  MemoryPlans &memory_plans() override { return _mem_plans; }
-
-private:
-  uint32_t _capacity = 0;
-  MemoryPlans _mem_plans;
-};
-
-/**
- * @brief Class to plan memory by firstfit way
- */
-class FirstFitPlanner : public IMemoryPlanner
-{
-public:
-  /**
-   * @brief Claim memory for operand by firstfit way
-   * @param[in] index The operand index
-   * @param[in] size The size of the memory
-   */
-  void claim(const ir::OperandIndex &, size_t) override;
-  /**
-   * @brief Release memory for operand by firstfit way
-   * @param[in] index The operand index
-   */
-  void release(const ir::OperandIndex &) override;
-  /**
-   * @brief Get capacity for memory planning
-   * @return The value of capacity
-   */
-  uint32_t capacity() override { return _capacity; }
-  /**
-   * @brief Get MemoryPlans
-   * @return MemoryPlans
-   */
-  MemoryPlans &memory_plans() override { return _mem_plans; }
-
-private:
-  uint32_t _capacity = 0;
-  MemoryPlans _mem_plans;
-  // Use std::map because claim() assumes that _claim_table is sorted by uint32_t(base_offset)
-  std::map<uint32_t, ir::OperandIndex> _claim_table;
-};
-
-/**
- * @brief Class to plan memory by Weighted Interval Color algorithm
- */
-class WICPlanner : public IMemoryPlanner
-{
-public:
-  WICPlanner();
-
-  /**
-   * @brief Claim memory for operand by WIC algorithm
-   * @param[in] index The operand index
-   * @param[in] size The size of the memory
-   */
-  void claim(const ir::OperandIndex &, size_t) override;
-  /**
-   * @brief Release memory for operand by WIC algorithm
-   * @param[in] index The operand index
-   */
-  void release(const ir::OperandIndex &) override;
-  /**
-   * @brief Get capacity for memory planning
-   * @return The value of capacity
-   */
-  uint32_t capacity() override
-  {
-    if (!_initialized)
-      buildMemoryPlans();
-    return _capacity;
-  }
-  /**
-   * @brief Get MemoryPlans
-   * @return MemoryPlans
-   */
-  MemoryPlans &memory_plans() override;
-
-private:
-  void buildMemoryPlans();
-
-  bool _initialized;
-  uint32_t _capacity;
-  MemoryPlans _mem_plans;
-  std::unordered_set<ir::OperandIndex> _live_operands;
-  ir::OperandIndexMap<std::unordered_set<ir::OperandIndex>> _interference_graph;
-  // Sort operands by descending order of size
-  std::multimap<uint32_t, ir::OperandIndex, std::greater<uint32_t>> _map_size_to_operands;
-  std::multimap<uint32_t, ir::OperandIndex> _claim_table;
-};
-
-} // namespace cpu_common
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_CPU_COMMON_MEMORY_PLANNER_H__
diff --git a/runtime/neurun/backend/cpu_common/MemoryPlanner.test.cc b/runtime/neurun/backend/cpu_common/MemoryPlanner.test.cc
deleted file mode 100644
index b2be7db24..000000000
--- a/runtime/neurun/backend/cpu_common/MemoryPlanner.test.cc
+++ /dev/null
@@ -1,193 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <gtest/gtest.h>
-
-#include "MemoryPlanner.h"
-#include "ir/Index.h"
-
-TEST(Allocator, allocate_test)
-{
-  ::neurun::backend::cpu_common::Allocator allocator(1024);
-  ASSERT_NE(allocator.base(), nullptr);
-}
-
-TEST(BumpPlanner, claim_test)
-{
-  ::neurun::backend::cpu_common::BumpPlanner planner;
-
-  auto claim = [&planner](uint32_t index, size_t size, uint32_t expected_offset) {
-    neurun::ir::OperandIndex mem_idx(index);
-    planner.claim(mem_idx, size);
-    auto mem_blk = planner.memory_plans()[mem_idx];
-    ASSERT_EQ(mem_blk.offset, expected_offset);
-    ASSERT_EQ(mem_blk.size, size);
-  };
-
-  claim(0, 10, 0);
-  claim(1, 20, 10);
-  claim(2, 30, 30);
-}
-
-TEST(FirstFitPlanner, claim_release_test)
-{
-  ::neurun::backend::cpu_common::FirstFitPlanner planner;
-
-  auto claim = [&planner](uint32_t index, size_t size, uint32_t expected_offset) {
-    neurun::ir::OperandIndex mem_idx(index);
-    planner.claim(mem_idx, size);
-    auto mem_blk = planner.memory_plans()[mem_idx];
-    ASSERT_EQ(mem_blk.offset, expected_offset);
-    ASSERT_EQ(mem_blk.size, size);
-  };
-
-  auto release = [&planner](uint32_t index) {
-    neurun::ir::OperandIndex mem_idx(index);
-    planner.release(mem_idx);
-  };
-
-  // 0 CLAIM - 10
-  claim(0, 10, 0);
-
-  // 1 CLAIM - 20
-  claim(1, 20, 10);
-
-  // 2 CLAIM - 30
-  claim(2, 30, 30);
-
-  // 0 RELEASE - 10
-  release(0);
-
-  // 3 CLAIM - 20
-  claim(3, 20, 60);
-
-  // 4 CLAIM - 5
-  claim(4, 5, 0);
-
-  // 5 CLAIM - 10
-  claim(5, 10, 80);
-
-  // 6 CLAIM - 5
-  claim(6, 5, 5);
-
-  // 2 RELEASE - 30
-  release(2);
-
-  // 7 CLAIM - 35
-  claim(7, 35, 90);
-
-  // 8 CLAIM - 10
-  claim(8, 10, 30);
-
-  // 4 RELEASE - 5
-  release(4);
-
-  // 9 CLAIM - 10
-  claim(9, 10, 40);
-
-  // 10 CLAIM - 10
-  claim(10, 10, 50);
-
-  // 6 RELEASE
-  release(6);
-
-  // 1 RELEASE
-  release(1);
-
-  // 8 RELEASE
-  release(8);
-
-  // 9 RELEASE
-  release(9);
-
-  // 10 RELEASE
-  release(10);
-
-  // 3 RELEASE
-  release(3);
-
-  // 5 RELEASE
-  release(5);
-
-  // 7 RELEASE
-  release(7);
-}
-
-TEST(WICPlanner, claim_release_test)
-{
-  ::neurun::backend::cpu_common::WICPlanner planner;
-
-  auto claim = [&planner](uint32_t index, size_t size) {
-    neurun::ir::OperandIndex mem_idx(index);
-    planner.claim(mem_idx, size);
-  };
-
-  auto release = [&planner](uint32_t index) {
-    neurun::ir::OperandIndex mem_idx(index);
-    planner.release(mem_idx);
-  };
-
-  auto verify = [&planner](uint32_t index, uint32_t size, uint32_t expected_offset) {
-    neurun::ir::OperandIndex mem_idx(index);
-    auto mem_blk = planner.memory_plans()[mem_idx];
-    ASSERT_EQ(mem_blk.offset, expected_offset);
-    ASSERT_EQ(mem_blk.size, size);
-  };
-
-  auto capacity = [&planner](uint32_t expected_capacity) {
-    auto actual_capacity = planner.capacity();
-    ASSERT_EQ(actual_capacity, expected_capacity);
-  };
-
-  claim(0, 20);
-  claim(1, 5);
-  release(0);
-  claim(2, 10);
-  release(1);
-  claim(3, 10);
-  release(2);
-  claim(4, 10);
-  release(3);
-  claim(5, 20);
-  release(4);
-  claim(6, 20);
-  release(5);
-  release(7);
-
-  // VERIFY 0 - 0
-  verify(0, 20, 0);
-
-  // VERIFY 1 - 20
-  verify(1, 5, 20);
-
-  // VERIFY 2 - 0
-  verify(2, 10, 0);
-
-  // VERIFY 3 - 10
-  verify(3, 10, 10);
-
-  // VERIFY 4 - 20
-  verify(4, 10, 20);
-
-  // VERIFY 5 - 0
-  verify(5, 20, 0);
-
-  // VERIFY 6 - 20
-  verify(6, 20, 20);
-
-  // CAPACITY - 40
-  capacity(40);
-}
diff --git a/runtime/neurun/backend/cpu_common/MemoryPlannerFactory.cc b/runtime/neurun/backend/cpu_common/MemoryPlannerFactory.cc
deleted file mode 100644
index 9da987aa4..000000000
--- a/runtime/neurun/backend/cpu_common/MemoryPlannerFactory.cc
+++ /dev/null
@@ -1,51 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "MemoryPlannerFactory.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu_common
-{
-
-MemoryPlannerFactory &MemoryPlannerFactory::get()
-{
-  static MemoryPlannerFactory instance;
-  return instance;
-}
-
-IMemoryPlanner *MemoryPlannerFactory::create(const std::string &key)
-{
-  if (key == "FirstFit")
-  {
-    return new FirstFitPlanner;
-  }
-  else if (key == "Bump")
-  {
-    return new BumpPlanner;
-  }
-  else if (key == "WIC")
-  {
-    return new WICPlanner;
-  }
-  return new FirstFitPlanner; // Default Planner
-}
-
-} // namespace cpu_common
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/cpu_common/MemoryPlannerFactory.h b/runtime/neurun/backend/cpu_common/MemoryPlannerFactory.h
deleted file mode 100644
index 829600e27..000000000
--- a/runtime/neurun/backend/cpu_common/MemoryPlannerFactory.h
+++ /dev/null
@@ -1,45 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_CPU_COMMON_MEMORY_PLANNER_FACTORY_H__
-#define __NEURUN_BACKEND_CPU_COMMON_MEMORY_PLANNER_FACTORY_H__
-
-#include "MemoryPlanner.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu_common
-{
-
-class MemoryPlannerFactory
-{
-public:
-  static MemoryPlannerFactory &get();
-
-private:
-  MemoryPlannerFactory() = default;
-
-public:
-  IMemoryPlanner *create(const std::string &key);
-};
-
-} // namespace cpu_common
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_CPU_COMMON_MEMORY_PLANNER_FACTORY_H__
diff --git a/runtime/neurun/backend/hi_perf_cpu/CMakeLists.txt b/runtime/neurun/backend/hi_perf_cpu/CMakeLists.txt
deleted file mode 100644
index 816edba5e..000000000
--- a/runtime/neurun/backend/hi_perf_cpu/CMakeLists.txt
+++ /dev/null
@@ -1,44 +0,0 @@
-set(LIB_NEURUN_BACKEND_HI_PERF_CPU neurun_backend_hi_perf)
-
-nnfw_find_package(NNPACK QUIET)
-
-option(BUILD_NEURUN_HI_PERF_CPU_BACKEND
-  "Build neurun HI_PERF_CPU backend"
-  ${NNPACK_FOUND} # Default value when there is no explicit user request
-)
-
-message(STATUS "Build neurun HI_PERF_CPU backend: ${BUILD_NEURUN_HI_PERF_CPU_BACKEND}")
-
-if(NOT BUILD_NEURUN_HI_PERF_CPU_BACKEND)
-  return()
-endif(NOT BUILD_NEURUN_HI_PERF_CPU_BACKEND)
-
-file(GLOB_RECURSE SOURCES "*.cc")
-file(GLOB_RECURSE TESTS "*.test.cc")
-list(REMOVE_ITEM SOURCES ${TESTS})
-
-add_library(${LIB_NEURUN_BACKEND_HI_PERF_CPU} SHARED ${SOURCES})
-
-target_link_libraries(${LIB_NEURUN_BACKEND_HI_PERF_CPU} PRIVATE nnfw_lib_misc)
-target_link_libraries(${LIB_NEURUN_BACKEND_HI_PERF_CPU} PRIVATE nnfw_lib_cpp14)
-target_link_libraries(${LIB_NEURUN_BACKEND_HI_PERF_CPU} PRIVATE neurun_core)
-target_link_libraries(${LIB_NEURUN_BACKEND_HI_PERF_CPU} PRIVATE nnfw_common)
-target_link_libraries(${LIB_NEURUN_BACKEND_HI_PERF_CPU} PRIVATE nnpack pthreadpool cpuinfo)
-target_link_libraries(${LIB_NEURUN_BACKEND_HI_PERF_CPU} PRIVATE nnfw_coverage)
-target_include_directories(${LIB_NEURUN_BACKEND_HI_PERF_CPU} PRIVATE ${NNPACK_INCLUDE_DIRS})
-
-set_target_properties(${LIB_NEURUN_BACKEND_HI_PERF_CPU} PROPERTIES OUTPUT_NAME backend_NNPACK)
-
-install(TARGETS ${LIB_NEURUN_BACKEND_HI_PERF_CPU} DESTINATION lib)
-
-# Unit Tests
-set(TEST_NEURUN_BACKEND_HI_PERF_CPU test_neurun_backend_hi_perf)
-
-add_executable(${TEST_NEURUN_BACKEND_HI_PERF_CPU} ${TESTS})
-
-target_link_libraries(${TEST_NEURUN_BACKEND_HI_PERF_CPU} ${LIB_NEURUN_BACKEND_HI_PERF_CPU})
-target_link_libraries(${TEST_NEURUN_BACKEND_HI_PERF_CPU} gtest gtest_main ${LIB_PTHREAD})
-target_link_libraries(${TEST_NEURUN_BACKEND_HI_PERF_CPU} nnpack)
-
-add_test(${TEST_NEURUN_BACKEND_HI_PERF_CPU} ${TEST_NEURUN_BACKEND_HI_PERF_CPU})
-install(TARGETS ${TEST_NEURUN_BACKEND_HI_PERF_CPU} DESTINATION unittest)
diff --git a/runtime/neurun/backend/hi_perf_cpu/HighPerformanceBackend.test.cc b/runtime/neurun/backend/hi_perf_cpu/HighPerformanceBackend.test.cc
deleted file mode 100644
index 625fe1c36..000000000
--- a/runtime/neurun/backend/hi_perf_cpu/HighPerformanceBackend.test.cc
+++ /dev/null
@@ -1,42 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <gtest/gtest.h>
-
-#include "nnpack.h"
-
-TEST(High_performance_backend, NNPACK_Test)
-{
-  // Check that it is possible to import
-  const enum nnp_status init_status = nnp_initialize();
-
-  // One of the allowed nnp status codes
-  ASSERT_GE(init_status, 0);
-  ASSERT_LE(init_status, 54);
-
-  // If it is possible to test, test relu
-  if (init_status == nnp_status_success)
-  {
-    float in[] = {-1, 1, -1, 1};
-    float out[4];
-    nnp_relu_output(1, 4, in, out, 0, nullptr);
-    for (int i = 0; i < 4; i++)
-    {
-      ASSERT_EQ(out[i], in[i] >= 0 ? in[i] : 0);
-    }
-  }
-  nnp_deinitialize();
-}
diff --git a/runtime/neurun/backend/hi_perf_cpu/KernelGenerator.cc b/runtime/neurun/backend/hi_perf_cpu/KernelGenerator.cc
deleted file mode 100644
index a13fe12b9..000000000
--- a/runtime/neurun/backend/hi_perf_cpu/KernelGenerator.cc
+++ /dev/null
@@ -1,18 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "KernelGenerator.h"
-// to force compilation
diff --git a/runtime/neurun/backend/hi_perf_cpu/KernelGenerator.h b/runtime/neurun/backend/hi_perf_cpu/KernelGenerator.h
deleted file mode 100644
index 3197995e1..000000000
--- a/runtime/neurun/backend/hi_perf_cpu/KernelGenerator.h
+++ /dev/null
@@ -1,47 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_HI_PERF_CPU_KERNEL_GENERATOR_H__
-#define __NEURUN_BACKEND_HI_PERF_CPU_KERNEL_GENERATOR_H__
-
-#include <backend/IKernelGenerator.h>
-
-#include "ir/Operands.h"
-#include "TensorBuilder.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace hi_perf_cpu
-{
-
-class KernelGenerator : public IKernelGenerator
-{
-public:
-  KernelGenerator(const Operands &ctx, const std::shared_ptr<TensorBuilder> &tensor_builder);
-  // TODO add more ops
-
-private:
-  const Operands &_ctx;
-  std::shared_ptr<TensorBuilder> _tensor_builder;
-};
-
-} // namespace hi_perf_cpu
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_HI_PERF_CPU_KERNEL_GENERATOR_H__
diff --git a/runtime/neurun/backend/hi_perf_cpu/TensorBuilder.cc b/runtime/neurun/backend/hi_perf_cpu/TensorBuilder.cc
deleted file mode 100644
index e6ebf5f0b..000000000
--- a/runtime/neurun/backend/hi_perf_cpu/TensorBuilder.cc
+++ /dev/null
@@ -1,18 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "TensorBuilder.h"
-// to force compilation
diff --git a/runtime/neurun/backend/hi_perf_cpu/TensorBuilder.h b/runtime/neurun/backend/hi_perf_cpu/TensorBuilder.h
deleted file mode 100644
index af879a41e..000000000
--- a/runtime/neurun/backend/hi_perf_cpu/TensorBuilder.h
+++ /dev/null
@@ -1,44 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef NNFW_TENSORBUILDER_H
-#define NNFW_TENSORBUILDER_H
-
-#include <unordered_map>
-
-#include <backend/ITensorBuilder.h>
-#include "ir/OperandIndexMap.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace hi_perf_cpu
-{
-
-class TensorBuilder : public ITensorBuilder
-{
-public:
-  TensorBuilder();
-
-private:
-};
-
-} // namespace hi_perf_cpu
-} // namespace backend
-} // namespace neurun
-
-#endif // NNFW_TENSORBUILDER_H
diff --git a/runtime/neurun/backend/srcn/Backend.h b/runtime/neurun/backend/srcn/Backend.h
deleted file mode 100644
index bc76a7ed2..000000000
--- a/runtime/neurun/backend/srcn/Backend.h
+++ /dev/null
@@ -1,64 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_SRCN_BACKEND_H__
-#define __NEURUN_BACKEND_SRCN_BACKEND_H__
-
-#include <memory>
-#include <backend/Backend.h>
-#include <ir/Operands.h>
-
-#include "Config.h"
-#include "ConstantInitializer.h"
-#include "KernelGenerator.h"
-#include "ShapeFixer.h"
-#include "TensorRegister.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-
-class Backend : public ::neurun::backend::Backend
-{
-public:
-  Backend() : _config{std::make_shared<Config>()} {}
-
-  std::shared_ptr<IConfig> config() const override { return _config; }
-
-  std::unique_ptr<BackendContext>
-  newContext(const ir::Operands &operands,
-             const std::shared_ptr<custom::IKernelBuilder> &kb) const override
-  {
-    auto tensor_builder = std::make_shared<TensorBuilder>();
-    return std::unique_ptr<BackendContext>{new BackendContext{
-        this, tensor_builder, std::make_shared<ConstantInitializer>(operands, tensor_builder),
-        std::make_shared<KernelGenerator>(operands, tensor_builder, kb),
-        std::make_shared<ShapeFixer>(operands, tensor_builder),
-        std::make_shared<TensorRegister>(operands, tensor_builder)}};
-  }
-
-private:
-  std::shared_ptr<IConfig> _config;
-};
-
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_SRCN_BACKEND_H__
diff --git a/runtime/neurun/backend/srcn/CMakeLists.txt b/runtime/neurun/backend/srcn/CMakeLists.txt
deleted file mode 100644
index 3b0440c8e..000000000
--- a/runtime/neurun/backend/srcn/CMakeLists.txt
+++ /dev/null
@@ -1,21 +0,0 @@
-if(NOT BUILD_SRCN_KERNEL)
-  message(STATUS "Skip building SRCN backend: SRCN kernel library is not build")
-  return()
-endif()
-
-set(LIB_NEURUN_BACKEND_SRCN neurun_backend_srcn)
-
-file(GLOB_RECURSE SOURCES "*.cc")
-
-add_library(${LIB_NEURUN_BACKEND_SRCN} SHARED ${SOURCES})
-
-target_link_libraries(${LIB_NEURUN_BACKEND_SRCN} PUBLIC nnfw_lib_cpp14)
-target_link_libraries(${LIB_NEURUN_BACKEND_SRCN} PRIVATE nnfw_lib_srcn)
-target_link_libraries(${LIB_NEURUN_BACKEND_SRCN} PRIVATE neurun_core)
-target_link_libraries(${LIB_NEURUN_BACKEND_SRCN} PRIVATE ${LIB_NEURUN_BACKEND_CPU_COMMON})
-target_link_libraries(${LIB_NEURUN_BACKEND_SRCN} PRIVATE nnfw_common)
-target_link_libraries(${LIB_NEURUN_BACKEND_SRCN} PRIVATE nnfw_coverage)
-
-set_target_properties(${LIB_NEURUN_BACKEND_SRCN} PROPERTIES OUTPUT_NAME backend_srcn)
-
-install(TARGETS ${LIB_NEURUN_BACKEND_SRCN} DESTINATION lib)
diff --git a/runtime/neurun/backend/srcn/Config.cc b/runtime/neurun/backend/srcn/Config.cc
deleted file mode 100644
index 6865657e7..000000000
--- a/runtime/neurun/backend/srcn/Config.cc
+++ /dev/null
@@ -1,30 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "Config.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-
-bool Config::initialize() { return true; }
-
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/srcn/Config.h b/runtime/neurun/backend/srcn/Config.h
deleted file mode 100644
index efc77fde2..000000000
--- a/runtime/neurun/backend/srcn/Config.h
+++ /dev/null
@@ -1,46 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_SRCN_CONFIG_H__
-#define __NEURUN_BACKEND_SRCN_CONFIG_H__
-
-#include <backend/IConfig.h>
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-
-class Config : public IConfig
-{
-public:
-  std::string id() override { return "srcn"; }
-  bool initialize() override;
-  bool SupportPermutation() override { return false; }
-  bool SupportSubTensorAlloc() override
-  {
-    // NOTE srcn allocator cannot support subtensor allocation yet
-    return false;
-  }
-};
-
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_SRCN_CONFIG_H__
diff --git a/runtime/neurun/backend/srcn/ConstantInitializer.cc b/runtime/neurun/backend/srcn/ConstantInitializer.cc
deleted file mode 100644
index f03628b1f..000000000
--- a/runtime/neurun/backend/srcn/ConstantInitializer.cc
+++ /dev/null
@@ -1,191 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ConstantInitializer.h"
-
-#include "kernel/OperationUtils.h"
-
-namespace
-{
-
-template <typename T>
-static void PermuteKernel(const neurun::ir::Operand &model_obj,
-                          neurun::backend::operand::ITensor &obj,
-                          const std::vector<int32_t> &permutation)
-{
-  const auto shape = model_obj.shape();
-  auto base = reinterpret_cast<const T *>(model_obj.data().base());
-
-  assert(shape.rank() == 4);
-  assert(permutation.size() == 4);
-  assert(permutation[0] != permutation[1] && permutation[0] != permutation[2] &&
-         permutation[0] != permutation[3]);
-  assert(permutation[1] != permutation[2] && permutation[1] != permutation[3]);
-  assert(permutation[2] != permutation[3]);
-  assert(permutation[0] < 4 && permutation[1] < 4 && permutation[2] < 4 && permutation[3] < 4);
-
-  obj.access([&](::neurun::backend::operand::ITensor &tensor) {
-    if (permutation[0] == 0 && permutation[1] == 1 && permutation[2] == 2 && permutation[3] == 3)
-    {
-      memcpy(tensor.buffer(), base, shape.num_elements() * sizeof(T));
-    }
-    else
-    {
-      const int32_t dim0 = shape.dim(0);
-      const int32_t dim1 = shape.dim(1);
-      const int32_t dim2 = shape.dim(2);
-      const int32_t dim3 = shape.dim(3);
-      for (auto i = 0; i < dim0; ++i)
-      {
-        for (auto j = 0; j < dim1; ++j)
-        {
-          for (auto k = 0; k < dim2; ++k)
-          {
-            for (auto l = 0; l < dim3; ++l)
-            {
-              Coordinates frontend_coords{i, j, k, l};
-              Coordinates coords = frontend_coords;
-              coords.set(0, frontend_coords[permutation[0]]);
-              coords.set(1, frontend_coords[permutation[1]]);
-              coords.set(2, frontend_coords[permutation[2]]);
-              coords.set(3, frontend_coords[permutation[3]]);
-              T *into = reinterpret_cast<T *>(tensor.buffer() + tensor.calcOffset(coords));
-              T value = *(base + i * dim1 * dim2 * dim3 + j * dim2 * dim3 + k * dim3 + l);
-              *into = value;
-            }
-          }
-        }
-      }
-    }
-  });
-}
-}
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-
-ConstantInitializer::ConstantInitializer(const ir::Operands &operands,
-                                         const std::shared_ptr<TensorBuilder> &tensor_builder)
-    : _operands{operands}, _tensor_builder{tensor_builder}
-{
-  // DO NOTHING
-}
-
-void ConstantInitializer::registerPermuteKernelInitializer(const ir::OperandIndex &index,
-                                                           const ir::Operand &obj,
-                                                           const std::vector<int32_t> &permutation)
-{
-  // For only CONSTANTS
-  if (!obj.isConstant())
-    return;
-
-  VERBOSE(FillOperandData) << "[SRCN] Fill data for operand " << index.value() << std::endl;
-
-  const auto type = obj.typeInfo().type();
-  using ir::DataType;
-  using namespace std::placeholders;
-
-  switch (type)
-  {
-    case DataType::FLOAT32:
-      _init_map[index] = std::bind(PermuteKernel<float>, _1, _2, permutation);
-      break;
-    case DataType::INT32:
-      _init_map[index] = std::bind(PermuteKernel<int32_t>, _1, _2, permutation);
-      break;
-    case DataType::UINT32:
-      _init_map[index] = std::bind(PermuteKernel<uint32_t>, _1, _2, permutation);
-      break;
-    case DataType::BOOL8:
-    case DataType::QUANT8_ASYMM:
-      _init_map[index] = std::bind(PermuteKernel<uint8_t>, _1, _2, permutation);
-      break;
-    case DataType::QUANT8_SYMM:
-      _init_map[index] = std::bind(PermuteKernel<int8_t>, _1, _2, permutation);
-      break;
-    default:
-      throw std::runtime_error("Not supported, yet");
-      break;
-  }
-}
-
-void ConstantInitializer::visit(const ir::operation::Conv2D &node)
-{
-  const auto &kernel_index = node.getInputs().at(ir::operation::Conv2D::KERNEL);
-  const auto &kernel_obj = _operands.at(kernel_index);
-  util::Coordinates permutation{0, 1, 2, 3};
-  const auto frontend_layout = _current_subg_layout;
-  const auto backend_layout = _tensor_builder->tensorAt(kernel_index)->layout();
-  assert(frontend_layout == ir::Layout::NHWC || frontend_layout == ir::Layout::NCHW);
-  assert(backend_layout == ir::Layout::NHWC || backend_layout == ir::Layout::NCHW);
-  const auto frontend_filter_layout =
-      frontend_layout == ir::Layout::NHWC ? kernel::FilterLayout::OHWI : kernel::FilterLayout::OIHW;
-  const auto backend_filter_layout =
-      backend_layout == ir::Layout::NHWC ? kernel::FilterLayout::HWIO : kernel::FilterLayout::OIHW;
-  registerPermuteKernelInitializer(
-      kernel_index, kernel_obj,
-      kernel::getFilterPermutation(frontend_filter_layout, backend_filter_layout));
-}
-
-void ConstantInitializer::visit(const ir::operation::DepthwiseConv2D &node)
-{
-  const auto &kernel_index = node.getInputs().at(ir::operation::DepthwiseConv2D::KERNEL);
-  const auto &kernel_obj = _operands.at(kernel_index);
-  util::Coordinates permutation{0, 1, 2, 3};
-  const auto frontend_layout = _current_subg_layout;
-  const auto backend_layout = _tensor_builder->tensorAt(kernel_index)->layout();
-  assert(frontend_layout == ir::Layout::NHWC || frontend_layout == ir::Layout::NCHW);
-  assert(backend_layout == ir::Layout::NHWC || backend_layout == ir::Layout::NCHW);
-  const auto frontend_filter_layout =
-      frontend_layout == ir::Layout::NHWC ? kernel::FilterLayout::OHWI : kernel::FilterLayout::OIHW;
-  const auto backend_filter_layout =
-      backend_layout == ir::Layout::NHWC ? kernel::FilterLayout::HWIO : kernel::FilterLayout::OIHW;
-  registerPermuteKernelInitializer(
-      kernel_index, kernel_obj,
-      kernel::getFilterPermutation(frontend_filter_layout, backend_filter_layout));
-}
-
-void ConstantInitializer::visit(const ir::operation::TransposeConv &node)
-{
-  // NOTE The srcn deconvolution layer takes a HWOI layout as kernel filter even though image layout
-  //      is NHWC.
-  //      This policy is the same with the tensorflow policy.
-  //      So for using srcn library, we need to change kernel layout to HWOI from OHWI or OIHW in
-  //      this case.
-  //      Also the srcn deconvolution layer takes a OIHW layout as kernel filter if image's layout
-  //      is NCHW
-  const auto &kernel_index = node.getInputs().at(ir::operation::TransposeConv::KERNEL);
-  const auto &kernel_obj = _operands.at(kernel_index);
-  const auto frontend_layout = _current_subg_layout;
-  const auto backend_layout = _tensor_builder->tensorAt(kernel_index)->layout();
-  assert(frontend_layout == ir::Layout::NHWC || frontend_layout == ir::Layout::NCHW);
-  assert(backend_layout == ir::Layout::NHWC || backend_layout == ir::Layout::NCHW);
-  const auto frontend_filter_layout =
-      frontend_layout == ir::Layout::NHWC ? kernel::FilterLayout::OHWI : kernel::FilterLayout::OIHW;
-  const auto backend_filter_layout =
-      backend_layout == ir::Layout::NHWC ? kernel::FilterLayout::HWOI : kernel::FilterLayout::IOHW;
-  registerPermuteKernelInitializer(
-      kernel_index, kernel_obj,
-      kernel::getFilterPermutation(frontend_filter_layout, backend_filter_layout));
-}
-
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/srcn/ConstantInitializer.h b/runtime/neurun/backend/srcn/ConstantInitializer.h
deleted file mode 100644
index eadfe2ae1..000000000
--- a/runtime/neurun/backend/srcn/ConstantInitializer.h
+++ /dev/null
@@ -1,60 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_COMPILER_SRCN_CONSTANT_INITIALIZER_H__
-#define __NEURUN_COMPILER_SRCN_CONSTANT_INITIALIZER_H__
-
-#include <backend/IConstantInitializer.h>
-#include <ir/Operands.h>
-#include "TensorBuilder.h"
-#include <util/Coordinates.h>
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-
-class ConstantInitializer : public IConstantInitializer
-{
-public:
-  ConstantInitializer(const ir::Operands &operands,
-                      const std::shared_ptr<TensorBuilder> &tensor_builder);
-
-public:
-  void registerPermuteKernelInitializer(const ir::OperandIndex &index, const ir::Operand &obj,
-                                        const std::vector<int32_t> &permutation);
-
-public:
-  void visit(const ir::operation::Conv2D &) override;
-  void visit(const ir::operation::DepthwiseConv2D &) override;
-  void visit(const ir::operation::TransposeConv &) override;
-
-private:
-  const ir::Operands &operands() const override { return _operands; }
-  std::shared_ptr<ITensorBuilder> tensor_builder() const override { return _tensor_builder; }
-
-private:
-  const ir::Operands &_operands;
-  std::shared_ptr<TensorBuilder> _tensor_builder;
-};
-
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_COMPILER_SRCN_CONSTANT_INITIALIZER_H__
diff --git a/runtime/neurun/backend/srcn/Convert.cc b/runtime/neurun/backend/srcn/Convert.cc
deleted file mode 100644
index 1d80b2c7c..000000000
--- a/runtime/neurun/backend/srcn/Convert.cc
+++ /dev/null
@@ -1,75 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "Convert.h"
-
-#include <cassert>
-#include <cpp14/memory.h>
-#include <ir/DataType.h>
-#include "Swizzle.h"
-#include <vector>
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-
-ir::Shape asKernelShape(const ir::Shape &shape, kernel::FilterLayout frontend_layout,
-                        kernel::FilterLayout backend_layout)
-{
-  assert(shape.rank() == 4);
-  if (frontend_layout == backend_layout)
-  {
-    return ir::Shape{shape.dim(0), shape.dim(1), shape.dim(2), shape.dim(3)};
-  }
-
-  const auto permutation = getFilterPermutation(frontend_layout, backend_layout);
-  if (permutation.size() == 0)
-  {
-    throw std::runtime_error("Not supported FilterLayout");
-  }
-  return ir::Shape{shape.dim(permutation[0]), shape.dim(permutation[1]), shape.dim(permutation[2]),
-                   shape.dim(permutation[3])};
-}
-
-ir::Shape asTensorShape(const ir::Shape &shape, ir::Layout frontend_layout,
-                        ir::Layout backend_layout)
-{
-  const uint32_t rank = shape.rank();
-
-  ir::Shape ret(rank);
-  for (uint32_t axis = 0; axis < rank; ++axis)
-  {
-    const auto ncnn_axis = ToNCNNAxis(rank, axis, frontend_layout, backend_layout);
-    ret.dim(ncnn_axis) = shape.dim(axis);
-  }
-
-  return ret;
-}
-
-ir::OperandInfo asTensorInfo(const ir::Shape &shape, const ir::TypeInfo &typeInfo,
-                             ir::Layout frontend_layout, ir::Layout backend_layout)
-{
-  ir::OperandInfo info(asTensorShape(shape, frontend_layout, backend_layout), typeInfo);
-
-  return info;
-}
-
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/srcn/Convert.h b/runtime/neurun/backend/srcn/Convert.h
deleted file mode 100644
index 64be46e60..000000000
--- a/runtime/neurun/backend/srcn/Convert.h
+++ /dev/null
@@ -1,46 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_SRCN_CONVERT_H__
-#define __NEURUN_BACKEND_SRCN_CONVERT_H__
-
-#include "kernel/OperationUtils.h"
-#include <ir/Layout.h>
-#include <ir/Shape.h>
-#include <ir/TypeInfo.h>
-#include <ir/OperandInfo.h>
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-
-ir::Shape asKernelShape(const ir::Shape &shape, kernel::FilterLayout frontend_layout,
-                        kernel::FilterLayout backend_layout);
-
-ir::Shape asTensorShape(const ir::Shape &shape, ir::Layout frontend_layout,
-                        ir::Layout backend_layout);
-
-ir::OperandInfo asTensorInfo(const ir::Shape &shape, const ir::TypeInfo &typeInfo,
-                             ir::Layout frontend_layout, ir::Layout backend_layout);
-
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_SRCN_CONVERT_H__
diff --git a/runtime/neurun/backend/srcn/KernelGenerator.cc b/runtime/neurun/backend/srcn/KernelGenerator.cc
deleted file mode 100644
index c096f9230..000000000
--- a/runtime/neurun/backend/srcn/KernelGenerator.cc
+++ /dev/null
@@ -1,275 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "KernelGenerator.h"
-
-#include <stdexcept>
-
-#include "cpp14/memory.h"
-#include "util/Padding.h"
-#include "kernel/ConvolutionLayer.h"
-#include "kernel/DepthwiseConvolutionLayer.h"
-#include "kernel/InstanceNormLayer.h"
-#include "kernel/TransposeConvLayer.h"
-#include "kernel/AddLayer.h"
-
-#include <backend/Backend.h>
-#include <backend/IConfig.h>
-
-#include "util/logging.h"
-
-#include "util/Utils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-
-KernelGenerator::KernelGenerator(const ir::Operands &operand_ctx,
-                                 const std::shared_ptr<TensorBuilder> &tensor_builder,
-                                 const std::shared_ptr<custom::IKernelBuilder> &kb)
-    : _ctx(operand_ctx), _tensor_builder(tensor_builder), _kernel_builder(kb),
-      _current_subg_layout(ir::Layout::UNKNOWN)
-{
-  // DO NOTHING
-}
-
-void KernelGenerator::visit(const ir::OpSequence &op_seq)
-{
-  _current_subg_layout = op_seq.getLayout();
-  for (const auto &e : op_seq.operations())
-  {
-    const auto &node = *(e.node);
-    _tensor_builder->preVisit(node);
-    node.accept(*this);
-    _tensor_builder->postVisit(node);
-  }
-}
-
-void KernelGenerator::visit(const ir::operation::Conv2D &node)
-{
-  using ir::operation::Conv2D;
-
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(Conv2D::Input::INPUT)};
-  const auto ker_index{node.getInputs().at(Conv2D::Input::KERNEL)};
-  const auto bias_index{node.getInputs().at(Conv2D::Input::BIAS)};
-
-  const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(_current_subg_layout);
-  const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(_current_subg_layout);
-  // Kernel format is [depth_out, kernel_height, kernel_width, depth_in] if NHWC and [depth_out,
-  // depth_in, kernel_height, kernel_width] if NCHW.
-  const auto &ker_shape = _ctx.at(ker_index).shape().asFeature(_current_subg_layout);
-  const auto ker_height = ker_shape.H;
-  const auto ker_width = ker_shape.W;
-  const auto stride = node.param().stride;
-  const auto padding = neurun::util::calculatePadding(node.param().padding, ifm_shape, ofm_shape,
-                                                      stride, ker_width, ker_height);
-  const int has_padding = padding.left + padding.right + padding.top + padding.bottom;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index);
-  auto ifm_alloc = _tensor_builder->at(ifm_index);
-  auto ker_alloc = _tensor_builder->at(ker_index);
-  auto bias_alloc = _tensor_builder->at(bias_index);
-  const auto backend_layout = ifm_alloc->layout();
-  assert(backend_layout == ofm_alloc->layout());
-
-  const auto ofm_backend_descr = ::neurun::backend::srcn::kernel::getTensorDescriptor(
-      _ctx.at(ofm_index), _current_subg_layout, backend_layout);
-  const auto ifm_backend_descr = ::neurun::backend::srcn::kernel::getTensorDescriptor(
-      _ctx.at(ifm_index), _current_subg_layout, backend_layout);
-  const auto ker_backend_descr = ::neurun::backend::srcn::kernel::getTensorDescriptor(
-      _ctx.at(ker_index), _current_subg_layout, backend_layout);
-  const auto bias_backend_descr = ::neurun::backend::srcn::kernel::getTensorDescriptor(
-      _ctx.at(bias_index), _current_subg_layout, backend_layout);
-
-  auto fn = nnfw::cpp14::make_unique<::neurun::backend::srcn::kernel::ConvolutionLayer>();
-
-  // TODO Support activation
-  fn->configure(ifm_alloc->buffer(), ifm_backend_descr, ker_alloc->buffer(), ker_backend_descr,
-                bias_alloc->buffer(), bias_backend_descr, has_padding, padding.left, padding.right,
-                padding.top, padding.bottom, stride.horizontal, stride.vertical,
-                /*activation,*/ ofm_alloc->buffer(), ofm_backend_descr, backend_layout);
-
-  _execution_builder->append(std::move(fn));
-}
-
-void KernelGenerator::visit(const ir::operation::InstanceNorm &node)
-{
-  using ir::operation::InstanceNorm;
-
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(InstanceNorm::Input::INPUT)};
-  const auto gamma_index{node.getInputs().at(InstanceNorm::Input::GAMMA)};
-  const auto beta_index{node.getInputs().at(InstanceNorm::Input::BETA)};
-
-  const auto epsilon = node.param().epsilon;
-  const auto activation = node.param().activation;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index);
-  auto ifm_alloc = _tensor_builder->at(ifm_index);
-  auto gamma_alloc = _tensor_builder->at(gamma_index);
-  auto beta_alloc = _tensor_builder->at(beta_index);
-
-  const auto backend_layout = ofm_alloc->layout();
-
-  const auto ofm_backend_descr = ::neurun::backend::srcn::kernel::getTensorDescriptor(
-      _ctx.at(ofm_index), _current_subg_layout, backend_layout);
-  const auto ifm_backend_descr = ::neurun::backend::srcn::kernel::getTensorDescriptor(
-      _ctx.at(ifm_index), _current_subg_layout, backend_layout);
-  const auto gamma_backend_descr = ::neurun::backend::srcn::kernel::getTensorDescriptor(
-      _ctx.at(gamma_index), _current_subg_layout, backend_layout);
-  const auto beta_backend_descr = ::neurun::backend::srcn::kernel::getTensorDescriptor(
-      _ctx.at(beta_index), _current_subg_layout, backend_layout);
-
-  auto fn = nnfw::cpp14::make_unique<::neurun::backend::srcn::kernel::InstanceNormLayer>();
-
-  fn->configure(ifm_alloc->buffer(), ifm_backend_descr, gamma_alloc->buffer(), gamma_backend_descr,
-                beta_alloc->buffer(), beta_backend_descr, ofm_alloc->buffer(), ofm_backend_descr,
-                epsilon, activation, backend_layout);
-
-  _execution_builder->append(std::move(fn));
-}
-
-void KernelGenerator::visit(const ir::operation::DepthwiseConv2D &node)
-{
-  using ir::operation::DepthwiseConv2D;
-
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(DepthwiseConv2D::Input::INPUT)};
-  const auto ker_index{node.getInputs().at(DepthwiseConv2D::Input::KERNEL)};
-  const auto bias_index{node.getInputs().at(DepthwiseConv2D::Input::BIAS)};
-
-  const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(_current_subg_layout);
-  const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(_current_subg_layout);
-  // Kernel format is [depth_out, kernel_height, kernel_width, depth_in] if NHWC and [depth_out,
-  // depth_in, kernel_height, kernel_width] if NCHW.
-  const auto &ker_shape = _ctx.at(ker_index).shape().asFeature(_current_subg_layout);
-  const auto ker_height = ker_shape.H;
-  const auto ker_width = ker_shape.W;
-  const auto stride = node.param().stride;
-  const auto padding = neurun::util::calculatePadding(node.param().padding, ifm_shape, ofm_shape,
-                                                      stride, ker_width, ker_height);
-  const int padding_type = padding.left + padding.right + padding.top + padding.bottom;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index);
-  auto ifm_alloc = _tensor_builder->at(ifm_index);
-  auto ker_alloc = _tensor_builder->at(ker_index);
-  auto bias_alloc = _tensor_builder->at(bias_index);
-  const auto backend_layout = ifm_alloc->layout();
-  assert(backend_layout == ofm_alloc->layout());
-
-  const auto ofm_backend_descr = ::neurun::backend::srcn::kernel::getTensorDescriptor(
-      _ctx.at(ofm_index), _current_subg_layout, backend_layout);
-  const auto ifm_backend_descr = ::neurun::backend::srcn::kernel::getTensorDescriptor(
-      _ctx.at(ifm_index), _current_subg_layout, backend_layout);
-  const auto ker_backend_descr = ::neurun::backend::srcn::kernel::getTensorDescriptor(
-      _ctx.at(ker_index), _current_subg_layout, backend_layout);
-  const auto bias_backend_descr = ::neurun::backend::srcn::kernel::getTensorDescriptor(
-      _ctx.at(bias_index), _current_subg_layout, backend_layout);
-
-  auto fn = nnfw::cpp14::make_unique<::neurun::backend::srcn::kernel::DepthwiseConvolutionLayer>();
-
-  // TODO Support activation
-  fn->configure(ifm_alloc->buffer(), ifm_backend_descr, ker_alloc->buffer(), ker_backend_descr,
-                bias_alloc->buffer(), bias_backend_descr, padding_type, padding.left, padding.right,
-                padding.top, padding.bottom, stride.horizontal, stride.vertical,
-                /*activation,*/ ofm_alloc->buffer(), ofm_backend_descr, backend_layout);
-
-  _execution_builder->append(std::move(fn));
-}
-
-void KernelGenerator::visit(const ir::operation::TransposeConv &node)
-{
-  using ir::operation::TransposeConv;
-
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(TransposeConv::Input::INPUT)};
-  const auto ker_index{node.getInputs().at(TransposeConv::Input::KERNEL)};
-  const auto output_shape_index{node.getInputs().at(TransposeConv::Input::OUTPUT_SHAPE)};
-
-  const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(_current_subg_layout);
-  const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(_current_subg_layout);
-  // Kernel format is [depth_out, kernel_height, kernel_width, depth_in] if NHWC and [depth_out,
-  // depth_in, kernel_height, kernel_width] if NCHW.
-  const auto &ker_shape = _ctx.at(ker_index).shape().asFeature(_current_subg_layout);
-  const auto ker_height = ker_shape.H;
-  const auto ker_width = ker_shape.W;
-  const auto stride = node.param().stride;
-  const int padding_type = (node.param().padding.type == ir::PaddingType::SAME);
-  const auto padding = neurun::util::calculatePadding(node.param().padding, ofm_shape, ifm_shape,
-                                                      stride, ker_width, ker_height);
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index);
-  auto ifm_alloc = _tensor_builder->at(ifm_index);
-  auto ker_alloc = _tensor_builder->at(ker_index);
-  const auto backend_layout = ofm_alloc->layout();
-  assert(backend_layout == ifm_alloc->layout());
-
-  const auto ofm_backend_descr = ::neurun::backend::srcn::kernel::getTensorDescriptor(
-      _ctx.at(ofm_index), _current_subg_layout, backend_layout);
-  const auto ifm_backend_descr = ::neurun::backend::srcn::kernel::getTensorDescriptor(
-      _ctx.at(ifm_index), _current_subg_layout, backend_layout);
-  const auto ker_backend_descr = ::neurun::backend::srcn::kernel::getTensorDescriptor(
-      _ctx.at(ker_index), _current_subg_layout, backend_layout);
-
-  auto fn = nnfw::cpp14::make_unique<::neurun::backend::srcn::kernel::TransposeConvLayer>();
-
-  fn->configure(ifm_alloc->buffer(), ifm_backend_descr, ker_alloc->buffer(), ker_backend_descr,
-                padding_type, padding.left, padding.right, padding.top, padding.bottom,
-                stride.horizontal, stride.vertical, ofm_alloc->buffer(), ofm_backend_descr,
-                backend_layout);
-
-  _execution_builder->append(std::move(fn));
-}
-
-void KernelGenerator::visit(const ir::operation::Add &node)
-{
-  using ir::operation::Add;
-
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto lhs_index{node.getInputs().at(Add::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(Add::Input::RHS)};
-
-  const auto activation = node.param().activation;
-
-  auto ofm_alloc = _tensor_builder->at(ofm_index).get();
-  auto lhs_alloc = _tensor_builder->at(lhs_index).get();
-  auto rhs_alloc = _tensor_builder->at(rhs_index).get();
-
-  const auto backend_layout = ofm_alloc->layout();
-  assert(backend_layout == lhs_alloc->layout() && backend_layout == rhs_alloc->layout());
-
-  const auto ofm_backend_descr = ::neurun::backend::srcn::kernel::getTensorDescriptor(
-      _ctx.at(ofm_index), _current_subg_layout, backend_layout);
-  const auto lhs_backend_descr = ::neurun::backend::srcn::kernel::getTensorDescriptor(
-      _ctx.at(lhs_index), _current_subg_layout, backend_layout);
-  const auto rhs_backend_descr = ::neurun::backend::srcn::kernel::getTensorDescriptor(
-      _ctx.at(rhs_index), _current_subg_layout, backend_layout);
-
-  auto fn = nnfw::cpp14::make_unique<::neurun::backend::srcn::kernel::AddLayer>();
-
-  fn->configure(lhs_alloc->buffer(), lhs_backend_descr, rhs_alloc->buffer(), rhs_backend_descr,
-                activation, ofm_alloc->buffer(), ofm_backend_descr, backend_layout);
-
-  _execution_builder->append(std::move(fn));
-}
-
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/srcn/KernelGenerator.h b/runtime/neurun/backend/srcn/KernelGenerator.h
deleted file mode 100644
index 879aeaf5b..000000000
--- a/runtime/neurun/backend/srcn/KernelGenerator.h
+++ /dev/null
@@ -1,59 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_SRCN_KERNEL_GENERATOR_H__
-#define __NEURUN_BACKEND_SRCN_KERNEL_GENERATOR_H__
-
-#include "backend/IKernelGenerator.h"
-#include "ir/Operands.h"
-#include "operand/Tensor.h"
-#include "backend/CustomKernelBuilder.h"
-#include "TensorBuilder.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-
-class KernelGenerator : public IKernelGenerator
-{
-public:
-  KernelGenerator(const ir::Operands &ctx, const std::shared_ptr<TensorBuilder> &tensor_builder,
-                  const std::shared_ptr<custom::IKernelBuilder> &kb);
-
-  using IKernelGenerator::visit;
-
-  void visit(const ir::OpSequence &) override;
-  void visit(const ir::operation::Conv2D &) override;
-  void visit(const ir::operation::DepthwiseConv2D &) override;
-  void visit(const ir::operation::InstanceNorm &) override;
-  void visit(const ir::operation::TransposeConv &) override;
-  void visit(const ir::operation::Add &) override;
-
-private:
-  const ir::Operands &_ctx;
-  std::shared_ptr<TensorBuilder> _tensor_builder;
-  std::shared_ptr<custom::IKernelBuilder> _kernel_builder;
-  ir::Layout _current_subg_layout;
-};
-
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_SRCN_KERNEL_GENERATOR_H__
diff --git a/runtime/neurun/backend/srcn/MemoryManager.cc b/runtime/neurun/backend/srcn/MemoryManager.cc
deleted file mode 100644
index aa07ab168..000000000
--- a/runtime/neurun/backend/srcn/MemoryManager.cc
+++ /dev/null
@@ -1,92 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "MemoryManager.h"
-
-#include <cassert>
-
-#include <MemoryPlannerFactory.h>
-#include "util/logging.h"
-#include "util/ConfigSource.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-
-MemoryManager::MemoryManager() : _mem_planner{createMemoryPlanner()}
-{
-  // DO NOTHING
-}
-
-MemoryManager::MemoryManager(const std::string planner_id)
-    : _mem_planner{createMemoryPlanner(planner_id)}
-{
-  // DO NOTHING
-}
-
-cpu_common::IMemoryPlanner *MemoryManager::createMemoryPlanner()
-{
-  auto planner_id = util::getConfigString(util::config::CPU_MEMORY_PLANNER);
-  return cpu_common::MemoryPlannerFactory::get().create(planner_id);
-}
-
-cpu_common::IMemoryPlanner *MemoryManager::createMemoryPlanner(const std::string planner_id)
-{
-  return cpu_common::MemoryPlannerFactory::get().create(planner_id);
-}
-
-void MemoryManager::buildTensor(const ir::OperandIndex &ind, const ir::OperandInfo &info,
-                                ir::Layout layout)
-{
-  auto tensor = std::make_shared<operand::Tensor>(info, layout);
-  _tensors[ind] = tensor;
-}
-
-void MemoryManager::claimPlan(const ir::OperandIndex &ind, uint32_t size)
-{
-  _mem_planner->claim(ind, size);
-}
-
-void MemoryManager::releasePlan(const ir::OperandIndex &ind) { _mem_planner->release(ind); }
-
-void MemoryManager::allocate(void)
-{
-  _mem_alloc = std::make_shared<cpu_common::Allocator>(_mem_planner->capacity());
-  assert(_mem_alloc->base());
-
-  for (auto &mem_plan : _mem_planner->memory_plans())
-  {
-    auto ind = mem_plan.first;
-    auto mem_blk = mem_plan.second;
-
-    uint8_t *buffer = _mem_alloc->base() + mem_blk.offset;
-    auto tensor = _tensors[ind];
-    tensor->setBuffer(buffer);
-
-    VERBOSE(CPU_MEMORYMANAGER) << "TENSOR(#" << ind.value() << "): " << static_cast<void *>(buffer)
-                               << std::endl;
-
-    // If we do not make tensor here currently, kernel generation would cause segmentation fault.
-    // See also : Comments in `allocate` method.
-  }
-}
-
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/srcn/MemoryManager.h b/runtime/neurun/backend/srcn/MemoryManager.h
deleted file mode 100644
index 05fa07622..000000000
--- a/runtime/neurun/backend/srcn/MemoryManager.h
+++ /dev/null
@@ -1,63 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_SRCN_MEMORY_MANAGER_H__
-#define __NEURUN_BACKEND_SRCN_MEMORY_MANAGER_H__
-
-#include "backend/IMemoryManager.h"
-#include <MemoryPlanner.h>
-#include "operand/Tensor.h"
-#include "ir/OperandIndexMap.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-
-class MemoryManager : public backend::IMemoryManager
-{
-public:
-  MemoryManager();
-  MemoryManager(const std::string);
-  virtual ~MemoryManager() = default;
-
-  void allocate(void) override;
-  void deallocate(void) override { _mem_alloc->release(); }
-
-  void buildTensor(const ir::OperandIndex &ind, const ir::OperandInfo &info, ir::Layout layout);
-  void claimPlan(const ir::OperandIndex &ind, uint32_t size);
-  void releasePlan(const ir::OperandIndex &ind);
-
-  ir::OperandIndexMap<std::shared_ptr<operand::Tensor>> &tensors(void) { return _tensors; }
-
-private:
-  cpu_common::IMemoryPlanner *createMemoryPlanner();
-  cpu_common::IMemoryPlanner *createMemoryPlanner(std::string);
-
-private:
-  ir::OperandIndexMap<std::shared_ptr<operand::Tensor>> _tensors;
-  ir::OperandIndexMap<cpu_common::Block> _tensor_mem_map;
-  std::shared_ptr<cpu_common::IMemoryPlanner> _mem_planner;
-  std::shared_ptr<cpu_common::Allocator> _mem_alloc;
-};
-
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_SRCN_MEMORY_MANAGER_H__
diff --git a/runtime/neurun/backend/srcn/PluginClassesAllocator.cc b/runtime/neurun/backend/srcn/PluginClassesAllocator.cc
deleted file mode 100644
index 9efc6aaaa..000000000
--- a/runtime/neurun/backend/srcn/PluginClassesAllocator.cc
+++ /dev/null
@@ -1,33 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <util/logging.h>
-
-#include "Backend.h"
-
-extern "C" {
-neurun::backend::Backend *neurun_backend_create()
-{
-  VERBOSE(neurun_backend_create) << "'srcn' loaded\n";
-  return new neurun::backend::srcn::Backend;
-}
-
-void neurun_backend_destroy(neurun::backend::Backend *backend)
-{
-  VERBOSE(neurun_backend_create) << "'srcn' unloaded\n";
-  delete backend;
-}
-}
diff --git a/runtime/neurun/backend/srcn/ShapeFixer.cc b/runtime/neurun/backend/srcn/ShapeFixer.cc
deleted file mode 100644
index 0ef190f28..000000000
--- a/runtime/neurun/backend/srcn/ShapeFixer.cc
+++ /dev/null
@@ -1,47 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ShapeFixer.h"
-
-#include <stdexcept>
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-
-ShapeFixer::ShapeFixer(const ir::Operands &operand_ctx,
-                       const std::shared_ptr<TensorBuilder> &tensor_builder)
-    : _ctx(operand_ctx), _tensor_builder(tensor_builder)
-{
-  assert(tensor_builder);
-}
-
-void ShapeFixer::visit(const ir::operation::Conv2D &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::DepthwiseConv2D &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::InstanceNorm &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::TransposeConv &) { /* DO NOTHING */}
-
-void ShapeFixer::visit(const ir::operation::Add &) { /* DO NOTHING */}
-
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/srcn/ShapeFixer.h b/runtime/neurun/backend/srcn/ShapeFixer.h
deleted file mode 100644
index 7da1ae731..000000000
--- a/runtime/neurun/backend/srcn/ShapeFixer.h
+++ /dev/null
@@ -1,53 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_SRCN_SHAPE_FIXER_H__
-#define __NEURUN_BACKEND_SRCN_SHAPE_FIXER_H__
-
-#include <backend/IShapeFixer.h>
-
-#include "ir/Operands.h"
-#include "operand/Tensor.h"
-#include "TensorBuilder.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-
-class ShapeFixer : public IShapeFixer
-{
-public:
-  ShapeFixer(const ir::Operands &ctx, const std::shared_ptr<TensorBuilder> &tensor_builder);
-
-  void visit(const ir::operation::Conv2D &) override;
-  void visit(const ir::operation::DepthwiseConv2D &) override;
-  void visit(const ir::operation::InstanceNorm &) override;
-  void visit(const ir::operation::TransposeConv &) override;
-  void visit(const ir::operation::Add &) override;
-
-private:
-  const ir::Operands &_ctx;
-  std::shared_ptr<TensorBuilder> _tensor_builder;
-};
-
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_SRCN_SHAPE_FIXER_H__
diff --git a/runtime/neurun/backend/srcn/Swizzle.h b/runtime/neurun/backend/srcn/Swizzle.h
deleted file mode 100644
index d1f922367..000000000
--- a/runtime/neurun/backend/srcn/Swizzle.h
+++ /dev/null
@@ -1,84 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_SRCN_SWIZZLE_H__
-#define __NEURUN_BACKEND_SRCN_SWIZZLE_H__
-
-#include <cassert>
-#include <ir/Layout.h>
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-
-// Convert axis in ncnn order
-inline uint32_t ToNCNNAxis(uint32_t rank, uint32_t axis,
-                           const ir::Layout org_layout = ir::Layout::UNKNOWN,
-                           const ir::Layout ncnn_layout = ir::Layout::UNKNOWN)
-{
-  assert(rank > axis);
-
-  if (rank >= 4 && org_layout == ir::Layout::NHWC && ncnn_layout == ir::Layout::NCHW)
-  {
-    // NHWC -> NCHW
-    // DEPTH
-    if (axis == 3)
-    {
-      return 1;
-    }
-    // WIDTH
-    if (axis == 2)
-    {
-      return 3;
-    }
-    // HEIGHT
-    if (axis == 1)
-    {
-      return 2;
-    }
-  }
-
-  if (rank >= 4 && org_layout == ir::Layout::NCHW && ncnn_layout == ir::Layout::NHWC)
-  {
-    // NCHW -> NHWC
-    // WIDTH
-    if (axis == 3)
-    {
-      return 2;
-    }
-    // HEIGHT
-    if (axis == 2)
-    {
-      return 1;
-    }
-    // DEPTH
-    if (axis == 1)
-    {
-      return 3;
-    }
-  }
-
-  return axis;
-}
-
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_SRCN_SWIZZLE_H__
diff --git a/runtime/neurun/backend/srcn/TensorBuilder.cc b/runtime/neurun/backend/srcn/TensorBuilder.cc
deleted file mode 100644
index 5ac25c33e..000000000
--- a/runtime/neurun/backend/srcn/TensorBuilder.cc
+++ /dev/null
@@ -1,107 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "TensorBuilder.h"
-
-#include <cassert>
-#include "Convert.h"
-#include "util/logging.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-
-TensorBuilder::TensorBuilder() : _tensor_mgr{new TensorManager()}
-{
-  // DO NOTHING
-}
-
-void TensorBuilder::registerTensorInfo(const ir::OperandIndex &ind,
-                                       const ir::OperandInfo &tensor_info,
-                                       ir::Layout backend_layout, bool as_const)
-{
-  _tensor_info_map.emplace(ind, tensor_info);
-  _tensor_layout_map.emplace(ind, backend_layout);
-
-  if (as_const)
-    _constants.append(ind);
-}
-
-void TensorBuilder::registerSubTensorInfo(const ir::OperandIndex &, const compiler::SubTensorInfo &)
-{
-  // Not supported yet
-  assert(false);
-}
-
-void TensorBuilder::notifyFirstUse(const ir::OperandIndex &ind)
-{
-  assert(_tensor_info_map.find(ind) != _tensor_info_map.end());
-  const auto &tensor_info = _tensor_info_map.at(ind);
-  const auto size = tensor_info.total_size();
-  const auto &backend_layout = _tensor_layout_map.at(ind);
-  _tensor_mgr->buildTensor(ind, tensor_info, backend_layout, _constants.contains(ind));
-  _tensor_mgr->claimPlan(ind, size);
-}
-
-void TensorBuilder::notifyLastUse(const ir::OperandIndex &ind) { _tensor_mgr->releasePlan(ind); }
-
-bool TensorBuilder::isRegistered(const ir::OperandIndex &ind) const
-{
-  return _tensor_info_map.find(ind) != _tensor_info_map.end();
-}
-
-void TensorBuilder::prepare(void)
-{
-  _tensor_mgr->allocateConsts();
-  _tensor_mgr->allocateNonconsts();
-}
-
-void TensorBuilder::allocateConsts()
-{
-  // NOTE For now nothing to do. Allocation is done in prepare stage, which is not appropriate
-  //      This is because SRCN kernels require `ITensor`s to be allocated before Kernel Generation.
-}
-
-void TensorBuilder::allocateNonconsts()
-{
-  // NOTE For now nothing to do. Allocation is done in prepare stage, which is not appropriate
-  //      This is because SRCN kernels require `ITensor`s to be allocated before Kernel Generation.
-}
-
-std::shared_ptr<::neurun::backend::operand::ITensor>
-TensorBuilder::tensorAt(const ir::OperandIndex &ind)
-{
-  return _tensor_mgr->at(ind);
-}
-
-void TensorBuilder::iterate(const IterateFunction &fn) { _tensor_mgr->iterate(fn); }
-
-std::shared_ptr<operand::Tensor> TensorBuilder::at(const ir::OperandIndex &ind)
-{
-  return _tensor_mgr->at(ind);
-}
-
-std::unique_ptr<ITensorManager> TensorBuilder::releaseTensorManager(void)
-{
-  return std::move(_tensor_mgr);
-}
-
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/srcn/TensorBuilder.h b/runtime/neurun/backend/srcn/TensorBuilder.h
deleted file mode 100644
index 38bd6dd89..000000000
--- a/runtime/neurun/backend/srcn/TensorBuilder.h
+++ /dev/null
@@ -1,89 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_SRCN_TENSOR_BUILDER_H__
-#define __NEURUN_BACKEND_SRCN_TENSOR_BUILDER_H__
-
-#include <unordered_map>
-
-#include <backend/ITensorBuilder.h>
-#include "operand/Tensor.h"
-#include "ir/OperandIndexMap.h"
-#include "TensorManager.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-
-class TensorBuilder : public ITensorBuilder
-{
-public:
-  TensorBuilder();
-
-  /**
-   * @brief     Register tensor information to allocate on CPU backend
-   * @param[in] ind    Operand index
-   * @param[in] info   Operand information
-   * @param[in] layout Operand data layout
-   */
-  void registerTensorInfo(const ir::OperandIndex &ind, const ir::OperandInfo &info,
-                          ir::Layout backend_layout, bool as_const) override;
-  /**
-   * @brief     Register subtensor information to allocate on CPU backend
-   * @param[in] ind   Operand index
-   * @param[in] info  Tensor information
-   */
-  void registerSubTensorInfo(const ir::OperandIndex &ind,
-                             const compiler::SubTensorInfo &info) override;
-
-  void notifyFirstUse(const ir::OperandIndex &) override;
-  void notifyLastUse(const ir::OperandIndex &) override;
-
-  bool isRegistered(const ir::OperandIndex &) const override;
-
-  void prepare(void) override;
-  void allocateConsts() override;
-  void allocateNonconsts() override;
-  void postFunctionPrepare() override { /* DO NOTHING */}
-  void finalize() override { /* DO NOTHING */}
-
-  std::shared_ptr<::neurun::backend::operand::ITensor>
-  tensorAt(const ir::OperandIndex &ind) override;
-
-  void iterate(const IterateFunction &fn) override;
-
-  void preVisit(const ir::Operation &) override { /* DO NOTHING */}
-  void postVisit(const ir::Operation &) override { /* DO NOTHING */}
-
-  std::unique_ptr<ITensorManager> releaseTensorManager(void) override;
-
-  std::shared_ptr<operand::Tensor> at(const ir::OperandIndex &ind);
-
-private:
-  std::unique_ptr<TensorManager> _tensor_mgr;
-  ir::OperandIndexMap<ir::OperandInfo> _tensor_info_map;
-  ir::OperandIndexMap<ir::Layout> _tensor_layout_map;
-  ir::OperandIndexSequence _constants;
-};
-
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_SRCN_TENSOR_BUILDER_H__
diff --git a/runtime/neurun/backend/srcn/TensorManager.cc b/runtime/neurun/backend/srcn/TensorManager.cc
deleted file mode 100644
index 717f1bbbc..000000000
--- a/runtime/neurun/backend/srcn/TensorManager.cc
+++ /dev/null
@@ -1,95 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "TensorManager.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-
-TensorManager::TensorManager()
-    : _const_mgr{new MemoryManager("Bump")}, _nonconst_mgr{new MemoryManager()}
-{
-  // DO NOTHING
-}
-
-void TensorManager::allocateConsts(void) { _const_mgr->allocate(); }
-
-void TensorManager::allocateNonconsts(void) { _nonconst_mgr->allocate(); }
-
-void TensorManager::deallocateConsts(void) { _const_mgr->deallocate(); }
-
-void TensorManager::deallocateNonconsts(void) { _nonconst_mgr->deallocate(); }
-
-void TensorManager::buildTensor(const ir::OperandIndex &ind, const ir::OperandInfo &tensor_info,
-                                ir::Layout layout, bool as_const)
-{
-  assert(_ind_to_mgr.find(ind) == _ind_to_mgr.end());
-  if (as_const)
-  {
-    _const_mgr->buildTensor(ind, tensor_info, layout);
-    _ind_to_mgr.insert({ind, *_const_mgr});
-  }
-  else
-  {
-    _nonconst_mgr->buildTensor(ind, tensor_info, layout);
-    _ind_to_mgr.insert({ind, *_nonconst_mgr});
-  }
-}
-
-void TensorManager::claimPlan(const ir::OperandIndex &ind, uint32_t size)
-{
-  assert(_ind_to_mgr.find(ind) != _ind_to_mgr.end());
-  _ind_to_mgr.at(ind).claimPlan(ind, size);
-}
-
-void TensorManager::releasePlan(const ir::OperandIndex &ind)
-{
-  assert(_ind_to_mgr.find(ind) != _ind_to_mgr.end());
-  _ind_to_mgr.at(ind).releasePlan(ind);
-}
-
-std::shared_ptr<operand::Tensor> TensorManager::at(const ir::OperandIndex &ind)
-{
-  assert(_ind_to_mgr.find(ind) != _ind_to_mgr.end());
-  return _ind_to_mgr.at(ind).tensors().at(ind);
-}
-
-ir::OperandIndexMap<std::shared_ptr<operand::Tensor>> &TensorManager::constTensors(void)
-{
-  return _const_mgr->tensors();
-}
-
-ir::OperandIndexMap<std::shared_ptr<operand::Tensor>> &TensorManager::nonconstTensors(void)
-{
-  return _nonconst_mgr->tensors();
-}
-
-void TensorManager::iterate(const std::function<void(const ir::OperandIndex &)> &fn)
-{
-  for (auto it : _nonconst_mgr->tensors())
-    fn(it.first);
-
-  for (auto it : _const_mgr->tensors())
-    fn(it.first);
-}
-
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/srcn/TensorManager.h b/runtime/neurun/backend/srcn/TensorManager.h
deleted file mode 100644
index d4390d80c..000000000
--- a/runtime/neurun/backend/srcn/TensorManager.h
+++ /dev/null
@@ -1,65 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_SRCN_TENSOR_MANAGER_H__
-#define __NEURUN_BACKEND_SRCN_TENSOR_MANAGER_H__
-
-#include "backend/ITensorManager.h"
-#include "MemoryManager.h"
-#include "ir/OperandIndexMap.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-
-class TensorManager : public backend::ITensorManager
-{
-public:
-  TensorManager();
-  virtual ~TensorManager() = default;
-
-  void allocateConsts(void) override;
-  void allocateNonconsts(void) override;
-  void deallocateConsts(void) override;
-  void deallocateNonconsts(void) override;
-
-  void buildTensor(const ir::OperandIndex &ind, const ir::OperandInfo &tensor_info,
-                   ir::Layout layout, bool as_const);
-
-  void claimPlan(const ir::OperandIndex &ind, uint32_t size);
-  void releasePlan(const ir::OperandIndex &ind);
-
-  std::shared_ptr<operand::Tensor> at(const ir::OperandIndex &ind);
-
-  ir::OperandIndexMap<std::shared_ptr<operand::Tensor>> &constTensors(void);
-  ir::OperandIndexMap<std::shared_ptr<operand::Tensor>> &nonconstTensors(void);
-
-  void iterate(const std::function<void(const ir::OperandIndex &)> &fn);
-
-private:
-  std::unique_ptr<MemoryManager> _const_mgr;
-  std::unique_ptr<MemoryManager> _nonconst_mgr;
-  ir::OperandIndexMap<MemoryManager &> _ind_to_mgr;
-};
-
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_SRCN_TENSOR_MANAGER_H__
diff --git a/runtime/neurun/backend/srcn/TensorRegister.cc b/runtime/neurun/backend/srcn/TensorRegister.cc
deleted file mode 100644
index 8c2f59aef..000000000
--- a/runtime/neurun/backend/srcn/TensorRegister.cc
+++ /dev/null
@@ -1,118 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "TensorRegister.h"
-
-#include "Convert.h"
-#include "kernel/OperationUtils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-
-TensorRegister::TensorRegister(const ir::Operands &operands,
-                               const std::shared_ptr<TensorBuilder> &tensor_builder)
-    : _operands{operands}, _tensor_builder{tensor_builder}
-{
-  assert(tensor_builder != nullptr);
-}
-
-void TensorRegister::visit(const ir::operation::Conv2D &node)
-{
-  // General cases
-  defaultRegisterTensorInfo(node.getInputs().at(ir::operation::Conv2D::INPUT));
-  defaultRegisterTensorInfo(node.getInputs().at(ir::operation::Conv2D::BIAS));
-  defaultRegisterTensorInfo(node.getOutputs().at(0));
-
-  // Special case
-  const auto &kernel_index = node.getInputs().at(ir::operation::Conv2D::KERNEL);
-  const auto &kernel_obj = _operands.at(kernel_index);
-
-  const auto frontend_layout = frontendLayout();
-  assert(frontend_layout == ir::Layout::NCHW || frontend_layout == ir::Layout::NHWC);
-  const auto frontend_filter_layout =
-      frontend_layout == ir::Layout::NHWC ? kernel::FilterLayout::OHWI : kernel::FilterLayout::OIHW;
-  const auto backend_layout = backendLayout(kernel_index);
-  assert(backend_layout == ir::Layout::NCHW || backend_layout == ir::Layout::NHWC);
-  const auto backend_filter_layout =
-      backend_layout == ir::Layout::NHWC ? kernel::FilterLayout::HWIO : kernel::FilterLayout::OIHW;
-
-  ir::OperandInfo backend_info{
-      asKernelShape(kernel_obj.shape(), frontend_filter_layout, backend_filter_layout),
-      kernel_obj.info().typeInfo()};
-  _tensor_builder->registerTensorInfo(kernel_index, backend_info, backend_layout,
-                                      kernel_obj.isConstant());
-}
-
-void TensorRegister::visit(const ir::operation::DepthwiseConv2D &node)
-{
-  // General cases
-  defaultRegisterTensorInfo(node.getInputs().at(ir::operation::DepthwiseConv2D::INPUT));
-  defaultRegisterTensorInfo(node.getInputs().at(ir::operation::DepthwiseConv2D::BIAS));
-  defaultRegisterTensorInfo(node.getOutputs().at(0));
-
-  // Special case
-  const auto &kernel_index = node.getInputs().at(ir::operation::DepthwiseConv2D::KERNEL);
-  const auto &kernel_obj = _operands.at(kernel_index);
-
-  const auto frontend_layout = frontendLayout();
-  assert(frontend_layout == ir::Layout::NCHW || frontend_layout == ir::Layout::NHWC);
-  const auto frontend_filter_layout =
-      frontend_layout == ir::Layout::NHWC ? kernel::FilterLayout::OHWI : kernel::FilterLayout::OIHW;
-  const auto backend_layout = backendLayout(kernel_index);
-  assert(backend_layout == ir::Layout::NCHW || backend_layout == ir::Layout::NHWC);
-  const auto backend_filter_layout =
-      backend_layout == ir::Layout::NHWC ? kernel::FilterLayout::HWIO : kernel::FilterLayout::OIHW;
-
-  ir::OperandInfo backend_info{
-      asKernelShape(kernel_obj.shape(), frontend_filter_layout, backend_filter_layout),
-      kernel_obj.info().typeInfo()};
-  _tensor_builder->registerTensorInfo(kernel_index, backend_info, backend_layout,
-                                      kernel_obj.isConstant());
-}
-
-void TensorRegister::visit(const ir::operation::TransposeConv &node)
-{
-  // General cases
-  defaultRegisterTensorInfo(node.getInputs().at(ir::operation::TransposeConv::INPUT));
-  defaultRegisterTensorInfo(node.getOutputs().at(0));
-
-  // Special case
-  const auto &kernel_index = node.getInputs().at(ir::operation::TransposeConv::KERNEL);
-  const auto &kernel_obj = _operands.at(kernel_index);
-
-  const auto frontend_layout = frontendLayout();
-  assert(frontend_layout == ir::Layout::NCHW || frontend_layout == ir::Layout::NHWC);
-  const auto frontend_filter_layout =
-      frontend_layout == ir::Layout::NHWC ? kernel::FilterLayout::OHWI : kernel::FilterLayout::OIHW;
-  const auto backend_layout = backendLayout(kernel_index);
-  assert(backend_layout == ir::Layout::NCHW || backend_layout == ir::Layout::NHWC);
-  const auto backend_filter_layout =
-      backend_layout == ir::Layout::NHWC ? kernel::FilterLayout::HWOI : kernel::FilterLayout::IOHW;
-
-  ir::OperandInfo backend_info{
-      asKernelShape(kernel_obj.shape(), frontend_filter_layout, backend_filter_layout),
-      kernel_obj.info().typeInfo()};
-  _tensor_builder->registerTensorInfo(kernel_index, backend_info, backend_layout,
-                                      kernel_obj.isConstant());
-}
-
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/srcn/TensorRegister.h b/runtime/neurun/backend/srcn/TensorRegister.h
deleted file mode 100644
index 765f29567..000000000
--- a/runtime/neurun/backend/srcn/TensorRegister.h
+++ /dev/null
@@ -1,55 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_SRCN_TENSOR_REGISTER_H__
-#define __NEURUN_BACKEND_SRCN_TENSOR_REGISTER_H__
-
-#include <backend/ITensorRegister.h>
-#include "TensorBuilder.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-
-class TensorRegister : public ITensorRegister
-{
-public:
-  TensorRegister(const ir::Operands &operands,
-                 const std::shared_ptr<TensorBuilder> &tensor_builder);
-
-public:
-  void visit(const ir::operation::Conv2D &) override;
-  void visit(const ir::operation::DepthwiseConv2D &) override;
-  void visit(const ir::operation::TransposeConv &) override;
-
-private:
-  const ir::Operands &operands() const override { return _operands; }
-  std::shared_ptr<ITensorBuilder> tensor_builder() const override { return _tensor_builder; }
-  bool supportSubTensor() const final { return false; }
-
-private:
-  const ir::Operands &_operands;
-  const std::shared_ptr<TensorBuilder> _tensor_builder;
-};
-
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_SRCN_TENSOR_REGISTER_H__
diff --git a/runtime/neurun/backend/srcn/kernel/AddLayer.cc b/runtime/neurun/backend/srcn/kernel/AddLayer.cc
deleted file mode 100644
index b53dfe89d..000000000
--- a/runtime/neurun/backend/srcn/kernel/AddLayer.cc
+++ /dev/null
@@ -1,123 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "AddLayer.h"
-
-#include "OperationUtils.h"
-#include "ncnn/layer/binaryop.h"
-
-#include "cpp14/memory.h"
-
-namespace
-{
-std::unique_ptr<nnfw::ncnn::Mat>
-convertMatIgnoreLayout(neurun::backend::srcn::kernel::TensorDescriptor &desc, void *data)
-{
-  if (desc.dimensions.size() == 1)
-  {
-    return nnfw::cpp14::make_unique<nnfw::ncnn::Mat>(desc.dimensions[0], data);
-  }
-  else if (desc.dimensions.size() == 2)
-  {
-    return nnfw::cpp14::make_unique<nnfw::ncnn::Mat>(desc.dimensions[1], desc.dimensions[0], data);
-  }
-  else if (desc.dimensions.size() == 3)
-  {
-    return nnfw::cpp14::make_unique<nnfw::ncnn::Mat>(desc.dimensions[2], desc.dimensions[1],
-                                                     desc.dimensions[0], data);
-  }
-  else // rank == 4 and N == 1
-  {
-    return nnfw::cpp14::make_unique<nnfw::ncnn::Mat>(desc.dimensions[3], desc.dimensions[2],
-                                                     desc.dimensions[1], data);
-  }
-}
-} // namespace
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-namespace kernel
-{
-
-void AddLayer::addFloat32()
-{
-  assert(_activation == ir::Activation::NONE);
-
-  // ncnn kernel support
-  // 1. rank < 4
-  // 2. broadcasting
-  //   2-1 lhs, rhs have same rank, or
-  //   2-2 model layout and backend layout is same
-  // For safety, block all broadcasting (enable when ready)
-
-  assert(_lhsDescr.dimensions.size() < 4 ||
-         (_lhsDescr.dimensions.size() == 4 && _lhsDescr.dimensions[0] == 1));
-  assert(_rhsDescr.dimensions.size() < 4 ||
-         (_rhsDescr.dimensions.size() == 4 && _rhsDescr.dimensions[0] == 1));
-  assert((_lhsDescr.dimensions.size() == _rhsDescr.dimensions.size()));
-
-  nnfw::ncnn::BinaryOpParam param;
-  param.op_type = nnfw::ncnn::BinaryOp::Operation_ADD;
-
-  auto lhs_mat = convertMatIgnoreLayout(_lhsDescr, _lhsData.v);
-  auto rhs_mat = convertMatIgnoreLayout(_rhsDescr, _rhsData.v);
-  auto out_mat = convertMatIgnoreLayout(_outputDescr, _outputData.v);
-
-  ::nnfw::ncnn::ncnn_binary_op(param, *lhs_mat.get(), *rhs_mat.get(), *out_mat.get());
-}
-
-void AddLayer::addQuant8()
-{
-  // quant8 add is not implemented yet
-  throw std::runtime_error{"NYI"};
-}
-
-void AddLayer::configure(uint8_t *lhsData, const TensorDescriptor &lhsDescr, uint8_t *rhsData,
-                         const TensorDescriptor &rhsDescr, const ir::Activation activation,
-                         uint8_t *outputData, const TensorDescriptor &outputDescr,
-                         const ir::Layout backendLayout)
-{
-  _lhsData.u8 = lhsData;
-  _lhsDescr = lhsDescr;
-  _rhsData.u8 = rhsData;
-  _rhsDescr = rhsDescr;
-  _inputType = lhsDescr.type;
-  _activation = activation;
-  _outputData.u8 = outputData;
-  _outputDescr = outputDescr;
-  _backendLayout = backendLayout;
-}
-
-void AddLayer::run()
-{
-  if (_inputType == OperandType::FLOAT32)
-  {
-    addFloat32();
-  }
-  else if (_inputType == OperandType::QUANT8_ASYMM)
-  {
-    addQuant8();
-  }
-}
-
-} // namespace kernel
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/srcn/kernel/AddLayer.h b/runtime/neurun/backend/srcn/kernel/AddLayer.h
deleted file mode 100644
index 1cae171b5..000000000
--- a/runtime/neurun/backend/srcn/kernel/AddLayer.h
+++ /dev/null
@@ -1,80 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_SRCN_KERNEL_ADD_LAYER_H__
-#define __NEURUN_BACKEND_SRCN_KERNEL_ADD_LAYER_H__
-
-#include <exec/IFunction.h>
-
-#include "OperationUtils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-namespace kernel
-{
-
-class AddLayer : public ::neurun::exec::IFunction
-{
-public:
-  AddLayer() : _lhsData(), _rhsData(), _outputData(), _lhsDescr(), _rhsDescr(), _outputDescr()
-  {
-    // DO NOTHING
-  }
-
-public:
-  void addFloat32();
-
-  void addQuant8();
-
-  void configure(uint8_t *lhsData, const TensorDescriptor &lhsDescr, uint8_t *rhsData,
-                 const TensorDescriptor &rhsDescr, const ir::Activation activation,
-                 uint8_t *outputData, const TensorDescriptor &outputDescr,
-                 const ir::Layout backendLayout);
-
-  void run();
-  void runSync()
-  {
-    // this abstract method is used just for profiling and called for
-    // backend::acl_common::AclFunction
-    run();
-  }
-
-private:
-  DataPtr _lhsData;
-  DataPtr _rhsData;
-  DataPtr _outputData;
-
-  TensorDescriptor _lhsDescr;
-  TensorDescriptor _rhsDescr;
-  TensorDescriptor _outputDescr;
-
-  ir::Activation _activation{ir::Activation::NONE};
-
-  OperandType _inputType{OperandType::FLOAT32};
-
-  ir::Layout _backendLayout{ir::Layout::UNKNOWN};
-};
-
-} // namespace kernel
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_SRCN_KERNEL_ADD_LAYER_H__
diff --git a/runtime/neurun/backend/srcn/kernel/ConvolutionLayer.cc b/runtime/neurun/backend/srcn/kernel/ConvolutionLayer.cc
deleted file mode 100644
index 4e70f6319..000000000
--- a/runtime/neurun/backend/srcn/kernel/ConvolutionLayer.cc
+++ /dev/null
@@ -1,233 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ConvolutionLayer.h"
-
-#include "OperationUtils.h"
-#include <ncnn/srcn/srcn_conv.h>
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-namespace kernel
-{
-
-ConvolutionLayer::ConvolutionLayer()
-    : _inputData(), _kernelData(), _biasData(), _outputData(), _inputDescr(), _kernelDescr(),
-      _biasDescr(), _outputDescr(), _paddingType(0), _paddingLeft(0), _paddingTop(0),
-      _paddingRight(0), _paddingBottom(0), _strideWidth(0), _strideHeight(0),
-      _inputType(OperandType::FLOAT32), _layout(nnfw::srcn::col_major), _winograd_weights(nullptr),
-      _sparse_weights(nullptr)
-{
-  // DO NOTHING
-}
-
-ConvolutionLayer::~ConvolutionLayer()
-{
-  // TODO Move managing constant _winograd_data and sparse
-  nnfw::srcn::winograd_release(_winograd_weights);
-  size_t depth_index = _layout == nnfw::srcn::col_major ? 3 : 1;
-  nnfw::srcn::sparse_release(_outputDescr.dimensions[depth_index], _sparse_weights);
-}
-
-void ConvolutionLayer::convFloat32()
-{
-  nnfw::srcn::convMat_t in_mat, out_mat, kernel_mat;
-  nnfw::srcn::convParams_t in_param;
-
-  assert(_layout == nnfw::srcn::col_major || _layout == nnfw::srcn::row_major);
-  size_t height_index = _layout == nnfw::srcn::col_major ? 1 : 2;
-  size_t width_index = _layout == nnfw::srcn::col_major ? 2 : 3;
-  size_t depth_index = _layout == nnfw::srcn::col_major ? 3 : 1;
-  size_t kernel_input_depth_index = _layout == nnfw::srcn::col_major ? 3 : 1;
-  size_t kernel_output_depth_index = 0;
-
-  const int batches = MatchingDim(_inputDescr, 0, _outputDescr, 0);
-  const int input_height = _inputDescr.dimensions[height_index];
-  const int input_width = _inputDescr.dimensions[width_index];
-  const int input_depth =
-      MatchingDim(_inputDescr, depth_index, _kernelDescr, kernel_input_depth_index);
-  in_mat.c = input_depth;
-  in_mat.w = input_width;
-  in_mat.h = input_height;
-  in_mat.n = batches;
-  in_mat.data = _inputData.f;
-
-  const int output_height = _outputDescr.dimensions[height_index];
-  const int output_width = _outputDescr.dimensions[width_index];
-  const int output_depth =
-      MatchingDim(_kernelDescr, kernel_output_depth_index, _outputDescr, depth_index);
-  out_mat.c = output_depth;
-  out_mat.w = output_width;
-  out_mat.h = output_height;
-  out_mat.n = batches;
-  out_mat.data = _outputData.f;
-
-  const int outch = _kernelDescr.dimensions[kernel_output_depth_index];
-  const int inch = _kernelDescr.dimensions[kernel_input_depth_index];
-  const int ker_height = _kernelDescr.dimensions[height_index];
-  const int ker_width = _kernelDescr.dimensions[width_index];
-  kernel_mat.c = input_depth;
-  kernel_mat.w = ker_width;
-  kernel_mat.h = ker_height;
-  kernel_mat.n = output_depth;
-  kernel_mat.data = _kernelData.f;
-
-  in_param.kernel_w = ker_width;
-  in_param.kernel_h = ker_height;
-  in_param.stride_w = _strideWidth;
-  in_param.stride_h = _strideHeight;
-  in_param.padding = _paddingType;
-  in_param.pad_w = _paddingLeft;
-  in_param.pad_h = _paddingTop;
-  in_param.dilation_w = 1;
-  in_param.dilation_h = 1;
-
-  nnfw::srcn::winogradParams_t winograd_param;
-  winograd_param.kernel_w = ker_width;
-  winograd_param.kernel_h = ker_height;
-  winograd_param.stride_w = _strideWidth;
-  winograd_param.stride_h = _strideHeight;
-  winograd_param.dilation_w = 1;
-  winograd_param.dilation_h = 1;
-  winograd_param.batch = batches;
-  winograd_param.w = ker_width;
-  winograd_param.h = ker_height;
-  winograd_param.inch = inch;
-  winograd_param.outch = outch;
-  winograd_param.num_threads = 4;
-
-  winograd_param.conv_type = _layout;
-  winograd_param.weight_data = _kernelData.f;
-
-  // Without winograd
-  if (nnfw::srcn::check_winograd(winograd_param))
-  {
-    _winograd_weights = nnfw::srcn::trans_weight2winograd(winograd_param, nullptr);
-  }
-  _sparse_weights = nnfw::srcn::trans_weight2sparse(kernel_mat);
-
-  nnfw::srcn::srcn_convolution2D(in_mat, kernel_mat, out_mat, in_param, _winograd_weights, 4,
-                                 _layout);
-
-  // Add biases
-  if (_biasData.f == nullptr)
-  {
-    return;
-  }
-  // TODO Optimize
-  uint32_t strides[4] = {
-      _outputDescr.dimensions[1] * _outputDescr.dimensions[2] * _outputDescr.dimensions[3],
-      _outputDescr.dimensions[2] * _outputDescr.dimensions[3], _outputDescr.dimensions[3], 1};
-  if (_layout == nnfw::srcn::convType_t::col_major)
-  {
-    for (uint32_t c = 0; c < _outputDescr.dimensions[3]; ++c)
-    {
-      if (_biasData.f[c] != 0)
-      {
-        for (uint32_t b = 0; b < _outputDescr.dimensions[0]; ++b)
-        {
-          for (uint32_t h = 0; h < _outputDescr.dimensions[1]; ++h)
-          {
-            for (uint32_t w = 0; w < _outputDescr.dimensions[2]; ++w)
-            {
-              _outputData.f[b * strides[0] + h * strides[1] + w * strides[2] + c * strides[3]] +=
-                  _biasData.f[c];
-            }
-          }
-        }
-      }
-    }
-  }
-  else if (_layout == nnfw::srcn::convType_t::row_major)
-  {
-    for (uint32_t c = 0; c < _outputDescr.dimensions[1]; ++c)
-    {
-      if (_biasData.f[c] != 0)
-      {
-        for (uint32_t b = 0; b < _outputDescr.dimensions[0]; ++b)
-        {
-          for (uint32_t h = 0; h < _outputDescr.dimensions[2]; ++h)
-          {
-            for (uint32_t w = 0; w < _outputDescr.dimensions[3]; ++w)
-            {
-              _outputData.f[b * strides[0] + c * strides[1] + h * strides[2] + w * strides[3]] +=
-                  _biasData.f[c];
-            }
-          }
-        }
-      }
-    }
-  }
-  else
-  {
-    throw std::runtime_error("Wrong Layout");
-  }
-}
-
-void ConvolutionLayer::configure(uint8_t *inputData, const TensorDescriptor inputDescr,
-                                 uint8_t *kernelData, const TensorDescriptor kernelDescr,
-                                 uint8_t *biasData, const TensorDescriptor biasDescr,
-                                 const uint32_t paddingType, const uint32_t paddingLeft,
-                                 const uint32_t paddingRight, const uint32_t paddingTop,
-                                 const uint32_t paddingBottom, const uint32_t strideWidth,
-                                 const uint32_t strideHeight, uint8_t *outputData,
-                                 const TensorDescriptor outputDescr, ir::Layout layout)
-{
-  assert(inputDescr.dimensions.size() == 4);
-  assert(kernelDescr.dimensions.size() == 4);
-  assert(biasDescr.dimensions.size() == 1);
-  assert(outputDescr.dimensions.size() == 4);
-  assert(inputDescr.type == kernelDescr.type && inputDescr.type == outputDescr.type);
-  // TODO Add assertions validating height and width with padding
-  _layout = convertLayout(layout);
-  _inputData.u8 = inputData;
-  _inputDescr = inputDescr;
-  _inputType = inputDescr.type;
-  _kernelData.u8 = kernelData;
-  _kernelDescr = kernelDescr;
-  _biasData.u8 = biasData;
-  _biasDescr = biasDescr;
-  _paddingType = paddingType;
-  _paddingLeft = paddingLeft;
-  _paddingRight = paddingRight;
-  _paddingTop = paddingTop;
-  _paddingBottom = paddingBottom;
-  _strideWidth = strideWidth;
-  _strideHeight = strideHeight;
-  _outputData.u8 = outputData;
-  _outputDescr = outputDescr;
-}
-
-void ConvolutionLayer::run()
-{
-  if (_inputType == OperandType::FLOAT32)
-  {
-    convFloat32();
-  }
-  else if (_inputType == OperandType::QUANT8_ASYMM)
-  {
-    throw std::runtime_error("NYI");
-  }
-}
-
-} // namespace kernel
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/srcn/kernel/ConvolutionLayer.h b/runtime/neurun/backend/srcn/kernel/ConvolutionLayer.h
deleted file mode 100644
index 4edafaa87..000000000
--- a/runtime/neurun/backend/srcn/kernel/ConvolutionLayer.h
+++ /dev/null
@@ -1,89 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_SRCN_KERNEL_CONVOLUTION_LAYER_H__
-#define __NEURUN_BACKEND_SRCN_KERNEL_CONVOLUTION_LAYER_H__
-
-#include <exec/IFunction.h>
-#include <ncnn/srcn/conv_type.h>
-
-#include "OperationUtils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-namespace kernel
-{
-
-class ConvolutionLayer : public ::neurun::exec::IFunction
-{
-public:
-  ConvolutionLayer();
-  ~ConvolutionLayer();
-
-public:
-  void convFloat32();
-  void configure(uint8_t *inputData, const TensorDescriptor inputDescr, uint8_t *kernelData,
-                 const TensorDescriptor kernelDescr, uint8_t *biasData,
-                 const TensorDescriptor biasDescr, const uint32_t paddingType,
-                 const uint32_t paddingLeft, const uint32_t paddingRight, const uint32_t paddingTop,
-                 const uint32_t paddingBottom, const uint32_t strideW, const uint32_t strideH,
-                 uint8_t *outputData, const TensorDescriptor outputDescr, ir::Layout layout);
-
-  void run();
-  void runSync()
-  {
-    // this abstract method is used just for profiling and called for
-    // backend::acl_common::AclFunction
-    run();
-  }
-
-private:
-  DataPtr _inputData;
-  DataPtr _kernelData;
-  DataPtr _biasData;
-  DataPtr _outputData;
-
-  TensorDescriptor _inputDescr;
-  TensorDescriptor _kernelDescr;
-  TensorDescriptor _biasDescr;
-  TensorDescriptor _outputDescr;
-
-  uint32_t _paddingType;
-  uint32_t _paddingLeft;
-  uint32_t _paddingTop;
-  uint32_t _paddingRight;
-  uint32_t _paddingBottom;
-
-  uint32_t _strideWidth;
-  uint32_t _strideHeight;
-
-  OperandType _inputType;
-  nnfw::srcn::convType_t _layout;
-
-  float *_winograd_weights;
-  void *_sparse_weights;
-};
-
-} // namespace kernel
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_SRCN_KERNEL_CONVOLUTION_LAYER_H__
diff --git a/runtime/neurun/backend/srcn/kernel/DepthwiseConvolutionLayer.cc b/runtime/neurun/backend/srcn/kernel/DepthwiseConvolutionLayer.cc
deleted file mode 100644
index a1718c500..000000000
--- a/runtime/neurun/backend/srcn/kernel/DepthwiseConvolutionLayer.cc
+++ /dev/null
@@ -1,212 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "DepthwiseConvolutionLayer.h"
-
-#include <ncnn/srcn/srcn_conv.h>
-#include "OperationUtils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-namespace kernel
-{
-
-DepthwiseConvolutionLayer::DepthwiseConvolutionLayer()
-    : _inputData(), _kernelData(), _biasData(), _outputData(), _inputDescr(), _kernelDescr(),
-      _biasDescr(), _outputDescr(), _paddingType(0), _paddingLeft(0), _paddingTop(0),
-      _paddingRight(0), _paddingBottom(0), _strideWidth(0), _strideHeight(0),
-      _inputType(OperandType::FLOAT32), _layout(nnfw::srcn::col_major)
-{
-  // DO NOTHING
-}
-
-void DepthwiseConvolutionLayer::convFloat32()
-{
-  nnfw::srcn::convMat_t in_mat, out_mat, kernel_mat, bias_mat;
-  nnfw::srcn::convParams_t in_param;
-
-  assert(_layout == nnfw::srcn::col_major || _layout == nnfw::srcn::row_major);
-  size_t height_index = _layout == nnfw::srcn::col_major ? 1 : 2;
-  size_t width_index = _layout == nnfw::srcn::col_major ? 2 : 3;
-  size_t depth_index = _layout == nnfw::srcn::col_major ? 3 : 1;
-
-  const int batches = MatchingDim(_inputDescr, 0, _outputDescr, 0);
-  const int input_height = _inputDescr.dimensions[height_index];
-  const int input_width = _inputDescr.dimensions[width_index];
-  const int input_depth = _inputDescr.dimensions[depth_index];
-  in_mat.c = input_depth;
-  in_mat.w = input_width;
-  in_mat.h = input_height;
-  in_mat.n = batches;
-  in_mat.data = _inputData.f;
-
-  const int output_height = _outputDescr.dimensions[height_index];
-  const int output_width = _outputDescr.dimensions[width_index];
-  const int output_depth = MatchingDim(_kernelDescr, depth_index, _outputDescr, depth_index);
-  out_mat.c = output_depth;
-  out_mat.w = output_width;
-  out_mat.h = output_height;
-  out_mat.n = batches;
-  out_mat.data = _outputData.f;
-
-  const int ker_height = _kernelDescr.dimensions[height_index];
-  const int ker_width = _kernelDescr.dimensions[width_index];
-  kernel_mat.c = MatchingDim(_kernelDescr, depth_index, _outputDescr, depth_index);
-  kernel_mat.w = ker_width;
-  kernel_mat.h = ker_height;
-  kernel_mat.n = 1;
-  kernel_mat.data = _kernelData.f;
-
-  const int bias_depth = MatchingDim(_biasDescr, 0, _outputDescr, depth_index);
-  bias_mat.c = bias_depth;
-  bias_mat.data = _biasData.f;
-
-  in_param.kernel_w = ker_width;
-  in_param.kernel_h = ker_height;
-  in_param.stride_w = _strideWidth;
-  in_param.stride_h = _strideHeight;
-  in_param.padding = _paddingType;
-  in_param.pad_w = _paddingLeft;
-  in_param.pad_h = _paddingTop;
-  in_param.dilation_w = 1;
-  in_param.dilation_h = 1;
-
-  nnfw::srcn::srcn_depthwise_conv(in_mat, kernel_mat, out_mat, bias_mat, in_param, 4, _layout);
-
-  // Add biases
-  if (_biasData.f == nullptr)
-  {
-    return;
-  }
-  // TODO Optimize
-  uint32_t strides[4] = {
-      _outputDescr.dimensions[1] * _outputDescr.dimensions[2] * _outputDescr.dimensions[3],
-      _outputDescr.dimensions[2] * _outputDescr.dimensions[3], _outputDescr.dimensions[3], 1};
-  if (_layout == nnfw::srcn::convType_t::col_major)
-  {
-    for (uint32_t c = 0; c < _outputDescr.dimensions[3]; ++c)
-    {
-      if (_biasData.f[c] != 0)
-      {
-        for (uint32_t b = 0; b < _outputDescr.dimensions[0]; ++b)
-        {
-          for (uint32_t h = 0; h < _outputDescr.dimensions[1]; ++h)
-          {
-            for (uint32_t w = 0; w < _outputDescr.dimensions[2]; ++w)
-            {
-              _outputData.f[b * strides[0] + h * strides[1] + w * strides[2] + c * strides[3]] +=
-                  _biasData.f[c];
-            }
-          }
-        }
-      }
-    }
-  }
-  else if (_layout == nnfw::srcn::convType_t::row_major)
-  {
-    for (uint32_t c = 0; c < _outputDescr.dimensions[1]; ++c)
-    {
-      if (_biasData.f[c] != 0)
-      {
-        for (uint32_t b = 0; b < _outputDescr.dimensions[0]; ++b)
-        {
-          for (uint32_t h = 0; h < _outputDescr.dimensions[2]; ++h)
-          {
-            for (uint32_t w = 0; w < _outputDescr.dimensions[3]; ++w)
-            {
-              _outputData.f[b * strides[0] + c * strides[1] + h * strides[2] + w * strides[3]] +=
-                  _biasData.f[c];
-            }
-          }
-        }
-      }
-    }
-  }
-  else
-  {
-    throw std::runtime_error("Wrong Layout");
-  }
-}
-
-void DepthwiseConvolutionLayer::configure(uint8_t *inputData, const TensorDescriptor inputDescr,
-                                          uint8_t *kernelData, const TensorDescriptor kernelDescr,
-                                          uint8_t *biasData, const TensorDescriptor biasDescr,
-                                          const uint32_t paddingType, const uint32_t paddingLeft,
-                                          const uint32_t paddingRight, const uint32_t paddingTop,
-                                          const uint32_t paddingBottom, const uint32_t strideWidth,
-                                          const uint32_t strideHeight, uint8_t *outputData,
-                                          const TensorDescriptor outputDescr, ir::Layout layout)
-{
-  assert(layout == ir::Layout::NHWC || layout == ir::Layout::NCHW);
-
-  const auto height_index = layout == ir::Layout::NHWC ? 1 : 2;
-  const auto width_index = layout == ir::Layout::NHWC ? 2 : 3;
-
-  if (layout == ir::Layout::NHWC)
-  {
-    throw std::runtime_error("DepthwiseConv of ncnn does not support layout yet in NHWC");
-  }
-
-  if (kernelDescr.dimensions[height_index] != 3 || kernelDescr.dimensions[width_index] != 3)
-  {
-    throw std::runtime_error("DepthwiseConv of ncnn supports only 3x3 kernel now");
-  }
-
-  assert(inputDescr.dimensions.size() == 4);
-  assert(kernelDescr.dimensions.size() == 4);
-  assert(biasDescr.dimensions.size() == 1);
-  assert(outputDescr.dimensions.size() == 4);
-  assert(inputDescr.type == kernelDescr.type && inputDescr.type == outputDescr.type);
-  // TODO Add assertions validating height and width with padding
-  _layout = convertLayout(layout);
-  _inputData.u8 = inputData;
-  _inputDescr = inputDescr;
-  _inputType = inputDescr.type;
-  _kernelData.u8 = kernelData;
-  _kernelDescr = kernelDescr;
-  _biasData.u8 = biasData;
-  _biasDescr = biasDescr;
-  _paddingType = paddingType;
-  _paddingLeft = paddingLeft;
-  _paddingRight = paddingRight;
-  _paddingTop = paddingTop;
-  _paddingBottom = paddingBottom;
-  _strideWidth = strideWidth;
-  _strideHeight = strideHeight;
-  _outputData.u8 = outputData;
-  _outputDescr = outputDescr;
-}
-
-void DepthwiseConvolutionLayer::run()
-{
-  if (_inputType == OperandType::FLOAT32)
-  {
-    convFloat32();
-  }
-  else if (_inputType == OperandType::QUANT8_ASYMM)
-  {
-    throw std::runtime_error("NYI");
-  }
-}
-
-} // namespace kernel
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/srcn/kernel/DepthwiseConvolutionLayer.h b/runtime/neurun/backend/srcn/kernel/DepthwiseConvolutionLayer.h
deleted file mode 100644
index e94acff08..000000000
--- a/runtime/neurun/backend/srcn/kernel/DepthwiseConvolutionLayer.h
+++ /dev/null
@@ -1,85 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_SRCN_KERNEL_DEPTHWISE_CONVOLUTION_LAYER_H__
-#define __NEURUN_BACKEND_SRCN_KERNEL_DEPTHWISE_CONVOLUTION_LAYER_H__
-
-#include <exec/IFunction.h>
-#include <ncnn/srcn/conv_type.h>
-
-#include "OperationUtils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-namespace kernel
-{
-
-class DepthwiseConvolutionLayer : public ::neurun::exec::IFunction
-{
-public:
-  DepthwiseConvolutionLayer();
-
-public:
-  void convFloat32();
-  void configure(uint8_t *inputData, const TensorDescriptor inputDescr, uint8_t *kernelData,
-                 const TensorDescriptor kernelDescr, uint8_t *biasData,
-                 const TensorDescriptor biasDescr, const uint32_t paddingType,
-                 const uint32_t paddingLeft, const uint32_t paddingRight, const uint32_t paddingTop,
-                 const uint32_t paddingBottom, const uint32_t strideW, const uint32_t strideH,
-                 uint8_t *outputData, const TensorDescriptor outputDescr, ir::Layout layout);
-
-  void run();
-  void runSync()
-  {
-    // this abstract method is used just for profiling and called for
-    // backend::acl_common::AclFunction
-    run();
-  }
-
-private:
-  DataPtr _inputData;
-  DataPtr _kernelData;
-  DataPtr _biasData;
-  DataPtr _outputData;
-
-  TensorDescriptor _inputDescr;
-  TensorDescriptor _kernelDescr;
-  TensorDescriptor _biasDescr;
-  TensorDescriptor _outputDescr;
-
-  uint32_t _paddingType;
-  uint32_t _paddingLeft;
-  uint32_t _paddingTop;
-  uint32_t _paddingRight;
-  uint32_t _paddingBottom;
-
-  uint32_t _strideWidth;
-  uint32_t _strideHeight;
-
-  OperandType _inputType;
-  nnfw::srcn::convType_t _layout;
-};
-
-} // namespace kernel
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_SRCN_KERNEL_DEPTHWISE_CONVOLUTION_LAYER_H__
diff --git a/runtime/neurun/backend/srcn/kernel/InstanceNormLayer.cc b/runtime/neurun/backend/srcn/kernel/InstanceNormLayer.cc
deleted file mode 100644
index c83fe6d67..000000000
--- a/runtime/neurun/backend/srcn/kernel/InstanceNormLayer.cc
+++ /dev/null
@@ -1,155 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "InstanceNormLayer.h"
-
-#include "OperationUtils.h"
-#include "ncnn/layer/instance_norm.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-namespace kernel
-{
-
-InstanceNormLayer::InstanceNormLayer()
-    : _inputData(), _gammaData(), _betaData(), _outputData(), _inputDescr(), _gammaDescr(),
-      _betaDescr(), _outputDescr(), _epsilon(1e-5), _activation(ir::Activation::NONE),
-      _inputType(OperandType::FLOAT32), _backendLayout(ir::Layout::UNKNOWN)
-{
-  // DO NOTHING
-}
-
-void InstanceNormLayer::instanceNormFloat32()
-{
-  // Call kernel for NCHW data layout
-  if (_backendLayout == ir::Layout::NCHW)
-  {
-    // Supports single batch only
-    assert(_inputDescr.dimensions[0] == 1);
-    const int input_channels = _inputDescr.dimensions[1];
-    const int input_height = _inputDescr.dimensions[2];
-    const int input_width = _inputDescr.dimensions[3];
-    nnfw::ncnn::Mat in_mat(input_width, input_height, input_channels, _inputData.f);
-
-    const int gamma_channels = _gammaDescr.dimensions[0];
-    nnfw::ncnn::Mat gamma_mat(gamma_channels, _gammaData.f);
-
-    const int beta_channels = _betaDescr.dimensions[0];
-    nnfw::ncnn::Mat beta_mat(beta_channels, _betaData.f);
-
-    assert(_outputDescr.dimensions[0] == 1);
-    const int output_channels = _outputDescr.dimensions[1];
-    const int output_height = _outputDescr.dimensions[2];
-    const int output_width = _outputDescr.dimensions[3];
-    nnfw::ncnn::Mat out_mat(output_width, output_height, output_channels, _outputData.f);
-
-    if (_activation == ir::Activation::NONE)
-    {
-      nnfw::ncnn::ncnn_instance_norm_rowmajor(in_mat, out_mat, gamma_mat, beta_mat, input_channels,
-                                              _epsilon);
-    }
-    else if (_activation == ir::Activation::RELU)
-    {
-      nnfw::ncnn::ncnn_instance_norm_with_relu_rowmajor(in_mat, out_mat, gamma_mat, beta_mat,
-                                                        input_channels, _epsilon, 0.f);
-    }
-    else
-    {
-      std::runtime_error("Unsupported activation type");
-    }
-  }
-  // Call kernel for NHWC data layout
-  else if (_backendLayout == ir::Layout::NHWC)
-  {
-    // Supports single batch only
-    assert(_inputDescr.dimensions[0] == 1);
-    const int input_height = _inputDescr.dimensions[1];
-    const int input_width = _inputDescr.dimensions[2];
-    const int input_channels = _inputDescr.dimensions[3];
-    nnfw::ncnn::Mat in_mat(input_channels, input_width, input_height, _inputData.f);
-
-    const int gamma_channels = _gammaDescr.dimensions[0];
-    nnfw::ncnn::Mat gamma_mat(gamma_channels, _gammaData.f);
-
-    const int beta_channels = _betaDescr.dimensions[0];
-    nnfw::ncnn::Mat beta_mat(beta_channels, _betaData.f);
-
-    assert(_outputDescr.dimensions[0] == 1);
-    const int output_height = _outputDescr.dimensions[1];
-    const int output_width = _outputDescr.dimensions[2];
-    const int output_channels = _outputDescr.dimensions[3];
-    nnfw::ncnn::Mat out_mat(output_channels, output_width, output_height, _outputData.f);
-
-    if (_activation == ir::Activation::NONE)
-    {
-      nnfw::ncnn::ncnn_instance_norm_colmajor(in_mat, out_mat, gamma_mat, beta_mat, input_channels,
-                                              _epsilon);
-    }
-    else if (_activation == ir::Activation::RELU)
-    {
-      nnfw::ncnn::ncnn_instance_norm_with_relu_colmajor(in_mat, out_mat, gamma_mat, beta_mat,
-                                                        input_channels, _epsilon, 0.f);
-    }
-    {
-      std::runtime_error("Unsupported activation type");
-    }
-  }
-  else
-  {
-    std::runtime_error("Unsupported backend layout");
-  }
-}
-
-void InstanceNormLayer::configure(uint8_t *inputData, const TensorDescriptor inputDescr,
-                                  uint8_t *gammaData, const TensorDescriptor gammaDescr,
-                                  uint8_t *betaData, const TensorDescriptor betaDescr,
-                                  uint8_t *outputData, const TensorDescriptor outputDescr,
-                                  float epsilon, ir::Activation activation,
-                                  ir::Layout backendLayout)
-{
-  _inputData.u8 = inputData;
-  _inputDescr = inputDescr;
-  _gammaData.u8 = gammaData;
-  _gammaDescr = gammaDescr;
-  _betaData.u8 = betaData;
-  _betaDescr = betaDescr;
-  _outputData.u8 = outputData;
-  _outputDescr = outputDescr;
-  _epsilon = epsilon;
-  _activation = activation;
-  _backendLayout = backendLayout;
-}
-
-void InstanceNormLayer::run()
-{
-  if (_inputType == OperandType::FLOAT32)
-  {
-    instanceNormFloat32();
-  }
-  else if (_inputType == OperandType::QUANT8_ASYMM)
-  {
-    throw std::runtime_error("NYI");
-  }
-}
-
-} // namespace kernel
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/srcn/kernel/InstanceNormLayer.h b/runtime/neurun/backend/srcn/kernel/InstanceNormLayer.h
deleted file mode 100644
index 0ac0cef3f..000000000
--- a/runtime/neurun/backend/srcn/kernel/InstanceNormLayer.h
+++ /dev/null
@@ -1,77 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_SRCN_KERNEL_INSTANCENORM_LAYER_H__
-#define __NEURUN_BACKEND_SRCN_KERNEL_INSTANCENORM_LAYER_H__
-
-#include <exec/IFunction.h>
-
-#include "OperationUtils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-namespace kernel
-{
-
-class InstanceNormLayer : public ::neurun::exec::IFunction
-{
-public:
-  InstanceNormLayer();
-
-public:
-  void instanceNormFloat32();
-  void configure(uint8_t *inputData, const TensorDescriptor inputDescr, uint8_t *gammaData,
-                 const TensorDescriptor gammaDescr, uint8_t *betaData,
-                 const TensorDescriptor betaDescr, uint8_t *outputData,
-                 const TensorDescriptor outputDescr, float epsilon, ir::Activation activation,
-                 ir::Layout backendLayout);
-
-  void run();
-  void runSync()
-  {
-    // this abstract method is used just for profiling and called for
-    // backend::acl_common::AclFunction
-    run();
-  }
-
-private:
-  DataPtr _inputData;
-  DataPtr _gammaData;
-  DataPtr _betaData;
-  DataPtr _outputData;
-
-  TensorDescriptor _inputDescr;
-  TensorDescriptor _gammaDescr;
-  TensorDescriptor _betaDescr;
-  TensorDescriptor _outputDescr;
-
-  float _epsilon;
-  ir::Activation _activation;
-
-  OperandType _inputType;
-  ir::Layout _backendLayout;
-};
-
-} // namespace kernel
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_SRCN_KERNEL_INSTANCENORM_LAYER_H__
diff --git a/runtime/neurun/backend/srcn/kernel/OperationUtils.cc b/runtime/neurun/backend/srcn/kernel/OperationUtils.cc
deleted file mode 100644
index 684573a51..000000000
--- a/runtime/neurun/backend/srcn/kernel/OperationUtils.cc
+++ /dev/null
@@ -1,139 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "OperationUtils.h"
-
-#include <cmath>
-#include <algorithm>
-#include <cassert>
-#include <map>
-
-#include "util/Utils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-namespace kernel
-{
-
-uint32_t MatchingDim(const TensorDescriptor &descr1, int index1, const TensorDescriptor &descr2,
-                     int index2)
-{
-  UNUSED_RELEASE(descr2);
-  UNUSED_RELEASE(index2);
-  assert(descr1.dimensions[index1] == descr2.dimensions[index2]);
-  return descr1.dimensions[index1];
-}
-
-std::vector<int32_t> getFilterPermutation(FilterLayout from_layout, FilterLayout to_layout)
-{
-  static std::map<std::pair<FilterLayout, FilterLayout>, std::vector<int32_t>> filter_permutation =
-      {{std::make_pair(FilterLayout::OHWI, FilterLayout::HWOI), {1, 2, 0, 3}},
-       {std::make_pair(FilterLayout::OHWI, FilterLayout::HWIO), {1, 2, 3, 0}},
-       {std::make_pair(FilterLayout::OHWI, FilterLayout::OIHW), {0, 3, 1, 2}},
-       {std::make_pair(FilterLayout::OHWI, FilterLayout::IOHW), {3, 0, 1, 2}},
-       {std::make_pair(FilterLayout::OIHW, FilterLayout::HWOI), {2, 3, 0, 1}},
-       {std::make_pair(FilterLayout::OIHW, FilterLayout::HWIO), {2, 3, 1, 0}},
-       {std::make_pair(FilterLayout::OIHW, FilterLayout::OHWI), {0, 2, 3, 1}},
-       {std::make_pair(FilterLayout::OIHW, FilterLayout::IOHW), {1, 0, 2, 3}}};
-
-  const auto pair = std::make_pair(from_layout, to_layout);
-  const auto it = filter_permutation.find(pair);
-  if (it == filter_permutation.end())
-  {
-    return std::vector<int32_t>{};
-  }
-  return it->second;
-}
-
-Coordinates convertCoordinates(const Coordinates &coordinates, FilterLayout from_layout,
-                               FilterLayout to_layout)
-{
-  assert(coordinates.size() == 4);
-  if (from_layout == to_layout)
-  {
-    return coordinates;
-  }
-
-  const auto permutation = getFilterPermutation(from_layout, to_layout);
-  if (permutation.size() == 0)
-  {
-    throw std::runtime_error("Not supported FilterLayout");
-  }
-  return Coordinates{coordinates[permutation[0]], coordinates[permutation[1]],
-                     coordinates[permutation[2]], coordinates[permutation[3]]};
-}
-
-nnfw::srcn::convType_t convertLayout(ir::Layout layout)
-{
-  if (layout == ir::Layout::NHWC)
-  {
-    return nnfw::srcn::col_major;
-  }
-  else if (layout == ir::Layout::NCHW)
-  {
-    return nnfw::srcn::row_major;
-  }
-  else
-  {
-    throw std::runtime_error("Not supported layout");
-  }
-}
-
-TensorDescriptor getTensorDescriptor(const ir::Operand &o, ir::Layout frontend_layout,
-                                     ir::Layout backend_layout)
-{
-  TensorDescriptor descriptor;
-
-  auto dims = o.shape().dims();
-  if (frontend_layout == ir::Layout::NHWC && backend_layout == ir::Layout::NCHW &&
-      o.shape().rank() == 4)
-  {
-    // NHWC -> NCHW
-    uint32_t permutation[4] = {0, 3, 1, 2};
-    for (int i = 0; i < o.shape().rank(); ++i)
-    {
-      dims.at(i) = o.shape().dim(permutation[i]);
-    }
-  }
-  else if (frontend_layout == ir::Layout::NCHW && backend_layout == ir::Layout::NHWC &&
-           o.shape().rank() == 4)
-  {
-    // NCHW -> NHWC
-    uint32_t permutation[4] = {0, 2, 3, 1};
-    for (int i = 0; i < o.shape().rank(); ++i)
-    {
-      dims.at(i) = o.shape().dim(permutation[i]);
-    }
-  }
-  descriptor.dimensions = std::vector<uint32_t>(dims.begin(), dims.end());
-  descriptor.type = static_cast<OperandType>(static_cast<int32_t>(o.typeInfo().type()));
-  descriptor.scale = o.typeInfo().scale();
-  descriptor.offset = o.typeInfo().offset();
-
-  // CPU backend assume that neurun internal descriptor's rank is always same or less than 4
-  assert(descriptor.dimensions.size() <= 4);
-
-  return descriptor;
-}
-
-} // namespace kernel
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/srcn/kernel/OperationUtils.h b/runtime/neurun/backend/srcn/kernel/OperationUtils.h
deleted file mode 100644
index aa163a1f3..000000000
--- a/runtime/neurun/backend/srcn/kernel/OperationUtils.h
+++ /dev/null
@@ -1,84 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_SRCN_OPERATION_UTILS_H__
-#define __NEURUN_BACKEND_SRCN_OPERATION_UTILS_H__
-
-#include <iostream>
-#include <limits>
-#include <vector>
-
-#include "ir/Operand.h"
-#include "ir/DataType.h"
-#include <ir/InternalType.h>
-#include <ncnn/srcn/conv_type.h>
-
-using OperandType = neurun::ir::DataType;
-using neurun::util::Coordinates;
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-namespace kernel
-{
-
-struct TensorDescriptor
-{
-  OperandType type;
-  std::vector<uint32_t> dimensions;
-  float scale;
-  int32_t offset;
-};
-
-union DataPtr {
-  uint8_t *u8;
-  int8_t *i8;
-  int32_t *i32;
-  float *f;
-  void *v;
-};
-
-enum FilterLayout
-{
-  OHWI = 0, // TfLite Kernel Layout when using NHWC image layout
-  HWOI,     // SRCN Transpose Conv Kernel Layout when using NHWC image layout
-  OIHW,     // SRCN Conv Kernel Layout when using NCHW image layout
-  HWIO,     // SRCN Conv Kernel Layout when using NHWC image layout
-  IOHW,     // SRCN Transpose Conv Kernel Layout when using NCHW image layout
-};
-
-uint32_t MatchingDim(const TensorDescriptor &shape1, int index1, const TensorDescriptor &shape2,
-                     int index2);
-
-std::vector<int32_t> getFilterPermutation(FilterLayout from_layout, FilterLayout to_layout);
-
-Coordinates convertCoordinates(const Coordinates &from_coordinates, FilterLayout from_layout,
-                               FilterLayout to_layout);
-
-nnfw::srcn::convType_t convertLayout(ir::Layout layout);
-
-TensorDescriptor getTensorDescriptor(const ir::Operand &o, ir::Layout frontend_layout,
-                                     ir::Layout backend_layout);
-
-} // namespace kernel
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_SRCN_OPERATION_UTILS_H__
diff --git a/runtime/neurun/backend/srcn/kernel/TransposeConvLayer.cc b/runtime/neurun/backend/srcn/kernel/TransposeConvLayer.cc
deleted file mode 100644
index 26469f728..000000000
--- a/runtime/neurun/backend/srcn/kernel/TransposeConvLayer.cc
+++ /dev/null
@@ -1,136 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "TransposeConvLayer.h"
-
-#include <cstring>
-#include "OperationUtils.h"
-#include "ncnn/srcn/srcn_conv.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-namespace kernel
-{
-
-TransposeConvLayer::TransposeConvLayer()
-    : _inputData(), _kernelData(), _outputData(), _inputDescr(), _kernelDescr(), _outputDescr(),
-      _paddingType(0), _paddingLeft(0), _paddingTop(0), _paddingRight(0), _paddingBottom(0),
-      _strideWidth(0), _strideHeight(0), _inputType(OperandType::FLOAT32),
-      _layout(nnfw::srcn::col_major)
-{
-  // DO NOTHING
-}
-
-void TransposeConvLayer::convFloat32()
-{
-  nnfw::srcn::convMat_t in_mat, out_mat, kernel_mat;
-  nnfw::srcn::convParams_t in_param;
-
-  assert(_layout == nnfw::srcn::col_major || _layout == nnfw::srcn::row_major);
-  size_t height_index = _layout == nnfw::srcn::col_major ? 1 : 2;
-  size_t width_index = _layout == nnfw::srcn::col_major ? 2 : 3;
-  size_t depth_index = _layout == nnfw::srcn::col_major ? 3 : 1;
-  size_t kernel_input_depth_index = _layout == nnfw::srcn::col_major ? 3 : 1;
-  size_t kernel_output_depth_index = 0;
-  const int batches = MatchingDim(_inputDescr, 0, _outputDescr, 0);
-  const int input_height = _inputDescr.dimensions[height_index];
-  const int input_width = _inputDescr.dimensions[width_index];
-  const int input_depth =
-      MatchingDim(_inputDescr, depth_index, _kernelDescr, kernel_input_depth_index);
-  in_mat.c = input_depth;
-  in_mat.w = input_width;
-  in_mat.h = input_height;
-  in_mat.n = batches;
-  in_mat.data = _inputData.f;
-
-  const int output_height = _outputDescr.dimensions[height_index];
-  const int output_width = _outputDescr.dimensions[width_index];
-  const int output_depth =
-      MatchingDim(_kernelDescr, kernel_output_depth_index, _outputDescr, depth_index);
-  out_mat.c = output_depth;
-  out_mat.w = output_width;
-  out_mat.h = output_height;
-  out_mat.n = batches;
-  out_mat.data = _outputData.f;
-
-  const int ker_height = _kernelDescr.dimensions[height_index];
-  const int ker_width = _kernelDescr.dimensions[width_index];
-  kernel_mat.c = input_depth;
-  kernel_mat.w = ker_width;
-  kernel_mat.h = ker_height;
-  kernel_mat.n = output_depth;
-  kernel_mat.data = _kernelData.f;
-
-  in_param.kernel_w = ker_width;
-  in_param.kernel_h = ker_height;
-  in_param.stride_w = _strideWidth;
-  in_param.stride_h = _strideHeight;
-  in_param.padding = _paddingType;
-  in_param.pad_w = _paddingLeft;
-  in_param.pad_h = _paddingTop;
-  in_param.dilation_w = 1;
-  in_param.dilation_h = 1;
-
-  memset(_outputData.f, 0, out_mat.n * out_mat.h * out_mat.w * out_mat.c * sizeof(float));
-
-  nnfw::srcn::srcn_deconvolution2D(in_mat, kernel_mat, out_mat, in_param, 4, _layout);
-}
-
-void TransposeConvLayer::configure(uint8_t *inputData, const TensorDescriptor inputDescr,
-                                   uint8_t *kernelData, const TensorDescriptor kernelDescr,
-                                   const uint32_t paddingType, const uint32_t paddingLeft,
-                                   const uint32_t paddingRight, const uint32_t paddingTop,
-                                   const uint32_t paddingBottom, const uint32_t strideWidth,
-                                   const uint32_t strideHeight, uint8_t *outputData,
-                                   const TensorDescriptor outputDescr, ir::Layout layout)
-{
-  _layout = convertLayout(layout);
-  _inputData.u8 = inputData;
-  _inputDescr = inputDescr;
-  _inputType = inputDescr.type;
-  _kernelData.u8 = kernelData;
-  _kernelDescr = kernelDescr;
-  _paddingType = paddingType;
-  _paddingLeft = paddingLeft;
-  _paddingRight = paddingRight;
-  _paddingTop = paddingTop;
-  _paddingBottom = paddingBottom;
-  _strideWidth = strideWidth;
-  _strideHeight = strideHeight;
-  _outputData.u8 = outputData;
-  _outputDescr = outputDescr;
-}
-
-void TransposeConvLayer::run()
-{
-  if (_inputType == OperandType::FLOAT32)
-  {
-    convFloat32();
-  }
-  else if (_inputType == OperandType::QUANT8_ASYMM)
-  {
-    throw std::runtime_error("NYI");
-  }
-}
-
-} // namespace kernel
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/srcn/kernel/TransposeConvLayer.h b/runtime/neurun/backend/srcn/kernel/TransposeConvLayer.h
deleted file mode 100644
index cd88d4127..000000000
--- a/runtime/neurun/backend/srcn/kernel/TransposeConvLayer.h
+++ /dev/null
@@ -1,83 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_SRCN_KERNEL_TRANSPOSECONV_LAYER_H__
-#define __NEURUN_BACKEND_SRCN_KERNEL_TRANSPOSECONV_LAYER_H__
-
-#include <exec/IFunction.h>
-#include <ncnn/srcn/conv_type.h>
-
-#include "OperationUtils.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-namespace kernel
-{
-
-class TransposeConvLayer : public ::neurun::exec::IFunction
-{
-public:
-  TransposeConvLayer();
-
-public:
-  void convFloat32();
-  void configure(uint8_t *inputData, const TensorDescriptor inputDescr, uint8_t *kernelData,
-                 const TensorDescriptor kernelDescr, const uint32_t paddingType,
-                 const uint32_t paddingLeft, const uint32_t paddingRight, const uint32_t paddingTop,
-                 const uint32_t paddingBottom, const uint32_t strideW, const uint32_t strideH,
-                 uint8_t *outputData, const TensorDescriptor outputDescr,
-                 ir::Layout backend_layout);
-
-  void run();
-  void runSync()
-  {
-    // this abstract method is used just for profiling and called for
-    // backend::acl_common::AclFunction
-    run();
-  }
-
-private:
-  DataPtr _inputData;
-  DataPtr _kernelData;
-  DataPtr _outputData;
-
-  TensorDescriptor _inputDescr;
-  TensorDescriptor _kernelDescr;
-  TensorDescriptor _outputDescr;
-
-  uint32_t _paddingType;
-  uint32_t _paddingLeft;
-  uint32_t _paddingTop;
-  uint32_t _paddingRight;
-  uint32_t _paddingBottom;
-
-  uint32_t _strideWidth;
-  uint32_t _strideHeight;
-
-  OperandType _inputType;
-  nnfw::srcn::convType_t _layout;
-};
-
-} // namespace kernel
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_SRCN_KERNEL_TRANSPOSECONV_LAYER_H__
diff --git a/runtime/neurun/backend/srcn/operand/Tensor.cc b/runtime/neurun/backend/srcn/operand/Tensor.cc
deleted file mode 100644
index 8a53f97c5..000000000
--- a/runtime/neurun/backend/srcn/operand/Tensor.cc
+++ /dev/null
@@ -1,45 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "Tensor.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-namespace operand
-{
-
-size_t Tensor::calcOffset(const neurun::util::Coordinates &coords) const
-{
-  size_t rank = num_dimensions();
-  size_t offset = 0;
-  for (size_t i = 0; i < rank; ++i)
-  {
-    offset = offset * dimension(i) + coords[i];
-  }
-  offset *= sizeOfDataType(data_type());
-  return offset;
-}
-
-void Tensor::access(const std::function<void(ITensor &)> &fn) { fn(*this); }
-
-} // namespace operand
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/backend/srcn/operand/Tensor.h b/runtime/neurun/backend/srcn/operand/Tensor.h
deleted file mode 100644
index e16234a81..000000000
--- a/runtime/neurun/backend/srcn/operand/Tensor.h
+++ /dev/null
@@ -1,79 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_SRCN_OPERAND_TENSOR_H__
-#define __NEURUN_BACKEND_SRCN_OPERAND_TENSOR_H__
-
-#include <backend/operand/ITensor.h>
-#include <ir/Layout.h>
-#include "ir/OperandInfo.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace srcn
-{
-namespace operand
-{
-
-class Tensor : public ::neurun::backend::operand::ITensor
-{
-public:
-  Tensor() = delete;
-
-public:
-  Tensor(const ir::OperandInfo &info, ir::Layout layout) : _info(info), _layout(layout)
-  {
-    // DO NOTHING
-  }
-
-public:
-  void setBuffer(uint8_t *buffer) { _buffer = buffer; }
-  ir::DataType data_type() const { return _info.typeInfo().type(); }
-
-public:
-  uint8_t *buffer() const override { return _buffer; }
-  /**
-   * @brief Get dimension by index
-   *
-   * @param index Index to get diemension
-   * @return size_t Dimension at index
-   * @note N : dimension(0)
-   *       H : dimension(1)
-   *       W : dimension(2)
-   *       C : dimension(3)
-   */
-  size_t dimension(size_t index) const override { return _info.shape().dim(index); }
-  size_t num_dimensions() const override { return _info.shape().rank(); }
-  size_t total_size() const override { return _info.total_size(); }
-  size_t calcOffset(const neurun::util::Coordinates &coords) const override;
-  ir::Layout layout() const override { return _layout; }
-  bool has_padding() const override { return false; }
-  void access(const std::function<void(ITensor &tensor)> &fn) final;
-
-private:
-  ir::OperandInfo _info;
-  uint8_t *_buffer = nullptr;
-  ir::Layout _layout;
-};
-
-} // namespace operand
-} // namespace srcn
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_SRCN_OPERAND_TENSOR_H__
diff --git a/runtime/neurun/core/CMakeLists.txt b/runtime/neurun/core/CMakeLists.txt
deleted file mode 100644
index 422d292bb..000000000
--- a/runtime/neurun/core/CMakeLists.txt
+++ /dev/null
@@ -1,18 +0,0 @@
-file(GLOB_RECURSE SOURCES "src/*.cc")
-
-add_library(neurun_core SHARED ${SOURCES})
-set_target_properties(neurun_core PROPERTIES POSITION_INDEPENDENT_CODE ON)
-target_include_directories(neurun_core PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/include)
-target_include_directories(neurun_core PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/src)
-target_link_libraries(neurun_core PUBLIC nnfw_lib_misc)
-target_link_libraries(neurun_core PUBLIC nnfw_lib_cpp14)
-target_link_libraries(neurun_core PRIVATE nnfw_lib_cker)
-target_link_libraries(neurun_core PRIVATE nnfw_common)
-target_link_libraries(neurun_core PRIVATE nnfw_coverage)
-target_link_libraries(neurun_core PRIVATE dl ${LIB_PTHREAD})
-
-if(ENVVAR_NEURUN_CONFIG)
-  target_compile_definitions(neurun_core PRIVATE ENVVAR_FOR_DEFAULT_CONFIG)
-endif(ENVVAR_NEURUN_CONFIG)
-
-install(TARGETS neurun_core DESTINATION lib)
diff --git a/runtime/neurun/core/include/backend/Backend.h b/runtime/neurun/core/include/backend/Backend.h
deleted file mode 100644
index 9c4484fca..000000000
--- a/runtime/neurun/core/include/backend/Backend.h
+++ /dev/null
@@ -1,67 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_BACKEND_H__
-#define __NEURUN_BACKEND_BACKEND_H__
-
-#include <memory>
-
-#include "ir/Operands.h"
-
-namespace neurun
-{
-namespace backend
-{
-
-namespace custom
-{
-class IKernelBuilder;
-}
-
-class Backend;
-struct IConfig;
-class IConstantInitializer;
-class IKernelGenerator;
-class IShapeFixer;
-class ITensorRegister;
-struct ITensorBuilder;
-
-class BackendContext
-{
-public:
-  const Backend *backend;
-  std::shared_ptr<ITensorBuilder> tensor_builder;
-  std::shared_ptr<IConstantInitializer> constant_initializer;
-  std::shared_ptr<IKernelGenerator> kernel_gen;
-  std::shared_ptr<IShapeFixer> shape_fixer;
-  std::shared_ptr<ITensorRegister> tensor_register;
-};
-
-class Backend
-{
-public:
-  virtual ~Backend() = default;
-  virtual std::shared_ptr<neurun::backend::IConfig> config() const = 0;
-
-  virtual std::unique_ptr<BackendContext>
-  newContext(const ir::Operands &operands,
-             const std::shared_ptr<backend::custom::IKernelBuilder> &kb) const = 0;
-};
-
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_BACKEND_H__
diff --git a/runtime/neurun/core/include/backend/CustomKernelBuilder.h b/runtime/neurun/core/include/backend/CustomKernelBuilder.h
deleted file mode 100644
index 848ebd595..000000000
--- a/runtime/neurun/core/include/backend/CustomKernelBuilder.h
+++ /dev/null
@@ -1,69 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_CUSTOM_KERNEL_BUILDER_H__
-#define __NEURUN_BACKEND_CUSTOM_KERNEL_BUILDER_H__
-
-#include "exec/IFunction.h"
-
-#include "misc/tensor/Shape.h"
-#include "ir/DataType.h"
-
-#include <vector>
-#include <memory>
-
-namespace neurun
-{
-namespace backend
-{
-namespace custom
-{
-
-using Shape = nnfw::misc::tensor::Shape;
-
-struct TypeInfo
-{
-  Shape shape;
-  ir::DataType dtype;
-};
-
-struct CustomKernelConfigParams
-{
-  std::vector<void *> input_allocations;
-  std::vector<TypeInfo> input_types;
-
-  std::vector<void *> output_allocations;
-  std::vector<TypeInfo> output_types;
-
-  char *userdata;
-  size_t userdata_size;
-};
-
-class IKernelBuilder
-{
-public:
-  virtual ~IKernelBuilder() = default;
-  virtual std::unique_ptr<exec::IFunction> buildKernel(const std::string &id,
-                                                       CustomKernelConfigParams &&params) const = 0;
-};
-
-} // namespace custom
-
-} // namespace backend
-
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_CUSTOM_KERNEL_BUILDER_H__
diff --git a/runtime/neurun/core/include/backend/ExecTime.h b/runtime/neurun/core/include/backend/ExecTime.h
deleted file mode 100644
index 4eaf49fab..000000000
--- a/runtime/neurun/core/include/backend/ExecTime.h
+++ /dev/null
@@ -1,111 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_EXEC_TIME_H__
-#define __NEURUN_BACKEND_EXEC_TIME_H__
-
-#include "backend/Backend.h"
-#include "backend/IConfig.h"
-#include "JSONExecTime.h"
-#include <memory>
-#include <limits>
-#include <map>
-#include <unordered_map>
-#include <vector>
-
-namespace neurun
-{
-namespace backend
-{
-class ExecTime
-{
-public:
-  explicit ExecTime(const std::vector<const Backend *> &backends) : _json(backends, _measurements)
-  {
-  }
-
-public:
-  /**
-   * @brief Get exec time of an operation with input size
-   *        or linearly interpolated value based on size if there is no record for given size
-   *
-   * @param[in] backend id of a backend
-   * @param[in] operation name of an operation
-   * @param[in] quant if input type quantized
-   * @param[in] op_size sum of operation's flattened sizes of inputs and outputs
-   * @return execution time for given input sizes
-   *         -1 if there are no records for given parameters (backend, op, quantization).
-   */
-  int64_t getOperationExecTime(const Backend *backend, const std::string &operation, bool quant,
-                               uint32_t op_size) const;
-  /**
-   * @brief Update exec time of the operation on a backend with given input size or
-   *        add new entity if there is no one.
-   *
-   * @param[in] backend id of a backend
-   * @param[in] operation name of an operation
-   * @param[in] quant if input type quantized
-   * @param[in] op_size sum of operation's flattened sizes of inputs and outputs
-   * @param[in] time real measured value
-   */
-  void updateOperationExecTime(const Backend *backend, const std::string &operation, bool quant,
-                               uint32_t op_size, int64_t time);
-  /**
-   * @brief Get the permute time from one backend to another
-   *
-   * @param[in] from_backend
-   * @param[in] to_backend
-   * @param[in] quant if input type quantized
-   * @param[in] op_size sum of operation's flattened sizes of inputs and outputs
-   * @return permutation time for operation size
-   */
-  int64_t getPermuteTime(const Backend *from_backend, const Backend *to_backend, bool quant,
-                         uint32_t op_size) const;
-  /**
-   * @brief Update permute time from one backend to another
-   *
-   * @param[in] from_backend
-   * @param[in] to_backend
-   * @param[in] quant if input type quantized
-   * @param[in] time measured permutation time
-   * @param[in] op_size sum of operation's flattened sizes of inputs and outputs
-   */
-  void updatePermuteTime(const Backend *from_backend, const Backend *to_backend, bool quant,
-                         uint32_t op_size, int64_t time);
-  /**
-   * @brief Get the max value of int32_t in int64_t
-   * @return max value
-   */
-  static int64_t getMax() { return _MAX; }
-  /**
-   * @brief Update metrics file with new data.
-   */
-  void uploadOperationsExecTime() const { _json.uploadOperationsExecTime(); }
-  static const int64_t NOT_FOUND = -1;
-
-private:
-  /// @brief Measurement data, which is shared with serializer
-  MeasurementData _measurements;
-  // int64_t::max may cause integer overflow
-  static const int64_t _MAX = std::numeric_limits<int32_t>::max();
-  /// @brief Serializer
-  JSON _json;
-};
-
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_EXEC_TIME_H__
diff --git a/runtime/neurun/core/include/backend/IConfig.h b/runtime/neurun/core/include/backend/IConfig.h
deleted file mode 100644
index 855f31ef9..000000000
--- a/runtime/neurun/core/include/backend/IConfig.h
+++ /dev/null
@@ -1,47 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ICONFIG_H__
-#define __NEURUN_BACKEND_ICONFIG_H__
-
-#include "util/ITimer.h"
-#include <memory>
-#include <string>
-
-namespace neurun
-{
-namespace backend
-{
-
-struct IConfig
-{
-  virtual ~IConfig() = default;
-
-  virtual std::string id() = 0;
-  virtual bool initialize() = 0;
-  // Support permute kernel
-  virtual bool SupportPermutation() = 0;
-  // Support subtensor allocation
-  virtual bool SupportSubTensorAlloc() = 0;
-
-  // Timer is used for backend profiling. In case of default (nullptr) timer profiler won't work.
-  virtual std::unique_ptr<util::ITimer> timer() { return nullptr; }
-};
-
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ICONFIG_H__
diff --git a/runtime/neurun/core/include/backend/IConstantInitializer.h b/runtime/neurun/core/include/backend/IConstantInitializer.h
deleted file mode 100644
index 3cc770b29..000000000
--- a/runtime/neurun/core/include/backend/IConstantInitializer.h
+++ /dev/null
@@ -1,288 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ICONSTANT_INITIALIZER_H__
-#define __NEURUN_BACKEND_ICONSTANT_INITIALIZER_H__
-
-#include <unordered_map>
-#include <functional>
-
-#include "ITensorBuilder.h"
-#include "ir/Layout.h"
-#include "ir/Operand.h"
-#include "ir/Operands.h"
-#include "ir/OperationVisitor.h"
-#include "ir/OpSequence.h"
-#include "util/logging.h"
-#include "util/Utils.h"
-
-namespace
-{
-template <typename T>
-static void Init(const neurun::ir::Operand &model_obj, neurun::backend::operand::ITensor &obj,
-                 const bool copy,
-                 const neurun::ir::Layout frontend_layout = neurun::ir::Layout::UNKNOWN)
-{
-  const auto shape = model_obj.shape();
-  auto base = reinterpret_cast<const T *>(model_obj.data().base());
-
-  obj.access([&](::neurun::backend::operand::ITensor &tensor) {
-    switch (shape.rank())
-    {
-      case 0:
-      {
-        assert(model_obj.data().size() == sizeof(T));
-        const auto value = *reinterpret_cast<const T *>(base);
-        T *into = reinterpret_cast<T *>(tensor.buffer());
-        *into = value;
-        break;
-      }
-      case 1:
-      {
-        auto vec_size = shape.dim(0);
-        for (int32_t n = 0; n < vec_size; ++n)
-        {
-          const T *from = reinterpret_cast<const T *>(base) + n;
-          const auto value = *from;
-
-          T *into = reinterpret_cast<T *>(tensor.buffer()) + n;
-
-          *into = value;
-        }
-        break;
-      }
-      case 2:
-      {
-        const int32_t copy_len = shape.dim(1);
-
-        for (auto i = 0; i < shape.dim(0); ++i)
-        {
-          neurun::util::Coordinates coords{i, 0};
-          memcpy(tensor.buffer() + tensor.calcOffset(coords), base + i * copy_len,
-                 copy_len * sizeof(T));
-        }
-        break;
-      }
-      case 3:
-      {
-        const int32_t width = shape.dim(1);
-        const int32_t copy_len = shape.dim(2);
-
-        for (auto i = 0; i < shape.dim(0); ++i)
-        {
-          for (auto j = 0; j < shape.dim(1); ++j)
-          {
-            neurun::util::Coordinates coords{i, j, 0};
-            memcpy(tensor.buffer() + tensor.calcOffset(coords),
-                   base + i * width * copy_len + j * copy_len, copy_len * sizeof(T));
-          }
-        }
-        break;
-      }
-      case 4:
-      {
-        const int32_t height = shape.dim(1);
-        const int32_t width = shape.dim(2);
-        const int32_t copy_len = shape.dim(3);
-        for (auto i = 0; i < shape.dim(0); ++i)
-        {
-          for (auto j = 0; j < shape.dim(1); ++j)
-          {
-            for (auto k = 0; k < shape.dim(2); ++k)
-            {
-              if (copy)
-              {
-                neurun::util::Coordinates coords{i, j, k, 0};
-                memcpy(tensor.buffer() + tensor.calcOffset(coords),
-                       base + i * height * width * copy_len + j * width * copy_len + k * copy_len,
-                       copy_len * sizeof(T));
-              }
-              else
-              {
-                for (auto l = 0; l < shape.dim(3); ++l)
-                {
-                  const auto coords = neurun::util::convertCoordinates(
-                      {i, j, k, l}, frontend_layout, tensor.layout());
-                  T *into = reinterpret_cast<T *>(tensor.buffer() + tensor.calcOffset(coords));
-                  T value = *(base + i * height * width * copy_len + j * width * copy_len +
-                              k * copy_len + l);
-                  *into = value;
-                }
-              }
-            }
-          }
-        }
-        break;
-      }
-      default:
-        throw std::runtime_error{"Not yet supported"};
-    }
-  });
-}
-
-template <typename T>
-void copyInit(const neurun::ir::Operand &model_obj, neurun::backend::operand::ITensor &obj)
-{
-  Init<T>(model_obj, obj, true);
-}
-
-template <typename T>
-void permuteInit(const neurun::ir::Operand &model_obj, neurun::backend::operand::ITensor &obj,
-                 const neurun::ir::Layout frontend_layout)
-{
-  const bool copy = frontend_layout == obj.layout();
-  Init<T>(model_obj, obj, copy, frontend_layout);
-}
-
-} // namespace
-
-namespace neurun
-{
-namespace backend
-{
-
-class IConstantInitializer : ir::OperationVisitor
-{
-public:
-  virtual ~IConstantInitializer() = default;
-
-public:
-  void run()
-  {
-    assert(tensor_builder().get());
-    for (const auto &it : _init_map)
-    {
-      const auto &ind = it.first;
-      const auto &fn = it.second;
-
-      const auto &model_obj = operands().at(ind);
-      auto tensor_obj = tensor_builder()->tensorAt(ind);
-      fn(model_obj, *tensor_obj);
-      VERBOSE(FillOperandData) << "Fill data for operand " << ind.value() << std::endl;
-    }
-    _init_map.clear();
-  }
-
-public:
-  using Initializer = std::function<void(const ir::Operand &, backend::operand::ITensor &)>;
-
-  void generate(const ir::OpSequence &subg, const ir::Operands &operands)
-  {
-    _current_subg_layout = subg.getLayout();
-    subg.accept(*this);
-    for (const auto &e : subg.operations())
-    {
-      for (const auto &ind : e.node->getInputs())
-      {
-        const auto &obj = operands.at(ind);
-        if (obj.isConstant() && !exist(ind))
-        {
-          registerPermuteInitializer(ind, obj);
-        }
-      }
-    }
-  }
-
-protected:
-  using OperationVisitor::visit;
-
-protected:
-  virtual const ir::Operands &operands() const = 0;
-  virtual std::shared_ptr<ITensorBuilder> tensor_builder() const = 0;
-
-protected:
-  void registerCopyInitializer(const ir::OperandIndex &index, const ir::Operand &obj)
-  {
-    // For only CONSTANTS
-    // TODO Add to check if tensor has been allocated
-    if (!obj.isConstant())
-      return;
-
-    const auto type = obj.typeInfo().type();
-    using ir::DataType;
-
-    switch (type)
-    {
-      case DataType::FLOAT32:
-        _init_map[index] = copyInit<float>;
-        break;
-      case DataType::INT32:
-        _init_map[index] = copyInit<int32_t>;
-        break;
-      case DataType::UINT32:
-        _init_map[index] = copyInit<uint32_t>;
-        break;
-      case DataType::BOOL8:
-      case DataType::QUANT8_ASYMM:
-        _init_map[index] = copyInit<uint8_t>;
-        break;
-      case DataType::QUANT8_SYMM:
-        _init_map[index] = copyInit<int8_t>;
-        break;
-      default:
-        throw std::runtime_error("Not supported, yet");
-        break;
-    }
-  }
-
-protected:
-  void registerPermuteInitializer(const ir::OperandIndex &index, const ir::Operand &obj)
-  {
-    // For only CONSTANTS
-    // TODO Add to check if tensor has been allocated
-    if (!obj.isConstant())
-      return;
-
-    const auto type = obj.typeInfo().type();
-    using ir::DataType;
-    using namespace std::placeholders;
-
-    switch (type)
-    {
-      case DataType::FLOAT32:
-        _init_map[index] = std::bind(permuteInit<float>, _1, _2, _current_subg_layout);
-        break;
-      case DataType::INT32:
-        _init_map[index] = std::bind(permuteInit<int32_t>, _1, _2, _current_subg_layout);
-        break;
-      case DataType::UINT32:
-        _init_map[index] = std::bind(permuteInit<uint32_t>, _1, _2, _current_subg_layout);
-        break;
-      case DataType::BOOL8:
-      case DataType::QUANT8_ASYMM:
-        _init_map[index] = std::bind(permuteInit<uint8_t>, _1, _2, _current_subg_layout);
-        break;
-      case DataType::QUANT8_SYMM:
-        _init_map[index] = std::bind(permuteInit<int8_t>, _1, _2, _current_subg_layout);
-        break;
-      default:
-        throw std::runtime_error("Not supported, yet");
-        break;
-    }
-  }
-
-private:
-  bool exist(const ir::OperandIndex &ind) { return _init_map.find(ind) != _init_map.end(); }
-
-protected:
-  std::unordered_map<ir::OperandIndex, Initializer> _init_map;
-  ir::Layout _current_subg_layout;
-};
-
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ICONSTANT_INITIALIZER_H__
diff --git a/runtime/neurun/core/include/backend/IKernelGenerator.h b/runtime/neurun/core/include/backend/IKernelGenerator.h
deleted file mode 100644
index 4540e81d2..000000000
--- a/runtime/neurun/core/include/backend/IKernelGenerator.h
+++ /dev/null
@@ -1,63 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_IKERNEL_GENERATOR_H__
-#define __NEURUN_BACKEND_IKERNEL_GENERATOR_H__
-
-#include <memory>
-#include <functional>
-
-#include "ITensorBuilder.h"
-#include "compiler/IExecutionBuilder.h"
-#include "ir/OperationVisitor.h"
-#include "ir/OpSequence.h"
-#include "cpp14/memory.h"
-
-namespace neurun
-{
-namespace backend
-{
-
-class IKernelGenerator : public ir::OperationVisitor
-{
-public:
-  virtual ~IKernelGenerator() = default;
-
-  void generate(const ir::OpSequence &subg, neurun::compiler::IExecutionBuilder *executionBuilder)
-  {
-    _execution_builder = executionBuilder;
-    subg.accept(*this);
-  }
-
-protected:
-  using OperationVisitor::visit;
-
-#define OP(InternalName)                                                                \
-  void visit(const ir::operation::InternalName &) override                              \
-  {                                                                                     \
-    throw std::runtime_error("KernelGenerator: NYI for operation '" #InternalName "'"); \
-  }
-#include "ir/Operations.lst"
-#undef OP
-
-protected:
-  neurun::compiler::IExecutionBuilder *_execution_builder;
-};
-
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_IKERNEL_GENERATOR_H__
diff --git a/runtime/neurun/core/include/backend/IMemoryManager.h b/runtime/neurun/core/include/backend/IMemoryManager.h
deleted file mode 100644
index b06bab872..000000000
--- a/runtime/neurun/core/include/backend/IMemoryManager.h
+++ /dev/null
@@ -1,49 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_IMEMORY_MANAGER_H__
-#define __NEURUN_BACKEND_IMEMORY_MANAGER_H__
-
-namespace neurun
-{
-namespace backend
-{
-
-struct IMemoryManager
-{
-  virtual ~IMemoryManager() = default;
-
-  virtual void allocate(void) = 0;
-  virtual void deallocate(void) = 0;
-};
-
-} // namespace backend
-} // namespace neurun
-
-#include <unordered_set>
-#include <memory>
-
-namespace neurun
-{
-namespace backend
-{
-
-using MemoryManagerSet = std::unordered_set<std::unique_ptr<backend::IMemoryManager>>;
-
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_IMEMORY_MANAGER_H__
diff --git a/runtime/neurun/core/include/backend/IShapeFixer.h b/runtime/neurun/core/include/backend/IShapeFixer.h
deleted file mode 100644
index 8168e2a11..000000000
--- a/runtime/neurun/core/include/backend/IShapeFixer.h
+++ /dev/null
@@ -1,55 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ISHAPE_FIXER_H__
-#define __NEURUN_BACKEND_ISHAPE_FIXER_H__
-
-#include <memory>
-#include <functional>
-
-#include "ir/LowerInfoMap.h"
-#include "ITensorBuilder.h"
-#include "ir/OperationVisitor.h"
-#include "ir/OpSequence.h"
-#include "cpp14/memory.h"
-
-namespace neurun
-{
-namespace backend
-{
-
-class IShapeFixer : public ir::OperationVisitor
-{
-public:
-  virtual ~IShapeFixer() = default;
-
-protected:
-#define OP(InternalName)                                                           \
-  void visit(const ir::operation::InternalName &) override                         \
-  {                                                                                \
-    throw std::runtime_error("ShapeFixer: NYI for operation '" #InternalName "'"); \
-  }
-#include "ir/Operations.lst"
-#undef OP
-
-public:
-  void fix(const ir::OpSequence &op_seq) { op_seq.accept(*this); };
-};
-
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ISHAPE_FIXER_H__
diff --git a/runtime/neurun/core/include/backend/ITensorBuilder.h b/runtime/neurun/core/include/backend/ITensorBuilder.h
deleted file mode 100644
index 5eb4ab2d8..000000000
--- a/runtime/neurun/core/include/backend/ITensorBuilder.h
+++ /dev/null
@@ -1,89 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ITENSOR_BUILDER_H__
-#define __NEURUN_BACKEND_ITENSOR_BUILDER_H__
-
-#include <map>
-
-#include "ir/Index.h"
-#include "ir/OperandInfo.h"
-#include "ir/Operation.h"
-#include "ir/Layout.h"
-#include "operand/ITensor.h"
-#include "compiler/SubTensorInfo.h"
-#include "ITensorManager.h"
-
-namespace neurun
-{
-namespace backend
-{
-
-struct ITensorBuilder
-{
-  using IterateFunction = std::function<void(const ir::OperandIndex &)>;
-
-  virtual ~ITensorBuilder(void) = default;
-
-  // TODO Merge registerTensorInfo and registerSubTensorInfo using abstraction by internal class
-  /**
-   * @brief Register tensor information to allocate on backend
-   */
-  virtual void registerTensorInfo(const ir::OperandIndex &, const ir::OperandInfo &,
-                                  ir::Layout backend_layout, bool as_const) = 0;
-  /**
-   * @brief     Register subtensor information to allocate on backend
-   */
-  virtual void registerSubTensorInfo(const ir::OperandIndex &, const compiler::SubTensorInfo &) = 0;
-
-  virtual void notifyFirstUse(const ir::OperandIndex &) = 0;
-  virtual void notifyLastUse(const ir::OperandIndex &) = 0;
-
-  virtual bool isRegistered(const ir::OperandIndex &) const = 0;
-
-  virtual void prepare(void) = 0;
-  virtual void allocateConsts() = 0;
-  virtual void allocateNonconsts() = 0;
-  virtual void postFunctionPrepare() = 0;
-  virtual void finalize() = 0;
-
-  virtual std::shared_ptr<::neurun::backend::operand::ITensor>
-  tensorAt(const ir::OperandIndex &ind) = 0;
-  virtual void iterate(const IterateFunction &fn) = 0;
-
-  virtual void preVisit(const ir::Operation &) = 0;
-  virtual void postVisit(const ir::Operation &) = 0;
-
-  virtual std::unique_ptr<ITensorManager> releaseTensorManager(void) = 0;
-};
-
-} // namespace backend
-} // namespace neurun
-
-#include <unordered_set>
-#include <memory>
-
-namespace neurun
-{
-namespace backend
-{
-
-using TensorBuilderSet = std::unordered_set<std::shared_ptr<backend::ITensorBuilder>>;
-
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ITENSOR_BUILDER_H__
diff --git a/runtime/neurun/core/include/backend/ITensorManager.h b/runtime/neurun/core/include/backend/ITensorManager.h
deleted file mode 100644
index 74506ef59..000000000
--- a/runtime/neurun/core/include/backend/ITensorManager.h
+++ /dev/null
@@ -1,56 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ITENSOR_MANAGER_H__
-#define __NEURUN_BACKEND_ITENSOR_MANAGER_H__
-
-namespace neurun
-{
-namespace backend
-{
-
-// NOTE This name ITensorManager has been discussed whether or not the name is proper.
-// Anyone can argue with any better name.
-/**
- * @brief Interface as an abstract tensor manager which has MemoryManager
- */
-struct ITensorManager
-{
-  virtual ~ITensorManager() = default;
-
-  virtual void allocateConsts(void) = 0;
-  virtual void allocateNonconsts(void) = 0;
-  virtual void deallocateConsts(void) = 0;
-  virtual void deallocateNonconsts(void) = 0;
-};
-
-} // namespace backend
-} // namespace neurun
-
-#include <unordered_set>
-#include <memory>
-
-namespace neurun
-{
-namespace backend
-{
-
-using TensorManagerSet = std::unordered_set<std::unique_ptr<backend::ITensorManager>>;
-
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ITENSOR_MANAGER_H__
diff --git a/runtime/neurun/core/include/backend/ITensorRegister.h b/runtime/neurun/core/include/backend/ITensorRegister.h
deleted file mode 100644
index 9e554ab77..000000000
--- a/runtime/neurun/core/include/backend/ITensorRegister.h
+++ /dev/null
@@ -1,164 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_ITENSOR_REGISTER_H__
-#define __NEURUN_BACKEND_ITENSOR_REGISTER_H__
-
-#include "compiler/SubTensorInfo.h"
-#include "ir/LowerInfoMap.h"
-#include "ir/operand/ParentInfo.h"
-#include "ITensorBuilder.h"
-#include "ir/Layout.h"
-#include "ir/OperandIndexSequence.h"
-#include "ir/OperandInfo.h"
-#include "ir/Operands.h"
-#include "ir/OperationVisitor.h"
-
-namespace
-{
-
-neurun::ir::Shape permuteTensorShape(const neurun::ir::Shape &shape,
-                                     neurun::ir::Layout frontend_layout,
-                                     neurun::ir::Layout backend_layout)
-{
-  assert(shape.rank() <= 4);
-  neurun::ir::Shape backend_shape{shape};
-  if (shape.rank() == 4 && frontend_layout == neurun::ir::Layout::NHWC &&
-      backend_layout == neurun::ir::Layout::NCHW)
-  {
-    backend_shape.dim(1) = shape.dim(3);
-    backend_shape.dim(2) = shape.dim(1);
-    backend_shape.dim(3) = shape.dim(2);
-  }
-  else if (shape.rank() == 4 && frontend_layout == neurun::ir::Layout::NCHW &&
-           backend_layout == neurun::ir::Layout::NHWC)
-  {
-    backend_shape.dim(1) = shape.dim(2);
-    backend_shape.dim(2) = shape.dim(3);
-    backend_shape.dim(3) = shape.dim(1);
-  }
-  return backend_shape;
-}
-} // namespace
-
-namespace neurun
-{
-namespace backend
-{
-
-class ITensorRegister : public ir::OperationVisitor
-{
-public:
-  virtual ~ITensorRegister() = default;
-
-public:
-  void registerTensors(const ir::OpSequence &subg, const ir::LowerInfoMap *lower_info_map)
-  {
-    _current_subg_layout = subg.getLayout();
-    _lower_info_map = lower_info_map;
-    assert(_lower_info_map != nullptr);
-    assert(tensor_builder().get() != nullptr);
-    subg.accept(*this);
-  }
-
-protected:
-  virtual const ir::Operands &operands() const = 0;
-  virtual std::shared_ptr<ITensorBuilder> tensor_builder() const = 0;
-  virtual bool supportSubTensor() const = 0;
-
-protected:
-#define OP(InternalName)                                       \
-  void visit(const ir::operation::InternalName &node) override \
-  {                                                            \
-    ir::OperandIndexSequence indices{node.getInputs()};        \
-    indices.append(node.getOutputs());                         \
-    for (const auto &index : indices)                          \
-    {                                                          \
-      defaultRegisterTensorInfo(index);                        \
-    }                                                          \
-  }
-#include "ir/Operations.lst"
-#undef OP
-
-protected:
-  void defaultRegisterTensorInfo(const ir::OperandIndex &index) const
-  {
-    if (tensor_builder()->isRegistered(index))
-    {
-      return;
-    }
-
-    const auto &obj = operands().at(index);
-    const auto frontend_layout = frontendLayout();
-    const auto backend_layout = backendLayout(index);
-    if (supportSubTensor() && obj.parent_info() != nullptr)
-    {
-      tensor_builder()->registerSubTensorInfo(
-          index, generateSubTensorInfo(obj, frontend_layout, backend_layout));
-    }
-    else
-    {
-      ir::OperandInfo backend_info{permuteTensorShape(obj.shape(), frontend_layout, backend_layout),
-                                   obj.typeInfo()};
-      tensor_builder()->registerTensorInfo(index, backend_info, backend_layout, obj.isConstant());
-    }
-  }
-
-protected:
-  ir::Layout frontendLayout() const { return _current_subg_layout; }
-  ir::Layout backendLayout(const ir::OperandIndex &index) const
-  {
-    assert(_lower_info_map != nullptr);
-    const auto lower_info = _lower_info_map->operand.at(index).get();
-    return lower_info->def_factors().getOnlyElement().layout();
-  }
-
-private:
-  compiler::SubTensorInfo generateSubTensorInfo(const ir::Operand &obj, ir::Layout frontend_layout,
-                                                ir::Layout backend_layout) const
-  {
-    assert(obj.shape().rank() <= 4);
-    const auto parent_index = obj.parent_info()->parent();
-    auto shape = obj.shape();
-    auto offset = obj.parent_info()->offset();
-    if (operands().at(parent_index).shape().rank() == 4 && frontend_layout == ir::Layout::NHWC &&
-        backend_layout == ir::Layout::NCHW)
-    {
-      shape.extendRank(4);
-      offset = {offset[0], offset[3], offset[1], offset[2]};
-    }
-    else if (operands().at(parent_index).shape().rank() == 4 &&
-             frontend_layout == ir::Layout::NHWC && backend_layout == ir::Layout::NCHW)
-    {
-      shape.extendRank(4);
-      offset = {offset[0], offset[2], offset[3], offset[1]};
-    }
-    ir::Operand subtensor_obj{permuteTensorShape(shape, frontend_layout, backend_layout),
-                              obj.typeInfo()};
-    subtensor_obj.parent_info(
-        nnfw::cpp14::make_unique<ir::operand::ParentInfo>(parent_index, offset));
-    return compiler::SubTensorInfo{subtensor_obj};
-  }
-
-private:
-  ir::Layout _current_subg_layout;
-  const ir::LowerInfoMap *_lower_info_map{nullptr};
-};
-
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ITENSOR_REGISTER_H__
diff --git a/runtime/neurun/core/include/backend/JSONExecTime.h b/runtime/neurun/core/include/backend/JSONExecTime.h
deleted file mode 100644
index 84505e10f..000000000
--- a/runtime/neurun/core/include/backend/JSONExecTime.h
+++ /dev/null
@@ -1,96 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_JSON_EXEC_TIME_H__
-#define __NEURUN_BACKEND_JSON_EXEC_TIME_H__
-
-#include <fstream>
-#include <unordered_map>
-#include <map>
-#include <vector>
-#include "backend/Backend.h"
-#include "backend/IConfig.h"
-
-namespace neurun
-{
-namespace backend
-{
-
-/**
- * @brief table, that contains execution time of an operation on some backend for different input
- * sizes and transfer time from one backend to another for various input sizes (permutation time)
- *
- *               backend ->  op ->  quant->  size   --> time
- * _measurements[Backend*]["string"][bool][uint32_t] = int64_t
- */
-using MeasurementData = std::unordered_map<
-    const Backend *,
-    std::unordered_map<std::string, std::unordered_map<bool, std::map<uint32_t, int64_t>>>>;
-
-class JSON
-{
-public:
-  explicit JSON(const std::vector<const Backend *> &backends, MeasurementData &measurements)
-      : _measurement_file("exec_time.json"), _backends(), _measurements(measurements)
-  {
-    for (const auto b : backends)
-    {
-      _backends.emplace(b->config()->id(), b);
-    }
-    loadOperationsExecTime();
-  };
-  /**
-   * @brief Update _operations_exec_time_file with new data.
-   */
-  void uploadOperationsExecTime() const;
-
-private:
-  ///@brief file containing measurements
-  std::string _measurement_file;
-  std::unordered_map<std::string, const Backend *> _backends;
-  std::unordered_map<
-      const Backend *,
-      std::unordered_map<std::string, std::unordered_map<bool, std::map<uint32_t, int64_t>>>>
-      &_measurements;
-  /**
-   * @brief Helper function for inserting data to OperationExecTimes
-   *
-   * @param backend String name of backend
-   * @param operation String name of operation
-   * @param quant if input type quantized
-   * @param stream File stream
-   */
-  void readOperation(const std::string &backend, const std::string &operation, bool quant,
-                     std::ifstream &stream);
-
-  /**
-   * @brief Helper function for writing OperationExecTimes to stream
-   *
-   * @param operation_info Map of operations execution information
-   * @param stream File stream
-   */
-  void printOperation(const std::map<uint32_t, int64_t> &operation_info,
-                      std::ofstream &stream) const;
-  /**
-   * @brief Parse and load operations_exec_time from _operations_exec_time_file.
-   */
-  void loadOperationsExecTime();
-};
-
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_JSON_EXEC_TIME_H__
diff --git a/runtime/neurun/core/include/backend/operand/ITensor.h b/runtime/neurun/core/include/backend/operand/ITensor.h
deleted file mode 100644
index c278b01a0..000000000
--- a/runtime/neurun/core/include/backend/operand/ITensor.h
+++ /dev/null
@@ -1,54 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_OPERAND_I_TENSOR_H__
-#define __NEURUN_BACKEND_OPERAND_I_TENSOR_H__
-
-#include <cstring>
-#include <cstdint>
-#include <functional>
-
-#include "ir/Layout.h"
-#include "util/Coordinates.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace operand
-{
-
-class ITensor
-{
-public:
-  virtual ~ITensor() = default;
-
-public:
-  virtual uint8_t *buffer() const = 0;
-  virtual size_t total_size() const = 0;
-  virtual size_t dimension(size_t index) const = 0;
-  virtual size_t num_dimensions() const = 0;
-  virtual size_t calcOffset(const neurun::util::Coordinates &coords) const = 0;
-  virtual ir::Layout layout() const = 0;
-  virtual bool has_padding() const = 0;
-  virtual void access(const std::function<void(ITensor &tensor)> &fn) = 0;
-};
-
-} // namespace operand
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_OPERAND_I_TENSOR_H__
diff --git a/runtime/neurun/core/include/compiler/Compiler.h b/runtime/neurun/core/include/compiler/Compiler.h
deleted file mode 100644
index 797fc77ad..000000000
--- a/runtime/neurun/core/include/compiler/Compiler.h
+++ /dev/null
@@ -1,91 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/**
- * @file  Compiler.h
- * @brief This file contains Compiler class to define and run compilation phase
- */
-
-#ifndef __NEURUN_COMPILER_COMPILE_H_
-#define __NEURUN_COMPILER_COMPILE_H_
-
-#include "ir/Graph.h"
-#include "exec/IExecutor.h"
-
-namespace neurun
-{
-
-namespace compiler
-{
-
-enum class State
-{
-  CREATED, // Before compilation
-  STARTED, // Compile is started
-  LOWERED, // Backend is decided
-  COMPILED // Success compilation
-};
-
-/**
- * @brief Class to compile graph model
- */
-class Compiler
-{
-public:
-  /**
-   * @brief     Construct a new Compiler object
-   * @param[in] model Graph model
-   */
-  Compiler(const std::shared_ptr<ir::Graph> &graph)
-      : _graph{graph}, _executor{nullptr}, _state{State::CREATED}
-  {
-    // DO NOTHING
-  }
-
-public:
-  /**
-   * @brief   Run compilation. Compilation result will be saved in _plan
-   */
-  void compile(void);
-  /**
-   * @brief       Pass plan reference
-   * @param[out]  plan  Plan reference to return\n
-   *                    Set nullptr if compile is not run yet
-   */
-  void release(std::shared_ptr<exec::IExecutor> &executor) { executor = _executor; }
-
-  void state(State state) { _state = state; }
-  State state(void) const { return _state; }
-
-  /**
-   * @brief   Check if model can compile
-   * @return  @c true if model can compile, otherwise @c false
-   * @note    This method don't check model correctness,\n
-   *          so model verification should be done before calling this method
-   */
-  bool checkCompilable();
-
-private:
-  std::shared_ptr<ir::Graph> _graph;
-  std::shared_ptr<exec::IExecutor> _executor;
-  State _state;
-};
-
-} // namespace compiler
-
-} // namespace neurun
-
-#endif // __NEURUN_COMPILER_COMPILE_H_
diff --git a/runtime/neurun/core/include/compiler/IExecutionBuilder.h b/runtime/neurun/core/include/compiler/IExecutionBuilder.h
deleted file mode 100644
index c5a06fec0..000000000
--- a/runtime/neurun/core/include/compiler/IExecutionBuilder.h
+++ /dev/null
@@ -1,39 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_COMPILER_I_EXECUTION_BUILDER_H__
-#define __NEURUN_COMPILER_I_EXECUTION_BUILDER_H__
-
-#include <memory>
-
-#include "exec/IFunction.h"
-
-namespace neurun
-{
-namespace compiler
-{
-
-struct IExecutionBuilder
-{
-  virtual ~IExecutionBuilder() = default;
-
-  virtual void append(std::unique_ptr<::neurun::exec::IFunction> &&f) = 0;
-};
-
-} // namespace compiler
-} // namespace neurun
-
-#endif // __NEURUN_COMPILER_I_EXECUTION_BUILDER_H__
diff --git a/runtime/neurun/core/include/compiler/SubTensorInfo.h b/runtime/neurun/core/include/compiler/SubTensorInfo.h
deleted file mode 100644
index 18cab466b..000000000
--- a/runtime/neurun/core/include/compiler/SubTensorInfo.h
+++ /dev/null
@@ -1,83 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/**
- * @file  SubTensorInfo.h
- * @brief This file contains SubTensorInfo to represent subsumption between tensors
- *        for backend tensor allocation
- */
-#ifndef __NEURUN_COMPILER_SUBTENSOR_INFO_H__
-#define __NEURUN_COMPILER_SUBTENSOR_INFO_H__
-
-#include "ir/Operand.h"
-
-namespace neurun
-{
-namespace compiler
-{
-
-/**
- * @brief Class to represent information of subtensor
- */
-class SubTensorInfo
-{
-public:
-  SubTensorInfo() = delete;
-
-  /**
-   * @brief     Construct a new SubTensorInfo object
-   * @param[in] obj SubTensor object
-   */
-  SubTensorInfo(const ir::Operand &obj)
-      : _parent{obj.parent_info()->parent()}, _shape{obj.shape()}, _type{obj.typeInfo()},
-        _offset{obj.parent_info()->offset()}
-  {
-    // DO NOTHING
-  }
-
-public:
-  /**
-   * @brief   Return parent tensor index
-   * @return  Parent tensor index
-   */
-  const ir::OperandIndex parent(void) const { return _parent; }
-  /**
-   * @brief   Return tensor shape
-   * @return  Tensor shape
-   */
-  const ir::Shape &shape(void) const { return _shape; }
-  /**
-   * @brief   Return tensor type
-   * @return  Tensor type
-   */
-  const ir::TypeInfo &type(void) const { return _type; }
-  /**
-   * @brief   Return tensor's offset in parent tensor
-   * @return  Tensor offset
-   */
-  const neurun::util::Coordinates offset(void) const { return _offset; }
-
-private:
-  const ir::OperandIndex _parent;
-  const ir::Shape _shape;
-  const ir::TypeInfo _type;
-  const neurun::util::Coordinates _offset;
-};
-
-} // compiler
-} // neurun
-
-#endif // __NEURUN_COMPILER_SUBTENSOR_INFO_H__
diff --git a/runtime/neurun/core/include/exec/Execution.h b/runtime/neurun/core/include/exec/Execution.h
deleted file mode 100644
index 7304f8aab..000000000
--- a/runtime/neurun/core/include/exec/Execution.h
+++ /dev/null
@@ -1,144 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/**
- * @file  Execution.h
- * @brief This file defines execution
- */
-#ifndef __NEURUN_EXEC_EXECUTION_H__
-#define __NEURUN_EXEC_EXECUTION_H__
-
-#include "ir/Layout.h"
-#include "exec/IExecutor.h"
-#include "IODescription.h"
-
-#include <thread>
-
-namespace neurun
-{
-namespace exec
-{
-
-/**
- * @brief Class to define execution instance to collect input/output information for inference
- *        and prepare executor run (TODO)
- */
-class Execution
-{
-
-public:
-  /**
-   * @brief     Construct a new Execution object
-   * @param[in] executor  Model executor
-   */
-  Execution(const std::shared_ptr<IExecutor> &executor);
-
-public:
-  /**
-   * @brief   Returns graph object
-   * @return  Graph object
-   */
-  const ir::Graph &graph() const { return _executor->graph(); }
-  /**
-   * @brief     Set input data's information
-   * @param[in] index   Input index
-   * @param[in] buffer  Input data's buffer pointer
-   * @param[in] length  Input data's length
-   * @param[in] layout  Input data's data format
-   */
-  void setInput(const ir::IOIndex &index, const void *buffer, size_t length,
-                ir::Layout layout = ir::Layout::NHWC);
-  /**
-   * @brief     Set input data's information, especially to specify unknown dimensions on model
-   * build time.
-   * @param[in] index   Input index
-   * @param[in] type    Input data's type info
-   * @param[in] shape   Input data's shape
-   * @param[in] buffer  Input data's buffer pointer
-   * @param[in] length  Input data's length
-   * @param[in] layout  Input data's data format
-   */
-  void setInput(const ir::IOIndex &index, const ir::TypeInfo &type, const ir::Shape &shape,
-                const void *buffer, size_t length, ir::Layout layout = ir::Layout::NHWC);
-  /**
-   * @brief     Set output data's information
-   * @param[in] index   Output index
-   * @param[in] buffer  Output data's buffer pointer
-   * @param[in] length  Output data's length
-   * @param[in] layout  Output data's data format
-   */
-  void setOutput(const ir::IOIndex &index, void *buffer, size_t length,
-                 ir::Layout layout = ir::Layout::NHWC);
-  /**
-   * @brief     Set output data's information, especially to specify unknown dimensions on model
-   * build time.
-   * @param[in] index   Output index
-   * @param[in] type    Output data's type info
-   * @param[in] shape   Output data's shape
-   * @param[in] buffer  Output data's buffer pointer
-   * @param[in] length  Output data's length
-   * @param[in] layout  Output data's data format
-   */
-  void setOutput(const ir::IOIndex &index, const ir::TypeInfo &type, const ir::Shape &shape,
-                 void *buffer, size_t length, ir::Layout layout = ir::Layout::NHWC);
-  /**
-   * @brief     Set input data's data format
-   * @param[in] index   Input index
-   * @param[in] layout  Input data's data format
-   */
-  void setInputLayout(const ir::IOIndex &index, ir::Layout layout);
-  /**
-   * @brief     Set output data's data format
-   * @param[in] index   Output index
-   * @param[in] layout  Output data's data format
-   */
-  void setOutputLayout(const ir::IOIndex &index, ir::Layout layout);
-  /**
-   * @brief  Execution
-   * @note   It should be called after setting input and output buffer
-   */
-  void execute();
-
-  /**
-   * @brief Start asynchronous execution
-   * @note  It returns after execution thread is started
-   *        It should be called after setting input and output buffer
-   */
-  void startExecute(void);
-
-  /**
-   * @brief Return when execution is finished
-   * @note  It waits until execution is finished
-   */
-  void waitFinish(void);
-
-  /**
-   * @brief   Check execution is finished
-   * @return  @c true if execution is finished, otherwise @c false
-   */
-  bool isFinished(void) const;
-
-private:
-  const std::shared_ptr<IExecutor> _executor;
-  IODescription _io_desc;
-  std::unique_ptr<std::thread> _exec_thread;
-  bool finished{false};
-};
-
-} // namespace exec
-} // namespace neurun
-
-#endif // __NEURUN_EXEC_EXECUTION_H__
diff --git a/runtime/neurun/core/include/exec/ExecutionObservers.h b/runtime/neurun/core/include/exec/ExecutionObservers.h
deleted file mode 100644
index ca658c706..000000000
--- a/runtime/neurun/core/include/exec/ExecutionObservers.h
+++ /dev/null
@@ -1,83 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_EXEC_OBSREVERS_H__
-#define __NEURUN_EXEC_OBSREVERS_H__
-
-#include "exec/IFunction.h"
-#include "ir/OpSequence.h"
-#include "backend/ExecTime.h"
-#include "util/ITimer.h"
-#include "IExecutor.h"
-#include "misc/EventCollector.h"
-#include "misc/EventRecorder.h"
-
-namespace neurun
-{
-namespace exec
-{
-class IExecutionObserver
-{
-public:
-  /// @brief Invoked just before model (not individual operation) execution begins
-  virtual void handleBegin(IExecutor *) { return; }
-
-  virtual void handleBegin(IExecutor *, const ir::OpSequence *, const backend::Backend *) = 0;
-  virtual void handleEnd(IExecutor *, const ir::OpSequence *, const backend::Backend *) = 0;
-
-  /// @brief Invoked just after model (not individual operation) execution ends
-  virtual void handleEnd(IExecutor *) { return; }
-
-  virtual ~IExecutionObserver() = default;
-};
-
-class ProfileObserver : public IExecutionObserver
-{
-public:
-  explicit ProfileObserver(std::shared_ptr<backend::ExecTime> et) : _et(std::move(et)) {}
-  void handleBegin(IExecutor *, const ir::OpSequence *, const backend::Backend *) override;
-  void handleEnd(IExecutor *, const ir::OpSequence *, const backend::Backend *) override;
-
-  void handleEnd(IExecutor *) override { _et->uploadOperationsExecTime(); }
-
-private:
-  std::unique_ptr<util::ITimer> _timer;
-  std::shared_ptr<backend::ExecTime> _et;
-};
-
-class ChromeTracingObserver : public IExecutionObserver
-{
-public:
-  ChromeTracingObserver(const std::string &filepath);
-  ~ChromeTracingObserver();
-  void handleBegin(IExecutor *) override;
-  void handleBegin(IExecutor *, const ir::OpSequence *, const backend::Backend *) override;
-  void handleEnd(IExecutor *, const ir::OpSequence *, const backend::Backend *) override;
-  void handleEnd(IExecutor *) override;
-
-private:
-  static std::string subgraphTag(const ir::OpSequence *op_seq);
-
-private:
-  std::ofstream _ofs;
-  EventRecorder _recorder;
-  EventCollector _collector;
-};
-
-} // namespace exec
-} // namespace neurun
-
-#endif // __NEURUN_EXEC_OBSREVERS_H__
diff --git a/runtime/neurun/core/include/exec/IExecutor.h b/runtime/neurun/core/include/exec/IExecutor.h
deleted file mode 100644
index de3291388..000000000
--- a/runtime/neurun/core/include/exec/IExecutor.h
+++ /dev/null
@@ -1,72 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/**
- * @file  IExecutor.h
- * @brief This file defines interface of Executor
- */
-#ifndef __NEURUN_EXEC_I_EXECUTOR_H_
-#define __NEURUN_EXEC_I_EXECUTOR_H_
-
-#include "ir/Graph.h"
-#include "IFunction.h"
-#include "IODescription.h"
-#include "ir/OperationIndexMap.h"
-
-namespace neurun
-{
-namespace exec
-{
-class IExecutionObserver;
-/**
- * @brief Struct to define interface of Executor
- */
-struct IExecutor
-{
-  /**
-   * @brief Construct a new IExecutor object
-   */
-  IExecutor() = default;
-  /**
-   * @brief Destroy the IExecutor object
-   */
-  virtual ~IExecutor() = default;
-
-  /**
-   * @brief Returns graph object
-   *
-   * @return Graph object
-   */
-  virtual const ir::Graph &graph() = 0;
-
-  /**
-   * @brief     Set an ordering on operations
-   * @param[in] ranks   The table encoding the ordering
-   */
-  virtual void setIndexedRanks(std::shared_ptr<ir::OperationIndexMap<int64_t>>) = 0;
-
-  /**
-   * @brief     Start execution
-   * @param[in] desc Input and output description
-   * @note      This method should be thread-safe
-   */
-  virtual void execute(const IODescription &desc) = 0;
-};
-
-} // namespace exec
-} // namespace neurun
-
-#endif // __NEURUN_EXEC_I_EXECUTOR_H_
diff --git a/runtime/neurun/core/include/exec/IFunction.h b/runtime/neurun/core/include/exec/IFunction.h
deleted file mode 100644
index 5cc29ea75..000000000
--- a/runtime/neurun/core/include/exec/IFunction.h
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_EXEC_I_FUNCTION_H__
-#define __NEURUN_EXEC_I_FUNCTION_H__
-
-namespace neurun
-{
-namespace exec
-{
-
-class IFunction
-{
-public:
-  virtual ~IFunction() = default;
-  virtual void run() = 0;
-  virtual void runSync() = 0;
-  virtual void prepare() {}
-};
-
-} // namespace exec
-} // namespace neurun
-
-#endif // __NEURUN_EXEC_I_FUNCTION_H__
diff --git a/runtime/neurun/core/include/exec/IODescription.h b/runtime/neurun/core/include/exec/IODescription.h
deleted file mode 100644
index bdcc78176..000000000
--- a/runtime/neurun/core/include/exec/IODescription.h
+++ /dev/null
@@ -1,66 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_EXEC_IO_DESCRIPTION_H__
-#define __NEURUN_EXEC_IO_DESCRIPTION_H__
-
-#include <vector>
-
-#include "ir/OperandInfo.h"
-
-namespace neurun
-{
-namespace exec
-{
-
-struct InputDesc
-{
-  const ir::OperandInfo info;
-  const void *buffer;
-  const size_t size;
-  const ir::Layout layout;
-
-  InputDesc(void) = delete;
-  InputDesc(const ir::OperandInfo &info, const void *buffer, const size_t size, ir::Layout layout)
-      : info(info), buffer(buffer), size(size), layout(layout)
-  {
-  }
-};
-
-struct OutputDesc
-{
-  const ir::OperandInfo info;
-  void *buffer;
-  const size_t size;
-  const ir::Layout layout;
-
-  OutputDesc(void) = delete;
-  OutputDesc(const ir::OperandInfo &info, void *buffer, const size_t size, ir::Layout layout)
-      : info(info), buffer(buffer), size(size), layout(layout)
-  {
-  }
-};
-
-struct IODescription
-{
-  std::vector<std::unique_ptr<InputDesc>> inputs;
-  std::vector<std::unique_ptr<OutputDesc>> outputs;
-};
-
-} // namespace exec
-} // namespace neurun
-
-#endif // __NEURUN_EXEC_IO_DESCRIPTION_H__
diff --git a/runtime/neurun/core/include/exec/NopFunction.h b/runtime/neurun/core/include/exec/NopFunction.h
deleted file mode 100644
index 5cbd7e5ce..000000000
--- a/runtime/neurun/core/include/exec/NopFunction.h
+++ /dev/null
@@ -1,54 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/**
- * @file  NopFunction.h
- * @brief This file defines NopFunction
- */
-#ifndef __NEURUN_EXEC_NOP_FUNCTION_H_
-#define __NEURUN_EXEC_NOP_FUNCTION_H_
-
-#include "IFunction.h"
-
-namespace neurun
-{
-namespace exec
-{
-
-/**
- * @brief A derivative of IFunction tha does nothing
- *
- */
-class NopFunction : public IFunction
-{
-public:
-  NopFunction() = default;
-  void run() override
-  {
-    // DO NOTHING
-  }
-  void runSync() override
-  {
-    // this abstract method is used just for profiling and called for
-    // backend::acl_common::AclFunction
-    run();
-  }
-};
-
-} // namespace exec
-} // namespace neurun
-
-#endif // __NEURUN_EXEC_NOP_FUNCTION_H_
diff --git a/runtime/neurun/core/include/ir/BackendSet.h b/runtime/neurun/core/include/ir/BackendSet.h
deleted file mode 100644
index 4979286df..000000000
--- a/runtime/neurun/core/include/ir/BackendSet.h
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_BACKEND_SET_H__
-#define __NEURUN_IR_BACKEND_SET_H__
-
-#include "util/Set.h"
-
-namespace neurun
-{
-namespace backend
-{
-class Backend;
-} // namespace backend
-} // namespace neurun
-
-namespace neurun
-{
-namespace ir
-{
-
-using BackendSet = util::Set<const backend::Backend *>;
-
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_BACKEND_SET_H__
diff --git a/runtime/neurun/core/include/ir/Data.h b/runtime/neurun/core/include/ir/Data.h
deleted file mode 100644
index a0a489553..000000000
--- a/runtime/neurun/core/include/ir/Data.h
+++ /dev/null
@@ -1,75 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_DATA_H__
-#define __NEURUN_IR_DATA_H__
-
-#include <algorithm>
-
-namespace neurun
-{
-namespace ir
-{
-
-struct Data
-{
-  virtual ~Data() = default;
-
-  virtual size_t size(void) const = 0;
-  virtual const uint8_t *base(void) const = 0;
-};
-
-class CachedData final : public Data
-{
-public:
-  CachedData(const uint8_t *base, size_t size) : _base{new uint8_t[size]}, _size{size}
-  {
-    std::copy(base, base + size, _base);
-  }
-
-public:
-  ~CachedData() { delete[] _base; }
-
-public:
-  size_t size(void) const override { return _size; }
-  const uint8_t *base(void) const override { return _base; }
-
-private:
-  uint8_t *_base;
-  size_t _size;
-};
-
-class ExternalData final : public Data
-{
-public:
-  ExternalData(const uint8_t *base, size_t size) : _base{base}, _size{size}
-  {
-    // DO NOTHING
-  }
-
-public:
-  size_t size(void) const override { return _size; }
-  const uint8_t *base(void) const override { return _base; }
-
-private:
-  const uint8_t *_base;
-  const size_t _size;
-};
-
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_DATA_H__
diff --git a/runtime/neurun/core/include/ir/DataType.h b/runtime/neurun/core/include/ir/DataType.h
deleted file mode 100644
index 6eca6eb27..000000000
--- a/runtime/neurun/core/include/ir/DataType.h
+++ /dev/null
@@ -1,62 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_DATATYPE_H__
-#define __NEURUN_IR_DATATYPE_H__
-
-#include <stdexcept>
-
-namespace neurun
-{
-namespace ir
-{
-
-enum class DataType
-{
-  FLOAT32 = 0,
-  INT32 = 1,
-  UINT32 = 2,
-  QUANT8_ASYMM = 3,
-  BOOL8 = 4,
-  UINT8 = 5,
-  QUANT8_SYMM = 6,
-};
-
-inline size_t sizeOfDataType(DataType data_type)
-{
-  switch (data_type)
-  {
-    case DataType::FLOAT32:
-      return sizeof(float);
-    case DataType::INT32:
-      return sizeof(int32_t);
-    case DataType::UINT32:
-      return sizeof(uint32_t);
-    case DataType::BOOL8:
-    case DataType::QUANT8_ASYMM:
-    case DataType::UINT8:
-      return sizeof(uint8_t);
-    case DataType::QUANT8_SYMM:
-      return sizeof(int8_t);
-    default:
-      throw std::runtime_error{"Unsupported type size"};
-  }
-}
-
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_DATATYPE_H__
diff --git a/runtime/neurun/core/include/ir/Graph.h b/runtime/neurun/core/include/ir/Graph.h
deleted file mode 100644
index 5105c3a42..000000000
--- a/runtime/neurun/core/include/ir/Graph.h
+++ /dev/null
@@ -1,153 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_GRAPH_H__
-#define __NEURUN_IR_GRAPH_H__
-
-#include <functional>
-
-#include "ir/Operands.h"
-#include "ir/Operations.h"
-#include "ir/LowerInfoMap.h"
-#include "ir/OpSequence.h"
-#include "ir/Subgraphs.h"
-
-namespace neurun
-{
-namespace compiler
-{
-class BackendResolver;
-} // namespace compiler
-} // namespace neurun
-
-namespace neurun
-{
-namespace backend
-{
-namespace custom
-{
-class IKernelBuilder;
-} // namespace custom
-} // namespace backend
-} // namespace neurun
-
-namespace neurun
-{
-namespace ir
-{
-
-class Graph
-{
-private:
-  enum class Phase
-  {
-    BUILDING,
-    MODEL
-  };
-
-public:
-  Graph(void);
-  ~Graph(void);
-
-  // Graph Building
-public:
-  OperandIndex addOperand(const Shape &shape, const TypeInfo &type);
-  OperationIndex addOperation(std::unique_ptr<Operation> &&node);
-  void setOperandValue(const OperandIndex &ind, std::unique_ptr<Data> &&data);
-  void addInput(const OperandIndex &ind);
-  void addOutput(const OperandIndex &ind);
-  void finishBuilding(void);
-  void lower(void);
-  void removeOperand(const OperandIndex &ind) { _operands.remove(ind); }
-  bool isBuildingPhase(void) const { return _phase == Phase::BUILDING; }
-
-private:
-  void initializeUseDef();
-
-  // Custom operations support
-public:
-  void
-  bindKernelBuilder(const std::shared_ptr<neurun::backend::custom::IKernelBuilder> &kernel_builder)
-  {
-    _kernel_builder = kernel_builder;
-  }
-
-  const std::shared_ptr<backend::custom::IKernelBuilder> &getKernelBuilder() const
-  {
-    return _kernel_builder;
-  }
-
-private:
-  std::shared_ptr<backend::custom::IKernelBuilder> _kernel_builder;
-
-  // Accessors
-public:
-  const OperandIndexSequence &getInputs() const { return _inputs; }
-  OperandIndexSequence &getInputs() { return _inputs; }
-  const OperandIndexSequence &getOutputs() const { return _outputs; }
-  OperandIndexSequence &getOutputs() { return _outputs; }
-  const Operands &operands() const { return _operands; }
-  Operands &operands() { return _operands; } // TODO Remove this non-const accessor
-  const Operations &operations() const { return _operations; }
-  Operations &operations() { return _operations; }
-  const compiler::BackendResolver *backend_resolver() const { return _backend_resolver.get(); }
-
-private:
-  Phase _phase{Phase::BUILDING};
-  Operations _operations;
-  Operands _operands;
-  OperandIndexSequence _inputs;
-  OperandIndexSequence _outputs;
-
-  // For LOWERED phase
-public:
-  const LowerInfoMap *getLowerInfo() const { return _lower_info_map.get(); }
-  const operation::LowerInfo *getLowerInfo(const SubgraphIndex &subg_index) const;
-  void setLowerInfo(const SubgraphIndex &subg_index,
-                    std::unique_ptr<operation::LowerInfo> &&lower_info);
-  void removeLowerInfo(const SubgraphIndex &subg_index);
-  const operand::LowerInfo *getLowerInfo(const OperandIndex &index) const;
-  operand::LowerInfo *getLowerInfo(const OperandIndex &index);
-  void setLowerInfo(const OperandIndex &index, std::unique_ptr<operand::LowerInfo> &&lower_info);
-  void removeLowerInfo(const OperandIndex &index);
-  Subgraphs &subgraphs()
-  {
-    assert(_op_seqs);
-    return *_op_seqs;
-  }
-  const Subgraphs *subgraphs() const { return _op_seqs.get(); }
-  void setBackendResolver(std::unique_ptr<compiler::BackendResolver> &&br);
-
-private:
-  void makeSubgraphs(OperandIndexMap<std::unique_ptr<operand::LowerInfo>> &operands_lower_info);
-  void
-  manipulateLowerInfo(OperandIndexMap<std::unique_ptr<operand::LowerInfo>> &operands_lower_info);
-  void dumpLowerInfo();
-  bool mergeable(const SubgraphIndex &subg_index, const OperationIndex &node_index, Layout layout);
-  SubgraphIndex appendFreshSingleOpSubgraph(const OperationIndex &node_index, const Operation &node,
-                                            Layout layout);
-
-private:
-  std::unique_ptr<compiler::BackendResolver> _backend_resolver;
-  std::unique_ptr<LowerInfoMap> _lower_info_map;
-  // Pass(for Perm) can accept only graph so that Graph has Subgraphs as a member
-  std::unique_ptr<Subgraphs> _op_seqs;
-};
-
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_GRAPH_H__
diff --git a/runtime/neurun/core/include/ir/Index.h b/runtime/neurun/core/include/ir/Index.h
deleted file mode 100644
index aebc64dcd..000000000
--- a/runtime/neurun/core/include/ir/Index.h
+++ /dev/null
@@ -1,42 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERAND_INDEX_H__
-#define __NEURUN_IR_OPERAND_INDEX_H__
-
-#include "util/Index.h"
-
-namespace neurun
-{
-namespace ir
-{
-
-struct OperationIndexTag;
-using OperationIndex = ::neurun::util::Index<uint32_t, OperationIndexTag>;
-
-struct OperandIndexTag;
-using OperandIndex = ::neurun::util::Index<uint32_t, OperandIndexTag>;
-
-struct IOIndexTag;
-using IOIndex = ::neurun::util::Index<uint32_t, IOIndexTag>;
-
-struct SubgraphIndexTag;
-using SubgraphIndex = ::neurun::util::Index<uint32_t, SubgraphIndexTag>;
-
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERAND_INDEX_H__
diff --git a/runtime/neurun/core/include/ir/InternalType.h b/runtime/neurun/core/include/ir/InternalType.h
deleted file mode 100644
index 91085f2f3..000000000
--- a/runtime/neurun/core/include/ir/InternalType.h
+++ /dev/null
@@ -1,68 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_INTERNAL_TYPE_H__
-#define __NEURUN_IR_INTERNAL_TYPE_H__
-
-#include <cstdint>
-
-namespace neurun
-{
-namespace ir
-{
-
-enum class Activation
-{
-  NONE = 0,
-  RELU = 1,
-  RELU1 = 2,
-  RELU6 = 3,
-  TANH = 4,
-  SIGMOID = 5
-};
-
-enum class PaddingType
-{
-  EXPLICIT = 0,
-  SAME = 1,
-  VALID = 2
-};
-
-struct ExplicitPadding
-{
-  uint32_t left;
-  uint32_t right;
-  uint32_t top;
-  uint32_t bottom;
-};
-
-// TODO Resolve explicit padding param at frontend and save in value field
-struct Padding
-{
-  PaddingType type;
-  ExplicitPadding param;
-};
-
-struct Stride
-{
-  uint32_t vertical;
-  uint32_t horizontal;
-};
-
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_INTERNAL_TYPE_H__
diff --git a/runtime/neurun/core/include/ir/Layout.h b/runtime/neurun/core/include/ir/Layout.h
deleted file mode 100644
index c4edb70db..000000000
--- a/runtime/neurun/core/include/ir/Layout.h
+++ /dev/null
@@ -1,67 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_LAYOUT_H__
-#define __NEURUN_IR_LAYOUT_H__
-
-#include <functional>
-#include <string>
-
-namespace neurun
-{
-namespace ir
-{
-
-enum class Layout
-{
-  UNKNOWN = 0,
-  NHWC,
-  NCHW
-};
-
-inline std::string to_string(Layout layout)
-{
-  switch (layout)
-  {
-    case Layout::NHWC:
-      return std::string{"NHWC"};
-    case Layout::NCHW:
-      return std::string{"NCHW"};
-    case Layout::UNKNOWN:
-      return std::string{"UNKNOWN"};
-    default:
-      throw std::runtime_error("WRONG LAYOUT");
-  }
-}
-
-} // namespace ir
-} // namespace neurun
-
-namespace std
-{
-
-template <> struct hash<neurun::ir::Layout>
-{
-  size_t operator()(neurun::ir::Layout value) const noexcept
-  {
-    using type = typename std::underlying_type<neurun::ir::Layout>::type;
-    return hash<type>()(static_cast<type>(value));
-  }
-};
-
-} // namespace std
-
-#endif // __NEURUN_IR_LAYOUT_H__
diff --git a/runtime/neurun/core/include/ir/LowerInfoMap.h b/runtime/neurun/core/include/ir/LowerInfoMap.h
deleted file mode 100644
index a8fd818b8..000000000
--- a/runtime/neurun/core/include/ir/LowerInfoMap.h
+++ /dev/null
@@ -1,42 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_LOWER_INFO_MAP_H__
-#define __NEURUN_IR_LOWER_INFO_MAP_H__
-
-#include <memory>
-#include <unordered_map>
-
-#include "ir/operand/LowerInfo.h"
-#include "ir/operation/LowerInfo.h"
-#include "ir/OperandIndexMap.h"
-#include "ir/Index.h"
-
-namespace neurun
-{
-namespace ir
-{
-
-struct LowerInfoMap
-{
-  std::unordered_map<SubgraphIndex, std::unique_ptr<operation::LowerInfo>> operation;
-  OperandIndexMap<std::unique_ptr<operand::LowerInfo>> operand;
-};
-
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_LOWER_INFO_MAP_H__
diff --git a/runtime/neurun/core/include/ir/OpCode.h b/runtime/neurun/core/include/ir/OpCode.h
deleted file mode 100644
index 2b466a212..000000000
--- a/runtime/neurun/core/include/ir/OpCode.h
+++ /dev/null
@@ -1,56 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OP_CODE_H__
-#define __NEURUN_IR_OP_CODE_H__
-
-#include <functional>
-#include <stdint.h>
-
-namespace neurun
-{
-namespace ir
-{
-
-enum class OpCode
-{
-  Invalid,             //< Unused
-#define OP(Name) Name, //< All operations
-#include "ir/Operations.lst"
-#undef OP
-  COUNT
-};
-
-const char *toString(OpCode opcode);
-
-} // namespace ir
-} // namespace neurun
-
-namespace std
-{
-
-template <> struct hash<neurun::ir::OpCode>
-{
-  size_t operator()(neurun::ir::OpCode value) const noexcept
-  {
-    using type = typename std::underlying_type<neurun::ir::OpCode>::type;
-    return hash<type>()(static_cast<type>(value));
-  }
-};
-
-} // namespace std
-
-#endif // __NEURUN_IR_OP_CODE_H__
diff --git a/runtime/neurun/core/include/ir/OpSequence.h b/runtime/neurun/core/include/ir/OpSequence.h
deleted file mode 100644
index 68632e589..000000000
--- a/runtime/neurun/core/include/ir/OpSequence.h
+++ /dev/null
@@ -1,106 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OP_SEQUENCE_H__
-#define __NEURUN_IR_OP_SEQUENCE_H__
-
-#include <vector>
-#include <string>
-#include <memory>
-
-#include "ir/Layout.h"
-#include "ir/Index.h"
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-
-// To support ValueSwappable, Element doesn't have members which are classes
-// as value(or can have members which are classes as value and the classes
-// support Swappable)
-struct Element
-{
-  OperationIndex index;
-  const Operation *node;
-
-  Element(const OperationIndex *i, const Operation *n) : index{*i}, node{n}
-  {
-    // DO NOTHING
-  }
-};
-
-class OpSequence
-{
-public:
-  explicit OpSequence(Layout layout);
-  OpSequence(const OpSequence &) = delete;
-
-public:
-  void accept(OperationVisitor &v) const;
-
-public:
-  const OperandIndexSequence &getInputs() const { return _inputs; }
-  const OperandIndexSequence &getOutputs() const { return _outputs; }
-  void setInputs(const OperandIndexSequence &indexes) { _inputs = indexes; }
-  void setOutputs(const OperandIndexSequence &indexes) { _outputs = indexes; }
-  void replaceInput(const OperandIndex &from, const OperandIndex &to) { _inputs.replace(from, to); }
-  void replaceOutput(const OperandIndex &from, const OperandIndex &to)
-  {
-    _outputs.replace(from, to);
-  }
-
-  void appendOperation(const OperationIndex &index, const Operation &node)
-  {
-    _operations.emplace_back(&index, &node);
-  }
-
-  std::vector<Element> &operations(void) { return _operations; }
-
-  const std::vector<Element> &operations(void) const { return _operations; }
-
-  uint32_t size(void) const { return _operations.size(); }
-
-  // TODO: Impl Dumper instead of this method
-  std::string getStr(void) const;
-
-public:
-  void remove(const OperationIndex &index);
-
-public:
-  Layout getLayout() const { return _layout; }
-
-public:
-  std::vector<Element>::const_iterator begin() const { return _operations.begin(); }
-  std::vector<Element>::const_iterator end() const { return _operations.end(); }
-
-private:
-  bool exist(const OperationIndex &index) const;
-
-private:
-  OperandIndexSequence _inputs;
-  OperandIndexSequence _outputs;
-  std::vector<Element> _operations;
-
-private:
-  Layout _layout;
-};
-
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OP_SEQUENCE_H__
diff --git a/runtime/neurun/core/include/ir/Operand.h b/runtime/neurun/core/include/ir/Operand.h
deleted file mode 100644
index b1f28de48..000000000
--- a/runtime/neurun/core/include/ir/Operand.h
+++ /dev/null
@@ -1,130 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERAND_H__
-#define __NEURUN_IR_OPERAND_H__
-
-#include <cassert>
-#include <cstdint>
-#include <cpp14/memory.h>
-#include <algorithm>
-
-#include "ir/Data.h"
-#include "ir/DataType.h"
-#include "ir/OperandInfo.h"
-#include "ir/operand/ParentInfo.h" // TODO Remove this dependency
-#include "ir/OperationIndexList.h"
-
-namespace neurun
-{
-namespace ir
-{
-
-class Operand
-{
-public:
-  explicit Operand(const Shape &shape, const TypeInfo &type) : _info{shape, type}
-  {
-    // DO NOTHING
-  }
-
-public:
-  const Shape &shape(void) const { return _info.shape(); }
-  const TypeInfo &typeInfo(void) const { return _info.typeInfo(); }
-  const OperandInfo &info(void) const { return _info; }
-  size_t operandSize(void) const;
-
-  const OperationIndexList &getUses() const { return _uses; }
-  const OperationIndexList &getDef() const { return _def; }
-  void appendUse(const OperationIndex &idx);
-  void removeUse(const OperationIndex &idx);
-  void appendDef(const OperationIndex &idx);
-  void removeDef(const OperationIndex &idx);
-
-public:
-  void type(const DataType type) { _info.type(type); };
-
-public:
-  void data(std::unique_ptr<Data> &&data) { _data = std::move(data); }
-  const Data &data(void) const
-  {
-    assert(_data);
-    return *_data;
-  }
-
-  /**
-   * @brief Get true if Operand has data, otherwise @c false
-   a @return @c true if Operand has data, otherwise @c false
-   */
-  bool isConstant(void) const { return _data != nullptr; }
-
-public:
-  template <typename T, typename... Args> void data(Args &&... args)
-  {
-    data(nnfw::cpp14::make_unique<T>(std::forward<Args>(args)...));
-  }
-
-public:
-  template <typename T> T asScalar(void) const
-  {
-    assert((shape().rank() == 0) || ((shape().rank() == 1) && (shape().dim(0) == 1)));
-    assert(_data != nullptr);
-    assert((_data->base() != nullptr) && (_data->size() == sizeof(T)));
-
-    return *(reinterpret_cast<const T *>(_data->base()));
-  }
-
-  template <typename T> std::vector<T> asVector() const
-  {
-    assert(isConstant());
-    assert(_data->size() % sizeof(T) == 0);
-
-    const auto *base = reinterpret_cast<const T *>(_data->base());
-    const std::size_t size = _data->size() / sizeof(T);
-    return std::vector<T>(base, base + size);
-  }
-
-public:
-  /**
-   * @brief     Set parent information
-   * @param[in] parent_info Parent information
-   */
-  void parent_info(std::unique_ptr<operand::ParentInfo> &&parent_info);
-  /**
-   * @brief   Return parent information pointer as constant
-   * @return  Parent information pointer
-   */
-  const operand::ParentInfo *parent_info() const;
-  /**
-   * @brief   Return parent information pointer
-   * @return  Perent information pointer
-   */
-  operand::ParentInfo *parent_info();
-
-private:
-  OperandInfo _info;
-  std::shared_ptr<Data> _data;
-
-  OperationIndexList _uses;
-  OperationIndexList _def; // size is 0 (constant) or 1 (from def operation)
-
-  std::shared_ptr<operand::ParentInfo> _parent_info;
-};
-
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERAND_H__
diff --git a/runtime/neurun/core/include/ir/OperandConstraint.h b/runtime/neurun/core/include/ir/OperandConstraint.h
deleted file mode 100644
index de6f21634..000000000
--- a/runtime/neurun/core/include/ir/OperandConstraint.h
+++ /dev/null
@@ -1,58 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_MODEL_OPERAND_CONSTRAINT_H__
-#define __NEURUN_MODEL_OPERAND_CONSTRAINT_H__
-
-#include <stdint.h>
-#include <limits>
-#include <set>
-
-namespace neurun
-{
-namespace ir
-{
-
-class OperandConstraint
-{
-private:
-  static const uint32_t INF = std::numeric_limits<uint32_t>::max();
-
-public:
-  static OperandConstraint createAny() { return OperandConstraint{0u, INF}; }
-  static OperandConstraint createExact(uint32_t exact) { return OperandConstraint{exact, exact}; }
-  static OperandConstraint createAtMost(uint32_t end) { return OperandConstraint{0u, end}; }
-  static OperandConstraint createAtLeast(uint32_t begin) { return OperandConstraint{begin, INF}; }
-  static OperandConstraint createInRange(uint32_t begin, uint32_t end)
-  {
-    return OperandConstraint{begin, end};
-  }
-
-private:
-  OperandConstraint(uint32_t begin, uint32_t end) : _begin{begin}, _end{end} {}
-
-public:
-  bool check(uint32_t ind) const { return _begin <= ind && ind <= _end; }
-
-private:
-  uint32_t _begin;
-  uint32_t _end;
-};
-
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_MODEL_OPERAND_CONSTRAINT_H__
diff --git a/runtime/neurun/core/include/ir/OperandIndexMap.h b/runtime/neurun/core/include/ir/OperandIndexMap.h
deleted file mode 100644
index c9234128e..000000000
--- a/runtime/neurun/core/include/ir/OperandIndexMap.h
+++ /dev/null
@@ -1,34 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERAND_INDEX_MAP_H__
-#define __NEURUN_IR_OPERAND_INDEX_MAP_H__
-
-#include <unordered_map>
-
-#include "ir/Index.h"
-
-namespace neurun
-{
-namespace ir
-{
-
-template <typename T> using OperandIndexMap = std::unordered_map<OperandIndex, T>;
-
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERAND_INDEX_MAP_H__
diff --git a/runtime/neurun/core/include/ir/OperandIndexSequence.h b/runtime/neurun/core/include/ir/OperandIndexSequence.h
deleted file mode 100644
index 7f8cec844..000000000
--- a/runtime/neurun/core/include/ir/OperandIndexSequence.h
+++ /dev/null
@@ -1,60 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_MODEL_OPERAND_INDEX_SEQUENCE_H__
-#define __NEURUN_MODEL_OPERAND_INDEX_SEQUENCE_H__
-
-#include <initializer_list>
-#include <vector>
-
-#include "ir/Index.h"
-
-namespace neurun
-{
-namespace ir
-{
-
-class OperandIndexSequence
-{
-public:
-  OperandIndexSequence(void) = default;
-  OperandIndexSequence(std::initializer_list<OperandIndex> list);
-  OperandIndexSequence(std::initializer_list<int32_t> list);
-  OperandIndexSequence(std::initializer_list<uint32_t> list);
-
-public:
-  void append(const OperandIndex &index) { _set.emplace_back(index); }
-  void append(const OperandIndexSequence &l) { _set.insert(_set.end(), l.begin(), l.end()); }
-
-public:
-  uint32_t size() const { return static_cast<uint32_t>(_set.size()); }
-  const OperandIndex &at(IOIndex set_index) const { return _set.at(set_index.value()); }
-  const OperandIndex &at(uint32_t index) const { return _set.at(index); }
-  bool contains(const OperandIndex &index) const;
-  void replace(const OperandIndex &from, const OperandIndex &to);
-
-public:
-  std::vector<OperandIndex>::const_iterator begin(void) const { return _set.begin(); }
-  std::vector<OperandIndex>::const_iterator end(void) const { return _set.end(); }
-
-private:
-  std::vector<OperandIndex> _set;
-};
-
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_MODEL_OPERAND_INDEX_SET_H__
diff --git a/runtime/neurun/core/include/ir/OperandInfo.h b/runtime/neurun/core/include/ir/OperandInfo.h
deleted file mode 100644
index 82ad7ef0f..000000000
--- a/runtime/neurun/core/include/ir/OperandInfo.h
+++ /dev/null
@@ -1,90 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/**
- * @file  OperandInfo.h
- * @brief This file contains OperandInfo class
- */
-#ifndef __NEURUN_IR_OPERAND_INFO_H__
-#define __NEURUN_IR_OPERAND_INFO_H__
-
-#include "ir/Shape.h"
-#include "ir/TypeInfo.h"
-#include "ir/Layout.h"
-
-namespace neurun
-{
-namespace ir
-{
-
-/**
- * @brief Class to save tensor's shape and type
- */
-class OperandInfo
-{
-public:
-  /**
-   * @brief Construct a new OperandInfo object (deleted)
-   */
-  OperandInfo() = delete;
-  /**
-   * @brief     Construct a new OperandInfo object
-   * @param[in] shape     Tensor shape
-   * @param[in] typeInfo  Tensor data type
-   */
-  OperandInfo(const Shape &shape, const TypeInfo &typeInfo) : _shape(shape), _typeInfo(typeInfo)
-  {
-    // DO NOTHING
-  }
-  /**
-   * @brief     Construct a new OperandInfo object
-   * @param[in] origin info for copy
-   */
-  OperandInfo(const OperandInfo &origin) : _shape(origin.shape()), _typeInfo(origin.typeInfo())
-  {
-    // DO NOTHING
-  }
-
-public:
-  /**
-   * @brief   Return tensor shape
-   * @return  Tensor shape
-   */
-  const Shape &shape() const { return _shape; }
-  /**
-   * @brief   Return tensor data type info
-   * @return  Tensor data type
-   */
-  const TypeInfo &typeInfo() const { return _typeInfo; }
-  /**
-   * @brief   Set tensor data type
-   */
-  void type(const DataType type) { _typeInfo.type(type); }
-  /**
-   * @brief   Return size of tensor (bytes)
-   * @return  Tensor size
-   */
-  size_t total_size() const { return _shape.num_elements() * sizeOfDataType(_typeInfo.type()); }
-
-private:
-  Shape _shape;
-  TypeInfo _typeInfo;
-};
-
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERAND_INFO_H__
diff --git a/runtime/neurun/core/include/ir/Operands.h b/runtime/neurun/core/include/ir/Operands.h
deleted file mode 100644
index c8d68c088..000000000
--- a/runtime/neurun/core/include/ir/Operands.h
+++ /dev/null
@@ -1,39 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERANDS_H__
-#define __NEURUN_IR_OPERANDS_H__
-
-#include <memory>
-#include <unordered_map>
-
-#include "ir/Operand.h"
-#include "ir/Index.h"
-#include "util/ObjectManager.h"
-
-namespace neurun
-{
-namespace ir
-{
-
-class Operands : public util::ObjectManager<OperandIndex, Operand>
-{
-};
-
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_MODEL_OPERAND_SET_H__
diff --git a/runtime/neurun/core/include/ir/Operation.h b/runtime/neurun/core/include/ir/Operation.h
deleted file mode 100644
index a02f980a5..000000000
--- a/runtime/neurun/core/include/ir/Operation.h
+++ /dev/null
@@ -1,71 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_H__
-#define __NEURUN_IR_OPERATION_H__
-
-#include <memory>
-
-#include "ir/OpCode.h"
-#include "ir/Operand.h"
-#include "ir/OperandIndexSequence.h"
-#include "ir/OperandConstraint.h"
-
-namespace neurun
-{
-namespace ir
-{
-
-struct OperationVisitor;
-
-class Operation
-{
-public:
-  Operation(OperandConstraint input_constr, const OperandIndexSequence &inputs,
-            const OperandIndexSequence &outputs);
-  explicit Operation(OperandConstraint input_constr);
-
-  Operation(const Operation &) = delete;
-  Operation(Operation &&) = default;
-  Operation &operator=(const Operation &) = delete;
-  Operation &operator=(Operation &&) = default;
-
-  virtual ~Operation();
-
-public:
-  virtual void accept(OperationVisitor &v) const = 0;
-  virtual std::string name() const { return std::string{toString(opcode())}; }
-  virtual OpCode opcode() const = 0;
-
-public:
-  void replaceInput(const OperandIndex &from, const OperandIndex &to);
-  void replaceOutput(const OperandIndex &from, const OperandIndex &to);
-  const OperandIndexSequence &getInputs() const { return _inputs; }
-  const OperandIndexSequence &getOutputs() const { return _outputs; }
-  // It's for only input/output tensors but const data.
-  void setInputs(const OperandIndexSequence &indexes);
-  void setOutputs(const OperandIndexSequence &indexes);
-
-private:
-  OperandConstraint _input_constr;
-  OperandIndexSequence _inputs;
-  OperandIndexSequence _outputs;
-};
-
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_H__
diff --git a/runtime/neurun/core/include/ir/OperationIndexList.h b/runtime/neurun/core/include/ir/OperationIndexList.h
deleted file mode 100644
index bf3ea3d6b..000000000
--- a/runtime/neurun/core/include/ir/OperationIndexList.h
+++ /dev/null
@@ -1,59 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_MODEL_OPERATION_INDEX_LIST_H__
-#define __NEURUN_MODEL_OPERATION_INDEX_LIST_H__
-
-#include <algorithm>
-#include <cassert>
-#include <initializer_list>
-#include <list>
-
-#include "ir/Index.h"
-
-namespace neurun
-{
-namespace ir
-{
-
-class OperationIndexList
-{
-public:
-  OperationIndexList(void) = default;
-  OperationIndexList(std::initializer_list<OperationIndex> list);
-
-public:
-  void append(const OperationIndex &index) { _list.push_back(index); }
-  void remove(const OperationIndex &index)
-  {
-    auto itr = std::find(_list.begin(), _list.end(), index);
-    assert(itr != _list.end());
-    _list.erase(itr);
-  }
-
-public:
-  uint32_t size() const { return static_cast<uint32_t>(_list.size()); }
-  const std::list<OperationIndex> &list() const { return _list; }
-  bool contains(const OperationIndex &index) const;
-
-private:
-  std::list<OperationIndex> _list;
-};
-
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_MODEL_OPERATION_INDEX_LIST_H__
diff --git a/runtime/neurun/core/include/ir/OperationIndexMap.h b/runtime/neurun/core/include/ir/OperationIndexMap.h
deleted file mode 100644
index 50b1db527..000000000
--- a/runtime/neurun/core/include/ir/OperationIndexMap.h
+++ /dev/null
@@ -1,34 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_INDEX_MAP_H__
-#define __NEURUN_IR_OPERATION_INDEX_MAP_H__
-
-#include <unordered_map>
-
-#include "ir/Index.h"
-
-namespace neurun
-{
-namespace ir
-{
-
-template <typename T> using OperationIndexMap = std::unordered_map<OperationIndex, T>;
-
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_INDEX_MAP_H__
diff --git a/runtime/neurun/core/include/ir/OperationVisitor.h b/runtime/neurun/core/include/ir/OperationVisitor.h
deleted file mode 100644
index 0eb6de2d3..000000000
--- a/runtime/neurun/core/include/ir/OperationVisitor.h
+++ /dev/null
@@ -1,52 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_VISITOR_H__
-#define __NEURUN_IR_OPERATION_VISITOR_H__
-
-#include "ir/Operations.Include.h"
-#include "ir/OpSequence.h"
-
-namespace neurun
-{
-namespace ir
-{
-
-struct OperationVisitor
-{
-  virtual ~OperationVisitor() = default;
-
-#define OP(InternalName) \
-  virtual void visit(const operation::InternalName &) {}
-#include "ir/Operations.lst"
-#undef OP
-
-  // This OpSequence node should be handled specially so that
-  // Op.lst doesn't have OpSequence
-  // TODO Remove by pushing it down to derived classes.
-  virtual void visit(const OpSequence &op_seq)
-  {
-    for (const auto &e : op_seq.operations())
-    {
-      e.node->accept(*this);
-    }
-  }
-};
-
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_VISITOR_H__
diff --git a/runtime/neurun/core/include/ir/Operations.Include.h b/runtime/neurun/core/include/ir/Operations.Include.h
deleted file mode 100644
index e14e18cc1..000000000
--- a/runtime/neurun/core/include/ir/Operations.Include.h
+++ /dev/null
@@ -1,83 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-// This file has no ifdef guard intentionally
-
-#include "ir/operation/BatchToSpaceND.h"
-#include "ir/operation/Conv2D.h"
-#include "ir/operation/MaxPool2D.h"
-#include "ir/operation/AvgPool2D.h"
-#include "ir/operation/Concat.h"
-#include "ir/operation/Reshape.h"
-#include "ir/operation/FullyConnected.h"
-#include "ir/operation/Softmax.h"
-#include "ir/operation/Transpose.h"
-#include "ir/operation/Permute.h"
-#include "ir/operation/ReduceSum.h"
-#include "ir/operation/Add.h"
-#include "ir/operation/Sub.h"
-#include "ir/operation/DepthwiseConv2D.h"
-#include "ir/operation/Slice.h"
-#include "ir/operation/StridedSlice.h"
-#include "ir/operation/Mul.h"
-#include "ir/operation/Squeeze.h"
-#include "ir/operation/Tanh.h"
-#include "ir/operation/Logistic.h"
-#include "ir/operation/Cast.h"
-#include "ir/operation/Div.h"
-#include "ir/operation/Exp.h"
-#include "ir/operation/ReduceMax.h"
-#include "ir/operation/Comparison.h"
-#include "ir/operation/LogicalAnd.h"
-#include "ir/operation/LogicalOr.h"
-#include "ir/operation/LogicalNot.h"
-#include "ir/operation/LSTM.h"
-#include "ir/operation/RSQRT.h"
-#include "ir/operation/ReLU.h"
-#include "ir/operation/ResizeBilinear.h"
-#include "ir/operation/ReLU1.h"
-#include "ir/operation/ReLU6.h"
-#include "ir/operation/RNN.h"
-#include "ir/operation/Floor.h"
-#include "ir/operation/SpaceToBatchND.h"
-#include "ir/operation/SpaceToDepth.h"
-#include "ir/operation/L2Pool2D.h"
-#include "ir/operation/EmbeddingLookup.h"
-#include "ir/operation/L2Normalization.h"
-#include "ir/operation/HashtableLookup.h"
-#include "ir/operation/InstanceNorm.h"
-#include "ir/operation/PReLU.h"
-#include "ir/operation/TransposeConv.h"
-#include "ir/operation/SQRT.h"
-#include "ir/operation/SquaredDifference.h"
-#include "ir/operation/TopKV2.h"
-#include "ir/operation/Gather.h"
-#include "ir/operation/Neg.h"
-#include "ir/operation/Abs.h"
-#include "ir/operation/ArgMax.h"
-#include "ir/operation/Dequantize.h"
-#include "ir/operation/Mean.h"
-#include "ir/operation/LocalResponseNormalization.h"
-#include "ir/operation/DepthToSpace.h"
-#include "ir/operation/Pack.h"
-#include "ir/operation/ReduceMin.h"
-#include "ir/operation/Split.h"
-#include "ir/operation/Unpack.h"
-#include "ir/operation/Pad.h"
-#include "ir/operation/Min.h"
-#include "ir/operation/Max.h"
-#include "ir/operation/Custom.h"
-#include "ir/operation/OneHot.h"
diff --git a/runtime/neurun/core/include/ir/Operations.h b/runtime/neurun/core/include/ir/Operations.h
deleted file mode 100644
index 9e2aecb43..000000000
--- a/runtime/neurun/core/include/ir/Operations.h
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATIONS_H__
-#define __NEURUN_IR_OPERATIONS_H__
-
-#include "ir/Index.h"
-#include "ir/Operation.h"
-#include "util/ObjectManager.h"
-
-namespace neurun
-{
-namespace ir
-{
-
-class Operations : public util::ObjectManager<OperationIndex, Operation>
-{
-};
-
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_MODEL_OPERATION_MANAGER_H__
diff --git a/runtime/neurun/core/include/ir/Operations.lst b/runtime/neurun/core/include/ir/Operations.lst
deleted file mode 100644
index 8c02857d9..000000000
--- a/runtime/neurun/core/include/ir/Operations.lst
+++ /dev/null
@@ -1,86 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef OP
-#error Define OP before including this file
-#endif
-
-// Internal Name
-OP(Add)
-OP(Sub)
-OP(BatchToSpaceND)
-OP(Cast)
-OP(Conv2D)
-OP(DepthwiseConv2D)
-OP(AvgPool2D)
-OP(MaxPool2D)
-OP(Concat)
-OP(FullyConnected)
-OP(ReduceSum)
-OP(Reshape)
-OP(Mul)
-OP(Softmax)
-OP(Squeeze)
-OP(Slice)
-OP(StridedSlice)
-OP(Tanh)
-OP(Logistic)
-OP(Div)
-OP(Transpose)
-OP(Exp)
-OP(ReduceMax)
-OP(Comparison)
-OP(LogicalAnd)
-OP(LogicalOr)
-OP(LogicalNot)
-OP(LSTM)
-OP(RSQRT)
-OP(ReLU)
-OP(ResizeBilinear)
-OP(ReLU1)
-OP(ReLU6)
-OP(RNN)
-OP(Floor)
-OP(SpaceToBatchND)
-OP(SpaceToDepth)
-OP(L2Pool2D)
-OP(EmbeddingLookup)
-OP(L2Normalization)
-OP(HashtableLookup)
-OP(InstanceNorm)
-OP(PReLU)
-OP(TransposeConv)
-OP(SQRT)
-OP(SquaredDifference)
-OP(TopKV2)
-OP(Gather)
-OP(Neg)
-OP(Abs)
-OP(ArgMax)
-OP(Dequantize)
-OP(Mean)
-OP(LocalResponseNormalization)
-OP(DepthToSpace)
-OP(Pack)
-OP(ReduceMin)
-OP(Split)
-OP(Unpack)
-OP(Pad)
-OP(Custom)
-OP(Permute)
-OP(Min)
-OP(Max)
-OP(OneHot)
diff --git a/runtime/neurun/core/include/ir/Shape.h b/runtime/neurun/core/include/ir/Shape.h
deleted file mode 100644
index a58af38ad..000000000
--- a/runtime/neurun/core/include/ir/Shape.h
+++ /dev/null
@@ -1,84 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_SHAPE_H__
-#define __NEURUN_IR_SHAPE_H__
-
-#include "ir/Layout.h"
-#include "misc/feature/Shape.h"
-
-#include <cstdint>
-#include <vector>
-
-namespace neurun
-{
-namespace ir
-{
-
-// TODO Remove this dependency.
-using FeatureShape = nnfw::misc::feature::Shape;
-
-struct Shape
-{
-public:
-  Shape() = default;
-
-  explicit Shape(int rank) : _dimensions(rank) {}
-
-  Shape(std::initializer_list<int32_t> dimensions) : _dimensions(dimensions) {}
-
-  int rank() const { return _dimensions.size(); }
-
-  const std::vector<int32_t> &dims() const { return _dimensions; }
-
-  int32_t dim(int i) const { return _dimensions.at(i); }
-
-  int32_t &dim(int i) { return _dimensions.at(i); }
-
-  uint64_t num_elements() const;
-
-public:
-  FeatureShape asFeature(Layout layout) const;
-
-  /**
-   * @brief Add dimension to the beginning
-   * @param[in] d dimension to add to the beginning
-   */
-  void prepend(int32_t d) { _dimensions.insert(_dimensions.cbegin(), d); }
-
-  /**
-   * @brief Add dimension to the end
-   * @param[in] d dimension to add to the end
-   */
-  void append(int32_t d) { _dimensions.emplace_back(d); }
-
-  /**
-   * @brief Extend rank of Shape object for operand with param.
-   * @param[in] to_rank The rank value to be extended to
-   */
-  void extendRank(int to_rank);
-
-private:
-  std::vector<int32_t> _dimensions;
-};
-
-inline bool operator==(const Shape &lhs, const Shape &rhs) { return lhs.dims() == rhs.dims(); }
-inline bool operator!=(const Shape &lhs, const Shape &rhs) { return lhs.dims() != rhs.dims(); }
-
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_SHAPE_H__
diff --git a/runtime/neurun/core/include/ir/Subgraphs.h b/runtime/neurun/core/include/ir/Subgraphs.h
deleted file mode 100644
index 716f09bcf..000000000
--- a/runtime/neurun/core/include/ir/Subgraphs.h
+++ /dev/null
@@ -1,87 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_SUBGRAPHS_H__
-#define __NEURUN_IR_SUBGRAPHS_H__
-
-#include "ir/Index.h"
-#include "ir/OpSequence.h"
-#include "util/ObjectManager.h"
-
-namespace neurun
-{
-namespace ir
-{
-
-/**
- * @brief Class that manages OpSequence objects
- */
-class Subgraphs : public util::ObjectManager<SubgraphIndex, OpSequence>
-{
-public:
-  /**
-   * @brief Create an instance of OpSequence with given op and push it to objects
-   *
-   * @param[in] op_idx Operation index that is emplaced
-   * @param[in] op Operation that is emplaced
-   * @param[in] layout OpSequence's layout
-   * @return SubgraphIndex
-   */
-  SubgraphIndex emplace(const OperationIndex &op_index, const Operation &op, Layout layout);
-
-  /**
-   * @brief Push an instance of OpSequence to objects
-   *
-   * @param[in] subg An instance of OpSequence
-   * @return SubgraphIndex
-   */
-  SubgraphIndex emplace(std::unique_ptr<OpSequence> &&subg);
-
-  /**
-   * @brief Check if an operation does exist in any subgraphs
-   *
-   * @param operation_index Operation index to find
-   * @return true If such operation exists in any subgraphs otherwise false
-   */
-  bool containsOperation(const OperationIndex &operation_index) const;
-  /**
-   * @brief Find an operation from all subgraphs
-   *
-   * @param operation_index Operation index to find
-   * @return SubgraphIndex Index of OpSequence that contains given operation index
-   */
-  SubgraphIndex getOperation(const OperationIndex &operation_index) const;
-  /**
-   * @brief Dump subgraphs
-   *
-   * @param msg Message that will be displayed
-   */
-  void dump(const std::string &msg) const;
-  /**
-   * @brief Remove an operation from OpSequence
-   *
-   * @param operation_index Operation index to be removed
-   */
-  void removeFromSubgraph(const OperationIndex &operation_index);
-
-private:
-  SubgraphIndex findOperation(const OperationIndex &operation_index) const;
-};
-
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_SUBGRAPHS_H__
diff --git a/runtime/neurun/core/include/ir/TypeInfo.h b/runtime/neurun/core/include/ir/TypeInfo.h
deleted file mode 100644
index 5b35046bb..000000000
--- a/runtime/neurun/core/include/ir/TypeInfo.h
+++ /dev/null
@@ -1,59 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_TYPEINFO_H__
-#define __NEURUN_IR_TYPEINFO_H__
-
-#include <cstdint>
-
-#include "ir/DataType.h"
-
-namespace neurun
-{
-namespace ir
-{
-
-class TypeInfo
-{
-public:
-  TypeInfo() = delete;
-
-  explicit TypeInfo(DataType type, float scale = 0, int32_t offset = 0)
-      : _type(type), _scale(scale), _offset(offset)
-  {
-  }
-
-public:
-  DataType type() const { return _type; }
-  float scale() const { return _scale; }
-  int32_t offset() const { return _offset; }
-
-public:
-  void type(const DataType type) { _type = type; }
-
-private:
-  DataType _type;
-  float _scale;
-  int32_t _offset;
-};
-
-bool operator==(const TypeInfo &lhs, const TypeInfo &rhs);
-bool operator!=(const TypeInfo &lhs, const TypeInfo &rhs);
-
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_TYPEINFO_H__
diff --git a/runtime/neurun/core/include/ir/operand/LowerInfo.h b/runtime/neurun/core/include/ir/operand/LowerInfo.h
deleted file mode 100644
index e0fb2c5c8..000000000
--- a/runtime/neurun/core/include/ir/operand/LowerInfo.h
+++ /dev/null
@@ -1,93 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERAND_LOWER_INFO_H__
-#define __NEURUN_IR_OPERAND_LOWER_INFO_H__
-
-#include <functional>
-#include <stdint.h>
-
-#include "ir/operand/PermuteFactor.h"
-#include "util/Set.h"
-
-namespace neurun
-{
-namespace backend
-{
-class Backend;
-} // namespace backend
-} // namespace neurun
-
-namespace neurun
-{
-namespace ir
-{
-namespace operand
-{
-using PermuteFactorSet = util::Set<PermuteFactor>;
-
-class LowerInfo
-{
-public:
-  class Shape4D
-  {
-  public:
-    Shape4D(uint32_t n, uint32_t h, uint32_t w, uint32_t c) : _n{n}, _h{h}, _w{w}, _c{c}
-    {
-      // DO NOTHING
-    }
-
-  public:
-    uint32_t n(void) const { return _n; }
-    uint32_t h(void) const { return _h; }
-    uint32_t w(void) const { return _w; }
-    uint32_t c(void) const { return _c; }
-
-  private:
-    uint32_t _n;
-    uint32_t _h;
-    uint32_t _w;
-    uint32_t _c;
-  };
-
-public:
-  LowerInfo(const Shape4D &shape) : _shape{shape}
-  {
-    // DO NOTHING
-  }
-
-public:
-  const Shape4D &shape(void) const { return _shape; }
-  const PermuteFactorSet &def_factors(void) const { return _def_factors; }
-  const PermuteFactorSet &use_factors(void) const { return _use_factors; }
-
-public:
-  void addDefPermuteFactor(const PermuteFactor &factor) { _def_factors.add(factor); }
-  void addUsePermuteFactor(const PermuteFactor &factor) { _use_factors.add(factor); }
-  void removeDefPermuteFactor(const PermuteFactor &factor) { _def_factors.remove(factor); }
-  void removeUsePermuteFactor(const PermuteFactor &factor) { _use_factors.remove(factor); }
-
-private:
-  Shape4D _shape;
-  PermuteFactorSet _def_factors;
-  PermuteFactorSet _use_factors;
-};
-
-} // namespace operand
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERAND_LOWER_INFO_H__
diff --git a/runtime/neurun/core/include/ir/operand/ParentInfo.h b/runtime/neurun/core/include/ir/operand/ParentInfo.h
deleted file mode 100644
index 92dac2b63..000000000
--- a/runtime/neurun/core/include/ir/operand/ParentInfo.h
+++ /dev/null
@@ -1,77 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/**
- * @file  ParentInfo.h
- * @brief This file contains ParentInfo class and internal Coordinate4D class
- *        to represent subsumption between operand
- */
-
-#ifndef __NEURUN_IR_OPERAND_PARENT_INFO_H__
-#define __NEURUN_IR_OPERAND_PARENT_INFO_H__
-
-#include <stdint.h>
-
-#include "ir/Index.h"
-#include "util/Coordinates.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operand
-{
-
-/**
- * @brief	Class to represent parent operand in child operand
- */
-class ParentInfo
-{
-public:
-  /**
-   * @brief     Construct a new ParentInfo object
-   * @param[in] parent      Index of parent operand
-   * @param[in] coordinate  Offset of child operand in parent operand
-   * @return
-   */
-  ParentInfo(const OperandIndex parent, const util::Coordinates &coordinate)
-      : _parent{parent}, _coordinate{coordinate}
-  {
-    // DO NOTHING
-  }
-
-public:
-  /**
-   * @brief   Return parent index
-   * @return  Parent index
-   */
-  OperandIndex parent(void) const { return _parent; }
-  /**
-   * @brief   Retern offset in parent
-   * @return  Offset
-   */
-  util::Coordinates offset(void) const { return _coordinate; }
-
-private:
-  OperandIndex _parent;
-  util::Coordinates _coordinate;
-};
-
-} // namespace operand
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERAND_PARENT_INFO_H__
diff --git a/runtime/neurun/core/include/ir/operand/PermuteFactor.h b/runtime/neurun/core/include/ir/operand/PermuteFactor.h
deleted file mode 100644
index 60d926b2d..000000000
--- a/runtime/neurun/core/include/ir/operand/PermuteFactor.h
+++ /dev/null
@@ -1,130 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/**
- * @file     PermuteFactor.h
- * @brief    This file contains neurun::ir::operand::PermuteFactor class
- * @ingroup  COM_AI_RUNTIME
- */
-
-#ifndef __NEURUN_IR_OPERAND_PERMUTE_FACTOR_H__
-#define __NEURUN_IR_OPERAND_PERMUTE_FACTOR_H__
-
-#include <functional>
-
-#include "ir/Layout.h"
-
-namespace neurun
-{
-namespace backend
-{
-class Backend;
-} // namespace backend
-} // namespace neurun
-
-namespace neurun
-{
-namespace ir
-{
-namespace operand
-{
-
-/**
- * @brief Class that has factors of permutation
- */
-class PermuteFactor
-{
-public:
-  /**
-   * @brief Construct PermuteFactor object.
-   * @param backend  The backend factor
-   * @param backend  The layout factor
-   */
-  PermuteFactor(const backend::Backend *backend, Layout layout) : _backend{backend}, _layout{layout}
-  {
-    // DO NOTHING
-  }
-  /**
-   * @brief Construct PermuteFactor object by copy semantics.
-   */
-  PermuteFactor(const PermuteFactor &f) : _backend{f._backend}, _layout{f._layout}
-  {
-    // DO NOTHING
-  }
-  /**
-   * @brief Construct PermuteFactor object by move semantics.
-   */
-  PermuteFactor(PermuteFactor &&) = default;
-
-public:
-  /**
-   * @brief Get backend
-   *
-   * @return Backend factor
-   */
-  const backend::Backend *backend() const { return _backend; }
-  /**
-   * @brief Get layout
-   *
-   * @return Layout factor
-   */
-  Layout layout() const { return _layout; }
-
-public:
-  /**
-   * @brief operator overloading function for `==`
-   *
-   * @return Whether two PermuteFactor are the same
-   */
-  bool operator==(const PermuteFactor &other) const
-  {
-    return _backend == other.backend() && _layout == other.layout();
-  }
-  /**
-   * @brief operator overloading function for `!=`
-   *
-   * @return Whether two PermuteFactor are differenct
-   */
-  bool operator!=(const PermuteFactor &other) const { return !(*this == other); }
-
-private:
-  const backend::Backend *_backend{nullptr};
-  Layout _layout{Layout::UNKNOWN};
-};
-
-} // namespace operand
-} // namespace ir
-} // namespace neurun
-
-namespace std
-{
-
-/**
- * @brief Structure that provides hash value of PermuteFactor
- */
-template <> struct hash<neurun::ir::operand::PermuteFactor>
-{
-  size_t operator()(const neurun::ir::operand::PermuteFactor &factor) const noexcept
-  {
-    hash<const neurun::backend::Backend *> b_hash{};
-    hash<neurun::ir::Layout> l_hash{};
-    return b_hash(factor.backend()) ^ (l_hash(factor.layout()) << 1);
-  }
-};
-
-} // namespace std
-
-#endif // __NEURUN_IR_OPERAND_PERMUTE_FACTOR_H__
diff --git a/runtime/neurun/core/include/ir/operation/Abs.h b/runtime/neurun/core/include/ir/operation/Abs.h
deleted file mode 100644
index 97293823b..000000000
--- a/runtime/neurun/core/include/ir/operation/Abs.h
+++ /dev/null
@@ -1,49 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_ABS_H__
-#define __NEURUN_IR_OPERATION_ABS_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class Abs : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0
-  };
-
-public:
-  Abs(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::Abs; }
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_ABS_H__
diff --git a/runtime/neurun/core/include/ir/operation/Add.h b/runtime/neurun/core/include/ir/operation/Add.h
deleted file mode 100644
index fc4d6a7e7..000000000
--- a/runtime/neurun/core/include/ir/operation/Add.h
+++ /dev/null
@@ -1,62 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_ADD_H__
-#define __NEURUN_IR_OPERATION_ADD_H__
-
-#include "ir/Operation.h"
-#include "ir/InternalType.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class Add : public Operation
-{
-public:
-  enum Input
-  {
-    LHS = 0,
-    RHS
-  };
-
-  struct Param
-  {
-    Activation activation;
-  };
-
-public:
-  Add(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::Add; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_ADD_H__
diff --git a/runtime/neurun/core/include/ir/operation/ArgMax.h b/runtime/neurun/core/include/ir/operation/ArgMax.h
deleted file mode 100644
index 23f52710f..000000000
--- a/runtime/neurun/core/include/ir/operation/ArgMax.h
+++ /dev/null
@@ -1,62 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_ARG_MAX_H__
-#define __NEURUN_IR_OPERATION_ARG_MAX_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class ArgMax : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT
-  };
-
-  struct Param
-  {
-    int axis;
-    int rank;
-  };
-
-public:
-  ArgMax(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-         const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::ArgMax; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_ARG_MAX_H__
diff --git a/runtime/neurun/core/include/ir/operation/AvgPool2D.h b/runtime/neurun/core/include/ir/operation/AvgPool2D.h
deleted file mode 100644
index a03628184..000000000
--- a/runtime/neurun/core/include/ir/operation/AvgPool2D.h
+++ /dev/null
@@ -1,69 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_AVGPOOL2D_H__
-#define __NEURUN_IR_OPERATION_AVGPOOL2D_H__
-
-#include <memory>
-
-#include "ir/Operation.h"
-#include "ir/InternalType.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class AvgPool2D : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0
-  };
-
-  struct Param
-  {
-    uint32_t kh;
-    uint32_t kw;
-
-    Stride stride;
-    Padding padding;
-    Activation activation;
-  };
-
-public:
-  AvgPool2D(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-            const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::AvgPool2D; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_AVGPOOL2D_H__
diff --git a/runtime/neurun/core/include/ir/operation/BatchToSpaceND.h b/runtime/neurun/core/include/ir/operation/BatchToSpaceND.h
deleted file mode 100644
index b90d2871d..000000000
--- a/runtime/neurun/core/include/ir/operation/BatchToSpaceND.h
+++ /dev/null
@@ -1,50 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_BATCH_TO_SPACE_ND_H__
-#define __NEURUN_IR_OPERATION_BATCH_TO_SPACE_ND_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class BatchToSpaceND : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0,
-    BLOCK_SIZE = 1
-  };
-
-public:
-  BatchToSpaceND(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::BatchToSpaceND; }
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_BATCH_TO_SPACE_ND_H__
diff --git a/runtime/neurun/core/include/ir/operation/Cast.h b/runtime/neurun/core/include/ir/operation/Cast.h
deleted file mode 100644
index a71087dd0..000000000
--- a/runtime/neurun/core/include/ir/operation/Cast.h
+++ /dev/null
@@ -1,49 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_CAST_H__
-#define __NEURUN_IR_OPERATION_CAST_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class Cast : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0
-  };
-
-public:
-  Cast(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::Cast; }
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_CAST_H__
diff --git a/runtime/neurun/core/include/ir/operation/Comparison.h b/runtime/neurun/core/include/ir/operation/Comparison.h
deleted file mode 100644
index 23c775c42..000000000
--- a/runtime/neurun/core/include/ir/operation/Comparison.h
+++ /dev/null
@@ -1,72 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_COMPARISON_H__
-#define __NEURUN_IR_OPERATION_COMPARISON_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class Comparison : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT0 = 0,
-    INPUT1
-  };
-
-  enum class ComparisonType
-  {
-    Equal,
-    NotEqual,
-    Greater,
-    GreaterEqual,
-    Less,
-    LessEqual
-  };
-
-  struct Param
-  {
-    ComparisonType comparison_type;
-  };
-
-public:
-  Comparison(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-             const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::Comparison; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_COMPARISON_H__
diff --git a/runtime/neurun/core/include/ir/operation/Concat.h b/runtime/neurun/core/include/ir/operation/Concat.h
deleted file mode 100644
index 8628ed398..000000000
--- a/runtime/neurun/core/include/ir/operation/Concat.h
+++ /dev/null
@@ -1,59 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_CONCAT_H__
-#define __NEURUN_IR_OPERATION_CONCAT_H__
-
-#include <memory>
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class Concat : public Operation
-{
-public:
-  struct Param
-  {
-    int32_t axis;
-    int32_t rank;
-  };
-
-public:
-  Concat(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-         const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::Concat; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_CONCAT_H__
diff --git a/runtime/neurun/core/include/ir/operation/Conv2D.h b/runtime/neurun/core/include/ir/operation/Conv2D.h
deleted file mode 100644
index 1215666e9..000000000
--- a/runtime/neurun/core/include/ir/operation/Conv2D.h
+++ /dev/null
@@ -1,68 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_CONV2D_H__
-#define __NEURUN_IR_OPERATION_CONV2D_H__
-
-#include <memory>
-
-#include "ir/Operation.h"
-#include "ir/InternalType.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class Conv2D : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0,
-    KERNEL,
-    BIAS
-  };
-
-  struct Param
-  {
-    Stride stride;
-    Padding padding;
-    Activation activation;
-  };
-
-public:
-  Conv2D(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-         const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::Conv2D; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_CONV2D_H__
diff --git a/runtime/neurun/core/include/ir/operation/Custom.h b/runtime/neurun/core/include/ir/operation/Custom.h
deleted file mode 100644
index 03501e8ef..000000000
--- a/runtime/neurun/core/include/ir/operation/Custom.h
+++ /dev/null
@@ -1,66 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#ifndef __NEURUN_IR_OPERATION_CUSTOM_H__
-#define __NEURUN_IR_OPERATION_CUSTOM_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class Custom : public Operation
-{
-public:
-  struct Userdata
-  {
-    char *data;
-    size_t size;
-  };
-
-  Custom(OperandConstraint input_constr, const OperandIndexSequence &inputs,
-         const OperandIndexSequence &outputs, std::string id, const Userdata &userdata);
-
-  void accept(OperationVisitor &v) const override;
-
-public:
-  /**
-   * @return unique operation identifier
-   */
-  const std::string &id() const;
-
-  std::string name() const override;
-  OpCode opcode() const final { return OpCode::Custom; }
-
-  /**
-   * @return user-provided data
-   */
-  const Userdata &userdata() const;
-
-  ~Custom() override;
-
-private:
-  std::string _id;
-  Userdata _userdata;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-#endif // __NEURUN_IR_OPERATION_CUSTOM_H__
diff --git a/runtime/neurun/core/include/ir/operation/DepthToSpace.h b/runtime/neurun/core/include/ir/operation/DepthToSpace.h
deleted file mode 100644
index 6e7aaf249..000000000
--- a/runtime/neurun/core/include/ir/operation/DepthToSpace.h
+++ /dev/null
@@ -1,63 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_DEPTH_TO_SPACE_H__
-#define __NEURUN_IR_OPERATION_DEPTH_TO_SPACE_H__
-
-#include <memory>
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class DepthToSpace : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0
-  };
-
-  struct Param
-  {
-    std::int32_t block_size;
-  };
-
-public:
-  DepthToSpace(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-               const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::DepthToSpace; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_DEPTH_TO_SPACE_H__
diff --git a/runtime/neurun/core/include/ir/operation/DepthwiseConv2D.h b/runtime/neurun/core/include/ir/operation/DepthwiseConv2D.h
deleted file mode 100644
index 1f0926fa8..000000000
--- a/runtime/neurun/core/include/ir/operation/DepthwiseConv2D.h
+++ /dev/null
@@ -1,69 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_DEPTHWISECONV2D_H__
-#define __NEURUN_IR_OPERATION_DEPTHWISECONV2D_H__
-
-#include <memory>
-
-#include "ir/Operation.h"
-#include "ir/InternalType.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class DepthwiseConv2D : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0,
-    KERNEL,
-    BIAS
-  };
-
-  struct Param
-  {
-    Stride stride;
-    Padding padding;
-    uint32_t multiplier;
-    Activation activation;
-  };
-
-public:
-  DepthwiseConv2D(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-                  const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::DepthwiseConv2D; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_DEPTHWISECONV2D_H__
diff --git a/runtime/neurun/core/include/ir/operation/Dequantize.h b/runtime/neurun/core/include/ir/operation/Dequantize.h
deleted file mode 100644
index dfca278cd..000000000
--- a/runtime/neurun/core/include/ir/operation/Dequantize.h
+++ /dev/null
@@ -1,49 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_DEQUANTIZE_H__
-#define __NEURUN_IR_OPERATION_DEQUANTIZE_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class Dequantize : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0
-  };
-
-public:
-  Dequantize(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::Dequantize; }
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_DEQUANTIZE_H__
diff --git a/runtime/neurun/core/include/ir/operation/Div.h b/runtime/neurun/core/include/ir/operation/Div.h
deleted file mode 100644
index d3e744472..000000000
--- a/runtime/neurun/core/include/ir/operation/Div.h
+++ /dev/null
@@ -1,62 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_DIV_H__
-#define __NEURUN_IR_OPERATION_DIV_H__
-
-#include "ir/Operation.h"
-#include "ir/InternalType.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class Div : public Operation
-{
-public:
-  enum Input
-  {
-    LHS = 0,
-    RHS
-  };
-
-  struct Param
-  {
-    Activation activation;
-  };
-
-public:
-  Div(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::Div; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_DIV_H__
diff --git a/runtime/neurun/core/include/ir/operation/EmbeddingLookup.h b/runtime/neurun/core/include/ir/operation/EmbeddingLookup.h
deleted file mode 100644
index 968b7b35a..000000000
--- a/runtime/neurun/core/include/ir/operation/EmbeddingLookup.h
+++ /dev/null
@@ -1,50 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_EMBEDDING_LOOKUP_H__
-#define __NEURUN_IR_OPERATION_EMBEDDING_LOOKUP_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class EmbeddingLookup : public Operation
-{
-public:
-  enum Input
-  {
-    LOOKUPS = 0,
-    VALUES = 1
-  };
-
-public:
-  EmbeddingLookup(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::EmbeddingLookup; }
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_EMBEDDING_LOOKUP_H__
diff --git a/runtime/neurun/core/include/ir/operation/Exp.h b/runtime/neurun/core/include/ir/operation/Exp.h
deleted file mode 100644
index 8e04f3f7f..000000000
--- a/runtime/neurun/core/include/ir/operation/Exp.h
+++ /dev/null
@@ -1,49 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_EXP_H__
-#define __NEURUN_IR_OPERATION_EXP_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class Exp : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0
-  };
-
-public:
-  Exp(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::Exp; }
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_EXP_H__
diff --git a/runtime/neurun/core/include/ir/operation/Floor.h b/runtime/neurun/core/include/ir/operation/Floor.h
deleted file mode 100644
index ca4cf9881..000000000
--- a/runtime/neurun/core/include/ir/operation/Floor.h
+++ /dev/null
@@ -1,51 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_FLOOR_H__
-#define __NEURUN_IR_OPERATION_FLOOR_H__
-
-#include <memory>
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class Floor : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0
-  };
-
-public:
-  Floor(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::Floor; }
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_FLOOR_H__
diff --git a/runtime/neurun/core/include/ir/operation/FullyConnected.h b/runtime/neurun/core/include/ir/operation/FullyConnected.h
deleted file mode 100644
index 1ffa1318d..000000000
--- a/runtime/neurun/core/include/ir/operation/FullyConnected.h
+++ /dev/null
@@ -1,66 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_FULLYCONNECTED_H__
-#define __NEURUN_IR_OPERATION_FULLYCONNECTED_H__
-
-#include <memory>
-
-#include "ir/Operation.h"
-#include "ir/InternalType.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class FullyConnected : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0,
-    WEIGHT,
-    BIAS
-  };
-
-  struct Param
-  {
-    Activation activation;
-  };
-
-public:
-  FullyConnected(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-                 const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::FullyConnected; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_FULLYCONNECTED_H__
diff --git a/runtime/neurun/core/include/ir/operation/Gather.h b/runtime/neurun/core/include/ir/operation/Gather.h
deleted file mode 100644
index 13540d413..000000000
--- a/runtime/neurun/core/include/ir/operation/Gather.h
+++ /dev/null
@@ -1,65 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_GATHER_H__
-#define __NEURUN_IR_OPERATION_GATHER_H__
-
-#include <memory>
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class Gather : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0,
-    INDICES,
-  };
-
-  struct Param
-  {
-    int32_t axis;
-    int32_t rank;
-  };
-
-public:
-  Gather(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-         const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::Gather; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_GATHER_H__
diff --git a/runtime/neurun/core/include/ir/operation/HashtableLookup.h b/runtime/neurun/core/include/ir/operation/HashtableLookup.h
deleted file mode 100644
index fb6c97607..000000000
--- a/runtime/neurun/core/include/ir/operation/HashtableLookup.h
+++ /dev/null
@@ -1,57 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_HASHTABLE_LOOKUP_H__
-#define __NEURUN_IR_OPERATION_HASHTABLE_LOOKUP_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class HashtableLookup : public Operation
-{
-public:
-  enum Input
-  {
-    LOOKUPS = 0,
-    KEYS = 1,
-    VALUES = 2
-  };
-
-  enum Output
-  {
-    OUTPUT = 0,
-    HITS = 1
-  };
-
-public:
-  HashtableLookup(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::HashtableLookup; }
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_HASHTABLE_LOOKUP_H__
diff --git a/runtime/neurun/core/include/ir/operation/InstanceNorm.h b/runtime/neurun/core/include/ir/operation/InstanceNorm.h
deleted file mode 100644
index cbd03ad1f..000000000
--- a/runtime/neurun/core/include/ir/operation/InstanceNorm.h
+++ /dev/null
@@ -1,65 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_INSTANCE_NORM_H__
-#define __NEURUN_IR_OPERATION_INSTANCE_NORM_H__
-
-#include "ir/Operation.h"
-#include "ir/InternalType.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class InstanceNorm : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0,
-    GAMMA,
-    BETA
-  };
-
-  struct Param
-  {
-    Activation activation;
-    float epsilon;
-  };
-
-public:
-  InstanceNorm(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-               const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::InstanceNorm; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_INSTANCE_NORM_H__
diff --git a/runtime/neurun/core/include/ir/operation/L2Normalization.h b/runtime/neurun/core/include/ir/operation/L2Normalization.h
deleted file mode 100644
index e2c1f4eee..000000000
--- a/runtime/neurun/core/include/ir/operation/L2Normalization.h
+++ /dev/null
@@ -1,62 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_L2_NORMALIZATION_H__
-#define __NEURUN_IR_OPERATION_L2_NORMALIZATION_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class L2Normalization : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0
-  };
-
-public:
-  struct Param
-  {
-    int32_t rank;
-  };
-
-public:
-  L2Normalization(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-                  const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::L2Normalization; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_L2_NORMALIZATION_H__
diff --git a/runtime/neurun/core/include/ir/operation/L2Pool2D.h b/runtime/neurun/core/include/ir/operation/L2Pool2D.h
deleted file mode 100644
index 6d4d72ee2..000000000
--- a/runtime/neurun/core/include/ir/operation/L2Pool2D.h
+++ /dev/null
@@ -1,68 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_L2_POOL_2D_H__
-#define __NEURUN_IR_OPERATION_L2_POOL_2D_H__
-
-#include <memory>
-
-#include "ir/Operation.h"
-#include "ir/InternalType.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class L2Pool2D : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0,
-  };
-
-  struct Param
-  {
-    Padding padding;
-    Stride stride;
-    uint32_t kw;
-    uint32_t kh;
-    Activation activation;
-  };
-
-public:
-  L2Pool2D(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-           const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::L2Pool2D; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_L2_POOL_2D_H__
diff --git a/runtime/neurun/core/include/ir/operation/LSTM.h b/runtime/neurun/core/include/ir/operation/LSTM.h
deleted file mode 100644
index 2ea09b1b7..000000000
--- a/runtime/neurun/core/include/ir/operation/LSTM.h
+++ /dev/null
@@ -1,89 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#ifndef __NEURUN_IR_OPERATION_LSTM_H__
-#define __NEURUN_IR_OPERATION_LSTM_H__
-
-#include "ir/InternalType.h"
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class LSTM : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0,
-    INPUT_TO_INPUT_WEIGHTS = 1,
-    INPUT_TO_FORGET_WEIGHTS = 2,
-    INPUT_TO_CELL_WEIGHTS = 3,
-    INPUT_TO_OUTPUT_WEIGHTS = 4,
-    RECURRENT_TO_INPUT_WEIGHTS = 5,
-    RECURRENT_TO_FORGET_WEIGHTS = 6,
-    RECURRENT_TO_CELL_WEIGHTS = 7,
-    RECURRENT_TO_OUTPUT_WEIGHTS = 8,
-    CELL_TO_INPUT_WEIGHTS = 9,
-    CELL_TO_FORGET_WEIGHTS = 10,
-    CELL_TO_OUTPUT_WEIGHTS = 11,
-    INPUT_GATE_BIAS = 12,
-    FORGET_GATE_BIAS = 13,
-    CELL_BIAS = 14,
-    OUTPUT_GATE_BIAS = 15,
-    PROJECTION_WEIGHTS = 16,
-    PROJECTION_BIAS = 17,
-    OUTPUT_STATE_IN = 18,
-    CELL_STATE_IN = 19,
-  };
-
-  enum Output
-  {
-    SCRATCH_BUFFER = 0,
-    OUTPUT_STATE_OUT = 1,
-    CELL_STATE_OUT = 2,
-    OUTPUT = 3
-  };
-
-  struct Param
-  {
-    Activation activation;
-    float cell_threshold;
-    float projection_threshold;
-  };
-
-public:
-  LSTM(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::LSTM; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_LSTM_H__
diff --git a/runtime/neurun/core/include/ir/operation/LocalResponseNormalization.h b/runtime/neurun/core/include/ir/operation/LocalResponseNormalization.h
deleted file mode 100644
index 3fbf2e4ae..000000000
--- a/runtime/neurun/core/include/ir/operation/LocalResponseNormalization.h
+++ /dev/null
@@ -1,66 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_LOCAL_RESPONSE_NORMALIZATION_H__
-#define __NEURUN_IR_OPERATION_LOCAL_RESPONSE_NORMALIZATION_H__
-
-#include <memory>
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class LocalResponseNormalization : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0
-  };
-
-  struct Param
-  {
-    int radius;
-    float bias;
-    float alpha;
-    float beta;
-  };
-
-public:
-  LocalResponseNormalization(const OperandIndexSequence &inputs,
-                             const OperandIndexSequence &outputs, const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::LocalResponseNormalization; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_LOCAL_RESPONSE_NORMALIZATION_H__
diff --git a/runtime/neurun/core/include/ir/operation/LogicalAnd.h b/runtime/neurun/core/include/ir/operation/LogicalAnd.h
deleted file mode 100644
index f7b03d62d..000000000
--- a/runtime/neurun/core/include/ir/operation/LogicalAnd.h
+++ /dev/null
@@ -1,50 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_LOGICAL_AND_H__
-#define __NEURUN_IR_OPERATION_LOGICAL_AND_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class LogicalAnd : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT0 = 0,
-    INPUT1 = 1,
-  };
-
-public:
-  LogicalAnd(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::LogicalAnd; }
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_LOGICAL_AND_H__
diff --git a/runtime/neurun/core/include/ir/operation/LogicalNot.h b/runtime/neurun/core/include/ir/operation/LogicalNot.h
deleted file mode 100644
index e689d57b2..000000000
--- a/runtime/neurun/core/include/ir/operation/LogicalNot.h
+++ /dev/null
@@ -1,49 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_LOGICAL_NOT_H__
-#define __NEURUN_IR_OPERATION_LOGICAL_NOT_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class LogicalNot : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0,
-  };
-
-public:
-  LogicalNot(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::LogicalNot; }
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_LOGICAL_NOT_H__
diff --git a/runtime/neurun/core/include/ir/operation/LogicalOr.h b/runtime/neurun/core/include/ir/operation/LogicalOr.h
deleted file mode 100644
index fda6b20d7..000000000
--- a/runtime/neurun/core/include/ir/operation/LogicalOr.h
+++ /dev/null
@@ -1,50 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_LOGICAL_OR_H__
-#define __NEURUN_IR_OPERATION_LOGICAL_OR_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class LogicalOr : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT0 = 0,
-    INPUT1 = 1,
-  };
-
-public:
-  LogicalOr(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::LogicalOr; }
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_LOGICAL_OR_H__
diff --git a/runtime/neurun/core/include/ir/operation/Logistic.h b/runtime/neurun/core/include/ir/operation/Logistic.h
deleted file mode 100644
index b23e7ef3f..000000000
--- a/runtime/neurun/core/include/ir/operation/Logistic.h
+++ /dev/null
@@ -1,49 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_LOGISTIC_H__
-#define __NEURUN_IR_OPERATION_LOGISTIC_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class Logistic : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0
-  };
-
-public:
-  Logistic(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::Logistic; }
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_LOGISTIC_H__
diff --git a/runtime/neurun/core/include/ir/operation/LowerInfo.h b/runtime/neurun/core/include/ir/operation/LowerInfo.h
deleted file mode 100644
index 856976a0c..000000000
--- a/runtime/neurun/core/include/ir/operation/LowerInfo.h
+++ /dev/null
@@ -1,54 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_LOWER_INFO_H__
-#define __NEURUN_IR_OPERATION_LOWER_INFO_H__
-
-#include <string>
-
-#include <ir/operand/PermuteFactor.h>
-
-namespace neurun
-{
-namespace backend
-{
-class Backend;
-} // namespace backend
-} // namespace neurun
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class LowerInfo
-{
-public:
-  LowerInfo(const backend::Backend *backend, Layout layout);
-  const backend::Backend *backend() const { return _permute_factor.backend(); }
-  Layout layout() const { return _permute_factor.layout(); }
-
-private:
-  operand::PermuteFactor _permute_factor;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_LOWER_INFO_H__
diff --git a/runtime/neurun/core/include/ir/operation/Max.h b/runtime/neurun/core/include/ir/operation/Max.h
deleted file mode 100644
index 1675f9f72..000000000
--- a/runtime/neurun/core/include/ir/operation/Max.h
+++ /dev/null
@@ -1,50 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_MAX_H__
-#define __NEURUN_IR_OPERATION_MAX_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class Max : public Operation
-{
-public:
-  enum Input
-  {
-    LHS = 0,
-    RHS
-  };
-
-public:
-  Max(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::Max; }
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_MAX_H__
diff --git a/runtime/neurun/core/include/ir/operation/MaxPool2D.h b/runtime/neurun/core/include/ir/operation/MaxPool2D.h
deleted file mode 100644
index c0f0939aa..000000000
--- a/runtime/neurun/core/include/ir/operation/MaxPool2D.h
+++ /dev/null
@@ -1,68 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_MAXPOOL2D_H__
-#define __NEURUN_IR_OPERATION_MAXPOOL2D_H__
-
-#include <memory>
-
-#include "ir/Operation.h"
-#include "ir/InternalType.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class MaxPool2D : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0
-  };
-
-  struct Param
-  {
-    uint32_t kh;
-    uint32_t kw;
-    Stride stride;
-    Padding padding;
-    Activation activation;
-  };
-
-public:
-  MaxPool2D(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-            const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::MaxPool2D; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_MAXPOOL2D_H__
diff --git a/runtime/neurun/core/include/ir/operation/Mean.h b/runtime/neurun/core/include/ir/operation/Mean.h
deleted file mode 100644
index cce8de377..000000000
--- a/runtime/neurun/core/include/ir/operation/Mean.h
+++ /dev/null
@@ -1,62 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_MEAN_H__
-#define __NEURUN_IR_OPERATION_MEAN_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class Mean : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT
-  };
-
-  struct Param
-  {
-    std::vector<int> axes;
-    bool keep_dims;
-    int32_t rank;
-  };
-
-public:
-  Mean(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::Mean; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_MEAN_H__
diff --git a/runtime/neurun/core/include/ir/operation/Min.h b/runtime/neurun/core/include/ir/operation/Min.h
deleted file mode 100644
index fac901f21..000000000
--- a/runtime/neurun/core/include/ir/operation/Min.h
+++ /dev/null
@@ -1,50 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_MIN_H__
-#define __NEURUN_IR_OPERATION_MIN_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class Min : public Operation
-{
-public:
-  enum Input
-  {
-    LHS = 0,
-    RHS
-  };
-
-public:
-  Min(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::Min; }
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_MIN_H__
diff --git a/runtime/neurun/core/include/ir/operation/Mul.h b/runtime/neurun/core/include/ir/operation/Mul.h
deleted file mode 100644
index f6cfe2afb..000000000
--- a/runtime/neurun/core/include/ir/operation/Mul.h
+++ /dev/null
@@ -1,62 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_MUL_H__
-#define __NEURUN_IR_OPERATION_MUL_H__
-
-#include "ir/Operation.h"
-#include "ir/InternalType.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class Mul : public Operation
-{
-public:
-  enum Input
-  {
-    LHS = 0,
-    RHS
-  };
-
-  struct Param
-  {
-    Activation activation;
-  };
-
-public:
-  Mul(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::Mul; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_MUL_H__
diff --git a/runtime/neurun/core/include/ir/operation/Neg.h b/runtime/neurun/core/include/ir/operation/Neg.h
deleted file mode 100644
index ec364f8ad..000000000
--- a/runtime/neurun/core/include/ir/operation/Neg.h
+++ /dev/null
@@ -1,49 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_NEG_H__
-#define __NEURUN_IR_OPERATION_NEG_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class Neg : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0
-  };
-
-public:
-  Neg(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::Neg; }
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_NEG_H__
diff --git a/runtime/neurun/core/include/ir/operation/OneHot.h b/runtime/neurun/core/include/ir/operation/OneHot.h
deleted file mode 100644
index 5fbc5d45f..000000000
--- a/runtime/neurun/core/include/ir/operation/OneHot.h
+++ /dev/null
@@ -1,60 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_ONEHOT_H__
-#define __NEURUN_IR_OPERATION_ONEHOT_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class OneHot : public Operation
-{
-public:
-  enum Input
-  {
-    INDICES = 0,
-    DEPTH = 1,
-    ON_VALUE = 2,
-    OFF_VALUE = 3,
-  };
-
-  struct Param
-  {
-    int axis;
-  };
-
-public:
-  OneHot(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-         const Param &param);
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::OneHot; }
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_ONEHOT_H__
diff --git a/runtime/neurun/core/include/ir/operation/PReLU.h b/runtime/neurun/core/include/ir/operation/PReLU.h
deleted file mode 100644
index 8c00c46fd..000000000
--- a/runtime/neurun/core/include/ir/operation/PReLU.h
+++ /dev/null
@@ -1,50 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_PRELU_H__
-#define __NEURUN_IR_OPERATION_PRELU_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class PReLU : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0,
-    ALPHA = 1
-  };
-
-public:
-  PReLU(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::PReLU; }
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_PRELU_H__
diff --git a/runtime/neurun/core/include/ir/operation/Pack.h b/runtime/neurun/core/include/ir/operation/Pack.h
deleted file mode 100644
index ccf73fe5c..000000000
--- a/runtime/neurun/core/include/ir/operation/Pack.h
+++ /dev/null
@@ -1,53 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#ifndef __NEURUN_IR_OPERATION_PACK_H__
-#define __NEURUN_IR_OPERATION_PACK_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-class Pack : public Operation
-{
-public:
-  struct Param
-  {
-    int32_t num;
-    int32_t axis;
-    int32_t rank;
-  };
-
-public:
-  Pack(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::Pack; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-#endif // __NEURUN_IR_OPERATION_PACK_H__
diff --git a/runtime/neurun/core/include/ir/operation/Pad.h b/runtime/neurun/core/include/ir/operation/Pad.h
deleted file mode 100644
index 8e8304fae..000000000
--- a/runtime/neurun/core/include/ir/operation/Pad.h
+++ /dev/null
@@ -1,63 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_PAD_H__
-#define __NEURUN_IR_OPERATION_PAD_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class Pad : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0,
-    PAD = 1,
-    // VALUE = 2 Not allow padding value operand yet
-  };
-
-public:
-  struct Param
-  {
-    int32_t rank;
-  };
-
-public:
-  Pad(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::Pad; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_PAD_H__
diff --git a/runtime/neurun/core/include/ir/operation/Permute.h b/runtime/neurun/core/include/ir/operation/Permute.h
deleted file mode 100644
index f91f9571b..000000000
--- a/runtime/neurun/core/include/ir/operation/Permute.h
+++ /dev/null
@@ -1,78 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_PERMUTE_H__
-#define __NEURUN_IR_OPERATION_PERMUTE_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace backend
-{
-class BackendContext;
-} // namespace backend
-} // namespace neurun
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class Permute : public Operation
-{
-public:
-  enum class Type
-  {
-    NHWC_TO_NCHW,
-    NCHW_TO_NHWC,
-    COPY
-  };
-
-  struct Param
-  {
-    const backend::BackendContext *input_backend_ctx;
-    const backend::BackendContext *output_backend_ctx;
-  };
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::Permute; }
-
-public:
-  Permute(const OperandIndex &input, const OperandIndex &output,
-          const backend::BackendContext *input_backend_ctx,
-          const backend::BackendContext *output_backend_ctx, Type type,
-          DataType data_type = DataType::FLOAT32);
-
-public:
-  const Param &param() const { return _param; }
-  DataType getDataType() const { return _dataType; }
-  Type getPermuteType() const { return _type; }
-
-private:
-  Param _param;
-  Type _type;
-  DataType _dataType;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_PERMUTE_H__
diff --git a/runtime/neurun/core/include/ir/operation/RNN.h b/runtime/neurun/core/include/ir/operation/RNN.h
deleted file mode 100644
index d812a6fc3..000000000
--- a/runtime/neurun/core/include/ir/operation/RNN.h
+++ /dev/null
@@ -1,70 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#ifndef __NEURUN_IR_OPERATION_RNN_H__
-#define __NEURUN_IR_OPERATION_RNN_H__
-
-#include "ir/InternalType.h"
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class RNN : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0,
-    WEIGHTS = 1,
-    RECURRENT_WEIGHTS = 2,
-    BIAS = 3,
-    HIDDEN_STATE_IN = 4
-  };
-
-  enum Output
-  {
-    OUTPUT = 0,
-    HIDDEN_STATE_OUT = 1
-  };
-
-  struct Param
-  {
-    Activation activation;
-  };
-
-public:
-  RNN(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::RNN; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_RNN_H__
diff --git a/runtime/neurun/core/include/ir/operation/RSQRT.h b/runtime/neurun/core/include/ir/operation/RSQRT.h
deleted file mode 100644
index 33648555a..000000000
--- a/runtime/neurun/core/include/ir/operation/RSQRT.h
+++ /dev/null
@@ -1,49 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_RSQRT_H__
-#define __NEURUN_IR_OPERATION_RSQRT_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class RSQRT : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0
-  };
-
-public:
-  RSQRT(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::RSQRT; }
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_RSQRT_H__
diff --git a/runtime/neurun/core/include/ir/operation/ReLU.h b/runtime/neurun/core/include/ir/operation/ReLU.h
deleted file mode 100644
index b6c7fdf01..000000000
--- a/runtime/neurun/core/include/ir/operation/ReLU.h
+++ /dev/null
@@ -1,49 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_RELU_H__
-#define __NEURUN_IR_OPERATION_RELU_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class ReLU : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0
-  };
-
-public:
-  ReLU(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::ReLU; }
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_RELU_H__
diff --git a/runtime/neurun/core/include/ir/operation/ReLU1.h b/runtime/neurun/core/include/ir/operation/ReLU1.h
deleted file mode 100644
index ac45fda05..000000000
--- a/runtime/neurun/core/include/ir/operation/ReLU1.h
+++ /dev/null
@@ -1,49 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_ReLU1_H__
-#define __NEURUN_IR_OPERATION_ReLU1_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class ReLU1 : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0
-  };
-
-public:
-  ReLU1(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::ReLU1; }
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_ReLU1_H__
diff --git a/runtime/neurun/core/include/ir/operation/ReLU6.h b/runtime/neurun/core/include/ir/operation/ReLU6.h
deleted file mode 100644
index 4d98dad55..000000000
--- a/runtime/neurun/core/include/ir/operation/ReLU6.h
+++ /dev/null
@@ -1,49 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_ReLU6_H__
-#define __NEURUN_IR_OPERATION_ReLU6_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class ReLU6 : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0
-  };
-
-public:
-  ReLU6(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::ReLU6; }
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_ReLU6_H__
diff --git a/runtime/neurun/core/include/ir/operation/ReduceMax.h b/runtime/neurun/core/include/ir/operation/ReduceMax.h
deleted file mode 100644
index da4d7c4cc..000000000
--- a/runtime/neurun/core/include/ir/operation/ReduceMax.h
+++ /dev/null
@@ -1,65 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_REDUCEMAX_H__
-#define __NEURUN_IR_OPERATION_REDUCEMAX_H__
-
-#include <memory>
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class ReduceMax : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0
-  };
-
-  struct Param
-  {
-    std::vector<int> axes;
-    bool keep_dims;
-    int32_t rank;
-  };
-
-public:
-  ReduceMax(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-            const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::ReduceMax; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_REDUCEMAX_H__
diff --git a/runtime/neurun/core/include/ir/operation/ReduceMin.h b/runtime/neurun/core/include/ir/operation/ReduceMin.h
deleted file mode 100644
index f79fdeaea..000000000
--- a/runtime/neurun/core/include/ir/operation/ReduceMin.h
+++ /dev/null
@@ -1,65 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_REDUCEMIN_H__
-#define __NEURUN_IR_OPERATION_REDUCEMIN_H__
-
-#include <memory>
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class ReduceMin : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0
-  };
-
-  struct Param
-  {
-    std::vector<int> axes;
-    bool keep_dims;
-    int32_t rank;
-  };
-
-public:
-  ReduceMin(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-            const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::ReduceMin; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_REDUCEMIN_H__
diff --git a/runtime/neurun/core/include/ir/operation/ReduceSum.h b/runtime/neurun/core/include/ir/operation/ReduceSum.h
deleted file mode 100644
index b5ab8ee75..000000000
--- a/runtime/neurun/core/include/ir/operation/ReduceSum.h
+++ /dev/null
@@ -1,63 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_REDUCE_SUM_H__
-#define __NEURUN_IR_OPERATION_REDUCE_SUM_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class ReduceSum : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0
-  };
-
-  struct Param
-  {
-    std::vector<int> axes;
-    bool keep_dims;
-    int32_t rank;
-  };
-
-public:
-  ReduceSum(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-            const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::ReduceSum; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_REDUCE_SUM_H__
diff --git a/runtime/neurun/core/include/ir/operation/Reshape.h b/runtime/neurun/core/include/ir/operation/Reshape.h
deleted file mode 100644
index e476d7fe1..000000000
--- a/runtime/neurun/core/include/ir/operation/Reshape.h
+++ /dev/null
@@ -1,51 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_RESHAPE_H__
-#define __NEURUN_IR_OPERATION_RESHAPE_H__
-
-#include <memory>
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class Reshape : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0
-  };
-
-public:
-  Reshape(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::Reshape; }
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_RESHAPE_H__
diff --git a/runtime/neurun/core/include/ir/operation/ResizeBilinear.h b/runtime/neurun/core/include/ir/operation/ResizeBilinear.h
deleted file mode 100644
index d937da00c..000000000
--- a/runtime/neurun/core/include/ir/operation/ResizeBilinear.h
+++ /dev/null
@@ -1,64 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_RESIZE_BILINEAR_H__
-#define __NEURUN_IR_OPERATION_RESIZE_BILINEAR_H__
-
-#include <memory>
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class ResizeBilinear : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0
-  };
-
-  struct Param
-  {
-    int32_t height_out;
-    int32_t width_out;
-  };
-
-public:
-  ResizeBilinear(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-                 const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::ResizeBilinear; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_RESIZE_BILINEAR_H__
diff --git a/runtime/neurun/core/include/ir/operation/SQRT.h b/runtime/neurun/core/include/ir/operation/SQRT.h
deleted file mode 100644
index 5e21315b4..000000000
--- a/runtime/neurun/core/include/ir/operation/SQRT.h
+++ /dev/null
@@ -1,49 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_SQRT_H__
-#define __NEURUN_IR_OPERATION_SQRT_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class SQRT : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0
-  };
-
-public:
-  SQRT(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::SQRT; }
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_SQRT_H__
diff --git a/runtime/neurun/core/include/ir/operation/Slice.h b/runtime/neurun/core/include/ir/operation/Slice.h
deleted file mode 100644
index 4b79f42a6..000000000
--- a/runtime/neurun/core/include/ir/operation/Slice.h
+++ /dev/null
@@ -1,64 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_SLICE_H__
-#define __NEURUN_IR_OPERATION_SLICE_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class Slice : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0,
-    BEGINS = 1,
-    SIZES = 2,
-  };
-
-public:
-  struct Param
-  {
-    int32_t rank;
-  };
-
-public:
-  Slice(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-        const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::Slice; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_SLICE_H__
diff --git a/runtime/neurun/core/include/ir/operation/Softmax.h b/runtime/neurun/core/include/ir/operation/Softmax.h
deleted file mode 100644
index a3e896fed..000000000
--- a/runtime/neurun/core/include/ir/operation/Softmax.h
+++ /dev/null
@@ -1,63 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_SOFTMAX_H__
-#define __NEURUN_IR_OPERATION_SOFTMAX_H__
-
-#include <memory>
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class Softmax : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0
-  };
-
-  struct Param
-  {
-    float beta;
-  };
-
-public:
-  Softmax(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-          const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::Softmax; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_SOFTMAX_H__
diff --git a/runtime/neurun/core/include/ir/operation/SpaceToBatchND.h b/runtime/neurun/core/include/ir/operation/SpaceToBatchND.h
deleted file mode 100644
index 4ca0978b0..000000000
--- a/runtime/neurun/core/include/ir/operation/SpaceToBatchND.h
+++ /dev/null
@@ -1,53 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_SPACE_TO_BATCH_ND_H__
-#define __NEURUN_IR_OPERATION_SPACE_TO_BATCH_ND_H__
-
-#include <memory>
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class SpaceToBatchND : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0,
-    BLOCK_SIZE = 1,
-    PADDINGS = 2
-  };
-
-public:
-  SpaceToBatchND(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::SpaceToBatchND; }
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_SPACE_TO_BATCH_ND_H__
diff --git a/runtime/neurun/core/include/ir/operation/SpaceToDepth.h b/runtime/neurun/core/include/ir/operation/SpaceToDepth.h
deleted file mode 100644
index 9e77bdae0..000000000
--- a/runtime/neurun/core/include/ir/operation/SpaceToDepth.h
+++ /dev/null
@@ -1,63 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_SPACE_TO_DEPTH_H__
-#define __NEURUN_IR_OPERATION_SPACE_TO_DEPTH_H__
-
-#include <memory>
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class SpaceToDepth : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0
-  };
-
-  struct Param
-  {
-    std::int32_t block_size;
-  };
-
-public:
-  SpaceToDepth(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-               const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::SpaceToDepth; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_SPACE_TO_DEPTH_H__
diff --git a/runtime/neurun/core/include/ir/operation/Split.h b/runtime/neurun/core/include/ir/operation/Split.h
deleted file mode 100644
index 7a2749e84..000000000
--- a/runtime/neurun/core/include/ir/operation/Split.h
+++ /dev/null
@@ -1,59 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#ifndef __NEURUN_IR_OPERATION_SPLIT_H__
-#define __NEURUN_IR_OPERATION_SPLIT_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-class Split : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0
-  };
-
-  struct Param
-  {
-    int axis;
-    int num_splits;
-    int rank;
-  };
-
-public:
-  Split(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-        const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::Split; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-#endif // __NEURUN_IR_OPERATION_SPLIT_H__
diff --git a/runtime/neurun/core/include/ir/operation/SquaredDifference.h b/runtime/neurun/core/include/ir/operation/SquaredDifference.h
deleted file mode 100644
index 46df419f5..000000000
--- a/runtime/neurun/core/include/ir/operation/SquaredDifference.h
+++ /dev/null
@@ -1,50 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_SQUARED_DIFFERENCE_H__
-#define __NEURUN_IR_OPERATION_SQUARED_DIFFERENCE_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class SquaredDifference : public Operation
-{
-public:
-  enum Input
-  {
-    LHS = 0,
-    RHS
-  };
-
-public:
-  SquaredDifference(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::SquaredDifference; }
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_SQUARED_DIFFERENCE_H__
diff --git a/runtime/neurun/core/include/ir/operation/Squeeze.h b/runtime/neurun/core/include/ir/operation/Squeeze.h
deleted file mode 100644
index d27b315b5..000000000
--- a/runtime/neurun/core/include/ir/operation/Squeeze.h
+++ /dev/null
@@ -1,62 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_SQUEEZE_H__
-#define __NEURUN_IR_OPERATION_SQUEEZE_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class Squeeze : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0
-  };
-
-  struct Param
-  {
-    // Please see tensorflow/lite/c/builtin_op_data.h and squeeze.cc.
-    // tensorflow lite supports only for ndim <= 8.
-    int dims[8];
-    int ndim;
-  };
-
-public:
-  Squeeze(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-          const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::Squeeze; }
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_SQUEEZE_H__
diff --git a/runtime/neurun/core/include/ir/operation/StridedSlice.h b/runtime/neurun/core/include/ir/operation/StridedSlice.h
deleted file mode 100644
index 868bda72c..000000000
--- a/runtime/neurun/core/include/ir/operation/StridedSlice.h
+++ /dev/null
@@ -1,69 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_STRIDED_SLICE_H__
-#define __NEURUN_IR_OPERATION_STRIDED_SLICE_H__
-
-#include <memory>
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class StridedSlice : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0,
-    STARTS = 1,
-    ENDS = 2,
-    STRIDES = 3
-  };
-
-  struct Param
-  {
-    std::int32_t begin_mask;
-    std::int32_t end_mask;
-    std::int32_t shrink_axis_mask;
-    int32_t rank;
-  };
-
-public:
-  StridedSlice(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-               const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::StridedSlice; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_STRIDED_SLICE_H__
diff --git a/runtime/neurun/core/include/ir/operation/Sub.h b/runtime/neurun/core/include/ir/operation/Sub.h
deleted file mode 100644
index e5850af8c..000000000
--- a/runtime/neurun/core/include/ir/operation/Sub.h
+++ /dev/null
@@ -1,62 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_SUB_H__
-#define __NEURUN_IR_OPERATION_SUB_H__
-
-#include "ir/Operation.h"
-#include "ir/InternalType.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class Sub : public Operation
-{
-public:
-  enum Input
-  {
-    LHS = 0,
-    RHS
-  };
-
-  struct Param
-  {
-    Activation activation;
-  };
-
-public:
-  Sub(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::Sub; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_SUB_H__
diff --git a/runtime/neurun/core/include/ir/operation/Tanh.h b/runtime/neurun/core/include/ir/operation/Tanh.h
deleted file mode 100644
index 814ceec5a..000000000
--- a/runtime/neurun/core/include/ir/operation/Tanh.h
+++ /dev/null
@@ -1,49 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_TANH_H__
-#define __NEURUN_IR_OPERATION_TANH_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class Tanh : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0
-  };
-
-public:
-  Tanh(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::Tanh; }
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_TANH_H__
diff --git a/runtime/neurun/core/include/ir/operation/TopKV2.h b/runtime/neurun/core/include/ir/operation/TopKV2.h
deleted file mode 100644
index a6971e843..000000000
--- a/runtime/neurun/core/include/ir/operation/TopKV2.h
+++ /dev/null
@@ -1,69 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_TOPK_V2_H__
-#define __NEURUN_IR_OPERATION_TOPK_V2_H__
-
-#include <memory>
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class TopKV2 : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT
-  };
-
-  enum Output
-  {
-    OUTPUT_VALUES = 0,
-    OUTPUT_INDICES,
-  };
-
-  struct Param
-  {
-    std::int32_t k;
-  };
-
-public:
-  TopKV2(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-         const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::TopKV2; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_TOPK_V2_H__
diff --git a/runtime/neurun/core/include/ir/operation/Transpose.h b/runtime/neurun/core/include/ir/operation/Transpose.h
deleted file mode 100644
index b1e08a506..000000000
--- a/runtime/neurun/core/include/ir/operation/Transpose.h
+++ /dev/null
@@ -1,64 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_TRANSPOSE_H__
-#define __NEURUN_IR_OPERATION_TRANSPOSE_H__
-
-#include "ir/Operation.h"
-
-#include <utility>
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class Transpose : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0, // for an n-D tensor, specifying the tensor to be transposed.
-  };
-
-  struct Param
-  {
-    std::vector<int> perm;
-    int32_t rank;
-  };
-
-public:
-  Transpose(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-            const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::Transpose; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_TRANSPOSE_H__
diff --git a/runtime/neurun/core/include/ir/operation/TransposeConv.h b/runtime/neurun/core/include/ir/operation/TransposeConv.h
deleted file mode 100644
index a561db4e4..000000000
--- a/runtime/neurun/core/include/ir/operation/TransposeConv.h
+++ /dev/null
@@ -1,67 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_OPERATION_TRANSPOSE_CONV_H__
-#define __NEURUN_IR_OPERATION_TRANSPOSE_CONV_H__
-
-#include <memory>
-
-#include "ir/Operation.h"
-#include "ir/InternalType.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-class TransposeConv : public Operation
-{
-public:
-  enum Input
-  {
-    OUTPUT_SHAPE = 0,
-    KERNEL,
-    INPUT
-  };
-
-  struct Param
-  {
-    Padding padding;
-    Stride stride;
-  };
-
-public:
-  TransposeConv(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-                const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::TransposeConv; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_OPERATION_TRANSPOSE_CONV_H__
diff --git a/runtime/neurun/core/include/ir/operation/Unpack.h b/runtime/neurun/core/include/ir/operation/Unpack.h
deleted file mode 100644
index fa698d3af..000000000
--- a/runtime/neurun/core/include/ir/operation/Unpack.h
+++ /dev/null
@@ -1,59 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#ifndef __NEURUN_IR_OPERATION_UNPACK_H__
-#define __NEURUN_IR_OPERATION_UNPACK_H__
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-class Unpack : public Operation
-{
-public:
-  enum Input
-  {
-    INPUT = 0
-  };
-
-  struct Param
-  {
-    int32_t num;
-    int32_t axis;
-    int32_t rank;
-  };
-
-public:
-  Unpack(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-         const Param &param);
-
-public:
-  void accept(OperationVisitor &v) const override;
-  OpCode opcode() const final { return OpCode::Unpack; }
-
-public:
-  const Param &param() const { return _param; }
-
-private:
-  Param _param;
-};
-} // namespace operation
-} // namespace ir
-} // namespace neurun
-#endif // __NEURUN_IR_OPERATION_UNPACK_H__
diff --git a/runtime/neurun/core/include/util/Config.lst b/runtime/neurun/core/include/util/Config.lst
deleted file mode 100644
index 046a0c4a8..000000000
--- a/runtime/neurun/core/include/util/Config.lst
+++ /dev/null
@@ -1,43 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef CONFIG
-#error  Define CONFIG before including this file
-#endif
-
-//     Name                    | Type         | Default
-CONFIG(GRAPH_DOT_DUMP          , int          , "0")
-CONFIG(BACKENDS                , std::string  , "acl_cl;acl_neon;cpu;srcn")
-CONFIG(OP_BACKEND_ALLOPS       , std::string  , "")
-CONFIG(OP_BACKEND_MAP          , std::string  , "")
-CONFIG(DISABLE_COMPILE         , bool         , "0")
-CONFIG(NEURUN_LOG_ENABLE       , bool         , "0")
-CONFIG(CPU_MEMORY_PLANNER      , std::string  , "WIC")
-CONFIG(EXECUTOR                , std::string  , "Linear")
-CONFIG(ACL_LAYOUT              , std::string  , "none")
-CONFIG(NCNN_LAYOUT             , std::string  , "NCHW")
-CONFIG(PROFILING_MODE          , bool         , "0")
-CONFIG(USE_SCHEDULER           , bool         , "0")
-CONFIG(SUBG_MAX_NODE           , int          , "0")
-CONFIG(TRACE_FILEPATH          , std::string  , "")
-
-// Auto-generate all operations
-
-#define OP(InternalName) \
-    CONFIG(OP_BACKEND_ ## InternalName, std::string, "")
-#include "ir/Operations.lst"
-#undef OP
-
diff --git a/runtime/neurun/core/include/util/ConfigSource.h b/runtime/neurun/core/include/util/ConfigSource.h
deleted file mode 100644
index b1fa9a87d..000000000
--- a/runtime/neurun/core/include/util/ConfigSource.h
+++ /dev/null
@@ -1,55 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_UTIL_CONFIG_SOURCE_H__
-#define __NEURUN_UTIL_CONFIG_SOURCE_H__
-
-#include <memory>
-
-#include "IConfigSource.h"
-
-namespace neurun
-{
-namespace util
-{
-
-void config_source(std::unique_ptr<IConfigSource> &&source);
-
-bool getConfigBool(const std::string &key);
-int getConfigInt(const std::string &key);
-std::string getConfigString(const std::string &key);
-
-} // namespace util
-} // namespace neurun
-
-namespace neurun
-{
-namespace util
-{
-namespace config
-{
-
-#define CONFIG(Name, Type, Default) extern const char *Name;
-
-#include "Config.lst"
-
-#undef CONFIG
-
-} // namespace config
-} // namespace util
-} // namespace neurun
-
-#endif // __NEURUN_UTIL_CONFIG_SOURCE_H__
diff --git a/runtime/neurun/core/include/util/Coordinates.h b/runtime/neurun/core/include/util/Coordinates.h
deleted file mode 100644
index 67947138f..000000000
--- a/runtime/neurun/core/include/util/Coordinates.h
+++ /dev/null
@@ -1,103 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_UTIL_COORDINATES_H__
-#define __NEURUN_UTIL_COORDINATES_H__
-
-#include <cassert>
-#include <stdint.h>
-#include <vector>
-
-namespace neurun
-{
-namespace util
-{
-
-/**
- * @brief Class to represent position(offset) of tensor.\n
- *        Assume that the front is higher dimensional.
- *        i.g. N: 0, C: 1, H: 2, W: 3 for NCHW layout
- */
-class Coordinates final
-{
-public:
-  static constexpr size_t num_max_dimensions = 4;
-
-public:
-  /**
-   * @brief     Construct a new Coordinates object
-   * @param[in] init The initialzer_list with coordinates
-   * @return
-   */
-  Coordinates(std::initializer_list<int32_t> init) : _coordinates{init}
-  {
-    assert(init.size() <= num_max_dimensions);
-  }
-
-public:
-  /**
-   * @brief  Set the coordinate of one of the coordinates.
-   *
-   * @param[in] dimension  Dimension for which the coordinate is set.
-   * @param[in] Coordinate Coordinate to be set for the dimension.
-   */
-  void set(size_t dimension, int32_t coordinate)
-  {
-    assert(dimension < num_max_dimensions);
-    if (dimension >= _coordinates.size())
-    {
-      _coordinates.resize(dimension + 1, 0);
-    }
-    _coordinates[dimension] = coordinate;
-  }
-
-public:
-  /**
-   * @brief Return size of coordinates
-   *
-   * @return size of coordinates
-   */
-  size_t size() const { return _coordinates.size(); }
-
-public:
-  int32_t operator[](size_t dimension) const
-  {
-    assert(dimension < _coordinates.size());
-    return _coordinates[dimension];
-  }
-
-public:
-  /**
-   * @brief begin() of const_iterator for this class
-   *
-   * @return The first iterator of the coordinates
-   */
-  std::vector<int32_t>::const_iterator begin() const { return _coordinates.begin(); }
-  /**
-   * @brief end() of const_iterator for this class
-   *
-   * @return The last iterator of the coordinates
-   */
-  std::vector<int32_t>::const_iterator end() const { return _coordinates.end(); }
-
-private:
-  std::vector<int32_t> _coordinates;
-};
-
-} // namespace util
-} // namespace neurun
-
-#endif // __NEURUN_UTIL_COORDINATES_H__
diff --git a/runtime/neurun/core/include/util/EnvConfigSource.h b/runtime/neurun/core/include/util/EnvConfigSource.h
deleted file mode 100644
index 77be15c4e..000000000
--- a/runtime/neurun/core/include/util/EnvConfigSource.h
+++ /dev/null
@@ -1,41 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_UTIL_ENV_CONFIG_SOURCE_H__
-#define __NEURUN_UTIL_ENV_CONFIG_SOURCE_H__
-
-#include <unordered_map>
-
-#include "util/GeneralConfigSource.h"
-
-namespace neurun
-{
-namespace util
-{
-
-class EnvConfigSource final : public GeneralConfigSource
-{
-public:
-  std::string get(const std::string &key) const override;
-
-private:
-  std::unordered_map<std::string, std::string> _default_attributes;
-};
-
-} // namespace util
-} // namespace neurun
-
-#endif // __NEURUN_UTIL_ENV_CONFIG_SOURCE_H__
diff --git a/runtime/neurun/core/include/util/EventCollectorGlobal.h b/runtime/neurun/core/include/util/EventCollectorGlobal.h
deleted file mode 100644
index 15e40844e..000000000
--- a/runtime/neurun/core/include/util/EventCollectorGlobal.h
+++ /dev/null
@@ -1,155 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_UTIL_EVENT_COLLECTOR_GLOBAL_H__
-#define __NEURUN_UTIL_EVENT_COLLECTOR_GLOBAL_H__
-
-#include "misc/EventRecorder.h"
-#include "misc/EventCollector.h"
-
-namespace neurun
-{
-namespace util
-{
-
-/**
- * @brief Singleton class for event collection from anywhere in code
- *
- */
-class EventCollectorGlobal
-{
-public:
-  /**
-   * @brief Get the singleton object of this class
-   *
-   * @return EventCollectorGlobal& Singleton object
-   */
-  static EventCollectorGlobal &get();
-
-public:
-  /**
-   * @brief Getter for event collector object
-   *
-   * @return EventCollector& Collector object
-   */
-  EventCollector &collector() { return _collector; }
-
-private:
-  EventCollectorGlobal();
-  ~EventCollectorGlobal();
-
-private:
-  EventRecorder _recorder;
-  EventCollector _collector;
-};
-
-/**
- * @brief Helper class for emitting duration event which is handled automatically with ctor/dtor
- *
- */
-class EventDurationBlock
-{
-public:
-  /**
-   * @brief Raise a duration event with type of BEGIN
-   *
-   * @param tag A label for the duration event
-   */
-  EventDurationBlock(const std::string &tag);
-  /**
-   * @brief Raise a duration event with type of END
-   *
-   */
-  ~EventDurationBlock();
-
-private:
-  std::string _tag;
-};
-
-/**
- * @brief Helper class for emitting duration event which is handled manually
- *
- *        Usage:
- *        {
- *          ...
- *          EventDurationManual duration("some tag");
- *          duration.begin();
- *          ...
- *          ... // Code for duration
- *          ...
- *          duration.end();
- *        }
- *
- */
-class EventDurationManual
-{
-public:
-  /**
-   * @brief Construct a new Event Duration Manual object
-   *
-   * @param tag A label for the duration object
-   */
-  EventDurationManual(const std::string &tag);
-  /**
-   * @brief Destroy the Event Duration Manual object
-   *
-   */
-  ~EventDurationManual();
-
-  /**
-   * @brief Raise a duration event with type of BEGIN
-   *
-   */
-  void begin();
-  /**
-   * @brief Raise a duration event with type of END
-   *
-   */
-  void end();
-
-private:
-  std::string _tag;
-  bool _pair;
-};
-
-} // namespace util
-} // namespace neurun
-
-/**
- * Helper Macro Definitions
- *
- * HOW TO USE
- *
- * void f(args)
- * {
- *   EVENT_DURATION_FUNCTION();
- *   ...
- *   if(cond)
- *   {
- *     EVENT_DURATION_REGION("if branch");
- *     ...
- *   }
- *   ...
- * }
- */
-
-#define EVENT_DURATION_FUNCTION() \
-  ::neurun::util::EventDurationBlock __event_duration__##__LINE__ { __FUNCTION__ }
-
-#define EVENT_DURATION_REGION(tag) \
-  ::neurun::util::EventDurationBlock __event_duration__##__LINE__ { tag }
-
-#endif // __NEURUN_UTIL_EVENT_COLLECTOR_GLOBAL_H__
diff --git a/runtime/neurun/core/include/util/GeneralConfigSource.h b/runtime/neurun/core/include/util/GeneralConfigSource.h
deleted file mode 100644
index 04e3332b3..000000000
--- a/runtime/neurun/core/include/util/GeneralConfigSource.h
+++ /dev/null
@@ -1,44 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_UTIL_GLOBAL_CONFIG_SOURCE_H__
-#define __NEURUN_UTIL_GLOBAL_CONFIG_SOURCE_H__
-
-#include <unordered_map>
-
-#include "util/IConfigSource.h"
-
-namespace neurun
-{
-namespace util
-{
-
-class GeneralConfigSource : public IConfigSource
-{
-public:
-  GeneralConfigSource() = default;
-
-  std::string get(const std::string &key) const override;
-  void set(const std::string &key, const std::string &val);
-
-private:
-  std::unordered_map<std::string, std::string> _map;
-};
-
-} // namespace util
-} // namespace neurun
-
-#endif // __NEURUN_UTIL_GLOBAL_CONFIG_SOURCE_H__
diff --git a/runtime/neurun/core/include/util/IConfigSource.h b/runtime/neurun/core/include/util/IConfigSource.h
deleted file mode 100644
index a52d87097..000000000
--- a/runtime/neurun/core/include/util/IConfigSource.h
+++ /dev/null
@@ -1,46 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_UTIL_I_CONFIG_SOURCE_H__
-#define __NEURUN_UTIL_I_CONFIG_SOURCE_H__
-
-#include <string>
-
-namespace neurun
-{
-namespace util
-{
-
-struct IConfigSource
-{
-  /**
-   * @brief Destroy the IConfigSource object
-   */
-  virtual ~IConfigSource() = default;
-
-  /**
-   * @brief get the value for the matching key
-   *
-   * @param key string key to search
-   * @return string value associated with the key
-   */
-  virtual std::string get(const std::string &key) const = 0;
-};
-
-} // namespace util
-} // namespace neurun
-
-#endif // __NEURUN_UTIL_I_CONFIG_SOURCE_H__
diff --git a/runtime/neurun/core/include/util/ITimer.h b/runtime/neurun/core/include/util/ITimer.h
deleted file mode 100644
index 79ecdd0ca..000000000
--- a/runtime/neurun/core/include/util/ITimer.h
+++ /dev/null
@@ -1,59 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_UTIL_ITIMER_H__
-#define __NEURUN_UTIL_ITIMER_H__
-
-#include <chrono>
-
-namespace neurun
-{
-namespace util
-{
-
-class ITimer
-{
-public:
-  virtual void handleBegin() = 0;
-  virtual void handleEnd() = 0;
-  int getTime() { return _timer_res; };
-
-  virtual ~ITimer() = default;
-
-protected:
-  int _timer_res{0};
-};
-
-class CPUTimer : public ITimer
-{
-public:
-  void handleBegin() override { _start_time = std::chrono::steady_clock::now(); };
-
-  void handleEnd() override
-  {
-    const auto end_time = std::chrono::steady_clock::now();
-    _timer_res =
-        std::chrono::duration_cast<std::chrono::microseconds>(end_time - _start_time).count();
-  };
-
-private:
-  std::chrono::steady_clock::time_point _start_time; // in microseconds
-};
-
-} // namespace util
-} // namespace neurun
-
-#endif // __NEURUN_UTIL_ITIMER_H__
diff --git a/runtime/neurun/core/include/util/Index.h b/runtime/neurun/core/include/util/Index.h
deleted file mode 100644
index bd8eeb38c..000000000
--- a/runtime/neurun/core/include/util/Index.h
+++ /dev/null
@@ -1,154 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_UTIL_INDEX_H__
-#define __NEURUN_UTIL_INDEX_H__
-
-#include <functional>
-#include <limits>
-#include <stdint.h>
-
-namespace neurun
-{
-namespace util
-{
-
-/**
- * @brief A wrapper class for unsigned integral Index
- *        NOTE : Max value of the underlying type is used as the invalid value
- *
- * @tparam T Underlying type. Must be unsigned integral type otherwise its behavior is undefined.
- * @tparam DummyTag Dummy type to distinguish types with a same underlying type. Using an opaque
- * type is recommended.
- */
-template <typename T, typename DummyTag> class Index
-{
-private:
-  static const T UNDEFINED = std::numeric_limits<T>::max();
-
-public:
-  /**
-   * @brief Construct a new Index object
-   */
-  explicit Index(void) : _index{UNDEFINED} {}
-  /**
-   * @brief Construct a new Index object with a value in the underlying type
-   *
-   * @param o Value in the underlying type
-   */
-  explicit Index(const T o) : _index{o} {}
-  /**
-   * @brief Copy Constructor
-   *
-   * @param o Object to be copied
-   */
-  Index(const Index &o) = default;
-
-  /**
-   * @brief Assign a value in the underlying time
-   *
-   * @param o Value in the underlying type
-   * @return Index& Reference of this pointer
-   */
-  Index &operator=(const T o)
-  {
-    _index = o;
-    return *this;
-  }
-
-  /**
-   * @brief Copy assignment operator
-   *
-   * @param o Object to be copied
-   * @return Index& Reference of this pointer
-   */
-  Index &operator=(const Index &o) = default;
-
-  /**
-   * @brief Equality operator
-   *
-   * @param o The other value in the underlying type to compare
-   * @return true if underlying value is the same, false otherwise
-   */
-  bool operator==(T o) const { return _index == o; }
-  /**
-   * @brief Equality operator
-   *
-   * @param o The other object to compare
-   * @return true if underlying value is the same, false otherwise
-   */
-  bool operator==(const Index &o) const { return _index == o._index; }
-  /**
-   * @brief Inquality operator
-   *
-   * @param o The other value in the underlying type to compare
-   * @return true if underlying value is different, false otherwise
-   */
-  bool operator!=(T o) const { return !(*this == o); }
-  /**
-   * @brief Inquality operator
-   *
-   * @param o The other object to compare
-   * @return true if underlying value is different, false otherwise
-   */
-  bool operator!=(const Index &o) const { return !(*this == o); }
-
-  /**
-   * @brief Post increment operator
-   *
-   * @return Index Index before increment
-   */
-  Index operator++(int)
-  {
-    Index temp = *this;
-    _index++;
-    return temp;
-  }
-
-  /**
-   * @brief Check whether the value is valid or not
-   *
-   * @return true if valid, false otherwise
-   */
-  bool valid() const { return _index != UNDEFINED; }
-  /**
-   * @brief Return underlying value
-   *
-   * @return T Underlying value
-   */
-  T value() const { return _index; }
-
-private:
-  T _index;
-};
-
-} // namespace util
-} // namespace neurun
-
-namespace std
-{
-
-template <typename T, typename Tag> struct hash<::neurun::util::Index<T, Tag>>
-{
-  size_t operator()(const ::neurun::util::Index<T, Tag> &index) const noexcept
-  {
-    return hash<T>()(index.value());
-  }
-};
-
-} // namespace std
-
-#endif // __NEURUN_UTIL_INDEX_H__
diff --git a/runtime/neurun/core/include/util/ObjectManager.h b/runtime/neurun/core/include/util/ObjectManager.h
deleted file mode 100644
index fd2c3f295..000000000
--- a/runtime/neurun/core/include/util/ObjectManager.h
+++ /dev/null
@@ -1,144 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_UTIL_OBJECT_MANAGER_H__
-#define __NEURUN_UTIL_OBJECT_MANAGER_H__
-
-#include <unordered_map>
-#include <memory>
-
-namespace neurun
-{
-namespace util
-{
-
-/**
- * @brief Class that owns objects and maps them with indices as a handle for them
- *
- */
-template <typename Index, typename Object> class ObjectManager
-{
-public:
-  ObjectManager() : _index_count{0u} {}
-
-public:
-  /**
-   * @brief Create an object with args and put it in the container with a new Index for that
-   *
-   * @param[in] args Arguments for creating Operand object
-   * @return Created index that is associated to the object
-   */
-  template <class... Args> Index emplace(Args &&... args)
-  {
-    auto index = generateIndex();
-    _objects.emplace(index, nnfw::cpp14::make_unique<Object>(std::forward<Args>(args)...));
-    return index;
-  }
-
-  /**
-   * @brief Put object in the container with a new Index for that
-   *
-   * @param[in] object Object to be pushed
-   * @return Created index that is associated to the object
-   */
-  Index push(std::unique_ptr<Object> &&object)
-  {
-    auto index = generateIndex();
-    _objects.emplace(index, std::move(object));
-    return index;
-  }
-
-  /**
-   * @brief Remove the object that is associated with the given index
-   *
-   * @param[in] index Index of the object to be removed
-   * @return N/A
-   */
-  void remove(const Index &index) { _objects.erase(index); };
-
-  /**
-   * @brief Get the object that is associated with the given index
-   *
-   * @param[in] index Index of the object to be returned
-   * @return Object
-   */
-  const Object &at(const Index &index) const { return *(_objects.at(index)); }
-  /**
-   * @brief Get the object that is associated with the given index
-   *
-   * @param[in] index Index of the object to be returned
-   * @return Object
-   */
-  Object &at(const Index &index) { return *(_objects.at(index)); }
-  /**
-   * @brief Get the object that is associated with the given index
-   *
-   * @param[in] index Index of the object to be returned
-   * @return true if such entry exists otherwise false
-   */
-  bool exist(const Index &index) const
-  {
-    auto it = _objects.find(index);
-    return it != _objects.end();
-  }
-  /**
-   * @brief Iterate over the container with given function
-   *
-   * @param[in] fn Function to be run for every container entry
-   * @return N/A
-   */
-  void iterate(const std::function<void(const Index &, const Object &)> &fn) const
-  {
-    for (const auto &e : _objects)
-    {
-      fn(e.first, *e.second);
-    }
-  }
-  /**
-   * @brief Iterate over the container with given function
-   *
-   * @param[in] fn Function to be run for every container entry
-   * @return N/A
-   */
-  void iterate(const std::function<void(const Index &, Object &)> &fn)
-  {
-    // TODO Remove this workaround
-    // This implementation is a workaround in case of adding operands while iteration
-    std::list<Index> l;
-
-    for (auto &e : _objects)
-    {
-      l.push_back(e.first);
-    }
-
-    for (auto index : l)
-    {
-      fn(index, *_objects[index]);
-    }
-  }
-
-private:
-  Index generateIndex() { return Index{_index_count++}; }
-
-private:
-  std::unordered_map<Index, std::unique_ptr<Object>> _objects;
-  uint32_t _index_count;
-};
-
-} // namespace util
-} // namespace neurun
-
-#endif // __NEURUN_UTIL_OBJECT_MANAGER_H__
diff --git a/runtime/neurun/core/include/util/Padding.h b/runtime/neurun/core/include/util/Padding.h
deleted file mode 100644
index 3c707b873..000000000
--- a/runtime/neurun/core/include/util/Padding.h
+++ /dev/null
@@ -1,41 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_UTIL_PADDING_H__
-#define __NEURUN_UTIL_PADDING_H__
-
-#include <stdint.h>
-
-#include "ir/Shape.h"
-#include "ir/InternalType.h"
-
-namespace neurun
-{
-namespace util
-{
-
-ir::ExplicitPadding validPadding(void);
-ir::ExplicitPadding samePadding(const ir::FeatureShape &ifm_shape,
-                                const ir::FeatureShape &ofm_shape, const ir::Stride &stride,
-                                uint32_t kw, uint32_t kh);
-ir::ExplicitPadding calculatePadding(const ir::Padding &padding, const ir::FeatureShape &ifm_shape,
-                                     const ir::FeatureShape &ofm_shape, const ir::Stride &stride,
-                                     uint32_t kw, uint32_t kh);
-
-} // namespace util
-} // namespace neurun
-
-#endif // __NEURUN_UTIL_PADDING_H__
diff --git a/runtime/neurun/core/include/util/Set.h b/runtime/neurun/core/include/util/Set.h
deleted file mode 100644
index 13213511d..000000000
--- a/runtime/neurun/core/include/util/Set.h
+++ /dev/null
@@ -1,166 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/**
- * @file     Set.h
- * @brief    This file contains neurun::util::Set class
- * @ingroup  COM_AI_RUNTIME
- */
-
-#ifndef __NEURUN_UTIL_SET_H__
-#define __NEURUN_UTIL_SET_H__
-
-#include <cassert>
-#include <unordered_set>
-
-namespace neurun
-{
-namespace util
-{
-
-/**
- * @brief Class for set of custom element
- & @tparam Element  Key type of Set
- */
-template <typename Element> class Set
-{
-public:
-  /**
-   * @brief Construct default Set object.
-   */
-  Set() = default;
-  /**
-   * @brief Construct Set object by copy semantics.
-   */
-  Set(const Set<Element> &) = default;
-  /**
-   * @brief Construct move Set object by move semantics.
-   */
-  Set(Set<Element> &&) = default;
-
-public:
-  /**
-   * @brief Add a given element to the set
-   *
-   * @param e Element added
-   */
-  void add(const Element &e) { _set.insert(e); }
-  /**
-   * @brief remove a given element from the set
-   *
-   * @param e Element removed
-   */
-  void remove(const Element &e) { _set.erase(e); }
-  /**
-   * @brief Get size of the set
-   *
-   * @return The size of the set
-   */
-  uint32_t size() const { return static_cast<uint32_t>(_set.size()); }
-  /**
-   * @brief Get whether the set is empty
-   *
-   * @return Whether the set is empty
-   */
-  bool empty() const { return _set.empty(); }
-  /**
-   * @brief Get whether a given element exists in the set
-   *
-   * @param e A given element
-   *
-   * @return Whether a given element exists in the set
-   */
-  bool contains(const Element &e) const { return _set.find(e) != _set.end(); }
-  /**
-   * @brief Get first element of the set
-   *
-   * @return first element of the set
-   */
-  const Element &getOnlyElement() const
-  {
-    assert(_set.size() == 1u);
-    return *_set.begin();
-  }
-
-public:
-  /**
-   * @brief operator overloading function for `|`
-   *
-   * @return A set with two sets combined
-   */
-  Set<Element> operator|(const Set<Element> &other) const // Union
-  {
-    auto ret = *this;
-    for (auto e : other)
-    {
-      ret.add(e);
-    }
-    return ret;
-  }
-  /**
-   * @brief operator overloading function for `&`
-   *
-   * @return A set of elements that overlap in two sets
-   */
-  Set<Element> operator&(const Set<Element> &other) const // Intersect
-  {
-    Set<Element> ret;
-    for (auto e : other)
-    {
-      if (contains(e))
-      {
-        ret.add(e);
-      }
-    }
-    return ret;
-  }
-  /**
-   * @brief operator overloading function for `-`
-   *
-   * @return A set of subtracted from another set
-   */
-  Set<Element> operator-(const Set<Element> &other) const // Minus
-  {
-    auto ret = *this;
-    for (auto e : other)
-    {
-      ret.remove(e);
-    }
-    return ret;
-  }
-
-public:
-  /**
-   * @brief begin() of const_iterator for this class
-   *
-   * @return The first iterator of the set
-   */
-  typename std::unordered_set<Element>::const_iterator begin() const { return _set.begin(); }
-  /**
-   * @brief end() of const_iterator for this class
-   *
-   * @return The last iterator of the set
-   */
-  typename std::unordered_set<Element>::const_iterator end() const { return _set.end(); }
-
-private:
-  std::unordered_set<Element> _set;
-};
-
-} // namespace util
-} // namespace neurun
-
-#endif // __NEURUN_UTIL_SET_H__
diff --git a/runtime/neurun/core/include/util/ShapeInference.h b/runtime/neurun/core/include/util/ShapeInference.h
deleted file mode 100644
index 097d61643..000000000
--- a/runtime/neurun/core/include/util/ShapeInference.h
+++ /dev/null
@@ -1,59 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_GRAPH_SHAPE_INFERENCE_H__
-#define __NEURUN_GRAPH_SHAPE_INFERENCE_H__
-
-#include "ir/operation/AvgPool2D.h"
-#include "ir/operation/Concat.h"
-#include "ir/operation/MaxPool2D.h"
-#include "ir/operation/Conv2D.h"
-#include "ir/operation/DepthwiseConv2D.h"
-#include "ir/Operands.h"
-#include "ir/Index.h"
-#include "ir/Layout.h"
-
-namespace neurun
-{
-namespace shape_inference
-{
-
-using Shapes = std::vector<ir::Shape>;
-
-Shapes inferEltwiseShape(const ir::Shape &lhs_shape, const ir::Shape &rhs_shape);
-
-Shapes inferAvgPoolShape(const ir::Shape &in_shape, const ir::operation::AvgPool2D::Param &param,
-                         ir::Layout layout = ir::Layout::NHWC);
-
-Shapes inferConcatShape(const Shapes &in_shapes, const ir::operation::Concat::Param &param);
-
-Shapes inferMaxPoolShape(const ir::Shape &in_shape, const ir::operation::MaxPool2D::Param &param,
-                         ir::Layout layout = ir::Layout::NHWC);
-
-Shapes inferConv2DShape(const ir::Shape &in_shape, const ir::Shape &ker_shape,
-                        const ir::operation::Conv2D::Param &param,
-                        ir::Layout layout = ir::Layout::NHWC);
-
-Shapes inferDepthwiseConv2DShape(const ir::Shape &in_shape, const ir::Shape &ker_shape,
-                                 const ir::operation::DepthwiseConv2D::Param &param,
-                                 ir::Layout layout = ir::Layout::NHWC);
-
-Shapes inferFullyConnectedShape(const ir::Shape &in_shape, const ir::Shape &ker_shape);
-
-} // namespace shape_inference
-} // namespace neurun
-
-#endif // __NEURUN_GRAPH_SHAPE_INFERENCE_H__
diff --git a/runtime/neurun/core/include/util/Utils.h b/runtime/neurun/core/include/util/Utils.h
deleted file mode 100644
index e7468dabd..000000000
--- a/runtime/neurun/core/include/util/Utils.h
+++ /dev/null
@@ -1,51 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/**
- * @file     Utils.h
- * @brief    This file contains utility functions
- * @ingroup  COM_AI_RUNTIME
- */
-
-#ifndef __NEURUN_UTIL_UTILS_H__
-#define __NEURUN_UTIL_UTILS_H__
-
-#include "ir/InternalType.h"
-#include "ir/Layout.h"
-#include "ir/Operand.h"
-#include "util/Coordinates.h"
-
-#define UNUSED_RELEASE(a) (void)(a)
-
-namespace neurun
-{
-namespace util
-{
-
-/**
- * @brief Converts a internal padding type to const char*
- * @param[in] type Padding type to be converted
- * @return A string holding the converted value
- */
-const char *to_string(ir::PaddingType type);
-
-Coordinates convertCoordinates(const Coordinates &from_coordinates, ir::Layout from_layout,
-                               ir::Layout to_layout);
-
-} // namespace util
-} // namespace neurun
-
-#endif // __NEURUN_UTIL_UTILS_H__
diff --git a/runtime/neurun/core/include/util/feature/Coordinate4D.h b/runtime/neurun/core/include/util/feature/Coordinate4D.h
deleted file mode 100644
index b020ed239..000000000
--- a/runtime/neurun/core/include/util/feature/Coordinate4D.h
+++ /dev/null
@@ -1,111 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_UTIL_FEATURE_COORDINATE_4D_H__
-#define __NEURUN_UTIL_FEATURE_COORDINATE_4D_H__
-
-#include <stdint.h>
-
-namespace neurun
-{
-namespace util
-{
-namespace feature
-{
-
-/**
- * @brief Class to represent position(offset) of subtensor.\n
- *        Assume that parent and child are already lowered (can get Shape4D).
- */
-class Coordinate4D
-{
-public:
-  /**
-   * @brief Construct a new Coordinate4D object
-   */
-  Coordinate4D(void) : _n{0}, _h{0}, _w{0}, _c{0}
-  {
-    // DO NOTHING
-  }
-  /**
-   * @brief     Construct a new Coordinate4D object
-   * @param[in] n Batch offset
-   * @param[in] h Height offset
-   * @param[in] w Width offset
-   * @param[in] c Channel offset
-   * @return
-   */
-  Coordinate4D(int32_t n, int32_t h, int32_t w, int32_t c) : _n{n}, _h{h}, _w{w}, _c{c}
-  {
-    // DO NOTHING
-  }
-
-public:
-  /**
-   * @brief   Set batch offset
-   * @param[in] n Batch offset
-   */
-  void n(int32_t n) { _n = n; }
-  /**
-   * @brief   Set height offset
-   * @param[in] h Height offset
-   */
-  void h(int32_t h) { _h = h; }
-  /**
-   * @brief   Set width offset
-   * @param[in] w Width offset
-   */
-  void w(int32_t w) { _w = w; }
-  /**
-   * @brief   Set channel offset
-   * @param[in] c Channel offset
-   */
-  void c(int32_t c) { _c = c; }
-
-public:
-  /**
-   * @brief   Return batch offset
-   * @return  Batch offset
-   */
-  int32_t n(void) const { return _n; }
-  /**
-   * @brief   Return height offset
-   * @return  Height offset
-   */
-  int32_t h(void) const { return _h; }
-  /**
-   * @brief   Return width offset
-   * @return  Width offset
-   */
-  int32_t w(void) const { return _w; }
-  /**
-   * @brief   Return channel offset
-   * @return  Channel offset
-   */
-  int32_t c(void) const { return _c; }
-
-private:
-  int32_t _n;
-  int32_t _h;
-  int32_t _w;
-  int32_t _c;
-};
-
-} // namespace feature
-} // namespace util
-} // namespace neurun
-
-#endif // __NEURUN_UTIL_FEATURE_COORDINATE_4D_H__
diff --git a/runtime/neurun/core/include/util/feature/nchw/Reader.h b/runtime/neurun/core/include/util/feature/nchw/Reader.h
deleted file mode 100644
index 0305bdf69..000000000
--- a/runtime/neurun/core/include/util/feature/nchw/Reader.h
+++ /dev/null
@@ -1,120 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_UTIL_FEATURE_NCHW_READER_H__
-#define __NEURUN_UTIL_FEATURE_NCHW_READER_H__
-
-#include <cassert>
-
-#include "backend/operand/ITensor.h"
-#include "misc/feature/Reader.h"
-#include "misc/feature/Shape.h"
-#include "util/Coordinates.h"
-#include "util/Utils.h"
-
-namespace neurun
-{
-namespace util
-{
-namespace feature
-{
-namespace nchw
-{
-
-template <typename T> class Reader final : public nnfw::misc::feature::Reader<T>
-{
-public:
-  // Construct for buffer of model inputs
-  Reader(const ::nnfw::misc::feature::Shape &shape, const T *ptr, size_t len)
-      : _shape{shape}, _ptr{reinterpret_cast<const uint8_t *>(ptr)}, _len{len}
-  {
-    assert(shape.N * shape.C * shape.H * shape.W * sizeof(T) == len);
-
-    // No padding
-    _strides.W = sizeof(T);
-    _strides.H = shape.W * sizeof(T);
-    _strides.C = shape.W * shape.H * sizeof(T);
-    _strides.N = shape.W * shape.H * shape.C * sizeof(T);
-  }
-
-  // Construct for backend tensor
-  Reader(backend::operand::ITensor *tensor)
-      : _ptr{tensor->buffer() + tensor->calcOffset({0, 0, 0, 0})}, _len{tensor->total_size()}
-  {
-    assert(tensor->layout() == ir::Layout::NCHW);
-
-    const auto start_offset = tensor->calcOffset({0, 0, 0, 0});
-    _strides.W = tensor->dimension(3) == 1 ? 0 : tensor->calcOffset({0, 0, 0, 1}) - start_offset;
-    _strides.H = tensor->dimension(2) == 1 ? 0 : tensor->calcOffset({0, 0, 1, 0}) - start_offset;
-    _strides.C = tensor->dimension(1) == 1 ? 0 : tensor->calcOffset({0, 1, 0, 0}) - start_offset;
-    _strides.N = tensor->dimension(0) == 1 ? 0 : tensor->calcOffset({1, 0, 0, 0}) - start_offset;
-
-    _shape.W = tensor->dimension(3);
-    _shape.H = tensor->dimension(2);
-    _shape.C = tensor->dimension(1);
-    _shape.N = tensor->dimension(0);
-  }
-
-public:
-  T at(uint32_t ch, uint32_t row, uint32_t col) const override
-  {
-    const auto offset = feature_index_to_byte_offset(0, ch, row, col);
-
-    const T *ptr = reinterpret_cast<const T *>(_ptr + offset);
-
-    return *ptr;
-  }
-  T at(uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) const override
-  {
-    const auto offset = feature_index_to_byte_offset(batch, ch, row, col);
-
-    const T *ptr = reinterpret_cast<const T *>(_ptr + offset);
-
-    return *ptr;
-  }
-
-private:
-  size_t feature_index_to_byte_offset(uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) const
-  {
-    assert(1u * _shape.N > batch); // shape.N > batch
-    assert(1u * _shape.C > ch);    // shape.C > ch
-    assert(1u * _shape.H > row);   // shape.H > row
-    assert(1u * _shape.W > col);   // shape.W > col
-
-    uint32_t res = 0;
-    res += batch * _strides.N;
-    res += ch * _strides.C;
-    res += row * _strides.H;
-    res += col * _strides.W;
-
-    return res;
-  }
-
-private:
-  // TODO Remove _shape
-  nnfw::misc::feature::Shape _shape;
-  using Strides = nnfw::misc::feature::Shape;
-  Strides _strides;
-  const uint8_t *_ptr;
-  size_t _len;
-};
-
-} // namespace nchw
-} // namespace feature
-} // namespace util
-} // namespace neurun
-
-#endif // __NEURUN_UTIL_FEATURE_NCHW_READER_H__
diff --git a/runtime/neurun/core/include/util/feature/nchw/View.h b/runtime/neurun/core/include/util/feature/nchw/View.h
deleted file mode 100644
index d747937ee..000000000
--- a/runtime/neurun/core/include/util/feature/nchw/View.h
+++ /dev/null
@@ -1,138 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_UTIL_FEATURE_NCHW_VIEW_H__
-#define __NEURUN_UTIL_FEATURE_NCHW_VIEW_H__
-
-#include "misc/feature/Reader.h"
-#include "misc/feature/Shape.h"
-
-#include "backend/operand/ITensor.h"
-#include "util/Coordinates.h"
-#include "util/Utils.h"
-
-#include <cassert>
-
-namespace neurun
-{
-namespace util
-{
-namespace feature
-{
-namespace nchw
-{
-
-template <typename T> class View final : public nnfw::misc::feature::Reader<T>
-{
-public:
-  // Construct for buffer of model inputs
-  View(const ::nnfw::misc::feature::Shape &shape, T *ptr, size_t len)
-      : _shape{shape}, _ptr{reinterpret_cast<uint8_t *>(ptr)}, _len{len}
-  {
-    assert(shape.N * shape.C * shape.H * shape.W * sizeof(T) == len);
-
-    _strides.W = sizeof(T);
-    _strides.H = shape.W * sizeof(T);
-    _strides.C = shape.W * shape.H * sizeof(T);
-    _strides.N = shape.W * shape.H * shape.C * sizeof(T);
-  }
-
-  // Construct for backend tensor
-  View(::neurun::backend::operand::ITensor *tensor)
-      : _ptr{tensor->buffer() + tensor->calcOffset({0, 0, 0, 0})}, _len{tensor->total_size()}
-  {
-    assert(tensor->layout() == ir::Layout::NCHW);
-
-    const auto start_offset = tensor->calcOffset({0, 0, 0, 0});
-    _strides.W = tensor->dimension(3) == 1 ? 0 : tensor->calcOffset({0, 0, 0, 1}) - start_offset;
-    _strides.H = tensor->dimension(2) == 1 ? 0 : tensor->calcOffset({0, 0, 1, 0}) - start_offset;
-    _strides.C = tensor->dimension(1) == 1 ? 0 : tensor->calcOffset({0, 1, 0, 0}) - start_offset;
-    _strides.N = tensor->dimension(0) == 1 ? 0 : tensor->calcOffset({1, 0, 0, 0}) - start_offset;
-
-    _shape.W = tensor->dimension(3);
-    _shape.H = tensor->dimension(2);
-    _shape.C = tensor->dimension(1);
-    _shape.N = tensor->dimension(0);
-  }
-
-public:
-  T at(uint32_t ch, uint32_t row, uint32_t col) const override
-  {
-    const auto offset = feature_index_to_byte_offset(0, ch, row, col);
-
-    T *ptr = reinterpret_cast<T *>(_ptr + offset);
-
-    return *ptr;
-  }
-  T at(uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) const override
-  {
-    const auto offset = feature_index_to_byte_offset(batch, ch, row, col);
-
-    T *ptr = reinterpret_cast<T *>(_ptr + offset);
-
-    return *ptr;
-  }
-
-public:
-  T &at(uint32_t ch, uint32_t row, uint32_t col)
-  {
-    const auto offset = feature_index_to_byte_offset(0, ch, row, col);
-
-    T *ptr = reinterpret_cast<T *>(_ptr + offset);
-
-    return *ptr;
-  }
-  T &at(uint32_t batch, uint32_t ch, uint32_t row, uint32_t col)
-  {
-    const auto offset = feature_index_to_byte_offset(batch, ch, row, col);
-
-    T *ptr = reinterpret_cast<T *>(_ptr + offset);
-
-    return *ptr;
-  }
-
-private:
-  size_t feature_index_to_byte_offset(uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) const
-  {
-    assert(1u * _shape.N > batch); // shape.N > batch
-    assert(1u * _shape.C > ch);    // shape.C > ch
-    assert(1u * _shape.H > row);   // shape.H > row
-    assert(1u * _shape.W > col);   // shape.W > col
-
-    uint32_t res = 0;
-    res += batch * _strides.N;
-    res += ch * _strides.C;
-    res += row * _strides.H;
-    res += col * _strides.W;
-
-    return res;
-  }
-
-private:
-  // TODO Remove _shape
-  nnfw::misc::feature::Shape _shape;
-  using Strides = nnfw::misc::feature::Shape;
-  Strides _strides;
-  uint8_t *_ptr;
-  size_t _len;
-};
-
-} // namespace nchw
-} // namespace feature
-} // namespace util
-} // namespace neurun
-
-#endif // __NEURUN_UTIL_FEATURE_NCHW_VIEW_H__
diff --git a/runtime/neurun/core/include/util/feature/nhwc/Reader.h b/runtime/neurun/core/include/util/feature/nhwc/Reader.h
deleted file mode 100644
index 0df7be4be..000000000
--- a/runtime/neurun/core/include/util/feature/nhwc/Reader.h
+++ /dev/null
@@ -1,121 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_UTIL_FEATURE_NHWC_READER_H__
-#define __NEURUN_UTIL_FEATURE_NHWC_READER_H__
-
-#include <cassert>
-
-#include "backend/operand/ITensor.h"
-#include "misc/feature/Reader.h"
-#include "misc/feature/Shape.h"
-#include "util/Coordinates.h"
-#include "util/Utils.h"
-
-namespace neurun
-{
-namespace util
-{
-namespace feature
-{
-namespace nhwc
-{
-
-template <typename T> class Reader final : public nnfw::misc::feature::Reader<T>
-{
-public:
-  // Construct for buffer of model inputs
-  Reader(const ::nnfw::misc::feature::Shape &shape, const T *ptr, size_t len)
-      : _shape{shape}, _ptr{reinterpret_cast<const uint8_t *>(ptr)}, _len{len}
-  {
-    UNUSED_RELEASE(len); // Workaround for unused variable in release mode
-    assert(shape.N * shape.C * shape.H * shape.W * sizeof(T) == len);
-
-    // No padding
-    _strides.C = sizeof(T);
-    _strides.W = shape.C * sizeof(T);
-    _strides.H = shape.C * shape.W * sizeof(T);
-    _strides.N = shape.C * shape.W * shape.H * sizeof(T);
-  }
-
-  // Construct for backend tensor
-  Reader(const backend::operand::ITensor *tensor)
-      : _ptr{tensor->buffer() + tensor->calcOffset({0, 0, 0, 0})}, _len{tensor->total_size()}
-  {
-    assert(tensor->layout() == ir::Layout::NHWC);
-
-    const auto start_offset = tensor->calcOffset({0, 0, 0, 0});
-    _strides.C = tensor->dimension(3) == 1 ? 0 : tensor->calcOffset({0, 0, 0, 1}) - start_offset;
-    _strides.W = tensor->dimension(2) == 1 ? 0 : tensor->calcOffset({0, 0, 1, 0}) - start_offset;
-    _strides.H = tensor->dimension(1) == 1 ? 0 : tensor->calcOffset({0, 1, 0, 0}) - start_offset;
-    _strides.N = tensor->dimension(0) == 1 ? 0 : tensor->calcOffset({1, 0, 0, 0}) - start_offset;
-
-    _shape.C = tensor->dimension(3);
-    _shape.W = tensor->dimension(2);
-    _shape.H = tensor->dimension(1);
-    _shape.N = tensor->dimension(0);
-  }
-
-public:
-  T at(uint32_t row, uint32_t col, uint32_t ch) const override
-  {
-    const auto offset = feature_index_to_byte_offset(0, row, col, ch);
-
-    const T *ptr = reinterpret_cast<const T *>(_ptr + offset);
-
-    return *ptr;
-  }
-  T at(uint32_t batch, uint32_t row, uint32_t col, uint32_t ch) const override
-  {
-    const auto offset = feature_index_to_byte_offset(batch, row, col, ch);
-
-    const T *ptr = reinterpret_cast<const T *>(_ptr + offset);
-
-    return *ptr;
-  }
-
-private:
-  size_t feature_index_to_byte_offset(uint32_t batch, uint32_t row, uint32_t col, uint32_t ch) const
-  {
-    assert(1u * _shape.N > batch); // shape.N > batch
-    assert(1u * _shape.H > row);   // shape.H > row
-    assert(1u * _shape.W > col);   // shape.W > col
-    assert(1u * _shape.C > ch);    // shape.C > ch
-
-    uint32_t res = 0;
-    res += batch * _strides.N;
-    res += row * _strides.H;
-    res += col * _strides.W;
-    res += ch * _strides.C;
-
-    return res;
-  }
-
-private:
-  // TODO Remove _shape
-  nnfw::misc::feature::Shape _shape;
-  using Strides = nnfw::misc::feature::Shape;
-  Strides _strides;
-  const uint8_t *_ptr;
-  size_t _len;
-};
-
-} // namespace nhwc
-} // namespace feature
-} // namespace util
-} // namespace neurun
-
-#endif // __NEURUN_UTIL_FEATURE_NHWC_READER_H__
diff --git a/runtime/neurun/core/include/util/feature/nhwc/View.h b/runtime/neurun/core/include/util/feature/nhwc/View.h
deleted file mode 100644
index b9d98e9fa..000000000
--- a/runtime/neurun/core/include/util/feature/nhwc/View.h
+++ /dev/null
@@ -1,140 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_UTIL_FEATURE_NHWC_VIEW_H__
-#define __NEURUN_UTIL_FEATURE_NHWC_VIEW_H__
-
-#include <cassert>
-#include <cstddef>
-
-#include "backend/operand/ITensor.h"
-#include "misc/feature/Reader.h"
-#include "misc/feature/Shape.h"
-#include "util/Coordinates.h"
-#include "util/Utils.h"
-
-namespace neurun
-{
-namespace util
-{
-namespace feature
-{
-namespace nhwc
-{
-
-template <typename T> class View final : public nnfw::misc::feature::Reader<T>
-{
-public:
-  // Construct for buffer of model inputs
-  View(const ::nnfw::misc::feature::Shape &shape, T *ptr, size_t len)
-      : _shape{shape}, _ptr{reinterpret_cast<uint8_t *>(ptr)}, _len{len}
-  {
-    UNUSED_RELEASE(len); // Workaround for unused variable in release mode
-    assert(shape.N * shape.H * shape.W * shape.C * sizeof(T) == len);
-
-    // No padding
-    _strides.C = sizeof(T);
-    _strides.W = shape.C * sizeof(T);
-    _strides.H = shape.C * shape.W * sizeof(T);
-    _strides.N = shape.C * shape.W * shape.H * sizeof(T);
-  }
-
-  // Construct for backend tensor
-  View(backend::operand::ITensor *tensor)
-      : _ptr{tensor->buffer() + tensor->calcOffset({0, 0, 0, 0})}, _len{tensor->total_size()}
-  {
-    assert(tensor->layout() == ir::Layout::NHWC);
-
-    const auto start_offset = tensor->calcOffset({0, 0, 0, 0});
-    _strides.C = tensor->dimension(3) == 1 ? 0 : tensor->calcOffset({0, 0, 0, 1}) - start_offset;
-    _strides.W = tensor->dimension(2) == 1 ? 0 : tensor->calcOffset({0, 0, 1, 0}) - start_offset;
-    _strides.H = tensor->dimension(1) == 1 ? 0 : tensor->calcOffset({0, 1, 0, 0}) - start_offset;
-    _strides.N = tensor->dimension(0) == 1 ? 0 : tensor->calcOffset({1, 0, 0, 0}) - start_offset;
-
-    _shape.C = tensor->dimension(3);
-    _shape.W = tensor->dimension(2);
-    _shape.H = tensor->dimension(1);
-    _shape.N = tensor->dimension(0);
-  }
-
-public:
-  T at(uint32_t row, uint32_t col, uint32_t ch) const override
-  {
-    const auto offset = feature_index_to_byte_offset(0, row, col, ch);
-
-    const T *ptr = reinterpret_cast<const T *>(_ptr + offset);
-
-    return *ptr;
-  }
-  T at(uint32_t batch, uint32_t row, uint32_t col, uint32_t ch) const override
-  {
-    const auto offset = feature_index_to_byte_offset(batch, row, col, ch);
-
-    const T *ptr = reinterpret_cast<const T *>(_ptr + offset);
-
-    return *ptr;
-  }
-
-  T &at(uint32_t row, uint32_t col, uint32_t ch)
-  {
-    const auto offset = feature_index_to_byte_offset(0, row, col, ch);
-
-    T *ptr = reinterpret_cast<T *>(_ptr + offset);
-
-    return *ptr;
-  }
-
-  T &at(uint32_t batch, uint32_t row, uint32_t col, uint32_t ch)
-  {
-    const auto offset = feature_index_to_byte_offset(batch, row, col, ch);
-
-    T *ptr = reinterpret_cast<T *>(_ptr + offset);
-
-    return *ptr;
-  }
-
-private:
-  size_t feature_index_to_byte_offset(uint32_t batch, uint32_t row, uint32_t col, uint32_t ch) const
-  {
-    assert(1u * _shape.N > batch); // shape.N > batch
-    assert(1u * _shape.H > row);   // shape.H > row
-    assert(1u * _shape.W > col);   // shape.W > col
-    assert(1u * _shape.C > ch);    // shape.C > ch
-
-    uint32_t res = 0;
-    res += batch * _strides.N;
-    res += row * _strides.H;
-    res += col * _strides.W;
-    res += ch * _strides.C;
-
-    return res;
-  }
-
-private:
-  // TODO Remove _shape
-  nnfw::misc::feature::Shape _shape;
-  using Strides = nnfw::misc::feature::Shape;
-  Strides _strides;
-  uint8_t *_ptr;
-  size_t _len;
-};
-
-} // namespace nhwc
-} // namespace feature
-} // namespace util
-} // namespace neurun
-
-#endif // __NEURUN_UTIL_FEATURE_NHWC_VIEW_H__
diff --git a/runtime/neurun/core/include/util/logging.h b/runtime/neurun/core/include/util/logging.h
deleted file mode 100644
index 8ecd0ac12..000000000
--- a/runtime/neurun/core/include/util/logging.h
+++ /dev/null
@@ -1,63 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_UTIL_LOGGING_H__
-#define __NEURUN_UTIL_LOGGING_H__
-
-#include <iostream>
-
-#include "util/ConfigSource.h"
-
-namespace neurun
-{
-namespace util
-{
-namespace logging
-{
-
-class Context
-{
-public:
-  Context() noexcept : _enabled{false}
-  {
-    const auto env = util::getConfigBool(util::config::NEURUN_LOG_ENABLE);
-
-    if (env)
-    {
-      _enabled = true;
-    }
-  }
-
-  static Context &get() noexcept;
-
-public:
-  bool enabled(void) const { return _enabled; }
-
-private:
-  bool _enabled;
-};
-
-static Context &ctx = Context::get();
-
-} // namespace logging
-} // namespace util
-} // namespace neurun
-
-#define VERBOSE(name)                         \
-  if (::neurun::util::logging::ctx.enabled()) \
-  std::cout << "[" << #name << "] "
-
-#endif // __NEURUN_UTIL_LOGGING_H__
diff --git a/runtime/neurun/core/src/backend/Backend.cc b/runtime/neurun/core/src/backend/Backend.cc
deleted file mode 100644
index c2f745f8f..000000000
--- a/runtime/neurun/core/src/backend/Backend.cc
+++ /dev/null
@@ -1,30 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "backend/Backend.h"
-
-#include "backend/IConfig.h"
-#include "backend/ITensorBuilder.h"
-#include "backend/IKernelGenerator.h"
-#include "backend/IShapeFixer.h"
-
-namespace neurun
-{
-namespace backend
-{
-
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/core/src/backend/BackendManager.cc b/runtime/neurun/core/src/backend/BackendManager.cc
deleted file mode 100644
index 32086e8b6..000000000
--- a/runtime/neurun/core/src/backend/BackendManager.cc
+++ /dev/null
@@ -1,150 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <memory>
-#include <dlfcn.h>
-#include "BackendManager.h"
-
-#include "backend/Backend.h"
-#include "backend/IConfig.h"
-#include "util/logging.h"
-#include "util/ConfigSource.h"
-#include "misc/string_helpers.h"
-
-namespace neurun
-{
-namespace backend
-{
-
-BackendManager &BackendManager::get()
-{
-  static BackendManager object;
-  return object;
-}
-
-template <typename T, class... Types>
-void BackendManager::loadObjectFromPlugin(std::shared_ptr<T> &object_of_plugin_class,
-                                          const std::string obj_creator_func_name, void *handle,
-                                          Types &&... args)
-{
-  T *(*allocate_obj)(Types && ... Args);
-  // load object creator function
-  allocate_obj = (T * (*)(Types && ... Args))dlsym(handle, obj_creator_func_name.c_str());
-  if (allocate_obj == nullptr)
-  {
-    fprintf(stderr, "BackendManager: unable to open function %s: %s\n",
-            obj_creator_func_name.c_str(), dlerror());
-    abort();
-  }
-
-  object_of_plugin_class.reset(allocate_obj(args...));
-}
-
-void BackendManager::loadBackend(const std::string &backend)
-{
-  const std::string backend_plugin = "libbackend_" + backend + ".so";
-  void *handle = dlopen(backend_plugin.c_str(), RTLD_LAZY | RTLD_LOCAL);
-  if (handle == nullptr)
-  {
-    VERBOSE(BackendManager::loadBackend) << "loadBackend failed to load plugin of "
-                                         << backend.c_str() << " backend: " << dlerror()
-                                         << std::endl;
-    return;
-  }
-
-  VERBOSE(BackendManager::loadBackend) << "loaded " << backend_plugin << " as a plugin of "
-                                       << backend << " backend\n";
-
-  {
-    // load object creator function
-    auto backend_create = (backend_create_t)dlsym(handle, "neurun_backend_create");
-    if (backend_create == nullptr)
-    {
-      fprintf(stderr, "BackendManager: unable to open function neurun_backend_create : %s\n",
-              dlerror());
-      abort();
-    }
-
-    // load object creator function
-    auto backend_destroy = (backend_destroy_t)dlsym(handle, "neurun_backend_destroy");
-    if (backend_destroy == nullptr)
-    {
-      fprintf(stderr, "BackendManager: unable to open function neurun_backend_destroy : %s\n",
-              dlerror());
-      abort();
-    }
-
-    auto backend_object =
-        std::unique_ptr<backend::Backend, backend_destroy_t>(backend_create(), backend_destroy);
-    auto backend_object_raw = backend_object.get();
-    bool initialized = backend_object->config()->initialize(); // Call initialize here?
-    if (!initialized)
-    {
-      VERBOSE(BackendManager::loadBackend)
-          << backend.c_str() << " backend initialization failed. Don't use this backend"
-          << std::endl;
-      dlclose(handle);
-      return;
-    }
-    _gen_map.emplace(backend_object->config()->id(), std::move(backend_object));
-    _available_backends.push_back(backend_object_raw);
-  }
-
-  // Save backend handle (avoid warning by handle lost without dlclose())
-  auto u_handle = std::unique_ptr<void, dlhandle_destroy_t>{handle, [](void *h) { dlclose(h); }};
-  _handle_map.emplace(backend, std::move(u_handle));
-}
-
-BackendManager::BackendManager()
-{
-  const auto backends = util::getConfigString(util::config::BACKENDS);
-  for (auto &backend_id : nnfw::misc::split(backends, ';'))
-  {
-    loadBackend(backend_id);
-  }
-
-  // No loaded backend
-  if (_available_backends.empty())
-  {
-    VERBOSE(BackendManager::loadBackend) << "There is no loaded backend\n";
-    abort();
-  }
-}
-
-Backend *BackendManager::get(const std::string &key)
-{
-  if (_gen_map.find(key) != _gen_map.end())
-  {
-    return _gen_map.at(key).get();
-  }
-
-  return nullptr;
-}
-
-const Backend *BackendManager::get(const std::string &key) const
-{
-  if (_gen_map.find(key) != _gen_map.end())
-  {
-    return _gen_map.at(key).get();
-  }
-
-  return nullptr;
-}
-
-const Backend *BackendManager::getDefault() const { return get("cpu"); }
-
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/core/src/backend/BackendManager.h b/runtime/neurun/core/src/backend/BackendManager.h
deleted file mode 100644
index 9c6483f9d..000000000
--- a/runtime/neurun/core/src/backend/BackendManager.h
+++ /dev/null
@@ -1,82 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_BACKEND_BACKEND_MANAGER_H__
-#define __NEURUN_BACKEND_BACKEND_MANAGER_H__
-
-#include <memory>
-#include <map>
-
-#include "ir/Operands.h"
-#include "backend/Backend.h"
-
-namespace neurun
-{
-namespace backend
-{
-
-class BackendManager
-{
-public:
-  using backend_create_t = Backend *(*)();
-  using backend_destroy_t = void (*)(Backend *);
-  using dlhandle_destroy_t = void (*)(void *);
-
-  static BackendManager &get();
-
-public:
-  Backend *get(const std::string &key);
-  const Backend *get(const std::string &key) const;
-  const Backend *getDefault() const;
-  const std::vector<const Backend *> &getAll() const { return _available_backends; };
-
-private:
-  BackendManager();
-
-private:
-  std::vector<const Backend *> _available_backends;
-  std::map<std::string, std::unique_ptr<void, dlhandle_destroy_t>> _handle_map;
-  std::map<std::string, std::unique_ptr<Backend, backend_destroy_t>> _gen_map;
-  /**
-   * @brief Allocate an object of a class of a plugin by loading a plugin function, that does
-   * allocation, and calling it
-   *
-   * @param object_of_plugin_class target object
-   * @param obj_creator_func_name name of the plugin function, that allocates an object
-   * @param handle handle of the plugin
-   * @param args arguments to pass to constructor of the plugin class
-   *
-   * @return
-   */
-  template <typename T, class... Types>
-  void loadObjectFromPlugin(std::shared_ptr<T> &object_of_plugin_class,
-                            const std::string obj_creator_func_name, void *handle,
-                            Types &&... args);
-
-  /**
-   * @brief load backend plugin
-   *
-   * @param backend backend to be loaded
-   *
-   * @return
-   */
-  void loadBackend(const std::string &backend);
-};
-
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_BACKEND_MANAGER_H__
diff --git a/runtime/neurun/core/src/backend/ExecTime.cc b/runtime/neurun/core/src/backend/ExecTime.cc
deleted file mode 100644
index d5aa679d7..000000000
--- a/runtime/neurun/core/src/backend/ExecTime.cc
+++ /dev/null
@@ -1,133 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "backend/ExecTime.h"
-
-#include <fstream>
-#include <cassert>
-#include <limits>
-#include <algorithm>
-
-namespace neurun
-{
-namespace backend
-{
-
-int64_t ExecTime::getOperationExecTime(const Backend *backend, const std::string &operation,
-                                       bool quant, uint32_t op_size) const
-{
-  auto found_backend = _measurements.find(backend);
-  if (found_backend == _measurements.end())
-    return NOT_FOUND; // no execution time for this backend
-
-  auto found_operation_with_type = found_backend->second.find(operation);
-  if (found_operation_with_type == found_backend->second.end())
-    // no execution time for this operation
-    return NOT_FOUND;
-
-  auto found_operation = found_operation_with_type->second.find(quant);
-  if (found_operation == found_operation_with_type->second.end())
-    // no execution time for this operation
-    return NOT_FOUND;
-
-  auto found_size = found_operation->second.find(op_size);
-  if (found_size != found_operation->second.end())
-    return found_size->second; // found execution time
-
-  // Try to interpolate
-  if (found_operation->second.size() < 2)
-    // not possible to do linear interpolation
-    return found_operation->second.begin()->second;
-
-  // if we reach here, then this means, that there is no record, that is equal to op_size
-  auto upper_bound = found_operation->second.upper_bound(op_size); // > op_size
-  auto lower_bound = upper_bound;
-
-  if (upper_bound == found_operation->second.end()) // all values <= op_size
-  {
-    upper_bound--;
-    lower_bound = upper_bound;
-    lower_bound--;
-  }
-  else if (upper_bound == found_operation->second.begin()) // all values > op_size
-  {
-    upper_bound++;
-  }
-  else // op_size between
-  {
-    lower_bound--;
-  }
-
-  // Linear interpolation
-  const auto x0 = static_cast<int64_t>(lower_bound->first); // size
-  const auto x1 = static_cast<int64_t>(upper_bound->first); // size
-  const int64_t y0 = lower_bound->second;                   // time
-  const int64_t y1 = upper_bound->second;                   // time
-  const auto x = static_cast<int64_t>(op_size);
-
-  int64_t interpolated_value = y0 + (x - x0) * (y1 - y0) / (x1 - x0);
-
-  // In some cases ops with smaller inputs is executed slower than the one
-  // with larger inputs, more likely because of a backend's load difference
-  if (interpolated_value < 0 && x > x1)
-  {
-    return y0;
-  }
-  // It must be non-positive ONLY if it's lesser than both of them
-  assert(interpolated_value > 0 || x < x0);
-
-  // execution time must be non-negative
-  return std::max<int64_t>(interpolated_value, 1);
-}
-
-void ExecTime::updateOperationExecTime(const Backend *backend, const std::string &operation,
-                                       bool quant, uint32_t op_size, int64_t time)
-{
-  // If the op is not implemented for some input, it should not be scheduled
-  const auto &recs = _measurements[backend][operation][quant];
-  if (time == getMax() ||
-      std::any_of(recs.begin(), recs.end(),
-                  [](std::pair<const uint32_t, const int64_t> p) { return p.second == getMax(); }))
-  {
-    _measurements[backend][operation][quant].clear();
-    _measurements[backend][operation][quant].emplace(op_size, getMax());
-  }
-  else
-  {
-    auto it = _measurements[backend][operation][quant].emplace(op_size, time);
-    if (!it.second)
-    {
-      // affect of the last measurement is bigger than the previous ones:
-      //   this prefers new metrics than older once, so will adapt backend changes
-      it.first->second = (it.first->second + time) / 2;
-    }
-  }
-}
-
-void ExecTime::updatePermuteTime(const Backend *from_backend, const Backend *to_backend, bool quant,
-                                 uint32_t op_size, int64_t time)
-{
-  updateOperationExecTime(from_backend, to_backend->config()->id(), quant, op_size, time);
-}
-
-int64_t ExecTime::getPermuteTime(const Backend *from_backend, const Backend *to_backend, bool quant,
-                                 uint32_t op_size) const
-{
-  return getOperationExecTime(from_backend, to_backend->config()->id(), quant, op_size);
-}
-
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/core/src/backend/JSONExecTime.cc b/runtime/neurun/core/src/backend/JSONExecTime.cc
deleted file mode 100644
index e2404b2c8..000000000
--- a/runtime/neurun/core/src/backend/JSONExecTime.cc
+++ /dev/null
@@ -1,231 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "backend/JSONExecTime.h"
-#include "backend/IConfig.h"
-#include <fstream>
-
-namespace neurun
-{
-namespace backend
-{
-/**
- * @brief Helper function for reading string from stream
- *
- * @param str Output string
- * @param stream File stream
- */
-void readString(std::string &str, std::ifstream &stream)
-{
-  str.clear();
-  char buf;
-  while (stream.good())
-  {
-    stream.get(buf);
-    if (buf == '"')
-      break;
-    str.push_back(buf);
-  }
-}
-
-/**
- * @brief Helper function for reading bool from stream
- *
- * @param quant Output bool
- * @param stream File stream
- */
-void readBool(bool &quant, std::ifstream &stream)
-{
-  char buf;
-  stream.get(buf);
-  quant = (buf == '1');
-  stream.get(buf);
-}
-
-void printString(const std::string &str, std::ofstream &stream) { stream << "\"" << str << "\""; }
-
-void printBool(bool quant, std::ofstream &stream) { stream << "\"" << quant << "\""; }
-
-void JSON::readOperation(const std::string &backend, const std::string &operation, bool quant,
-                         std::ifstream &stream)
-{
-  uint32_t size = 0;
-  int64_t time = 0;
-
-  std::string int_buf;
-  char buf;
-  int number_of_closed_braces = 0;
-  int number_of_commas = 0;
-
-  while (stream.good())
-  {
-    stream.get(buf);
-
-    switch (buf)
-    {
-      case ']':
-      {
-        number_of_closed_braces++;
-        break;
-      }
-      case '[':
-      {
-        number_of_closed_braces--;
-        break;
-      }
-      default:
-      {
-        if (std::isdigit(buf))
-        {
-          int_buf.push_back(buf);
-        }
-        break;
-      }
-    }
-
-    if (number_of_closed_braces == 1)
-      break;
-
-    if ((buf == ']' && number_of_closed_braces == 0) ||
-        (buf == ',' && number_of_closed_braces == -1))
-    {
-      switch (number_of_commas % 2)
-      {
-        case 0:
-        {
-          size = static_cast<uint32_t>(std::atoi(int_buf.c_str()));
-          break;
-        }
-        case 1:
-        {
-          time = static_cast<int64_t>(std::atol(int_buf.c_str()));
-          auto bf = _backends.find(backend);
-          if (bf != _backends.end())
-          {
-            _measurements[bf->second][operation][quant][size] = time;
-          } // we ignore the records for unsupported backends
-          break;
-        }
-      }
-      number_of_commas++;
-      int_buf.clear();
-    }
-  }
-}
-void JSON::printOperation(const std::map<uint32_t, int64_t> &operation_info,
-                          std::ofstream &stream) const
-{
-  for (const auto &items : operation_info)
-  {
-    stream << "[" << items.first << ", " << items.second << "], ";
-  }
-  stream.seekp(-2, std::ofstream::end);
-}
-
-void JSON::uploadOperationsExecTime() const
-{
-  std::ofstream stream(_measurement_file);
-  if (!stream.is_open())
-  {
-    throw std::runtime_error("Failed to save backend config file");
-  }
-  else
-  {
-    stream << "{";
-    for (const auto &backend : _measurements)
-    {
-      printString(backend.first->config()->id(), stream);
-      stream << ": {";
-      for (const auto &operation : backend.second)
-      {
-        printString(operation.first, stream);
-        stream << ": {";
-        for (const auto &type : operation.second)
-        {
-          printBool(type.first, stream);
-          stream << ": [";
-          printOperation(type.second, stream);
-          stream << "], ";
-        }
-        stream.seekp(-2, std::ofstream::end);
-        stream << "}, ";
-      }
-      stream.seekp(-2, std::ofstream::end);
-      stream << "}, ";
-    }
-    stream.seekp(-2, std::ofstream::end);
-    stream << "}";
-    stream.close();
-  }
-}
-
-void JSON::loadOperationsExecTime()
-{
-  std::ifstream stream(_measurement_file);
-  if (stream.is_open())
-  {
-    std::string backend;
-    std::string operation;
-    bool quant = false;
-    char buf;
-    int number_of_open_braces = 0;
-
-    while (stream.good())
-    {
-      stream.get(buf);
-      switch (buf)
-      {
-        case '{':
-          number_of_open_braces++;
-          break;
-        case '}':
-          number_of_open_braces--;
-          break;
-        case '"':
-        {
-          if (number_of_open_braces == 1)
-          {
-            // read backend string
-            readString(backend, stream);
-          }
-          if (number_of_open_braces == 2)
-          {
-            // read operation string
-            readString(operation, stream);
-          }
-          if (number_of_open_braces == 3)
-          {
-            // read operation string
-            readBool(quant, stream);
-          }
-          break;
-        }
-        case '[':
-        {
-          // reading and creating all info for operation
-          readOperation(backend, operation, quant, stream);
-          break;
-        }
-        default:
-          break;
-      }
-    }
-    stream.close();
-  }
-}
-
-} // namespace backend
-} // namespace neurun
diff --git a/runtime/neurun/core/src/compiler/BackendResolver.cc b/runtime/neurun/core/src/compiler/BackendResolver.cc
deleted file mode 100644
index 0c544190c..000000000
--- a/runtime/neurun/core/src/compiler/BackendResolver.cc
+++ /dev/null
@@ -1,47 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "BackendResolver.h"
-
-namespace neurun
-{
-namespace compiler
-{
-
-BackendResolver::BackendResolver(const BackendResolver &obj)
-    : _context_manager{}, _gen_map{obj._gen_map}
-{
-  for (const auto &e : obj._context_manager)
-  {
-    _context_manager.emplace(e.first, nnfw::cpp14::make_unique<backend::BackendContext>(*e.second));
-  }
-}
-
-BackendResolver &BackendResolver::operator=(const BackendResolver &obj)
-{
-  _gen_map = obj._gen_map;
-
-  _context_manager.clear();
-  for (const auto &e : obj._context_manager)
-  {
-    _context_manager.emplace(e.first, nnfw::cpp14::make_unique<backend::BackendContext>(*e.second));
-  }
-
-  return *this;
-}
-
-} // namespace compiler
-} // namespace neurun
diff --git a/runtime/neurun/core/src/compiler/BackendResolver.h b/runtime/neurun/core/src/compiler/BackendResolver.h
deleted file mode 100644
index ddcae793a..000000000
--- a/runtime/neurun/core/src/compiler/BackendResolver.h
+++ /dev/null
@@ -1,102 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_COMPILER_BACKEND_RESOLVER_H__
-#define __NEURUN_COMPILER_BACKEND_RESOLVER_H__
-
-#include <unordered_map>
-#include <typeindex>
-
-#include "util/logging.h"
-#include "backend/Backend.h"
-#include "backend/BackendManager.h"
-#include "backend/ITensorBuilder.h"
-#include "ir/OperationIndexMap.h"
-
-namespace neurun
-{
-namespace compiler
-{
-
-class BackendResolver
-{
-public:
-  BackendResolver(const ir::Operands &operands,
-                  const std::vector<const backend::Backend *> &backends,
-                  const std::shared_ptr<backend::custom::IKernelBuilder> &kb)
-  {
-    for (const auto backend : backends)
-    {
-      _context_manager.emplace(backend, backend->newContext(operands, kb));
-    }
-  }
-
-  ~BackendResolver() = default;
-  BackendResolver(const BackendResolver &obj);
-  BackendResolver(BackendResolver &&obj) = default;
-  BackendResolver &operator=(const BackendResolver &obj);
-  BackendResolver &operator=(BackendResolver &&obj) = default;
-
-public:
-  const backend::BackendContext *getBackendContext(const ir::OperationIndex &index) const
-  {
-    return _context_manager.at(_gen_map.at(index)).get();
-  }
-
-  const backend::BackendContext *getBackendContext(const backend::Backend *backend) const
-  {
-    return _context_manager.at(backend).get();
-  }
-
-  backend::TensorBuilderSet tensor_builders() const
-  {
-    backend::TensorBuilderSet ret;
-    for (const auto &e : _context_manager)
-    {
-      ret.insert(e.second->tensor_builder);
-    }
-    return ret;
-  }
-
-  const backend::Backend *getBackend(const ir::OperationIndex &index) const
-  {
-    return getBackendContext(index)->backend;
-  }
-
-  void setBackend(const ir::OperationIndex &index, const backend::Backend *backend)
-  {
-    _gen_map[index] = backend;
-  }
-
-  void iterate(const std::function<void(const ir::OperationIndex &,
-                                        const backend::BackendContext &)> &fn) const
-  {
-    for (const auto &e : _gen_map)
-    {
-      fn(e.first, *_context_manager.at(e.second));
-    }
-  }
-
-private:
-  std::unordered_map<const backend::Backend *, std::unique_ptr<backend::BackendContext>>
-      _context_manager;
-  ir::OperationIndexMap<const backend::Backend *> _gen_map;
-};
-
-} // namespace compiler
-} // namespace neurun
-
-#endif // __NEURUN_COMPILER_BACKEND_RESOLVER_H__
diff --git a/runtime/neurun/core/src/compiler/CodeWithInfo.h b/runtime/neurun/core/src/compiler/CodeWithInfo.h
deleted file mode 100644
index 73dd1055b..000000000
--- a/runtime/neurun/core/src/compiler/CodeWithInfo.h
+++ /dev/null
@@ -1,44 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_COMPILER_CODE_WITH_INFO_H__
-#define __NEURUN_COMPILER_CODE_WITH_INFO_H__
-
-#include <memory>
-
-#include "compiler/Linear.h"
-#include "exec/IFunction.h"
-
-namespace neurun
-{
-namespace compiler
-{
-
-struct CodeWithInfo
-{
-  Linear::Element elem;
-  std::unique_ptr<exec::IFunction> fn;
-
-  CodeWithInfo(const Linear::Element &elem, std::unique_ptr<exec::IFunction> &&fn)
-      : elem{elem}, fn{std::move(fn)}
-  {
-  }
-};
-
-} // namespace compiler
-} // namespace neurun
-
-#endif // __NEURUN_COMPILER_CODE_WITH_INFO_H__
diff --git a/runtime/neurun/core/src/compiler/Compiler.cc b/runtime/neurun/core/src/compiler/Compiler.cc
deleted file mode 100644
index d5ee39d85..000000000
--- a/runtime/neurun/core/src/compiler/Compiler.cc
+++ /dev/null
@@ -1,143 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "compiler/Compiler.h"
-
-#include "BackendResolver.h"
-#include "ParamChecker.h"
-#include "ExecutorFactory.h"
-#include "OperationValidator.h"
-
-#include "compiler/IScheduler.h"
-#include "compiler/ManualScheduler.h"
-#include "compiler/HEScheduler.h"
-#include "backend/ExecTime.h"
-#include "ir/operation/LowerInfo.h"
-#include "dumper/dot/DotDumper.h"
-#include "compiler/Linear.h"
-#include "exec/interp/ExecManager.h"
-#include "backend/ExecTime.h"
-#include "util/ConfigSource.h"
-
-namespace neurun
-{
-
-namespace compiler
-{
-
-static void checkProfilerConditions()
-{
-  if (!util::getConfigBool(util::config::USE_SCHEDULER))
-    throw std::runtime_error("Heterogeneous scheduler must be enabled during profiling.");
-
-  if (util::getConfigString(util::config::EXECUTOR) != "Dataflow")
-    throw std::runtime_error("Profiling mode works only with 'Dataflow' executor");
-}
-
-void Compiler::compile(void)
-{
-  _state = State::STARTED;
-
-  /***************************************************
-   * Prepare compilation phase
-   ***************************************************/
-
-  // Operation validation check
-  OperationValidator{*_graph}();
-
-  // Compilable check
-  if (!checkCompilable())
-  {
-    _executor = std::make_shared<exec::interp::ExecManager>(*_graph);
-    return;
-  }
-
-  // Mode check
-  if (util::getConfigBool(util::config::PROFILING_MODE))
-    checkProfilerConditions();
-
-  /***************************************************
-   * Backend independent analysis & optimization phase
-   ***************************************************/
-  // Schedule
-  std::unique_ptr<BackendResolver> br;
-  std::shared_ptr<ir::OperationIndexMap<int64_t>> indexed_ranks;
-  if (util::getConfigBool(util::config::USE_SCHEDULER))
-  {
-    auto scheduler = compiler::HEScheduler(
-        _graph->operands(), backend::BackendManager::get().getAll(), _graph->getKernelBuilder());
-    br = scheduler.schedule(*_graph);
-    indexed_ranks = scheduler.getIndexedRanks();
-  }
-  else
-  {
-    auto scheduler = compiler::ManualScheduler();
-    br = scheduler.schedule(*_graph);
-  }
-  _graph->setBackendResolver(std::move(br));
-  /*************************************************************
-   *  Backend independent analysis & optimization phase finished
-   *************************************************************/
-
-  // dump graph to .dot
-  auto dump_level =
-      static_cast<dumper::dot::DotDumper::Level>(util::getConfigInt(util::config::GRAPH_DOT_DUMP));
-  neurun::dumper::dot::DotDumper dot_dumper(*_graph, dump_level);
-  dot_dumper.dump("before_lower");
-
-  // Lower: decide backend
-  _graph->lower();
-  _state = State::LOWERED;
-
-  dot_dumper.dump("after_lower");
-
-  const std::string executor_str = util::getConfigString(util::config::EXECUTOR);
-
-  _executor =
-      std::shared_ptr<exec::IExecutor>{ExecutorFactory::get().create(executor_str, *_graph)};
-  _executor->setIndexedRanks(indexed_ranks);
-  /********************************
-   * Code generation phase finished
-   ********************************/
-  _state = State::COMPILED;
-}
-
-bool Compiler::checkCompilable()
-{
-  // Disable compile phase
-  // When ready to use interpreter backend, remove this config and use backend setting
-  const auto env_disable_compile = util::getConfigBool(util::config::DISABLE_COMPILE);
-  if (env_disable_compile)
-  {
-    return false;
-  }
-
-  // TODO check unspecified operand shape
-
-  // Check compilable parameter
-  ParamChecker paramChecker{_graph};
-  paramChecker();
-  if (paramChecker.haveNoneConstParam())
-  {
-    return false;
-  }
-
-  return true;
-}
-
-} // namespace compiler
-
-} // namespace neurun
diff --git a/runtime/neurun/core/src/compiler/ExecutorFactory.cc b/runtime/neurun/core/src/compiler/ExecutorFactory.cc
deleted file mode 100644
index 59de6c4a4..000000000
--- a/runtime/neurun/core/src/compiler/ExecutorFactory.cc
+++ /dev/null
@@ -1,379 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ExecutorFactory.h"
-
-#include <functional>
-#include "exec/ExecutionObservers.h"
-#include "exec/LinearExecutor.h"
-#include "exec/DataflowExecutor.h"
-#include "exec/ParallelExecutor.h"
-#include "compiler/BackendResolver.h"
-#include "backend/ExecTime.h"
-#include "compiler/Linear.h"
-#include "ir/dumper/Dumper.h"
-#include "SubTensorAnalyzer.h"
-#include "backend/IConstantInitializer.h"
-#include "backend/IKernelGenerator.h"
-#include "backend/IShapeFixer.h"
-#include "backend/ITensorRegister.h"
-#include "cpp14/memory.h"
-#include "CodeWithInfo.h"
-
-namespace neurun
-{
-namespace compiler
-{
-
-ExecutorFactory &ExecutorFactory::get()
-{
-  static ExecutorFactory singleton;
-  return singleton;
-}
-
-ExecutorFactory::ExecutorFactory()
-{
-  _map["Linear"] = createLinearExecutor;
-  _map["Dataflow"] = std::bind(createDataflowExecutor, std::placeholders::_1, false);
-  _map["Parallel"] = std::bind(createDataflowExecutor, std::placeholders::_1, true);
-}
-
-exec::IExecutor *ExecutorFactory::create(const std::string &id, ir::Graph &graph)
-{
-  return _map.at(id)(graph);
-}
-
-exec::IExecutor *ExecutorFactory::createLinearExecutor(ir::Graph &graph)
-{
-  auto operand_context = std::make_shared<OperandContext>();
-
-  // linearize
-  assert(!graph.isBuildingPhase());
-  auto linear = nnfw::cpp14::make_unique<Linear>(graph);
-
-  // Dump ops
-  linear->accept(ir::dumper::Dumper{});
-
-  /*************************************************
-   * Backend dependent analysis & optimization phase
-   *************************************************/
-
-  // SubTensorInfo should be generated after lower, before shape correction and finalize
-  // because SubTensorAnalyzer assume that insert permutation is already finished
-  //    lower: decide backend and insert permutation
-  //    fix shapes: prepare codegen to optimization
-  //    generate tensor objects: generate tensor using subtensor info
-  //    generate kernels
-  //    allocate tesor memory
-  //    constant intialization: fill the constants with values
-  // Generated SubTensorInfo is in operand(Object)
-  // for easy pass SubTensorInfo to plan builder and tensor builder
-  linear->accept(SubTensorAnalyzer{graph});
-
-  /**********************************************************
-   * Backend dependent analysis & optimization phase finished
-   **********************************************************/
-
-  /***********************
-   * Code generation phase
-   ***********************/
-
-  // Fix shapes
-  linear->iterate([&](const compiler::Linear::Element &element) {
-    auto backend = element.lower_info->backend();
-    auto shape_fixer = graph.backend_resolver()->getBackendContext(backend)->shape_fixer;
-    shape_fixer->fix(*element.op_seq);
-  });
-
-  linear->planTensors();
-
-  auto tensor_builders = graph.backend_resolver()->tensor_builders();
-
-  // Prepare tensors
-  for (auto &tensor_builder : tensor_builders)
-  {
-    tensor_builder->prepare();
-  }
-
-  // Generate initializers
-  linear->generateConstantInitializers();
-
-  class ExecutionBuilder final : public IExecutionBuilder
-  {
-  public:
-    void append(std::unique_ptr<exec::IFunction> &&f) override
-    {
-      _code.emplace_back(_next_elem, std::move(f));
-    }
-
-    void setNextElem(const compiler::Linear::Element &next_elem) { _next_elem = next_elem; }
-    std::vector<CodeWithInfo> releaseCode() { return std::move(_code); }
-
-  private:
-    compiler::Linear::Element _next_elem;
-    std::vector<CodeWithInfo> _code;
-  };
-
-  ExecutionBuilder builder;
-
-  // Generate kernels
-  linear->iterate([&](const compiler::Linear::Element &element) {
-    auto backend = element.lower_info->backend();
-    builder.setNextElem(element);
-    auto kernel_gen = graph.backend_resolver()->getBackendContext(backend)->kernel_gen;
-    kernel_gen->generate(*element.op_seq, &builder);
-  });
-
-  auto code = builder.releaseCode();
-
-  for (auto &tensor_builder : tensor_builders)
-  {
-    tensor_builder->allocateConsts();
-  }
-
-  // TODO Add optimization passes
-
-  // Initialize constant tensors
-  for (const auto backend : backend::BackendManager::get().getAll())
-  {
-    graph.backend_resolver()->getBackendContext(backend)->constant_initializer->run();
-  }
-
-  for (auto &&e : code)
-  {
-    e.fn->prepare();
-    auto backend = e.elem.lower_info->backend();
-    auto tensor_builder = graph.backend_resolver()->getBackendContext(backend)->tensor_builder;
-    tensor_builder->postFunctionPrepare();
-  }
-
-  for (auto &tensor_builder : tensor_builders)
-  {
-    tensor_builder->allocateNonconsts();
-  }
-
-  for (auto &tensor_builder : tensor_builders)
-  {
-    tensor_builder->finalize();
-  }
-
-  // Wrap tensors as Object and store them to plan
-  for (auto &tensor_builder : tensor_builders)
-  {
-    tensor_builder->iterate([&](const ir::OperandIndex &index) {
-      auto object = tensor_builder->tensorAt(index);
-      operand_context->set(index, object);
-    });
-  }
-
-  // Prepare each TensorManager on each backend
-  auto tensor_mgrs = nnfw::cpp14::make_unique<backend::TensorManagerSet>();
-  for (auto &tensor_builder : tensor_builders)
-  {
-    tensor_mgrs->insert(tensor_builder->releaseTensorManager());
-  }
-
-  auto exec =
-      new exec::LinearExecutor{graph, operand_context, std::move(tensor_mgrs), std::move(code)};
-
-  const std::string trace_filepath = util::getConfigString(util::config::TRACE_FILEPATH);
-  if (!trace_filepath.empty())
-  {
-    std::unique_ptr<exec::IExecutionObserver> ctp =
-        nnfw::cpp14::make_unique<exec::ChromeTracingObserver>(trace_filepath);
-    exec->addObserver(std::move(ctp));
-  }
-
-  return exec;
-}
-
-exec::IExecutor *ExecutorFactory::createDataflowExecutor(ir::Graph &graph, bool parallel)
-{
-  auto operand_context = std::make_shared<OperandContext>();
-
-  graph.subgraphs().iterate([&](const ir::SubgraphIndex &, const ir::OpSequence &subg) {
-    auto subtensor_analyzer = SubTensorAnalyzer{graph};
-    subg.accept(subtensor_analyzer);
-  });
-
-  // Fix shapes and register tensors
-  graph.subgraphs().iterate([&](const ir::SubgraphIndex &subg_index, const ir::OpSequence &subg) {
-    auto backend = graph.getLowerInfo(subg_index)->backend();
-    auto shape_fixer = graph.backend_resolver()->getBackendContext(backend)->shape_fixer;
-    shape_fixer->fix(subg);
-    const auto tensor_register =
-        graph.backend_resolver()->getBackendContext(backend)->tensor_register;
-    tensor_register->registerTensors(subg, graph.getLowerInfo());
-  });
-
-  graph.operands().iterate([&](const ir::OperandIndex &ind, const ir::Operand &obj) {
-    const auto lower_info = graph.getLowerInfo(ind);
-    for (auto factor : lower_info->def_factors())
-    {
-      auto backend = factor.backend();
-      auto tensor_builder = graph.backend_resolver()->getBackendContext(backend)->tensor_builder;
-
-      if (!tensor_builder->isRegistered(ind))
-      {
-        // These tensors do not exist in any op_seq (No use and def)
-        // These tensors cannot be a SubTensor
-        assert(obj.parent_info() == nullptr);
-
-        const auto info = obj.info();
-        const auto backend_layout = lower_info->def_factors().getOnlyElement().layout();
-        // TODO Change tensor info to have permuted shape
-        tensor_builder->registerTensorInfo(ind, info, backend_layout, obj.isConstant());
-      }
-
-      // Is not SubTensor?
-      if (!backend->config()->SupportSubTensorAlloc() || obj.parent_info() == nullptr)
-      {
-        // To make this never be deallocated, this is a workaround to use static memory planner
-        tensor_builder->notifyFirstUse(ind);
-      }
-    }
-  });
-
-  auto tensor_builders = graph.backend_resolver()->tensor_builders();
-
-  for (auto &tensor_builder : tensor_builders)
-  {
-    tensor_builder->prepare();
-  }
-
-  class ExecutionBuilder : public IExecutionBuilder
-  {
-  public:
-    void append(std::unique_ptr<exec::IFunction> &&fn) override
-    {
-      auto itr = _code_map.find(_next_index);
-      if (itr == _code_map.end())
-      {
-        _code_map[_next_index] = nnfw::cpp14::make_unique<exec::FunctionSequence>();
-      }
-      _code_map[_next_index]->append(std::move(fn));
-    };
-
-    // TODO Remove this method and make `append` to get index value as an argument
-    void setNextIndex(const ir::SubgraphIndex next_index) { _next_index = next_index; }
-
-    exec::DataflowExecutor::CodeMap &&releaseCodeMap() { return std::move(_code_map); }
-
-  private:
-    ir::SubgraphIndex _next_index;
-    exec::DataflowExecutor::CodeMap _code_map;
-  };
-
-  auto execution_builder = nnfw::cpp14::make_unique<ExecutionBuilder>();
-
-  // Generate kernels
-  graph.subgraphs().iterate([&](const ir::SubgraphIndex &subg_index, const ir::OpSequence &subg) {
-    auto backend = graph.getLowerInfo(subg_index)->backend();
-    auto constant_initializer =
-        graph.backend_resolver()->getBackendContext(backend)->constant_initializer;
-    constant_initializer->generate(subg, graph.operands());
-    // TODO This approach is temporal. See declaration of `setNextIndex`.
-    execution_builder->setNextIndex(subg_index);
-    auto kernel_gen = graph.backend_resolver()->getBackendContext(backend)->kernel_gen;
-    kernel_gen->generate(subg, execution_builder.get());
-  });
-
-  for (const auto &tensor_builder : tensor_builders)
-  {
-    tensor_builder->allocateConsts();
-  }
-
-  // Initialize constant tensors
-  for (const auto backend : backend::BackendManager::get().getAll())
-  {
-    graph.backend_resolver()->getBackendContext(backend)->constant_initializer->run();
-  }
-
-  exec::DataflowExecutor::CodeMap code_map = execution_builder->releaseCodeMap();
-
-  for (auto &it : code_map)
-  {
-    auto subg_index = it.first;
-    auto &function_sequence = *(it.second);
-
-    function_sequence.iterate([&](exec::IFunction &ifunc) {
-      // NOTE. It may need avoiding prepare() for some operations
-      // Ref: https://github.sec.samsung.net/STAR/nnfw/issues/7326
-      ifunc.prepare();
-      auto backend = graph.getLowerInfo(subg_index)->backend();
-      auto tensor_builder = graph.backend_resolver()->getBackendContext(backend)->tensor_builder;
-      tensor_builder->postFunctionPrepare();
-    });
-  }
-
-  for (const auto &tensor_builder : tensor_builders)
-  {
-    tensor_builder->allocateNonconsts();
-  }
-
-  for (auto &tensor_builder : tensor_builders)
-  {
-    tensor_builder->finalize();
-  }
-
-  // Wrap tensors as Object and store them to plan
-  for (auto &tensor_builder : tensor_builders)
-  {
-    tensor_builder->iterate([&](const ir::OperandIndex &index) {
-      auto object = tensor_builder->tensorAt(index);
-      operand_context->set(index, object);
-    });
-  }
-
-  // Prepare each TensorManager on each backend
-  auto tensor_mgrs = nnfw::cpp14::make_unique<backend::TensorManagerSet>();
-  for (auto &tensor_builder : tensor_builders)
-  {
-    tensor_mgrs->insert(tensor_builder->releaseTensorManager());
-  }
-
-  exec::ExecutorBase *exec = nullptr;
-  if (parallel)
-  {
-    exec = new exec::ParallelExecutor{graph, operand_context, std::move(tensor_mgrs),
-                                      std::move(code_map)};
-  }
-  else
-  {
-    exec = new exec::DataflowExecutor{graph, operand_context, std::move(tensor_mgrs),
-                                      std::move(code_map)};
-    if (util::getConfigBool(util::config::PROFILING_MODE))
-    {
-      auto et = std::make_shared<backend::ExecTime>(backend::BackendManager::get().getAll());
-      std::unique_ptr<exec::IExecutionObserver> obs =
-          nnfw::cpp14::make_unique<exec::ProfileObserver>(et);
-      exec->addObserver(std::move(obs));
-    }
-  }
-
-  const std::string trace_filepath = util::getConfigString(util::config::TRACE_FILEPATH);
-  if (!trace_filepath.empty())
-  {
-    std::unique_ptr<exec::IExecutionObserver> ctp =
-        nnfw::cpp14::make_unique<exec::ChromeTracingObserver>(trace_filepath);
-    exec->addObserver(std::move(ctp));
-  }
-
-  return exec;
-}
-
-} // namespace compiler
-} // namespace neurun
diff --git a/runtime/neurun/core/src/compiler/ExecutorFactory.h b/runtime/neurun/core/src/compiler/ExecutorFactory.h
deleted file mode 100644
index 6da4ffc5e..000000000
--- a/runtime/neurun/core/src/compiler/ExecutorFactory.h
+++ /dev/null
@@ -1,52 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_COMPILER_EXECUTOR_FACTORY_H__
-#define __NEURUN_COMPILER_EXECUTOR_FACTORY_H__
-
-#include <unordered_map>
-
-#include "exec/IExecutor.h"
-#include "ir/Graph.h"
-
-namespace neurun
-{
-namespace compiler
-{
-
-class ExecutorFactory
-{
-public:
-  static ExecutorFactory &get();
-
-public:
-  exec::IExecutor *create(const std::string &id, ir::Graph &graph);
-
-private:
-  ExecutorFactory();
-
-private:
-  static exec::IExecutor *createLinearExecutor(ir::Graph &graph);
-  static exec::IExecutor *createDataflowExecutor(ir::Graph &graph, bool parallel);
-
-private:
-  std::unordered_map<std::string, std::function<exec::IExecutor *(ir::Graph &)>> _map;
-};
-
-} // namespace compiler
-} // namespace neurun
-
-#endif // __NEURUN_COMPILER_EXECUTOR_FACTORY_H__
diff --git a/runtime/neurun/core/src/compiler/HEScheduler.cc b/runtime/neurun/core/src/compiler/HEScheduler.cc
deleted file mode 100644
index aec68d655..000000000
--- a/runtime/neurun/core/src/compiler/HEScheduler.cc
+++ /dev/null
@@ -1,628 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/Operand.h"
-#include "compiler/HEScheduler.h"
-#include "ir/Graph.h"
-#include "util/ConfigSource.h"
-#include "compiler/IExecutionBuilder.h"
-#include "compiler/BackendResolver.h"
-#include "backend/IShapeFixer.h"
-#include "util/logging.h"
-#include "util/Utils.h"
-#include "exec/FunctionSequence.h"
-#include <cassert>
-#include <cmath>
-#include <chrono>
-
-namespace neurun
-{
-
-namespace compiler
-{
-static uint32_t getOperationsFlattenedIOSize(const ir::Graph &graph, const ir::Operation &node)
-{
-  uint32_t size = 0;
-  for (const auto &input : node.getInputs())
-  {
-    size += graph.operands().at(input).info().total_size();
-  }
-  for (const auto &output : node.getOutputs())
-  {
-    size += graph.operands().at(output).info().total_size();
-  }
-  return size;
-}
-
-static bool isQuant(const ir::Graph &graph, const ir::Operation &node)
-{
-  for (const auto &input : node.getInputs())
-  {
-    const auto &obj = graph.operands().at(input);
-    if (obj.typeInfo().type() == ir::DataType::QUANT8_ASYMM)
-    {
-      return true;
-    }
-  }
-  return false;
-}
-
-static bool isWorkaroundSkip(const ir::Graph &graph, const backend::Backend *backend,
-                             const ir::Operation &node, bool quant)
-{
-  /* TODO: this is workaround, come up with better solution if have.
-      Adding exception in stage doesn't help. Because if there is a record for add without
-      broadcast, scheduling will select it since it doesn't distinguish broadcast and
-      non-broadcast like it does for quant non-quantized*/
-  if (backend->config()->id() == "cpu" &&
-      (node.opcode() == ir::OpCode::Add || node.opcode() == ir::OpCode::Sub ||
-       node.opcode() == ir::OpCode::Mul))
-  {
-    const auto lhs_index{node.getInputs().at(ir::operation::Add::Input::LHS)};
-    const auto rhs_index{node.getInputs().at(ir::operation::Add::Input::RHS)};
-    /*Broadcasting isn't supported on CPU: no way to differ the existing exec_time record with and
-     * without broadcasting*/
-    if (!(graph.operands().at(lhs_index).shape() == graph.operands().at(rhs_index).shape()))
-    {
-      return true;
-    }
-  }
-  /* TODO: this is workaround, come up with better solution if have.
-          Adding exception in stage doesn't help. Because if there is a record for Mul without
-          broadcast, scheduling will select it since it doesn't distinguish broadcast and
-          non-broadcast like it does for quant non-quantized*/
-  else if (backend->config()->id() == "acl_neon" && node.opcode() == ir::OpCode::Mul)
-  {
-    const auto lhs_index{node.getInputs().at(ir::operation::Mul::Input::LHS)};
-    const auto rhs_index{node.getInputs().at(ir::operation::Mul::Input::RHS)};
-
-    // Nontrivial broadcasting isn't supported yet
-    if (quant ||
-        !(graph.operands().at(lhs_index).shape() == graph.operands().at(rhs_index).shape()))
-    {
-      return true;
-    }
-  }
-  return false;
-}
-
-// if a node can be merged into op_seq
-static bool isMergeable(const ir::Graph &graph, const ir::Operation &node)
-{
-  size_t prev_op_cnt = 0;
-  for (const auto &input : node.getInputs())
-  {
-    // only valid_inputs
-    const auto &operand = graph.operands().at(input);
-    if (operand.isConstant())
-      continue;
-
-    // This operand is output of operation, not weight or bias
-    if (operand.getDef().list().size() > 0)
-      ++prev_op_cnt;
-
-    // Current node has multiple inputs as concat or at the beginning of the separated branch
-    if (prev_op_cnt > 1 || operand.getUses().list().size() > 1)
-    {
-      return false;
-    }
-  }
-  return true;
-}
-
-void HEScheduler::scheduleShufflingBackends()
-{
-  VERBOSE(HEScheduler::schedule)
-      << "Started task scheduling: uses all backends to get more metrics for data transfer"
-      << std::endl;
-  size_t backend_ind = 0;
-  for (const auto &rank : _rank_to_op)
-  {
-    VERBOSE(HEScheduler::schedule) << "scheduling (" << rank.second.value() << ")" << std::endl;
-    const auto &node = _graph->operations().at(rank.second);
-    const bool quant = isQuant(*_graph, node);
-    const auto size = getOperationsFlattenedIOSize(*_graph, node);
-    for (size_t i = 0;; ++i)
-    {
-      if (i == _all_backends.size())
-      {
-        // wasn't able to find backend
-        assert(false);
-        break;
-      }
-      if (backend_ind == _all_backends.size())
-      {
-        backend_ind = 0;
-      }
-      if (isWorkaroundSkip(*_graph, _all_backends[backend_ind], node, quant))
-      {
-        ++backend_ind;
-        continue;
-      }
-      const auto exec_time =
-          _exec_time->getOperationExecTime(_all_backends[backend_ind], node.name(), quant, size);
-      // Scheduling to measure data transfer must be done after measuring all backends separately
-      assert(exec_time != _exec_time->NOT_FOUND);
-      if (exec_time == _exec_time->getMax())
-      {
-        ++backend_ind;
-        continue;
-      }
-      _backend_resolver->setBackend(rank.second, _all_backends[backend_ind]);
-      VERBOSE(HEScheduler::schedule) << "backend for " << node.name() << " is "
-                                     << _all_backends[backend_ind]->config()->id() << std::endl;
-      ++backend_ind;
-      break;
-    }
-  }
-}
-
-bool HEScheduler::isNodeProfiled(const ir::Operation &node)
-{
-  const bool quant = isQuant(*_graph, node);
-  const auto size = getOperationsFlattenedIOSize(*_graph, node);
-  for (const auto *backend : _all_backends)
-  {
-    const auto exec_time = _exec_time->getOperationExecTime(backend, node.name(), quant, size);
-    if (exec_time == _exec_time->NOT_FOUND)
-      return false;
-  }
-  return true;
-}
-
-void HEScheduler::scheduleBranch(const ir::OperationIndex &index,
-                                 ir::OperationIndexMap<bool> &scheduled)
-{
-  auto loc_index = index;
-  const backend::Backend *parent_backend = nullptr;
-  while (true)
-  {
-    if (scheduled[loc_index])
-    {
-      return;
-    }
-    if (!schedule(loc_index, parent_backend))
-    {
-      return;
-    }
-    scheduled[loc_index] = true;
-    parent_backend = _backend_resolver->getBackend(loc_index);
-
-    const auto &node = _graph->operations().at(loc_index);
-    /* get the only output operand, that is input of the next single operation
-     *   and just this nodes output.*/
-    if (node.getOutputs().size() != 1)
-    {
-      return;
-    }
-    const auto &only_out_operand = _graph->operands().at(*node.getOutputs().begin());
-    loc_index = only_out_operand.getUses().list().front();
-    /* verify, that next node is neither beginning nor ending node of a branch*/
-    const auto &next_node = _graph->operations().at(loc_index);
-    if (!isMergeable(*_graph, next_node))
-    {
-      return;
-    }
-  }
-}
-
-std::unique_ptr<compiler::BackendResolver> HEScheduler::schedule(const ir::Graph &graph)
-{
-  _graph = &graph;
-  VERBOSE(HEScheduler::schedule) << "task scheduling started" << std::endl;
-  // Make ranks and save in descending order
-  makeRank();
-
-  for (const auto *backend : _all_backends)
-  {
-    _backends_avail_time.emplace(backend, std::map<int64_t, int64_t>{{0, 0}});
-  }
-
-  const bool is_profiling = util::getConfigBool(util::config::PROFILING_MODE);
-  if (is_profiling)
-  {
-    // Check if profiling info about all backend/node pairs already exists
-    bool all_nodes_are_profiled = true;
-    _graph->operations().iterate([&](const ir::OperationIndex &, const ir::Operation &op) {
-      if (all_nodes_are_profiled)
-        all_nodes_are_profiled = isNodeProfiled(op);
-    });
-
-    // If all nodes are already profiled - schedule backends in such order, so more profiling
-    // information about between-backends data transfer could be collected
-    if (all_nodes_are_profiled)
-    {
-      scheduleShufflingBackends();
-      VERBOSE(HEScheduler::schedule) << "task scheduling finished" << std::endl;
-      return std::move(_backend_resolver);
-    }
-  }
-
-  ir::OperationIndexMap<bool> visited;
-  graph.operations().iterate(
-      [&](const ir::OperationIndex &index, const ir::Operation &) { visited[index] = false; });
-  // for each task select the backend with the smallest earliest finishing time(eft)
-  for (const auto &rank : _rank_to_op)
-  {
-    scheduleBranch(rank.second, visited);
-  }
-  VERBOSE(HEScheduler::schedule) << "task scheduling finished" << std::endl;
-  return std::move(_backend_resolver);
-}
-
-int64_t HEScheduler::getOpTime(const backend::Backend *backend, const std::string &operation,
-                               bool quant, uint32_t size)
-{
-  const auto time = _exec_time->getOperationExecTime(backend, operation, quant, size);
-  if (time != _exec_time->NOT_FOUND)
-    return time;
-
-  return _is_supported.at(backend).at(operation) ? 1 : _exec_time->getMax();
-}
-
-int64_t HEScheduler::getPermuteTime(const backend::Backend *src_backend,
-                                    const backend::Backend *dst_backend, bool quant, uint32_t size)
-{
-  const auto time = _exec_time->getPermuteTime(src_backend, dst_backend, quant, size);
-  if (time != _exec_time->NOT_FOUND)
-    return time;
-
-  // Makes the scheduler prefer keeping computations on one backend
-  return size / 200;
-}
-
-int64_t HEScheduler::tryBackend(const ir::Operation &node, const backend::Backend *backend)
-{
-  // if there is no profiling info don't use this backend during scheduling
-  if (!util::getConfigBool(util::config::PROFILING_MODE))
-  {
-    VERBOSE(HEScheduler::tryBackend)
-        << "Trying to HE schedule while there is no profiling info for " << node.name()
-        << " on backend " << backend->config()->id() << ". So this backend won't be used. "
-        << std::endl;
-    _is_supported[backend][node.name()] = false;
-    return _exec_time->getMax();
-  }
-  auto iter = _is_supported.find(backend);
-  if (iter != _is_supported.end())
-  {
-    auto it2 = iter->second.find(node.name());
-    if (it2 != iter->second.end())
-    {
-      return _is_supported[backend][node.name()] ? 1 : _exec_time->getMax();
-    }
-  }
-  try
-  {
-    node.accept(*_backend_resolver->getBackendContext(backend)->shape_fixer);
-
-    _is_supported[backend][node.name()] = true;
-  }
-  catch (std::runtime_error &e)
-  {
-    _is_supported[backend][node.name()] = false;
-  }
-  return _is_supported[backend][node.name()] ? 1 : _exec_time->getMax();
-}
-
-void HEScheduler::makeRank()
-{
-  VERBOSE(HEScheduler::makeRank) << "task prioritizing" << std::endl;
-
-  _graph->operations().iterate(
-      [&](const ir::OperationIndex &index, const ir::Operation &) { DFSMaxRank(index); });
-
-  // Check that ranks are calculated for all operations(nodes)
-  _graph->operations().iterate([&](const ir::OperationIndex &index, const ir::Operation &) {
-    UNUSED_RELEASE(index);
-    assert(_op_to_rank->find(index) != _op_to_rank->end());
-  });
-  VERBOSE(HEScheduler::makeRank) << "task prioritizing finished" << std::endl;
-}
-
-int64_t HEScheduler::DFSMaxRank(const ir::OperationIndex &index)
-{
-  auto op_to_rank_it = _op_to_rank->find(index);
-  if (op_to_rank_it != _op_to_rank->end())
-    return op_to_rank_it->second;
-
-  const auto &node = _graph->operations().at(index);
-  int64_t rank = 0;
-  const bool quant = isQuant(*_graph, node);
-  const auto size = getOperationsFlattenedIOSize(*_graph, node);
-  auto supported_backends_quantity = static_cast<int64_t>(_all_backends.size());
-
-  const auto max_child_rank = DFSChildrenMaxRank(index);
-
-  // get average exec time of this op
-  for (const auto &backend : _all_backends)
-  {
-    auto exec_time = _exec_time->getOperationExecTime(backend, node.name(), quant, size);
-    if (exec_time == _exec_time->NOT_FOUND)
-    {
-      exec_time = tryBackend(node, backend);
-    }
-    if (exec_time < _exec_time->getMax())
-    {
-      rank += exec_time;
-    }
-    else
-    {
-      // this operation isn't supported in this backend
-      --supported_backends_quantity;
-    }
-  }
-  if (supported_backends_quantity == 0)
-  {
-    throw std::runtime_error{"Encountered unsupported op: " + node.name()};
-  }
-  rank /= supported_backends_quantity;
-
-  // get standard deviation
-  int64_t std = 0;
-  for (const auto backend : _all_backends)
-  {
-    const auto exec_time = getOpTime(backend, node.name(), quant, size);
-    if (exec_time < _exec_time->getMax())
-    {
-      std += (exec_time - rank) * (exec_time - rank);
-    }
-  }
-  std /= supported_backends_quantity;
-  if (std > 0)
-  {
-    std = static_cast<int>(std::sqrt(std));
-    rank *= std;
-  }
-  rank += max_child_rank;
-
-  assert(rank >= 0);
-  _rank_to_op.emplace(rank, index);
-  _op_to_rank->emplace(index, rank);
-  VERBOSE(HEScheduler::DFSMaxRank) << "rank of operation (" << index.value() << ")" << node.name()
-                                   << " is " << rank << std::endl;
-
-  return rank;
-}
-
-int64_t HEScheduler::DFSChildrenMaxRank(const ir::OperationIndex &index)
-{
-  const auto &node = _graph->operations().at(index);
-  int64_t max_child_rank = 0;
-  for (const auto &output : node.getOutputs())
-  {
-    const auto &operand = _graph->operands().at(output);
-    const bool quant = operand.typeInfo().type() == ir::DataType::QUANT8_ASYMM;
-    // average data transfer cost of this operand's data
-    int64_t avg_transfer_cost = 1;
-    for (const auto *backend : _all_backends)
-    {
-      for (const auto *other_backend : _all_backends)
-      {
-        if (backend == other_backend)
-        {
-          continue;
-        }
-        auto transfer_cost =
-            _exec_time->getPermuteTime(backend, other_backend, quant, operand.info().total_size());
-        if (transfer_cost == _exec_time->NOT_FOUND)
-        {
-          // Makes the scheduler prefer keeping computations on one backend
-          transfer_cost = operand.info().total_size() / 100;
-        }
-        avg_transfer_cost += transfer_cost;
-      }
-    }
-    avg_transfer_cost /= _all_backends.size();
-    for (const auto &use : operand.getUses().list())
-    {
-      const auto cur_child_rank = DFSMaxRank(use);
-      max_child_rank = std::max(max_child_rank, cur_child_rank + avg_transfer_cost);
-    }
-  }
-  return max_child_rank;
-}
-
-int64_t HEScheduler::backendAvailableTime(const backend::Backend *backend,
-                                          const int64_t &starting_time, const int64_t &time_amount)
-{
-  const auto backend_times = _backends_avail_time.at(backend);
-  // finishing and starting times of an op, that will come after current op
-  auto next_op_fst = backend_times.upper_bound(starting_time);
-  // finishing time of an op, that will come before current op
-  auto prev_op_ft = starting_time;
-  // until reach the "hole/gap", that is enough to run this op
-  while (next_op_fst != backend_times.end() && next_op_fst->second - prev_op_ft <= time_amount)
-  {
-    prev_op_ft = next_op_fst->first + 1;
-    ++next_op_fst;
-  }
-  return prev_op_ft;
-}
-
-bool HEScheduler::schedule(const ir::OperationIndex &index, const backend::Backend *parent_backend)
-{
-  VERBOSE(HEScheduler::schedule) << "scheduling (" << index.value() << ")" << std::endl;
-  int64_t eft = std::numeric_limits<int64_t>::max(), selected_exec_time = 0;
-  const auto &node = _graph->operations().at(index);
-
-  std::multimap<int64_t, int64_t> selected_transfer_st_exec_time;
-  // select the backend with the smallest eft of this task
-  const backend::Backend *chosen_backend = nullptr;
-  for (const auto *backend : _all_backends)
-  {
-    std::multimap<int64_t, int64_t> transfer_st_exec_time;
-    const auto est_and_et = ESTAndExecTime(backend, index, transfer_st_exec_time);
-
-    if (eft > est_and_et.first + est_and_et.second)
-    {
-      eft = est_and_et.first + est_and_et.second;
-      selected_exec_time = est_and_et.second;
-      chosen_backend = backend;
-      selected_transfer_st_exec_time = transfer_st_exec_time;
-    }
-  }
-
-  if (chosen_backend == nullptr)
-  {
-    throw std::runtime_error{"Fail to choose backend on scheduler"};
-  }
-
-  // this is part of a branch and it is assigned another backend
-  if (parent_backend && parent_backend != chosen_backend)
-  {
-    return false;
-  }
-  for (const auto &it : selected_transfer_st_exec_time)
-  {
-    auto prev_op_ft = backendAvailableTime(_cpu_backend, it.first, it.second);
-    _backends_avail_time[_cpu_backend].insert({prev_op_ft + it.second, prev_op_ft});
-  }
-
-  _ops_eft[index] = eft;
-  _backends_avail_time[chosen_backend].emplace(eft, eft - selected_exec_time);
-  _backend_resolver->setBackend(index, chosen_backend);
-
-  VERBOSE(HEScheduler::schedule) << "backend for " << node.name() << " is "
-                                 << chosen_backend->config()->id() << ". Its eft: " << eft
-                                 << std::endl;
-  return true;
-}
-
-std::pair<int64_t, int64_t>
-HEScheduler::ESTAndExecTime(const backend::Backend *backend, const ir::OperationIndex &index,
-                            std::multimap<int64_t, int64_t> &transfer_st_exec_time)
-{
-  const bool is_linear_exec = "Linear" == util::getConfigString(util::config::EXECUTOR);
-  const bool is_parallel_exec = "Parallel" == util::getConfigString(util::config::EXECUTOR);
-  // Permutation will cause creating a separate op_seq that contains just this permutation node.
-  // This isn't needed for Linear executor since it doesn't use subgraphs
-  // Number 1 ms is picked experimentally
-  int64_t permute_fine = 1000;
-  // Multiply cpu operations' exec time by 2 because in parallel executor it might be busy with
-  // permutation on other branches or non-nnfw specific tasks and have to wait for it.
-  // Number 2 is picked experimentally
-  const int64_t CPU_DELAY = 2;
-  const auto &node = _graph->operations().at(index);
-  const bool quant = isQuant(*_graph, node);
-  const auto size = getOperationsFlattenedIOSize(*_graph, node);
-  // if this node can be part of a op_seq, then assigning different backend will cause creating
-  // another op_seq
-  if (isMergeable(*_graph, node))
-  {
-    permute_fine *= 2;
-  }
-  if (isWorkaroundSkip(*_graph, backend, node, quant))
-  {
-    return {_exec_time->getMax(), _exec_time->getMax()};
-  }
-  // get average exec time of the op on this backend
-  auto exec_time = getOpTime(backend, node.name(), quant, size);
-  if (backend->config()->id() == "cpu" && is_parallel_exec)
-  {
-    exec_time *= CPU_DELAY;
-  }
-
-  // get max eft of direct (one level above) predecessors
-  auto max_pred_eft = predMaxEFT(backend, node, transfer_st_exec_time);
-
-  int64_t total_transfer_cost = 0;
-  std::vector<std::multimap<int64_t, int64_t>::iterator> inserted_permutations;
-  // Find free time for data transferring and insert it into backend taskset. This is needed:
-  //  1. Time for multiple permutations for this node's input is found correctly
-  //  2. If backend==cpu, then free time for this node must come after permutations
-  for (auto &it : transfer_st_exec_time)
-  {
-    if (is_parallel_exec)
-    {
-      it.second *= CPU_DELAY;
-    }
-    if (!is_linear_exec)
-    {
-      it.second += permute_fine;
-    }
-    total_transfer_cost += it.second;
-
-    const auto prev_op_ft = backendAvailableTime(_cpu_backend, it.first, it.second);
-
-    max_pred_eft = std::max(max_pred_eft, prev_op_ft + it.second);
-
-    const auto tmp = _backends_avail_time[_cpu_backend].emplace(prev_op_ft + it.second, prev_op_ft);
-    inserted_permutations.push_back(tmp.first);
-  }
-  // find the hole/gap, where this op can be put or the finishing time of the last assigned op
-  auto prev_op_ft = backendAvailableTime(backend, max_pred_eft, exec_time);
-
-  // Remove inserted permutation from cpu's task set
-  for (const auto &it : inserted_permutations)
-  {
-    _backends_avail_time[_cpu_backend].erase(it);
-  }
-
-  /* In case non-parallel executor measure just exec time and data transfer time
-   * because EFT(prev_op_ft) is the same for all backends. Since two operations
-   * can't be run simultaneously, finish of running operation must be waited for.
-   * When an operation starts, all backends are free. So, they need time just for
-   * data transfer.*/
-  if (!is_parallel_exec)
-  {
-    VERBOSE(HEScheduler::ESTAndExecTime)
-        << "exec_time of (" << index.value() << ") " << node.name() << " quant==" << quant << " on "
-        << backend->config()->id() << " is " << exec_time
-        << " microseconds. Data transfer cost: " << total_transfer_cost << std::endl;
-
-    return {total_transfer_cost, exec_time};
-  }
-  VERBOSE(HEScheduler::ESTAndExecTime)
-      << "exec_time of (" << index.value() << ") " << node.name() << " quant==" << quant << " on "
-      << backend->config()->id() << ": " << exec_time
-      << " microseconds. Backend available time: " << prev_op_ft
-      << " Parent's max eft: " << max_pred_eft - total_transfer_cost
-      << " data transfer cost: " << total_transfer_cost << std::endl;
-
-  return {prev_op_ft, exec_time};
-}
-
-int64_t HEScheduler::predMaxEFT(const backend::Backend *backend, const ir::Operation &node,
-                                std::multimap<int64_t, int64_t> &transfer_st_exec_time)
-{
-  int64_t max_pred_eft = 0;
-  for (const auto &input_operand_idx : node.getInputs())
-  {
-    const auto &input_operand = _graph->operands().at(input_operand_idx);
-    const bool quant = input_operand.typeInfo().type() == ir::DataType::QUANT8_ASYMM;
-
-    for (const auto &input_node_idx : input_operand.getDef().list())
-    {
-      // Data transfer cost from parent's node backend to current node's backend:
-      auto parent_backend = _backend_resolver->getBackend(input_node_idx);
-
-      max_pred_eft = std::max(max_pred_eft, _ops_eft.at(input_node_idx));
-      if (parent_backend != backend)
-      {
-        // Multiply operand size by 2 because size must describe input+output size
-        int64_t transfer_cost =
-            getPermuteTime(parent_backend, backend, quant, input_operand.info().total_size() * 2);
-        transfer_st_exec_time.emplace(_ops_eft.at(input_node_idx), transfer_cost);
-      }
-    }
-  }
-  return max_pred_eft;
-}
-
-} // namespace compiler
-
-} // namespace neurun
diff --git a/runtime/neurun/core/src/compiler/HEScheduler.h b/runtime/neurun/core/src/compiler/HEScheduler.h
deleted file mode 100644
index 538427065..000000000
--- a/runtime/neurun/core/src/compiler/HEScheduler.h
+++ /dev/null
@@ -1,175 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/**
- * @file  HEScheduler.h
- * @brief This file contains HEScheduler class to define and run task Heterogeneous Execution
- * Scheduler
- */
-
-#ifndef __NEURUN_COMPILER_H_E_SCHEDULER_H_
-#define __NEURUN_COMPILER_H_E_SCHEDULER_H_
-
-#include "compiler/IScheduler.h"
-#include "ir/Graph.h"
-#include "backend/ExecTime.h"
-#include "backend/Backend.h"
-#include "cpp14/memory.h"
-#include "ir/OperationIndexMap.h"
-#include <map>
-#include <memory>
-
-namespace neurun
-{
-
-namespace compiler
-{
-/**
- * @brief Class to schedule tasks
- */
-class HEScheduler : IScheduler
-{
-public:
-  /**
-   * @brief     Construct a new Heterogeneous Execution Scheduler object
-   * @param[in] model Graph model
-   * @param[in] backend_resolver backend resolver
-   */
-  HEScheduler(const ir::Operands &operands, std::vector<const backend::Backend *> backends,
-              const std::shared_ptr<backend::custom::IKernelBuilder> &kb)
-      : _is_supported{}, _backends_avail_time{}, _ops_eft{},
-        _op_to_rank{std::make_shared<ir::OperationIndexMap<int64_t>>()},
-        _all_backends(std::move(backends))
-  {
-    _backend_resolver =
-        nnfw::cpp14::make_unique<compiler::BackendResolver>(operands, _all_backends, kb);
-    _exec_time = nnfw::cpp14::make_unique<backend::ExecTime>(_all_backends);
-
-    // Find cpu backend
-    auto cpu_backend_it = std::find_if(
-        _all_backends.begin(), _all_backends.end(),
-        [](const backend::Backend *backend) { return backend->config()->id() == "cpu"; });
-    if (cpu_backend_it == _all_backends.end())
-      throw std::runtime_error("HEScheduler could be used only if 'cpu' backend is available");
-    _cpu_backend = *cpu_backend_it;
-  }
-
-public:
-  /**
-   * @brief   Task scheduling
-   *
-   * @note    The main idea is taken from HSIP algo:
-   *          https://www.hindawi.com/journals/sp/2016/3676149/
-   */
-  std::unique_ptr<compiler::BackendResolver> schedule(const ir::Graph &graph) final;
-  std::shared_ptr<ir::OperationIndexMap<int64_t>> getIndexedRanks() { return _op_to_rank; }
-
-private:
-  bool isNodeProfiled(const ir::Operation &);
-
-  bool schedule(const ir::OperationIndex &, const backend::Backend *parent_backend);
-  /**
-   * @brief   Get earliest starting time and execution time of an operation on a backend.
-   *
-   * @note  Returns a time when operation's inputs are ready and backend is available
-   *        It also returns exec time. If this is "cpu" backend, then exec_time*CPU_DELAY
-   *
-   * @param[in] backend: backend, for which to return the time
-   * @param[in] index: index of an operation
-   * @param[out] transfer_st_exec_time: est and exec time of data transfer operation
-   *
-   * @return earliest starting time and execution time
-   */
-  std::pair<int64_t, int64_t>
-  ESTAndExecTime(const backend::Backend *backend, const ir::OperationIndex &index,
-                 std::multimap<int64_t, int64_t> &transfer_st_exec_time);
-  /**
-   * @brief   Returns the latest finishing time of parents of a node.
-   *
-   * @param[in] backend: backend, for which to return the time
-   * @param[in] node: node to get eft of parents
-   * @param[out] transfer_st_exec_time: est and exec time of data transfer operation
-   *
-   * @return earliest finishing time of parent nodes
-   */
-  int64_t predMaxEFT(const backend::Backend *backend, const ir::Operation &node,
-                     std::multimap<int64_t, int64_t> &transfer_st_exec_time);
-
-  void makeRank();
-
-  int64_t DFSMaxRank(const ir::OperationIndex &index);
-
-  int64_t DFSChildrenMaxRank(const ir::OperationIndex &index);
-  /**
-   * @brief   Returns the time, when backend is available for at least given amount of time.
-   *
-   * @note  Returns either hole/gap between two performing two already scheduled operations,
-   *        or the finishing time of the last scheduled operation
-   *
-   * @param[in] backend backend, for which to return the time
-   * @param[in] starting_time time, starting which to look for gap
-   * @param[in] time_amount amount of the time, for which to look gap
-   *
-   * @return time, when backend has at least time_amount free time
-   */
-  int64_t backendAvailableTime(const backend::Backend *backend, const int64_t &starting_time,
-                               const int64_t &time_amount);
-
-  int64_t getOpTime(const backend::Backend *backend, const std::string &operation, bool quant,
-                    uint32_t size);
-
-  int64_t getPermuteTime(const backend::Backend *src_backend, const backend::Backend *dst_backend,
-                         bool quant, uint32_t size);
-
-  void scheduleShufflingBackends();
-
-  int64_t tryBackend(const ir::Operation &node, const backend::Backend *backend);
-
-  /**
-   * @brief   Schedule a node and its successor until:
-   *            1. there is no branching or connection of multiple branches
-   *            2. for subsequent nodes: other than predecessor's backend is prefered
-   *
-   * @param[in] index: index of an operation
-   * @param[in] scheduled: a map to check if this node has already been scheduled
-   *
-   * @return N/A
-   */
-  void scheduleBranch(const ir::OperationIndex &index, ir::OperationIndexMap<bool> &scheduled);
-
-private:
-  // This variable stores backend/node pairs with unknown execution time, and hints scheduler
-  // whether it should assign these backends to these nodes:
-  // * It stores false for unsupported nodes
-  // * During rank calculation with enabled profiling mode it stores true for supported nodes
-  std::unordered_map<const backend::Backend *, std::unordered_map<std::string, bool>> _is_supported;
-  // Finishing and starting time of each backend
-  std::unordered_map<const backend::Backend *, std::map<int64_t, int64_t>> _backends_avail_time;
-  ir::OperationIndexMap<int64_t> _ops_eft;
-  std::multimap<int64_t, ir::OperationIndex, std::greater<int64_t>> _rank_to_op;
-  std::shared_ptr<ir::OperationIndexMap<int64_t>> _op_to_rank;
-  std::unique_ptr<compiler::BackendResolver> _backend_resolver;
-  std::unique_ptr<backend::ExecTime> _exec_time;
-  const ir::Graph *_graph{nullptr};
-  const std::vector<const backend::Backend *> _all_backends;
-  const backend::Backend *_cpu_backend{nullptr};
-};
-
-} // namespace compiler
-
-} // namespace neurun
-
-#endif // __NEURUN_COMPILER_H_E_SCHEDULER_H_
diff --git a/runtime/neurun/core/src/compiler/IScheduler.h b/runtime/neurun/core/src/compiler/IScheduler.h
deleted file mode 100644
index dc1373ff9..000000000
--- a/runtime/neurun/core/src/compiler/IScheduler.h
+++ /dev/null
@@ -1,38 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_CORE_COMPILER_I_SCHEDULER_H__
-#define __NEURUN_CORE_COMPILER_I_SCHEDULER_H__
-
-#include "BackendResolver.h"
-#include "ir/Graph.h"
-
-namespace neurun
-{
-namespace compiler
-{
-
-struct IScheduler
-{
-  virtual ~IScheduler() = default;
-
-  virtual std::unique_ptr<BackendResolver> schedule(const ir::Graph &graph) = 0;
-};
-
-} // namespace compiler
-} // namespace neurun
-
-#endif // __NEURUN_CORE_COMPILER_I_SCHEDULER_H__
diff --git a/runtime/neurun/core/src/compiler/Linear.cc b/runtime/neurun/core/src/compiler/Linear.cc
deleted file mode 100644
index b18dcea02..000000000
--- a/runtime/neurun/core/src/compiler/Linear.cc
+++ /dev/null
@@ -1,317 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <algorithm>
-
-#include "Linear.h"
-
-#include "backend/IShapeFixer.h"
-#include "backend/IConfig.h"
-#include "backend/IConstantInitializer.h"
-#include "backend/ITensorRegister.h"
-#include "backend/Backend.h"
-#include "compiler/SubTensorInfo.h"
-
-#include "util/logging.h"
-
-namespace neurun
-{
-namespace compiler
-{
-
-Linear::Linear(ir::Graph &graph) : _graph(graph)
-{
-  // Get SubgraphSequence by topological sorting
-  {
-    ir::Subgraphs &subgraphs = _graph.subgraphs();
-    ir::Operands &operands = _graph.operands();
-    // subgraphs can't access a op_seq by an operand so that input_to_subgs can offer it
-    std::unordered_map<ir::OperandIndex, std::list<ir::SubgraphIndex>> input_to_subgs;
-
-    // Get the relations between input/op_seq to be used for dfs-post-iter
-    //
-    //      [0]             # input -> _input_to_op_seqes[0] = {SUBG0}
-    //       |
-    //     [SUBG0]
-    //       |
-    //      [1]-----.       # input -> _input_to_op_seqes[1] = {SUBG1, SUBG2}
-    //       |      |
-    //     [SUBG1] [SUBG2]
-    //       |      |
-    //      [2]    [3]      # input -> _input_to_op_seqes[2] = {SUBG3}
-    //       \      /       # input -> _input_to_op_seqes[3] = {SUBG3}
-    //       [SUBG3]
-    //         |
-    //        [4]
-    subgraphs.iterate([&](const ir::SubgraphIndex &subg_idx, ir::OpSequence &subg) {
-      for (auto input : subg.getInputs())
-      {
-        // only valid_inputs
-        const auto &operand = operands.at(input);
-        if (operand.isConstant())
-          continue;
-
-        auto it = input_to_subgs.find(input);
-        if (it == input_to_subgs.end())
-        {
-          std::list<ir::SubgraphIndex> list{subg_idx};
-          input_to_subgs[input] = list;
-        }
-        else
-        {
-          it->second.push_back(subg_idx);
-        }
-      }
-    });
-
-    std::unordered_map<ir::SubgraphIndex, bool> visited;
-    subgraphs.iterate(
-        [&](const ir::SubgraphIndex &index, const ir::OpSequence &) { visited[index] = false; });
-
-    std::function<void(const ir::SubgraphIndex &, ir::OpSequence &)> dfs_recursive =
-        [&](const ir::SubgraphIndex &index, ir::OpSequence &subg) -> void {
-      if (visited[index])
-        return;
-      visited[index] = true;
-
-      // The outputs should be not constants
-      for (auto output : subg.getOutputs())
-      {
-        const auto it = input_to_subgs.find(output);
-        if (it != input_to_subgs.end())
-        {
-          const auto &subg_index_list = it->second;
-          for (const auto &index : subg_index_list)
-          {
-            auto &subg = subgraphs.at(index);
-            dfs_recursive(index, subg);
-          }
-        }
-      }
-
-      _elements.emplace_back(&subgraphs.at(index), _graph.getLowerInfo(index));
-    };
-
-    subgraphs.iterate(dfs_recursive);
-
-    // All of the nodes must have been visited.
-    assert(std::all_of(visited.begin(), visited.end(),
-                       [](const std::pair<const ir::SubgraphIndex, bool> &v) { return v.second; }));
-
-    // NOTE. Now these op_seq are on the reverse order
-    std::reverse(_elements.begin(), _elements.end());
-  }
-
-  {
-    const auto &backendToString = [](const neurun::backend::Backend *backend) {
-      assert(backend);
-      std::string str;
-      str += backend->config()->id();
-      str += " ";
-      return "{ " + str + "}";
-    };
-
-    VERBOSE(Linear) << "Final SubgraphSequence" << std::endl;
-    for (const auto &element : _elements)
-    {
-      const auto subg = element.op_seq;
-      const auto lower_info = element.lower_info;
-      VERBOSE(Linear) << "* SUBG"
-                      << " " << backendToString(lower_info->backend()) << " " << subg->getStr()
-                      << std::endl;
-    }
-  }
-}
-
-void Linear::accept(ir::OperationVisitor &&visitor) const
-{
-  for (const auto &e : _elements)
-  {
-    e.op_seq->accept(visitor);
-  }
-}
-
-void Linear::planTensors()
-{
-  ir::OperandIndexMap<std::shared_ptr<backend::ITensorBuilder>> tensor_builder_map;
-
-  // NOTE
-  // While current ITensorBuilder exposes registerSubTensorInfo for subtensor,
-  // this stage uses registerSubTensorInfo() and notify{First|Last}Use()
-  // but handling subtensor should be processed on each backend. See #5726.
-  ir::OperandIndexMap<uint32_t> uses_map;
-  ir::OperandIndexMap<uint32_t> def_map;
-  ir::OperandIndexSequence constants;
-
-  iterate([&](const neurun::compiler::Linear::Element &element) {
-    const auto backend = element.lower_info->backend();
-    const auto tensor_register =
-        _graph.backend_resolver()->getBackendContext(backend)->tensor_register;
-    tensor_register->registerTensors(*element.op_seq, _graph.getLowerInfo());
-  });
-
-  // Prepare scanning
-  _graph.operands().iterate([&](const ir::OperandIndex &ind, const ir::Operand &obj) {
-    const auto lower_info = _graph.getLowerInfo(ind);
-    // TODO Remove if neurun doesn't support anymore such as
-    // GeneratedTests.reshape_quant8_weights_as_inputs
-    if (lower_info->def_factors().size() == 0 && lower_info->use_factors().size() == 0 &&
-        !_graph.getInputs().contains(ind))
-    {
-      VERBOSE(LINEAR) << "Operand #" << ind.value() << " will be not used. no more process."
-                      << std::endl;
-      return;
-    }
-
-    uses_map[ind] = obj.getUses().size();
-    def_map[ind] = obj.getDef().size(); // should be 1 or 0
-
-    bool is_const = obj.isConstant();
-    if (is_const)
-    {
-      constants.append(ind);
-    }
-
-    for (auto factor : lower_info->def_factors())
-    {
-      auto backend = factor.backend();
-      auto tensor_builder = _graph.backend_resolver()->getBackendContext(backend)->tensor_builder;
-
-      if (!tensor_builder->isRegistered(ind))
-      {
-        // These tensors do not exist in any op_seq (No use and def)
-        // These tensors cannot be a SubTensor
-        assert(obj.parent_info() == nullptr);
-
-        const auto info = obj.info();
-        const auto backend_layout = lower_info->def_factors().getOnlyElement().layout();
-        // TODO Change tensor info to have permuted shape
-        tensor_builder->registerTensorInfo(ind, info, backend_layout, is_const);
-      }
-
-      tensor_builder_map[ind] = tensor_builder;
-    }
-  });
-
-  // If a tensor is model output, increase the use of the tensor.
-  // This aim is same to above one.
-  for (const auto &ind : _graph.getOutputs())
-  {
-    uses_map[ind]++;
-  }
-
-  // Start scanning to do notify{First|Last}Use for each tensor
-
-  // If a tensor is a constant, increase the use of the tensor.
-  // It makes the tensor not be dealloced. It means these will be deallocated last.
-  // And allocate constant operands first
-  VERBOSE(LINEAR) << "TENSORS as CONSTANT" << std::endl;
-  for (const auto &ind : constants)
-  {
-    uses_map[ind]++;
-    tensor_builder_map[ind]->notifyFirstUse(ind);
-  }
-
-  // Allocate Model's inputs
-  VERBOSE(LINEAR) << "TENSORS as MODEL INPUT" << std::endl;
-  for (const auto &ind : _graph.getInputs())
-  {
-    auto tensor_builder = tensor_builder_map[ind];
-    if (!tensor_builder) // for GeneratedTests.xxx_weights_as_inputs
-      continue;
-    tensor_builder->notifyFirstUse(ind);
-  }
-
-  // At each operation,
-  // 1. Scan DEF of outputs. If the DEF, allocate it
-  // 2. Scan USE of inputs. Decrease the USE and deallocate if the USE is 0
-  VERBOSE(LINEAR) << "TENSORS" << std::endl;
-  for (const auto &e : _elements)
-  {
-    for (const auto &op : e.op_seq->operations())
-    {
-      for (const auto &ind : op.node->getOutputs())
-      {
-        assert(def_map.find(ind) != def_map.end());
-        if (def_map[ind])
-        {
-          def_map[ind] = 0;
-          tensor_builder_map[ind]->notifyFirstUse(ind);
-        }
-      }
-
-      for (const auto &ind : op.node->getInputs())
-      {
-        assert(uses_map.find(ind) != uses_map.end());
-        assert(uses_map[ind] > 0);
-        uses_map[ind]--;
-        if (uses_map[ind] == 0)
-        {
-          tensor_builder_map[ind]->notifyLastUse(ind);
-        }
-      }
-    }
-  }
-
-  // Dispose and validate
-  for (const auto &ind : _graph.getOutputs())
-  {
-    --uses_map[ind];
-    if (uses_map[ind] == 0) // To prevent notifyLastUse from being called twice
-    {
-      tensor_builder_map[ind]->notifyLastUse(ind);
-    }
-  }
-
-  for (const auto &ind : constants)
-  {
-    --uses_map[ind];
-    if (uses_map[ind] == 0) // To prevent notifyLastUse from being called twice
-    {
-      tensor_builder_map[ind]->notifyLastUse(ind);
-    }
-  }
-
-  assert(
-      std::all_of(uses_map.begin(), uses_map.end(),
-                  [](std::pair<const ir::OperandIndex, uint32_t> it) { return it.second == 0; }));
-
-  assert(
-      std::all_of(def_map.begin(), def_map.end(),
-                  [](std::pair<const ir::OperandIndex, uint32_t> it) { return it.second == 0; }));
-}
-
-void Linear::iterate(const std::function<void(const Element &element)> &fn) const
-{
-  for (const auto &e : _elements)
-  {
-    fn(e);
-  }
-}
-
-void Linear::generateConstantInitializers(void) const
-{
-  iterate([&](const compiler::Linear::Element &element) {
-    auto backend = element.lower_info->backend();
-
-    auto constant_initializer =
-        _graph.backend_resolver()->getBackendContext(backend)->constant_initializer;
-    constant_initializer->generate(*element.op_seq, _graph.operands());
-  });
-}
-
-} // namespace compiler
-} // namespace neurun
diff --git a/runtime/neurun/core/src/compiler/Linear.h b/runtime/neurun/core/src/compiler/Linear.h
deleted file mode 100644
index e10d03695..000000000
--- a/runtime/neurun/core/src/compiler/Linear.h
+++ /dev/null
@@ -1,81 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_COMPILER_LINEAR_H__
-#define __NEURUN_COMPILER_LINEAR_H__
-
-#include <vector>
-#include <memory>
-
-#include "ir/Subgraphs.h"
-#include "backend/ITensorBuilder.h"
-#include "ir/Graph.h"
-#include "compiler/BackendResolver.h"
-
-namespace neurun
-{
-namespace ir
-{
-struct OperationVisitor;
-} // namespace ir
-} // namespace neurun
-
-namespace neurun
-{
-namespace compiler
-{
-
-class Linear
-{
-public:
-  struct Element
-  {
-    const ir::OpSequence *op_seq;
-    const ir::operation::LowerInfo *lower_info;
-
-    Element() : op_seq{nullptr}, lower_info{nullptr} {}
-
-    Element(const ir::OpSequence *op_seq, const ir::operation::LowerInfo *lower_info)
-        : op_seq{op_seq}, lower_info{lower_info}
-    {
-      // DO NOTHING
-    }
-  };
-
-public:
-  Linear(ir::Graph &graph);
-
-public:
-  Linear(const Linear &linear) = delete;
-
-public:
-  void accept(ir::OperationVisitor &&visitor) const;
-
-  void planTensors();
-
-  void iterate(const std::function<void(const Element &element)> &fn) const;
-
-  void generateConstantInitializers(void) const;
-
-private:
-  ir::Graph &_graph;
-  std::vector<Element> _elements;
-};
-
-} // namespace compiler
-} // namespace neurun
-
-#endif // __NEURUN_COMPILER_LINEAR_H__
diff --git a/runtime/neurun/core/src/compiler/ManualScheduler.cc b/runtime/neurun/core/src/compiler/ManualScheduler.cc
deleted file mode 100644
index 1fb9d3759..000000000
--- a/runtime/neurun/core/src/compiler/ManualScheduler.cc
+++ /dev/null
@@ -1,142 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ManualScheduler.h"
-#include "ir/OpCode.h"
-#include "ir/Operations.Include.h"
-#include "backend/Backend.h"
-#include "backend/BackendManager.h"
-#include "backend/IConfig.h"
-#include "util/ConfigSource.h"
-#include "misc/string_helpers.h"
-
-namespace neurun
-{
-namespace compiler
-{
-
-std::unique_ptr<BackendResolver> ManualScheduler::schedule(const ir::Graph &graph)
-{
-  auto backend_resolver = nnfw::cpp14::make_unique<compiler::BackendResolver>(
-      graph.operands(), backend::BackendManager::get().getAll(), graph.getKernelBuilder());
-
-  // 1. Backend for All operations
-  auto backend_all_str = util::getConfigString(util::config::OP_BACKEND_ALLOPS);
-  backend::Backend *backend_all = nullptr;
-
-  if (backend_all_str.empty())
-  {
-    const auto backends_str = util::getConfigString(util::config::BACKENDS);
-    size_t prev_pos = 0;
-    auto pos = backends_str.find(";");
-    while (pos != std::string::npos)
-    {
-      backend_all_str = backends_str.substr(prev_pos, pos - prev_pos);
-      backend_all = backend::BackendManager::get().get(backend_all_str);
-      if (backend_all != nullptr)
-        break;
-
-      prev_pos = pos + 1;
-      pos = backends_str.find(";", prev_pos);
-    }
-    // if backends doesn't terminate with ";"
-    if (backend_all == nullptr && prev_pos < backends_str.size())
-    {
-      backend_all_str = backends_str.substr(prev_pos);
-      backend_all = backend::BackendManager::get().get(backend_all_str);
-    }
-  }
-  else
-  {
-    backend_all = backend::BackendManager::get().get(backend_all_str);
-  }
-
-  VERBOSE(ManualScheduler) << "Default backend for all ops: " << backend_all_str << std::endl;
-
-  graph.operations().iterate([&](const ir::OperationIndex &index, const ir::Operation &) {
-    backend_resolver->setBackend(index, backend_all);
-  });
-
-  // 2. Backend per operation type
-  std::unordered_map<ir::OpCode, backend::Backend *> op_type_map;
-  // By default, Custom uses cpu backend
-  op_type_map[ir::OpCode::Custom] = backend::BackendManager::get().get("cpu");
-
-#define OP(InternalName)                                                                      \
-  {                                                                                           \
-    const auto &backend_str = util::getConfigString(util::config::OP_BACKEND_##InternalName); \
-    if (!backend_str.empty())                                                                 \
-    {                                                                                         \
-      auto backend = backend::BackendManager::get().get(backend_str);                         \
-      VERBOSE(Lower) << "backend for " << #InternalName << ": " << backend_str << std::endl;  \
-      op_type_map[ir::OpCode::InternalName] = backend;                                        \
-    }                                                                                         \
-  }
-#include "ir/Operations.lst"
-#undef OP
-
-  graph.operations().iterate([&](const ir::OperationIndex &index, const ir::Operation &operation) {
-    auto itr = op_type_map.find(operation.opcode());
-    if (itr != op_type_map.end())
-    {
-      backend_resolver->setBackend(index, itr->second);
-    }
-  });
-
-  // 3. Backend per operation
-  try
-  {
-    auto map_str = util::getConfigString(util::config::OP_BACKEND_MAP);
-    auto key_val_list = nnfw::misc::split(map_str, ';');
-    for (const auto &key_val_str : key_val_list)
-    {
-      if (key_val_str.empty())
-      {
-        continue;
-      }
-
-      auto key_val = nnfw::misc::split(key_val_str, '=');
-      const auto &key_str = key_val.at(0);
-      const auto &val = key_val.at(1);
-      auto key = static_cast<uint32_t>(std::stoi(key_str));
-
-      graph.operations().at(ir::OperationIndex{key}); // Check if exist, or this wil throw
-      backend_resolver->setBackend(ir::OperationIndex{key},
-                                   backend::BackendManager::get().get(val));
-    }
-  }
-  catch (...)
-  {
-    VERBOSE(ManualScheduler) << "Invalid value from " << util::config::OP_BACKEND_MAP
-                             << ". Some of the given values are ignored" << std::endl;
-  }
-
-  // 4. Operations that are specially handled
-  //    All configuration above will be ignored(overwritten)
-  op_type_map[ir::OpCode::Permute] = backend::BackendManager::get().get("cpu");
-
-  // Dump final assignment
-  backend_resolver->iterate(
-      [&](const ir::OperationIndex &index, const backend::BackendContext &backend_ctx) {
-        VERBOSE(ManualScheduler) << "backend for operation #" << index.value() << ": "
-                                 << backend_ctx.backend->config()->id() << std::endl;
-      });
-
-  return backend_resolver;
-}
-
-} // namespace compiler
-} // namespace neurun
diff --git a/runtime/neurun/core/src/compiler/ManualScheduler.h b/runtime/neurun/core/src/compiler/ManualScheduler.h
deleted file mode 100644
index a442cec08..000000000
--- a/runtime/neurun/core/src/compiler/ManualScheduler.h
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_CORE_COMPILER_MANUAL_SCHEDULER_H__
-#define __NEURUN_CORE_COMPILER_MANUAL_SCHEDULER_H__
-
-#include "IScheduler.h"
-
-namespace neurun
-{
-namespace compiler
-{
-
-class ManualScheduler : public IScheduler
-{
-public:
-  std::unique_ptr<BackendResolver> schedule(const ir::Graph &graph) override;
-};
-
-} // namespace compiler
-} // namespace neurun
-
-#endif // __NEURUN_CORE_COMPILER_MANUAL_SCHEDULER_H__
diff --git a/runtime/neurun/core/src/compiler/OperandContext.cc b/runtime/neurun/core/src/compiler/OperandContext.cc
deleted file mode 100644
index c06f6157b..000000000
--- a/runtime/neurun/core/src/compiler/OperandContext.cc
+++ /dev/null
@@ -1,45 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "OperandContext.h"
-
-#include <cassert>
-
-namespace neurun
-{
-namespace compiler
-{
-
-OperandContext &OperandContext::set(const ir::OperandIndex &id,
-                                    const std::shared_ptr<backend::operand::ITensor> &tensor)
-{
-  // Only one tensor for an id
-  assert(_tensors.find(id) == _tensors.end());
-  _tensors[id] = tensor;
-  return (*this);
-}
-
-void OperandContext::iterate(
-    const std::function<void(const ir::OperandIndex &, backend::operand::ITensor &)> &fn)
-{
-  for (auto &e : _tensors)
-  {
-    fn(e.first, *e.second);
-  }
-}
-
-} // namespace compiler
-} // namespace neurun
diff --git a/runtime/neurun/core/src/compiler/OperandContext.h b/runtime/neurun/core/src/compiler/OperandContext.h
deleted file mode 100644
index da1a51bb9..000000000
--- a/runtime/neurun/core/src/compiler/OperandContext.h
+++ /dev/null
@@ -1,60 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_COMPILER_OPERAND_CONTEXT_H__
-#define __NEURUN_COMPILER_OPERAND_CONTEXT_H__
-
-#include "backend/operand/ITensor.h"
-#include "ir/OperandIndexMap.h"
-#include <unordered_map>
-#include <memory>
-
-namespace neurun
-{
-namespace compiler
-{
-
-class OperandContext
-{
-public:
-  OperandContext &set(const ir::OperandIndex &ind,
-                      const std::shared_ptr<backend::operand::ITensor> &tensor);
-
-public:
-  bool exist(const ir::OperandIndex &ind) const { return _tensors.find(ind) != _tensors.end(); }
-
-public:
-  std::shared_ptr<backend::operand::ITensor> at(const ir::OperandIndex &ind) const
-  {
-    return _tensors.at(ind);
-  }
-
-  std::shared_ptr<backend::operand::ITensor> &at(const ir::OperandIndex &ind)
-  {
-    return _tensors.at(ind);
-  }
-
-  void
-  iterate(const std::function<void(const ir::OperandIndex &, backend::operand::ITensor &)> &fn);
-
-private:
-  ir::OperandIndexMap<std::shared_ptr<backend::operand::ITensor>> _tensors;
-};
-
-} // namespace compiler
-} // namespace neurun
-
-#endif // __NEURUN_COMPILER_OPERAND_CONTEXT_H__
diff --git a/runtime/neurun/core/src/compiler/OperationValidator.cc b/runtime/neurun/core/src/compiler/OperationValidator.cc
deleted file mode 100644
index 74f76bdbe..000000000
--- a/runtime/neurun/core/src/compiler/OperationValidator.cc
+++ /dev/null
@@ -1,985 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "OperationValidator.h"
-
-#include <typeinfo>
-
-#include "ir/Graph.h"
-#include "ir/operation/LowerInfo.h"
-
-#include "util/logging.h"
-#include "util/Utils.h"
-
-namespace neurun
-{
-namespace compiler
-{
-
-OperationValidator::OperationValidator(const ir::Graph &graph)
-    : _graph{graph}, _ctx{graph.operands()}, _current_subg_layout{ir::Layout::UNKNOWN}
-{
-}
-
-void OperationValidator::operator()()
-{
-  // TODO Get frontend layout from graph
-  _current_subg_layout = ir::Layout::NHWC;
-
-  _graph.operations().iterate(
-      [&](const ir::OperationIndex &, const ir::Operation &node) { node.accept(*this); });
-}
-
-void OperationValidator::visit(const ir::operation::BatchToSpaceND &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::BatchToSpaceND::Input::INPUT)};
-  const auto block_size_index{
-      node.getInputs().at(ir::operation::BatchToSpaceND::Input::BLOCK_SIZE)};
-
-  const auto frontend_layout = _current_subg_layout;
-  const auto input_shape = _ctx.at(ifm_index).shape().asFeature(frontend_layout);
-  const auto output_shape = _ctx.at(ofm_index).shape().asFeature(frontend_layout);
-
-  UNUSED_RELEASE(input_shape);
-  UNUSED_RELEASE(output_shape);
-
-  // All assertions as per NNAPI specification.
-  assert(_ctx.at(ifm_index).shape().rank() == 4);
-  assert(_ctx.at(ofm_index).shape().rank() == 4);
-  assert(_ctx.at(block_size_index).shape().rank() == 1);
-
-  assert(_ctx.at(block_size_index).shape().dim(0) == 2);
-
-  assert(_ctx.at(block_size_index).isConstant());
-
-  assert(input_shape.C == output_shape.C);
-}
-
-void OperationValidator::visit(const ir::operation::Cast &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(0)};
-
-  UNUSED_RELEASE(output_index);
-  UNUSED_RELEASE(input_index);
-
-  assert(_ctx.at(output_index).shape() == _ctx.at(input_index).shape());
-}
-
-void OperationValidator::visit(const ir::operation::Comparison &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto lhs_index{node.getInputs().at(ir::operation::Comparison::Input::INPUT0)};
-  const auto rhs_index{node.getInputs().at(ir::operation::Comparison::Input::INPUT1)};
-
-  UNUSED_RELEASE(output_index);
-  UNUSED_RELEASE(lhs_index);
-  UNUSED_RELEASE(rhs_index);
-
-  assert(_ctx.at(lhs_index).typeInfo().type() == _ctx.at(rhs_index).typeInfo().type());
-  assert(_ctx.at(output_index).typeInfo().type() == ir::DataType::BOOL8);
-}
-
-void OperationValidator::visit(const ir::operation::Softmax &node)
-{
-  VERBOSE(Softmax) << "Configure SOFTMAX operation" << std::endl;
-
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(0)};
-
-  UNUSED_RELEASE(output_index);
-  UNUSED_RELEASE(input_index);
-
-  assert(_ctx.at(output_index).shape().rank() == _ctx.at(input_index).shape().rank());
-}
-
-void OperationValidator::visit(const ir::operation::InstanceNorm &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::InstanceNorm::Input::INPUT)};
-  const auto gamma_index{node.getInputs().at(ir::operation::InstanceNorm::Input::GAMMA)};
-  const auto beta_index{node.getInputs().at(ir::operation::InstanceNorm::Input::BETA)};
-
-  UNUSED_RELEASE(ofm_index);
-  UNUSED_RELEASE(ifm_index);
-  UNUSED_RELEASE(gamma_index);
-  UNUSED_RELEASE(beta_index);
-
-  assert(_ctx.at(ifm_index).shape().rank() == 4);
-  assert(_ctx.at(ifm_index).shape() == _ctx.at(ofm_index).shape());
-  assert(_ctx.at(gamma_index).shape().rank() == 1);
-  assert(_ctx.at(beta_index).shape().rank() == 1);
-}
-
-void OperationValidator::visit(const ir::operation::Permute &node)
-{
-  VERBOSE(Permute) << "Configure Permute operation" << std::endl;
-
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(0)};
-
-  UNUSED_RELEASE(output_index);
-  UNUSED_RELEASE(input_index);
-
-  assert(_ctx.at(output_index).shape().rank() == _ctx.at(input_index).shape().rank());
-}
-
-void OperationValidator::visit(const ir::operation::ReduceSum &node)
-{
-  VERBOSE(Permute) << "Configure ReduceSum operation" << std::endl;
-
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::ReduceSum::Input::INPUT)};
-  const auto &axes = node.param().axes;
-
-  UNUSED_RELEASE(output_index);
-  UNUSED_RELEASE(input_index);
-  UNUSED_RELEASE(axes);
-
-  const auto input_shape = _ctx.at(input_index).shape();
-  const auto output_shape = _ctx.at(output_index).shape();
-
-  UNUSED_RELEASE(output_shape);
-  UNUSED_RELEASE(input_shape);
-
-  assert(input_shape.rank() <= 4);
-  assert(output_shape.rank() <= input_shape.rank());
-
-  // NOTE For the 4-dimensions, if the rank of input and output are different, this runtime only
-  // supports cases reducing height and width or reducing depth.
-  // TODO We have to support all cases of dimensions up to 4.
-  // For correct permuting, we have to set output's shape to be equal in dimension position of the
-  // input. But the positions of the same dimensions in the input and output may be set differently.
-  // For example {2,3,4,5}(input's shape) can be reduced to {3,5}(output's shape). The original
-  // output shape should be {1,3,1,5}, but real output shape may be {3,5}. If you simply try to
-  // extend it in 4 dimensions, it should be {1,1,3,5}.
-  // Even if output shape is changed to {1,3,1,5}, there is another problem. It is that shape of
-  // output tensor used at next operation is changed to {1,3,1,5} after this operation even if the
-  // next operation is not desired.
-  if (input_shape.rank() == 4 && input_shape.rank() != output_shape.rank())
-  {
-    if (output_shape.rank() == 2)
-    {
-      // Reducing HW
-      assert(input_shape.dim(0) == output_shape.dim(0) &&
-             input_shape.dim(3) == output_shape.dim(1));
-    }
-    else if (output_shape.rank() == 3)
-    {
-      // Reducing C or
-      // (Reducing H and C(input and output) == 1) or (Reducing W and C(input and output) == 1)
-      assert((input_shape.dim(0) == output_shape.dim(0) &&
-              input_shape.dim(1) == output_shape.dim(1) &&
-              input_shape.dim(2) == output_shape.dim(2)) ||
-             (input_shape.dim(0) == output_shape.dim(0) &&
-              (input_shape.dim(1) == output_shape.dim(1) ||
-               input_shape.dim(2) == output_shape.dim(1)) &&
-              input_shape.dim(3) == 1 && output_shape.dim(2) == 1));
-    }
-  }
-}
-
-void OperationValidator::visit(const ir::operation::Transpose &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::Transpose::Input::INPUT)};
-  const auto &perm{node.param().perm};
-
-  const auto &output_shape = _ctx.at(output_index).shape();
-  const auto &input_shape = _ctx.at(input_index).shape();
-
-  UNUSED_RELEASE(output_shape);
-  UNUSED_RELEASE(input_shape);
-  UNUSED_RELEASE(perm);
-
-  assert(input_shape.rank() == static_cast<int>(perm.size()));
-  assert(input_shape.rank() == output_shape.rank());
-}
-
-void OperationValidator::visit(const ir::operation::ReduceMax &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::ReduceMax::Input::INPUT)};
-  const auto &axes = node.param().axes;
-
-  auto output_shape = _ctx.at(output_index).shape();
-  auto input_shape = _ctx.at(input_index).shape();
-
-  UNUSED_RELEASE(output_shape);
-  UNUSED_RELEASE(input_shape);
-  UNUSED_RELEASE(axes);
-
-  assert(input_shape.rank() <= 4);
-  assert(output_shape.rank() <= input_shape.rank());
-
-  // NOTE For the 4-dimensions, if the rank of input and output are different, this runtime only
-  // supports cases reducing height and width or reducing depth.
-  // TODO We have to support all cases of dimensions up to 4.
-  // For correct permuting, we have to set output's shape to be equal in dimension position of the
-  // input. But the positions of the same dimensions in the input and output may be set differently.
-  // For example {2,3,4,5}(input's shape) can be reduced to {3,5}(output's shape). The original
-  // output shape should be {1,3,1,5}, but real output shape may be {3,5}. If you simply try to
-  // extend it in 4 dimensions, it should be {1,1,3,5}.
-  // Even if output shape is changed to {1,3,1,5}, there is another problem. It is that shape of
-  // output tensor used at next operation is changed to {1,3,1,5} after this operation even if the
-  // next operation is not desired.
-  if (input_shape.rank() == 4 && input_shape.rank() != output_shape.rank())
-  {
-    if (output_shape.rank() == 2)
-    {
-      // Reducing HW
-      assert(input_shape.dim(0) == output_shape.dim(0) &&
-             input_shape.dim(3) == output_shape.dim(1));
-    }
-    else if (output_shape.rank() == 3)
-    {
-      // Reducing C or
-      // (Reducing H and C(ifm and ofm) == 1) or (Reducing W and C(ifm and ofm) == 1)
-      assert((input_shape.dim(0) == output_shape.dim(0) &&
-              input_shape.dim(1) == output_shape.dim(1) &&
-              input_shape.dim(2) == output_shape.dim(2)) ||
-             (input_shape.dim(0) == output_shape.dim(0) &&
-              (input_shape.dim(1) == output_shape.dim(1) ||
-               input_shape.dim(2) == output_shape.dim(1)) &&
-              input_shape.dim(3) == 1 && output_shape.dim(2) == 1));
-    }
-  }
-}
-
-void OperationValidator::visit(const ir::operation::RNN &node)
-{
-  // NOTE This validation is for static rnn(non-dynamic shape), but not for dynamic rnn
-  // TODO Support dynamic rnn
-  const auto output_index{node.getOutputs().at(ir::operation::RNN::Output::OUTPUT)};
-  const auto hidden_state_out_index{
-      node.getOutputs().at(ir::operation::RNN::Output::HIDDEN_STATE_OUT)};
-
-  const auto input_index{node.getInputs().at(ir::operation::RNN::Input::INPUT)};
-  const auto weights_index{node.getInputs().at(ir::operation::RNN::Input::WEIGHTS)};
-  const auto recurrent_weights_index{
-      node.getInputs().at(ir::operation::RNN::Input::RECURRENT_WEIGHTS)};
-  const auto bias_index{node.getInputs().at(ir::operation::RNN::Input::BIAS)};
-  const auto hidden_state_in_index{node.getInputs().at(ir::operation::RNN::Input::HIDDEN_STATE_IN)};
-
-  const auto batch_size = _ctx.at(output_index).shape().dim(0);
-  const auto num_units = _ctx.at(output_index).shape().dim(1);
-
-  UNUSED_RELEASE(output_index);
-  UNUSED_RELEASE(hidden_state_out_index);
-  UNUSED_RELEASE(input_index);
-  UNUSED_RELEASE(weights_index);
-  UNUSED_RELEASE(recurrent_weights_index);
-  UNUSED_RELEASE(bias_index);
-  UNUSED_RELEASE(hidden_state_in_index);
-  UNUSED_RELEASE(batch_size);
-  UNUSED_RELEASE(num_units);
-
-  assert(_ctx.at(output_index).shape().rank() == 2 &&
-         _ctx.at(hidden_state_out_index).shape().rank() == 2 &&
-         _ctx.at(input_index).shape().rank() == 2 && _ctx.at(weights_index).shape().rank() == 2 &&
-         _ctx.at(recurrent_weights_index).shape().rank() == 2 &&
-         _ctx.at(hidden_state_in_index).shape().rank() == 2);
-  assert(_ctx.at(bias_index).shape().rank() == 1);
-
-  assert(batch_size == _ctx.at(input_index).shape().dim(0) &&
-         batch_size == _ctx.at(hidden_state_in_index).shape().dim(0) &&
-         batch_size == _ctx.at(hidden_state_out_index).shape().dim(0));
-  assert(_ctx.at(input_index).shape().dim(1) == _ctx.at(weights_index).shape().dim(1));
-
-  assert(num_units == _ctx.at(weights_index).shape().dim(0) &&
-         num_units == _ctx.at(recurrent_weights_index).shape().dim(0) &&
-         num_units == _ctx.at(bias_index).shape().dim(0));
-  assert(num_units == _ctx.at(output_index).shape().dim(1) &&
-         num_units == _ctx.at(recurrent_weights_index).shape().dim(1) &&
-         num_units == _ctx.at(hidden_state_in_index).shape().dim(1) &&
-         num_units == _ctx.at(hidden_state_out_index).shape().dim(1));
-}
-
-void OperationValidator::visit(const ir::operation::SpaceToBatchND &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::SpaceToBatchND::Input::INPUT)};
-  const auto block_size_index{
-      node.getInputs().at(ir::operation::SpaceToBatchND::Input::BLOCK_SIZE)};
-  const auto paddings_index{node.getInputs().at(ir::operation::SpaceToBatchND::Input::PADDINGS)};
-
-  const auto frontend_layout = _current_subg_layout;
-  const auto input_shape = _ctx.at(ifm_index).shape().asFeature(frontend_layout);
-  const auto output_shape = _ctx.at(ofm_index).shape().asFeature(frontend_layout);
-
-  UNUSED_RELEASE(input_shape);
-  UNUSED_RELEASE(output_shape);
-
-  // All assertions as per NNAPI specification.
-  assert(_ctx.at(ifm_index).shape().rank() == 4);
-  assert(_ctx.at(ofm_index).shape().rank() == 4);
-  assert(_ctx.at(block_size_index).shape().rank() == 1);
-  assert(_ctx.at(paddings_index).shape().rank() == 2);
-
-  assert(_ctx.at(block_size_index).shape().dim(0) == 2);
-  assert(_ctx.at(paddings_index).shape().dim(0) == 2);
-  assert(_ctx.at(paddings_index).shape().dim(1) == 2);
-
-  assert(_ctx.at(block_size_index).isConstant());
-  assert(_ctx.at(paddings_index).isConstant());
-
-  assert(input_shape.C == output_shape.C);
-}
-
-void OperationValidator::visit(const ir::operation::SpaceToDepth &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::SpaceToDepth::Input::INPUT)};
-
-  const auto frontend_layout = _current_subg_layout;
-  const auto input_shape = _ctx.at(ifm_index).shape().asFeature(frontend_layout);
-  const auto output_shape = _ctx.at(ofm_index).shape().asFeature(frontend_layout);
-  const auto block_size = node.param().block_size;
-
-  UNUSED_RELEASE(input_shape);
-  UNUSED_RELEASE(output_shape);
-  UNUSED_RELEASE(block_size);
-
-  // All assertions as per NNAPI specification.
-  assert(_ctx.at(ifm_index).shape().rank() == 4);
-  assert(_ctx.at(ofm_index).shape().rank() == 4);
-  assert((block_size >= 1) && (input_shape.H % block_size == 0) &&
-         (input_shape.W % block_size == 0));
-  assert(input_shape.N == output_shape.N);
-  assert(input_shape.C * block_size * block_size == output_shape.C);
-}
-
-void OperationValidator::visit(const ir::operation::EmbeddingLookup &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto lookups_index{node.getInputs().at(ir::operation::EmbeddingLookup::Input::LOOKUPS)};
-  const auto values_index{node.getInputs().at(ir::operation::EmbeddingLookup::Input::VALUES)};
-
-  const auto &output_obj = _ctx.at(output_index);
-  const auto &lookups_obj = _ctx.at(lookups_index);
-  const auto &values_obj = _ctx.at(values_index);
-
-  UNUSED_RELEASE(output_obj);
-  UNUSED_RELEASE(lookups_obj);
-  UNUSED_RELEASE(values_obj);
-
-  // Verify operand here, not at SimpleEmbeddingLookup::configure() to avoid acl's modifying
-  // TensorShape sometimes(Issue: https://github.sec.samsung.net/STAR/nnfw/issues/729)
-  {
-    assert(lookups_obj.typeInfo().type() == ir::DataType::INT32);
-
-    const auto &output_shape = output_obj.shape();
-    const auto &lookups_shape = lookups_obj.shape();
-    const auto &values_shape = values_obj.shape();
-
-    UNUSED_RELEASE(output_shape);
-    UNUSED_RELEASE(lookups_shape);
-    UNUSED_RELEASE(values_shape);
-
-    assert(lookups_shape.rank() == 1);
-    assert(values_shape.rank() >= 2);
-
-    // output should be a n-D tensor with the same rank and shape as the values tensor, except for
-    // the first dimension which has the same size as lookups' only dimension.
-    assert(output_shape.rank() == values_shape.rank());
-    assert(output_shape.dim(0) == lookups_shape.dim(0));
-    for (int n = 1; n < output_shape.rank(); ++n)
-    {
-      assert(output_shape.dim(n) == values_shape.dim(n));
-    }
-  }
-}
-
-void OperationValidator::visit(const ir::operation::Exp &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::Exp::Input::INPUT)};
-
-  UNUSED_RELEASE(output_index);
-  UNUSED_RELEASE(input_index);
-
-  assert(_ctx.at(output_index).shape() == _ctx.at(input_index).shape());
-  assert(_ctx.at(output_index).typeInfo().type() == _ctx.at(input_index).typeInfo().type());
-}
-
-void OperationValidator::visit(const ir::operation::Floor &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::Floor::Input::INPUT)};
-
-  UNUSED_RELEASE(output_index);
-  UNUSED_RELEASE(input_index);
-
-  assert(_ctx.at(output_index).shape() == _ctx.at(input_index).shape());
-  assert(_ctx.at(output_index).typeInfo().type() == _ctx.at(input_index).typeInfo().type());
-}
-
-void OperationValidator::visit(const ir::operation::HashtableLookup &node)
-{
-  const auto output_index{node.getOutputs().at(ir::operation::HashtableLookup::Output::OUTPUT)};
-  const auto hits_index{node.getOutputs().at(ir::operation::HashtableLookup::Output::HITS)};
-
-  const auto lookups_index{node.getInputs().at(ir::operation::HashtableLookup::Input::LOOKUPS)};
-  const auto keys_index{node.getInputs().at(ir::operation::HashtableLookup::Input::KEYS)};
-  const auto values_index{node.getInputs().at(ir::operation::HashtableLookup::Input::VALUES)};
-
-  const auto &output_obj = _ctx.at(output_index);
-  const auto &hits_obj = _ctx.at(hits_index);
-
-  const auto &lookups_obj = _ctx.at(lookups_index);
-  const auto &keys_obj = _ctx.at(keys_index);
-  const auto &values_obj = _ctx.at(values_index);
-
-  assert(lookups_obj.typeInfo().type() == ir::DataType::INT32);
-  assert(keys_obj.typeInfo().type() == ir::DataType::INT32);
-  assert(hits_obj.typeInfo().type() == ir::DataType::QUANT8_ASYMM);
-
-  const auto &output_shape = output_obj.shape();
-  const auto &hits_shape = hits_obj.shape();
-
-  const auto &lookups_shape = lookups_obj.shape();
-  const auto &keys_shape = keys_obj.shape();
-  const auto &values_shape = values_obj.shape();
-
-  UNUSED_RELEASE(output_shape);
-  UNUSED_RELEASE(hits_shape);
-  UNUSED_RELEASE(lookups_shape);
-  UNUSED_RELEASE(keys_shape);
-  UNUSED_RELEASE(values_shape);
-
-  assert(values_shape.rank() == output_shape.rank());
-  assert(lookups_shape.rank() == 1);
-  assert(keys_shape.rank() == 1);
-  assert(values_shape.dim(0) == keys_shape.dim(0));
-  assert(lookups_shape.dim(0) == output_shape.dim(0));
-}
-
-void OperationValidator::visit(const ir::operation::TransposeConv &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::TransposeConv::Input::INPUT)};
-  const auto ker_index{node.getInputs().at(ir::operation::TransposeConv::Input::KERNEL)};
-
-  // Only 4D tensors are supported
-  assert(_ctx.at(ofm_index).shape().rank() == 4);
-  assert(_ctx.at(ofm_index).shape().rank() == _ctx.at(ifm_index).shape().rank());
-  assert(_ctx.at(ofm_index).shape().rank() == _ctx.at(ker_index).shape().rank());
-
-  const auto frontend_layout = _current_subg_layout;
-  const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(frontend_layout);
-  const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(frontend_layout);
-  // The kernel has only IHWO layout on frontend
-  // So ker_shape is treated here below
-  // I -> N
-  // H -> H
-  // W -> W
-  // O -> C
-  const auto ker_shape = _ctx.at(ker_index).shape().asFeature(ir::Layout::NHWC);
-
-  UNUSED_RELEASE(ofm_shape);
-  UNUSED_RELEASE(ifm_shape);
-  UNUSED_RELEASE(ker_shape);
-
-  assert((node.param().padding.type == ir::PaddingType::SAME) ||
-         (node.param().padding.type == ir::PaddingType::VALID));
-  assert(ifm_shape.N == ofm_shape.N);
-  assert(ifm_shape.C == ker_shape.C);
-  assert(ker_shape.N == ofm_shape.C);
-}
-
-void OperationValidator::visit(const ir::operation::Gather &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-
-  const auto ifm_index{node.getInputs().at(ir::operation::Gather::Input::INPUT)};
-  const auto indices_index{node.getInputs().at(ir::operation::Gather::Input::INDICES)};
-
-  const auto axis = node.param().axis;
-
-  const auto ifm_shape = _ctx.at(ifm_index).shape();
-  const auto indices_shape = _ctx.at(indices_index).shape();
-  const auto ofm_shape = _ctx.at(ofm_index).shape();
-
-  UNUSED_RELEASE(ifm_shape);
-  UNUSED_RELEASE(indices_shape);
-  UNUSED_RELEASE(ofm_shape);
-  UNUSED_RELEASE(axis);
-
-  assert(ifm_shape.rank() <= 4);
-  assert(indices_shape.rank() <= 3);
-  assert(ofm_shape.rank() <= 4);
-}
-
-void OperationValidator::visit(const ir::operation::Dequantize &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::Dequantize::Input::INPUT)};
-
-  UNUSED_RELEASE(output_index);
-  UNUSED_RELEASE(input_index);
-
-  assert(_ctx.at(input_index).shape().rank() <= 4);
-  assert(_ctx.at(input_index).shape() == _ctx.at(output_index).shape());
-  assert(_ctx.at(input_index).typeInfo().type() == ir::DataType::QUANT8_ASYMM);
-  assert(_ctx.at(output_index).typeInfo().type() == ir::DataType::FLOAT32);
-}
-
-void OperationValidator::visit(const ir::operation::Mean &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::Mean::Input::INPUT)};
-
-  const auto ifm_shape = _ctx.at(ifm_index).shape();
-  const auto ofm_shape = _ctx.at(ofm_index).shape();
-
-  // NOTE For the 4-dimensions, if the rank of input and output are different, this runtime only
-  // supports cases reducing height and width or reducing depth.
-  // TODO We have to support all cases of dimensions up to 4.
-  // For correct permuting, we have to set output's shape to be equal in dimension position of the
-  // input. But the positions of the same dimensions in the input and output may be set differently.
-  // For example {2,3,4,5}(input's shape) can be reduced to {3,5}(output's shape). The original
-  // output shape should be {1,3,1,5}, but real output shape may be {3,5}. If you simply try to
-  // extend it in 4 dimensions, it should be {1,1,3,5}.
-  // Even if output shape is changed to {1,3,1,5}, there is another problem. It is that shape of
-  // output tensor used at next operation is changed to {1,3,1,5} after this operation even if the
-  // next operation is not desired.
-  if (ifm_shape.rank() == 4 && ifm_shape.rank() != ofm_shape.rank())
-  {
-    if (ofm_shape.rank() == 2)
-    {
-      // Reducing HW
-      assert(ifm_shape.dim(0) == ofm_shape.dim(0) && ifm_shape.dim(3) == ofm_shape.dim(1));
-    }
-    else if (ofm_shape.rank() == 3)
-    {
-      // Reducing C or
-      // (Reducing H and C(ifm and ofm) == 1) or (Reducing W and C(ifm and ofm) == 1)
-      assert((ifm_shape.dim(0) == ofm_shape.dim(0) && ifm_shape.dim(1) == ofm_shape.dim(1) &&
-              ifm_shape.dim(2) == ofm_shape.dim(2)) ||
-             (ifm_shape.dim(0) == ofm_shape.dim(0) &&
-              (ifm_shape.dim(1) == ofm_shape.dim(1) || ifm_shape.dim(2) == ofm_shape.dim(1)) &&
-              ifm_shape.dim(3) == 1 && ofm_shape.dim(2) == 1));
-    }
-  }
-}
-
-void OperationValidator::visit(const ir::operation::DepthToSpace &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto input_index{node.getInputs().at(ir::operation::DepthToSpace::Input::INPUT)};
-
-  const auto frontend_layout = _current_subg_layout;
-  const auto output_shape = _ctx.at(output_index).shape().asFeature(frontend_layout);
-  const auto input_shape = _ctx.at(input_index).shape().asFeature(frontend_layout);
-
-  UNUSED_RELEASE(output_shape);
-  UNUSED_RELEASE(input_shape);
-
-  assert(_ctx.at(input_index).shape().rank() == 4);
-  assert(_ctx.at(output_index).shape().rank() == 4);
-
-  int32_t block_size = node.param().block_size;
-
-  UNUSED_RELEASE(block_size);
-
-  assert(block_size > 0);
-
-  { // assertions block
-    assert(output_shape.N == input_shape.N);
-    assert(output_shape.H == input_shape.H * block_size);
-    assert(output_shape.W == input_shape.W * block_size);
-    assert(input_shape.C % (block_size * block_size) == 0);
-    assert(output_shape.C == input_shape.C / (block_size * block_size));
-  }
-}
-
-void OperationValidator::visit(const ir::operation::Pack &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto num{node.param().num};
-  const auto axis{node.param().axis};
-
-  const auto &output_shape = _ctx.at(output_index).shape();
-  const auto output_rank = static_cast<int32_t>(output_shape.rank());
-
-  const auto input1_index{node.getInputs().at(0)};
-  const auto input_shape = _ctx.at(input1_index).shape();
-
-  UNUSED_RELEASE(num);
-  UNUSED_RELEASE(axis);
-  UNUSED_RELEASE(output_rank);
-
-  assert(num == static_cast<int32_t>(node.getInputs().size()));
-  assert(axis >= -output_rank && axis < output_rank);
-  for (const auto &index : node.getInputs())
-  {
-    UNUSED_RELEASE(index);
-    assert(input_shape == _ctx.at(index).shape());
-  }
-}
-
-void OperationValidator::visit(const ir::operation::ReduceMin &node)
-{
-  const auto ofm_index{node.getOutputs().at(0)};
-  const auto ifm_index{node.getInputs().at(ir::operation::ReduceMin::Input::INPUT)};
-  const auto &axes = node.param().axes;
-
-  auto ifm_shape = _ctx.at(ifm_index).shape();
-  auto ofm_shape = _ctx.at(ofm_index).shape();
-
-  UNUSED_RELEASE(ifm_shape);
-  UNUSED_RELEASE(ofm_shape);
-  UNUSED_RELEASE(axes);
-
-  assert(ifm_shape.rank() <= 4);
-  assert(ofm_shape.rank() <= ifm_shape.rank());
-
-  // NOTE For the 4-dimensions, if the rank of input and output are different, this runtime only
-  // supports cases reducing height and width or reducing depth.
-  // TODO We have to support all cases of dimensions up to 4.
-  // For correct permuting, we have to set output's shape to be equal in dimension position of the
-  // input. But the positions of the same dimensions in the input and output may be set differently.
-  // For example {2,3,4,5}(input's shape) can be reduced to {3,5}(output's shape). The original
-  // output shape should be {1,3,1,5}, but real output shape may be {3,5}. If you simply try to
-  // extend it in 4 dimensions, it should be {1,1,3,5}.
-  // Even if output shape is changed to {1,3,1,5}, there is another problem. It is that shape of
-  // output tensor used at next operation is changed to {1,3,1,5} after this operation even if the
-  // next operation is not desired.
-  if (ifm_shape.rank() == 4 && ifm_shape.rank() != ofm_shape.rank())
-  {
-    if (ofm_shape.rank() == 2)
-    {
-      // Reducing HW
-      assert(ifm_shape.dim(0) == ofm_shape.dim(0) && ifm_shape.dim(3) == ofm_shape.dim(1));
-    }
-    else if (ofm_shape.rank() == 3)
-    {
-      // Reducing C or
-      // (Reducing H and C(ifm and ofm) == 1) or (Reducing W and C(ifm and ofm) == 1)
-      assert((ifm_shape.dim(0) == ofm_shape.dim(0) && ifm_shape.dim(1) == ofm_shape.dim(1) &&
-              ifm_shape.dim(2) == ofm_shape.dim(2)) ||
-             (ifm_shape.dim(0) == ofm_shape.dim(0) &&
-              (ifm_shape.dim(1) == ofm_shape.dim(1) || ifm_shape.dim(2) == ofm_shape.dim(1)) &&
-              ifm_shape.dim(3) == 1 && ofm_shape.dim(2) == 1));
-    }
-  }
-}
-
-void OperationValidator::visit(const ir::operation::LSTM &node)
-{
-  // NOTE This validation is for static rnn(non-dynamic shape), but not for dynamic rnn
-  // TODO Support dynamic rnn
-  const auto scratch_buffer_index{
-      node.getOutputs().at(ir::operation::LSTM::Output::SCRATCH_BUFFER)};
-  const auto output_state_out_index{
-      node.getOutputs().at(ir::operation::LSTM::Output::OUTPUT_STATE_OUT)};
-  const auto cell_state_out_index{
-      node.getOutputs().at(ir::operation::LSTM::Output::CELL_STATE_OUT)};
-  const auto output_index{node.getOutputs().at(ir::operation::LSTM::Output::OUTPUT)};
-
-  const auto input_index{node.getInputs().at(ir::operation::LSTM::Input::INPUT)};
-  const auto input_to_input_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::INPUT_TO_INPUT_WEIGHTS)};
-  const auto input_to_forget_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::INPUT_TO_FORGET_WEIGHTS)};
-  const auto input_to_cell_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::INPUT_TO_CELL_WEIGHTS)};
-  const auto input_to_output_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::INPUT_TO_OUTPUT_WEIGHTS)};
-  const auto recurrent_to_input_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::RECURRENT_TO_INPUT_WEIGHTS)};
-  const auto recurrent_to_forget_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::RECURRENT_TO_FORGET_WEIGHTS)};
-  const auto recurrent_to_cell_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::RECURRENT_TO_CELL_WEIGHTS)};
-  const auto recurrent_to_output_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::RECURRENT_TO_OUTPUT_WEIGHTS)};
-  const auto cell_to_input_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::CELL_TO_INPUT_WEIGHTS)};
-  const auto cell_to_forget_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::CELL_TO_FORGET_WEIGHTS)};
-  const auto cell_to_output_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::CELL_TO_OUTPUT_WEIGHTS)};
-  const auto input_gate_bias_index{
-      node.getInputs().at(ir::operation::LSTM::Input::INPUT_GATE_BIAS)};
-  const auto forget_gate_bias_index{
-      node.getInputs().at(ir::operation::LSTM::Input::FORGET_GATE_BIAS)};
-  const auto cell_bias_index{node.getInputs().at(ir::operation::LSTM::Input::CELL_BIAS)};
-  const auto output_gate_bias_index{
-      node.getInputs().at(ir::operation::LSTM::Input::OUTPUT_GATE_BIAS)};
-  const auto projection_weights_index{
-      node.getInputs().at(ir::operation::LSTM::Input::PROJECTION_WEIGHTS)};
-  const auto projection_bias_index{
-      node.getInputs().at(ir::operation::LSTM::Input::PROJECTION_BIAS)};
-  const auto output_state_in_index{
-      node.getInputs().at(ir::operation::LSTM::Input::OUTPUT_STATE_IN)};
-  const auto cell_state_in_index{node.getInputs().at(ir::operation::LSTM::Input::CELL_STATE_IN)};
-
-  UNUSED_RELEASE(scratch_buffer_index);
-  UNUSED_RELEASE(output_state_out_index);
-  UNUSED_RELEASE(cell_state_out_index);
-  UNUSED_RELEASE(output_index);
-
-  UNUSED_RELEASE(input_index);
-  UNUSED_RELEASE(input_to_input_weights_index);
-  UNUSED_RELEASE(input_to_forget_weights_index);
-  UNUSED_RELEASE(input_to_cell_weights_index);
-  UNUSED_RELEASE(input_to_output_weights_index);
-  UNUSED_RELEASE(recurrent_to_input_weights_index);
-  UNUSED_RELEASE(recurrent_to_forget_weights_index);
-  UNUSED_RELEASE(recurrent_to_cell_weights_index);
-  UNUSED_RELEASE(recurrent_to_output_weights_index);
-  UNUSED_RELEASE(cell_to_input_weights_index);
-  UNUSED_RELEASE(cell_to_forget_weights_index);
-  UNUSED_RELEASE(cell_to_output_weights_index);
-  UNUSED_RELEASE(input_gate_bias_index);
-  UNUSED_RELEASE(forget_gate_bias_index);
-  UNUSED_RELEASE(cell_bias_index);
-  UNUSED_RELEASE(output_gate_bias_index);
-  UNUSED_RELEASE(projection_weights_index);
-  UNUSED_RELEASE(projection_bias_index);
-  UNUSED_RELEASE(output_state_in_index);
-  UNUSED_RELEASE(cell_state_in_index);
-
-  assert(_ctx.at(scratch_buffer_index).shape().rank() == 2 &&
-         _ctx.at(output_state_out_index).shape().rank() == 2 &&
-         _ctx.at(cell_state_out_index).shape().rank() == 2 &&
-         _ctx.at(output_index).shape().rank() == 2 && _ctx.at(input_index).shape().rank() == 2 &&
-         _ctx.at(input_to_input_weights_index).shape().rank() == 2 &&
-         _ctx.at(input_to_forget_weights_index).shape().rank() == 2 &&
-         _ctx.at(input_to_cell_weights_index).shape().rank() == 2 &&
-         _ctx.at(input_to_output_weights_index).shape().rank() == 2 &&
-         _ctx.at(recurrent_to_input_weights_index).shape().rank() == 2 &&
-         _ctx.at(recurrent_to_forget_weights_index).shape().rank() == 2 &&
-         _ctx.at(recurrent_to_cell_weights_index).shape().rank() == 2 &&
-         _ctx.at(recurrent_to_output_weights_index).shape().rank() == 2 &&
-         _ctx.at(projection_weights_index).shape().rank() == 2 &&
-         _ctx.at(output_state_in_index).shape().rank() == 2 &&
-         _ctx.at(cell_state_in_index).shape().rank() == 2);
-
-  assert(_ctx.at(cell_to_input_weights_index).shape().rank() == 1 &&
-         _ctx.at(cell_to_forget_weights_index).shape().rank() == 1 &&
-         _ctx.at(cell_to_output_weights_index).shape().rank() == 1 &&
-         _ctx.at(input_gate_bias_index).shape().rank() == 1 &&
-         _ctx.at(forget_gate_bias_index).shape().rank() == 1 &&
-         _ctx.at(cell_bias_index).shape().rank() == 1 &&
-         _ctx.at(output_gate_bias_index).shape().rank() == 1 &&
-         _ctx.at(projection_bias_index).shape().rank() == 1);
-
-  // CIFG assertion
-  assert((_ctx.at(input_to_input_weights_index).shape().dim(0) == 0 &&
-          _ctx.at(input_to_input_weights_index).shape().dim(1) == 0 &&
-          _ctx.at(recurrent_to_input_weights_index).shape().dim(0) == 0 &&
-          _ctx.at(recurrent_to_input_weights_index).shape().dim(1) == 0 &&
-          _ctx.at(input_gate_bias_index).shape().dim(0) == 0 &&
-          _ctx.at(cell_to_input_weights_index).shape().dim(0) == 0) ||
-         (_ctx.at(input_to_input_weights_index).shape().dim(0) != 0 &&
-          _ctx.at(input_to_input_weights_index).shape().dim(1) != 0 &&
-          _ctx.at(recurrent_to_input_weights_index).shape().dim(0) != 0 &&
-          _ctx.at(recurrent_to_input_weights_index).shape().dim(1) != 0 &&
-          _ctx.at(input_gate_bias_index).shape().dim(0) != 0));
-
-  // Peephole assertion
-  assert((_ctx.at(cell_to_forget_weights_index).shape().dim(0) == 0 &&
-          _ctx.at(cell_to_output_weights_index).shape().dim(0) == 0) ||
-         (_ctx.at(cell_to_forget_weights_index).shape().dim(0) != 0 &&
-          _ctx.at(cell_to_output_weights_index).shape().dim(0) != 0));
-
-  bool has_input_to_input_weights = _ctx.at(input_to_input_weights_index).shape().dim(0) != 0 &&
-                                    _ctx.at(input_to_input_weights_index).shape().dim(1) != 0;
-  bool has_recurrent_to_input_weights =
-      _ctx.at(recurrent_to_input_weights_index).shape().dim(0) != 0 &&
-      _ctx.at(recurrent_to_input_weights_index).shape().dim(1) != 0;
-  bool has_input_gate_bias = _ctx.at(input_gate_bias_index).shape().dim(0) != 0;
-  bool has_cell_to_input_weights = _ctx.at(cell_to_input_weights_index).shape().dim(0) != 0;
-  bool has_cell_to_forget_weights = _ctx.at(cell_to_forget_weights_index).shape().dim(0) != 0;
-  bool has_cell_to_output_weights = _ctx.at(cell_to_output_weights_index).shape().dim(0) != 0;
-  bool has_projection_weights = _ctx.at(projection_weights_index).shape().dim(0) != 0 &&
-                                _ctx.at(projection_weights_index).shape().dim(1) != 0;
-  bool has_projection_bias = _ctx.at(projection_bias_index).shape().dim(0);
-
-  // NOTE The cell_to_input_weights do not exist in non-peephole although regular LSTM(non-CIFG).
-  // true: no CIFG
-  // false: CIFG
-  bool has_cifg_param = has_input_to_input_weights && has_recurrent_to_input_weights;
-
-  // NOTE The cell_to_input_weights do not exist in regular CIFG although peephole.
-  // true: peephole
-  // false: no peephole
-  bool has_peephole_param = has_cell_to_forget_weights && has_cell_to_output_weights;
-
-  // NOTE The projection weights may have data but the projection bias may not.
-  bool has_projection_param = has_projection_weights;
-
-  UNUSED_RELEASE(has_input_to_input_weights);
-  UNUSED_RELEASE(has_recurrent_to_input_weights);
-  UNUSED_RELEASE(has_input_gate_bias);
-  UNUSED_RELEASE(has_cell_to_input_weights);
-  UNUSED_RELEASE(has_cell_to_forget_weights);
-  UNUSED_RELEASE(has_cell_to_output_weights);
-  UNUSED_RELEASE(has_projection_weights);
-  UNUSED_RELEASE(has_projection_bias);
-  UNUSED_RELEASE(has_cifg_param);
-  UNUSED_RELEASE(has_peephole_param);
-  UNUSED_RELEASE(has_projection_param);
-
-  const auto batch_size = _ctx.at(input_index).shape().dim(0);
-  UNUSED_RELEASE(batch_size);
-  assert(batch_size == _ctx.at(output_state_in_index).shape().dim(0) &&
-         batch_size == _ctx.at(cell_state_in_index).shape().dim(0) &&
-         batch_size == _ctx.at(scratch_buffer_index).shape().dim(0) &&
-         batch_size == _ctx.at(output_state_out_index).shape().dim(0) &&
-         batch_size == _ctx.at(cell_state_out_index).shape().dim(0) &&
-         batch_size == _ctx.at(output_index).shape().dim(0));
-
-  const auto input_size = _ctx.at(input_index).shape().dim(1);
-  UNUSED_RELEASE(input_size);
-  assert(input_size == _ctx.at(input_to_forget_weights_index).shape().dim(1) &&
-         input_size == _ctx.at(input_to_cell_weights_index).shape().dim(1) &&
-         input_size == _ctx.at(input_to_output_weights_index).shape().dim(1));
-
-  const auto num_units = _ctx.at(cell_state_out_index).shape().dim(1);
-  UNUSED_RELEASE(num_units);
-  assert(num_units == _ctx.at(input_to_forget_weights_index).shape().dim(0) &&
-         num_units == _ctx.at(input_to_cell_weights_index).shape().dim(0) &&
-         num_units == _ctx.at(input_to_output_weights_index).shape().dim(0) &&
-         num_units == _ctx.at(recurrent_to_forget_weights_index).shape().dim(0) &&
-         num_units == _ctx.at(recurrent_to_cell_weights_index).shape().dim(0) &&
-         num_units == _ctx.at(recurrent_to_output_weights_index).shape().dim(0) &&
-         num_units == _ctx.at(forget_gate_bias_index).shape().dim(0) &&
-         num_units == _ctx.at(cell_bias_index).shape().dim(0) &&
-         num_units == _ctx.at(output_gate_bias_index).shape().dim(0) &&
-         num_units == _ctx.at(cell_state_in_index).shape().dim(1) &&
-         (((num_units * 3) == _ctx.at(scratch_buffer_index).shape().dim(1)) ||
-          ((num_units * 4) == _ctx.at(scratch_buffer_index).shape().dim(1))));
-
-  const auto output_size = _ctx.at(output_index).shape().dim(1);
-  UNUSED_RELEASE(output_size);
-  assert(output_size == _ctx.at(recurrent_to_forget_weights_index).shape().dim(1) &&
-         output_size == _ctx.at(recurrent_to_cell_weights_index).shape().dim(1) &&
-         output_size == _ctx.at(recurrent_to_output_weights_index).shape().dim(1) &&
-         output_size == _ctx.at(output_state_in_index).shape().dim(1) &&
-         output_size == _ctx.at(output_state_out_index).shape().dim(1));
-
-  if (has_cifg_param)
-  {
-    assert(input_size == _ctx.at(input_to_input_weights_index).shape().dim(1));
-    assert(num_units == _ctx.at(input_to_input_weights_index).shape().dim(0) &&
-           num_units == _ctx.at(recurrent_to_input_weights_index).shape().dim(0) &&
-           (num_units == _ctx.at(cell_to_input_weights_index).shape().dim(0) ||
-            _ctx.at(cell_to_input_weights_index).shape().dim(0) == 0 /* non-peephole */) &&
-           num_units == _ctx.at(input_gate_bias_index).shape().dim(0));
-    assert(output_size == _ctx.at(recurrent_to_input_weights_index).shape().dim(1));
-    assert(has_input_to_input_weights && has_recurrent_to_input_weights && has_input_gate_bias);
-    if (has_cell_to_input_weights)
-    {
-      // NOTE The cell_to_input_weights exist only in case of non-CIFG and peephole.
-      assert(has_peephole_param);
-    }
-    assert(_ctx.at(scratch_buffer_index).shape().dim(1) == num_units * 4);
-  }
-  else
-  {
-    assert(_ctx.at(scratch_buffer_index).shape().dim(1) == num_units * 3);
-  }
-
-  if (has_peephole_param)
-  {
-    assert(num_units == _ctx.at(cell_to_forget_weights_index).shape().dim(0) &&
-           num_units == _ctx.at(cell_to_output_weights_index).shape().dim(0) &&
-           (num_units == _ctx.at(cell_to_input_weights_index).shape().dim(0) ||
-            _ctx.at(cell_to_input_weights_index).shape().dim(0) == 0 /* CIFG */));
-  }
-
-  if (has_projection_param)
-  {
-    assert(num_units == _ctx.at(projection_weights_index).shape().dim(1));
-    assert(output_size == _ctx.at(projection_weights_index).shape().dim(0));
-    if (has_projection_bias)
-    {
-      assert(output_size == _ctx.at(projection_bias_index).shape().dim(0));
-    }
-  }
-}
-
-void OperationValidator::visit(const ir::operation::Unpack &node)
-{
-  const auto input_index{node.getInputs().at(ir::operation::Unpack::Input::INPUT)};
-  const auto num{node.param().num};
-  const auto axis{node.param().axis};
-
-  const auto &input_shape = _ctx.at(input_index).shape();
-  const auto input_rank = static_cast<int32_t>(input_shape.rank());
-
-  UNUSED_RELEASE(num);
-  UNUSED_RELEASE(axis);
-  UNUSED_RELEASE(input_rank);
-
-  assert(num == static_cast<int32_t>(node.getOutputs().size()));
-  assert(axis >= -input_rank && axis < input_rank);
-}
-
-void OperationValidator::visit(const ir::operation::Pad &node)
-{
-  const auto input_index{node.getInputs().at(ir::operation::Pad::Input::INPUT)};
-  const auto pad_index{node.getInputs().at(ir::operation::Pad::Input::PAD)};
-  const auto output_index{node.getInputs().at(0)};
-
-  const auto &pad_shape = _ctx.at(pad_index).shape();
-  const auto input_rank = static_cast<int32_t>(_ctx.at(input_index).shape().rank());
-
-  UNUSED_RELEASE(pad_shape);
-  UNUSED_RELEASE(input_rank);
-  UNUSED_RELEASE(output_index);
-
-  assert(pad_shape.rank() == 2);
-  assert(pad_shape.dim(0) == input_rank);
-  assert(pad_shape.dim(1) == 2);
-  assert(_ctx.at(pad_index).typeInfo().type() == ir::DataType::INT32);
-  assert(_ctx.at(input_index).shape().rank() == _ctx.at(output_index).shape().rank());
-}
-
-void OperationValidator::visit(const ir::operation::Min &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto lhs_index{node.getInputs().at(ir::operation::Min::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::Min::Input::RHS)};
-
-  UNUSED_RELEASE(output_index);
-  UNUSED_RELEASE(lhs_index);
-  UNUSED_RELEASE(rhs_index);
-
-  assert(_ctx.at(lhs_index).typeInfo().type() == _ctx.at(rhs_index).typeInfo().type());
-  assert(_ctx.at(lhs_index).typeInfo().type() == _ctx.at(output_index).typeInfo().type());
-}
-
-void OperationValidator::visit(const ir::operation::Max &node)
-{
-  const auto output_index{node.getOutputs().at(0)};
-  const auto lhs_index{node.getInputs().at(ir::operation::Max::Input::LHS)};
-  const auto rhs_index{node.getInputs().at(ir::operation::Max::Input::RHS)};
-
-  UNUSED_RELEASE(output_index);
-  UNUSED_RELEASE(lhs_index);
-  UNUSED_RELEASE(rhs_index);
-
-  assert(_ctx.at(lhs_index).typeInfo().type() == _ctx.at(rhs_index).typeInfo().type());
-  assert(_ctx.at(lhs_index).typeInfo().type() == _ctx.at(output_index).typeInfo().type());
-}
-
-} // namespace compiler
-} // namespace neurun
diff --git a/runtime/neurun/core/src/compiler/OperationValidator.h b/runtime/neurun/core/src/compiler/OperationValidator.h
deleted file mode 100644
index 6ff3c7815..000000000
--- a/runtime/neurun/core/src/compiler/OperationValidator.h
+++ /dev/null
@@ -1,86 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_COMPILER_OPERATION_VALIDATOR_H__
-#define __NEURUN_COMPILER_OPERATION_VALIDATOR_H__
-
-#include "ir/Layout.h"
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-class Graph;
-class Operands;
-} // namespace ir
-} // namespace neurun
-
-namespace neurun
-{
-namespace compiler
-{
-
-class OperationValidator : public ir::OperationVisitor
-{
-public:
-  OperationValidator(void) = delete;
-  OperationValidator(const ir::Graph &graph);
-
-public:
-  void operator()();
-
-public:
-  void visit(const ir::operation::BatchToSpaceND &node) override;
-  void visit(const ir::operation::Cast &node) override;
-  void visit(const ir::operation::Comparison &node) override;
-  void visit(const ir::operation::Softmax &node) override;
-  void visit(const ir::operation::InstanceNorm &node) override;
-  void visit(const ir::operation::Permute &node) override;
-  void visit(const ir::operation::ReduceSum &node) override;
-  void visit(const ir::operation::Transpose &node) override;
-  void visit(const ir::operation::ReduceMax &node) override;
-  void visit(const ir::operation::RNN &node) override;
-  void visit(const ir::operation::SpaceToBatchND &node) override;
-  void visit(const ir::operation::SpaceToDepth &node) override;
-  void visit(const ir::operation::EmbeddingLookup &node) override;
-  void visit(const ir::operation::Exp &node) override;
-  void visit(const ir::operation::Floor &node) override;
-  void visit(const ir::operation::HashtableLookup &node) override;
-  void visit(const ir::operation::TransposeConv &node) override;
-  void visit(const ir::operation::Gather &node) override;
-  void visit(const ir::operation::Dequantize &node) override;
-  void visit(const ir::operation::Mean &node) override;
-  void visit(const ir::operation::DepthToSpace &node) override;
-  void visit(const ir::operation::Pack &node) override;
-  void visit(const ir::operation::ReduceMin &node) override;
-  void visit(const ir::operation::LSTM &node) override;
-  void visit(const ir::operation::Unpack &node) override;
-  void visit(const ir::operation::Pad &node) override;
-  void visit(const ir::operation::Min &node) override;
-  void visit(const ir::operation::Max &node) override;
-
-private:
-  // TODO Remove _ctx field
-  const ir::Graph &_graph;
-  const ir::Operands &_ctx;
-  ir::Layout _current_subg_layout;
-};
-
-} // namespace compiler
-} // namespace neurun
-
-#endif // __NEURUN_COMPILER_OPERATION_VALIDATOR_H__
diff --git a/runtime/neurun/core/src/compiler/ParamChecker.cc b/runtime/neurun/core/src/compiler/ParamChecker.cc
deleted file mode 100644
index bf172b53f..000000000
--- a/runtime/neurun/core/src/compiler/ParamChecker.cc
+++ /dev/null
@@ -1,33 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ParamChecker.h"
-
-#include "ir/Graph.h"
-
-namespace neurun
-{
-namespace compiler
-{
-
-void ParamChecker::operator()()
-{
-  _model->operations().iterate(
-      [&](const ir::OperationIndex &, const ir::Operation &node) { node.accept(*this); });
-}
-
-} // namespace compiler
-} // namespace neurun
diff --git a/runtime/neurun/core/src/compiler/ParamChecker.h b/runtime/neurun/core/src/compiler/ParamChecker.h
deleted file mode 100644
index 7e88ff185..000000000
--- a/runtime/neurun/core/src/compiler/ParamChecker.h
+++ /dev/null
@@ -1,73 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/**
- * @file ParamChecker.h
- * @brief This file contains ParamChecker to check\n
- *        operations' parameters are compilable at machine independent phase\n
- *        ex) Check param is constant
- */
-#ifndef __NEURUN_COMPILER_PARAM_CHECKER_H__
-#define __NEURUN_COMPILER_PARAM_CHECKER_H__
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-class Graph;
-} // namespace ir
-} // namespace neurun
-
-namespace neurun
-{
-namespace compiler
-{
-
-class ParamChecker : public ir::OperationVisitor
-{
-public:
-  /**
-   * @brief Construct a new Param Checker object (deleted)
-   */
-  ParamChecker(void) = delete;
-  /**
-   * @brief Construct a new Param Checker object
-   * @param[in] model Graph model to check
-   */
-  ParamChecker(std::shared_ptr<ir::Graph> model) : _model{model} {}
-
-public:
-  /**
-   * @brief Run parameter analysis
-   */
-  void operator()();
-  /**
-   * @brief   Return analysis result if model have non-const parameter
-   * @return  @c true if there is non-const parameter, otherwise @c false
-   */
-  bool haveNoneConstParam(void) { return _nonConstParam; }
-
-private:
-  const std::shared_ptr<ir::Graph> _model;
-  bool _nonConstParam{false};
-};
-
-} // namespace compiler
-} // namespace neurun
-
-#endif // __NEURUN_COMPILER_OPERATION_VALIDATOR_H__
diff --git a/runtime/neurun/core/src/compiler/SubTensorAnalyzer.cc b/runtime/neurun/core/src/compiler/SubTensorAnalyzer.cc
deleted file mode 100644
index dae1a74ff..000000000
--- a/runtime/neurun/core/src/compiler/SubTensorAnalyzer.cc
+++ /dev/null
@@ -1,96 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "SubTensorAnalyzer.h"
-
-#include <typeinfo>
-
-#include "cpp14/memory.h"
-#include "ir/OperandIndexSequence.h"
-#include "util/logging.h"
-#include "util/Coordinates.h"
-
-namespace neurun
-{
-namespace compiler
-{
-
-void SubTensorAnalyzer::visit(const ir::operation::Concat &node)
-{
-  //  If operator is concat (or other operators related with subsumption), fill subsumption info
-  //  TODO: if one tensor is subset of many parents or model input
-  //        Solution 1. Handle 1st parent only, ignore others (need to invert for other children)
-  //        Solution 2. Insert copy operation for other parents
-  int32_t axis_raw = node.param().axis;
-
-  const auto &output_index = node.getOutputs().at(0);
-  const auto &inputs = node.getInputs();
-
-  int32_t axis_point = 0;
-  const auto rank = _graph.operands().at(output_index).shape().rank();
-  int32_t axis = axis_raw < 0 ? (axis_raw + rank) : axis_raw;
-  assert(rank > axis);
-
-  for (const auto &input_index : inputs)
-  {
-    // NOTE Not support multiple parent tensor yet
-    //
-    // Let us consider the following example (where OP_i is not a CONCAT):
-    //
-    //   %0 = OP_0
-    //   %1 = OP_1
-    //   %2 = OP_2
-    //   %3 = CONCAT(%0, %1)
-    //   %4 = CONCAT(%0, %2)
-    //
-    // %0 and %1 SHOULD be consecutive in order to eliminate the former CONCAT operation,
-    // which makes it impossible to eliminate the latter CONCAT operation.
-    // - Note that %0 and %2 cannot be consecutive.
-    if (_graph.operands().at(input_index).parent_info() != nullptr)
-    {
-      return;
-    }
-
-    // NOTE Not support the case that concat's input is a constant or a input of model
-    if (_graph.operands().at(input_index).isConstant() || _graph.getInputs().contains(input_index))
-    {
-      return;
-    }
-  }
-
-  for (const auto &input_index : inputs)
-  {
-    auto input_shape = _graph.operands().at(input_index).shape();
-    assert(rank == input_shape.rank());
-
-    neurun::util::Coordinates coordinate_info{};
-    for (int i = 0; i < rank; i++)
-    {
-      coordinate_info.set(i, 0);
-    }
-    coordinate_info.set(axis, axis_point);
-
-    auto parentInfo =
-        nnfw::cpp14::make_unique<ir::operand::ParentInfo>(output_index, coordinate_info);
-
-    _graph.operands().at(input_index).parent_info(std::move(parentInfo));
-
-    axis_point += input_shape.dim(axis);
-  }
-}
-
-} // namespace compiler
-} // namespace neurun
diff --git a/runtime/neurun/core/src/compiler/SubTensorAnalyzer.h b/runtime/neurun/core/src/compiler/SubTensorAnalyzer.h
deleted file mode 100644
index 54d41e460..000000000
--- a/runtime/neurun/core/src/compiler/SubTensorAnalyzer.h
+++ /dev/null
@@ -1,59 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/**
- * @file  SubTensorAnalyzer.h
- * @brief This file contains SubTensorAnalyzer to analyze tensor subsumption
- *        using operation visitor
- */
-
-#ifndef __NEURUN_COMPILER_SUBTENSOR_ANALYZER_H__
-#define __NEURUN_COMPILER_SUBTENSOR_ANALYZER_H__
-
-#include "ir/OperationVisitor.h"
-#include "ir/Graph.h"
-
-namespace neurun
-{
-namespace compiler
-{
-
-/**
- * @brief Class to analyze tensor subsumption
- */
-class SubTensorAnalyzer : public ir::OperationVisitor
-{
-public:
-  /**
-   * @brief     Construct a new SubTensorAnalyzer object
-   * @param[in] ctx Graph operand set
-   */
-  SubTensorAnalyzer(ir::Graph &graph) : _graph{graph}
-  {
-    // DO NOTHING
-  }
-
-public:
-  void visit(const ir::operation::Concat &) override;
-
-private:
-  ir::Graph &_graph; // TODO Refactor : Do not update Operands
-};
-
-} // namespace compiler
-} // namespace neurun
-
-#endif // __NEURUN_COMPILER_SUBTENSOR_ANALYZER_H__
diff --git a/runtime/neurun/core/src/dumper/dot/DotBuilder.cc b/runtime/neurun/core/src/dumper/dot/DotBuilder.cc
deleted file mode 100644
index 4c7089a9c..000000000
--- a/runtime/neurun/core/src/dumper/dot/DotBuilder.cc
+++ /dev/null
@@ -1,83 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "DotBuilder.h"
-
-namespace neurun
-{
-namespace dumper
-{
-namespace dot
-{
-
-// DotDumper
-DotBuilder::DotBuilder() {}
-
-void DotBuilder::update(const Node &node_info)
-{
-  add(node_info);
-  for (auto edge : node_info.edges())
-  {
-    addEdge(node_info, *edge);
-  }
-}
-
-void DotBuilder::addSubgraph(const DotSubgraphInfo &subgraph_info)
-{
-  _dot << "op_seq cluster_" << subgraph_info.index().value() << " {\n";
-  _dot << "  label=\"" << subgraph_info.label() << "\";\n";
-  _dot << "  style=filled;\n";
-  _dot << "  color=lightgrey;\n";
-  _dot << "  ";
-  for (auto op : subgraph_info.operations())
-  {
-    _dot << "operation" << op.value() << "; ";
-  }
-  for (auto op : subgraph_info.operands())
-  {
-    _dot << "operand" << op.value() << "; ";
-  }
-  _dot << "\n";
-  _dot << "}\n";
-}
-
-void DotBuilder::writeDot(std::ostream &os)
-{
-  os << "digraph D {\n"
-     << _dot.str() << "\n"
-     << "}\n";
-}
-
-void DotBuilder::add(const Node &node)
-{
-  _dot << node.id();
-  std::stringstream ss;
-  _dot << "[";
-  for (auto attr : node.attributes())
-  {
-    _dot << attr.first << "=\"" << attr.second << "\" ";
-  }
-  _dot << "];\n";
-}
-
-void DotBuilder::addEdge(const Node &node1, const Node &node2)
-{
-  _dot << node1.id() << " -> " << node2.id() << ";\n";
-}
-
-} // namespace dot
-} // namespace dumper
-} // namespace neurun
diff --git a/runtime/neurun/core/src/dumper/dot/DotBuilder.h b/runtime/neurun/core/src/dumper/dot/DotBuilder.h
deleted file mode 100644
index c04f6bc52..000000000
--- a/runtime/neurun/core/src/dumper/dot/DotBuilder.h
+++ /dev/null
@@ -1,62 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_DUMPER_DOT_DOT_BUILDER_H__
-#define __NEURUN_DUMPER_DOT_DOT_BUILDER_H__
-
-#include <sstream>
-
-#include "ir/Index.h"
-#include "ir/Operation.h"
-#include "ir/Operand.h"
-
-#include "OperationNode.h"
-#include "OperandNode.h"
-#include "DotSubgraphInfo.h"
-
-using Operation = neurun::ir::Operation;
-using Object = neurun::ir::Operand;
-
-namespace neurun
-{
-namespace dumper
-{
-namespace dot
-{
-
-class DotBuilder
-{
-public:
-  DotBuilder();
-
-public:
-  void update(const Node &dotinfo);
-  void addSubgraph(const DotSubgraphInfo &subgraph_info);
-
-  void writeDot(std::ostream &os);
-
-private:
-  void add(const Node &dotinfo);
-  void addEdge(const Node &dotinfo1, const Node &dotinfo2);
-
-  std::stringstream _dot;
-};
-
-} // namespace dot
-} // namespace dumper
-} // namespace neurun
-
-#endif // __NEURUN_DUMPER_DOT_DOT_BUILDER_H__
diff --git a/runtime/neurun/core/src/dumper/dot/DotDumper.cc b/runtime/neurun/core/src/dumper/dot/DotDumper.cc
deleted file mode 100644
index 44313a657..000000000
--- a/runtime/neurun/core/src/dumper/dot/DotDumper.cc
+++ /dev/null
@@ -1,199 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <fstream>
-#include <unordered_map>
-
-#include "DotDumper.h"
-#include "DotBuilder.h"
-#include "DotSubgraphInfo.h"
-#include "ir/OpSequence.h"
-#include "ir/OperationIndexMap.h"
-#include "backend/Backend.h"
-#include "backend/BackendManager.h"
-#include "backend/IConfig.h"
-
-namespace neurun
-{
-namespace dumper
-{
-namespace dot
-{
-
-void DotDumper::dump(const std::string &tag)
-{
-  if (_level == Level::OFF)
-  {
-    return;
-  }
-
-  neurun::dumper::dot::DotBuilder dot_builder;
-
-  auto &operations = _graph.operations();
-  auto &operands = _graph.operands();
-
-  ir::OperationIndexMap<std::unique_ptr<Operation>> operation_nodes;
-  std::unordered_map<ir::OperandIndex, std::unique_ptr<Operand>> operand_nodes;
-
-  operations.iterate([&](const ir::OperationIndex &index, const ir::Operation &op) {
-    auto node = nnfw::cpp14::make_unique<Operation>(index, op);
-
-    for (auto output : op.getOutputs())
-    {
-      using neurun::dumper::dot::Operand;
-      auto child = std::make_shared<Operand>(output, Operand::Type::MODEL_OUTPUT);
-      node->addEdge(child);
-    }
-
-    operation_nodes.emplace(index, std::move(node));
-  });
-
-  auto backend_to_fillcolor = [](const backend::Backend *backend) {
-    static const auto map = []() {
-      std::unordered_map<const backend::Backend *, std::string> ret;
-      uint32_t index = 1; // Start from 1 to avoid 0(red) which is too dark :(
-      for (const auto backend : backend::BackendManager::get().getAll())
-      {
-        ret.emplace(backend, Node::BG_COLORS[index]);
-        index = (index + 1) % (sizeof(Node::BG_COLORS) / sizeof(Node::BG_COLORS[0]));
-      }
-      return ret;
-    }();
-
-    auto itr = map.find(backend);
-    if (itr == map.end())
-    {
-      return Node::DEFAULT_FILLCOLOR;
-    }
-    else
-    {
-      return itr->second;
-    }
-  };
-
-  util::Set<ir::OperandIndex> shown_operand_set;
-
-  operands.iterate([&](const ir::OperandIndex &index, const ir::Operand &object) {
-    bool showing_cond = false;
-    if (_level == Level::ALL)
-    {
-      showing_cond = true;
-    }
-    else
-    {
-      showing_cond = !object.isConstant();
-    }
-    if (object.isConstant() || _graph.getInputs().contains(index))
-    {
-      showing_cond = showing_cond && (object.getUses().size() > 0);
-    }
-    if (showing_cond)
-    {
-      shown_operand_set.add(index);
-
-      auto type = [&]() {
-        using neurun::dumper::dot::Operand;
-        if (_graph.getInputs().contains(index))
-          return Operand::Type::MODEL_INPUT;
-        if (_graph.getOutputs().contains(index))
-          return Operand::Type::MODEL_OUTPUT;
-        return Operand::Type::INTERNAL;
-      }();
-
-      auto lower_info = _graph.getLowerInfo(index);
-      auto node = nnfw::cpp14::make_unique<Operand>(index, type);
-
-      {
-        // Display LowerInfo attributes
-        std::string label = std::to_string(index.value());
-        std::string fillcolor = "";
-        if (lower_info)
-        {
-          const auto &def_factors = lower_info->def_factors();
-          if (def_factors.size() > 0)
-          {
-            label += "\\n[";
-            label += def_factors.getOnlyElement().backend()->config()->id();
-            label += "]";
-
-            fillcolor = backend_to_fillcolor(lower_info->def_factors().getOnlyElement().backend());
-          }
-        }
-        node->setAttribute("label", label);
-        node->setAttribute("fillcolor", fillcolor);
-      }
-
-      for (auto operation_index : object.getUses().list())
-      {
-        auto &operation = operations.at(operation_index);
-        auto child = std::make_shared<Operation>(operation_index, operation);
-        node->addEdge(child);
-      }
-
-      operand_nodes.emplace(index, std::move(node));
-    }
-  });
-
-  const auto subgraphs = _graph.subgraphs();
-  if (subgraphs)
-  {
-    subgraphs->iterate([&](const ir::SubgraphIndex &index, const ir::OpSequence &op_seq) {
-      const auto lower_info = _graph.getLowerInfo(index);
-      auto fillcolor = backend_to_fillcolor(lower_info->backend());
-      std::string label =
-          std::to_string(index.value()) + " [" + lower_info->backend()->config()->id() + "]";
-      DotSubgraphInfo subgraph_info{index, op_seq, shown_operand_set};
-      subgraph_info.label(label);
-      subgraph_info.fillcolor(fillcolor);
-      dot_builder.addSubgraph(subgraph_info);
-
-      // Set fillcolor of all operations in the op_seq
-      for (const auto &op : op_seq.operations())
-      {
-        auto found = operation_nodes.find(op.index);
-        if (found != operation_nodes.end())
-        {
-          auto &&op = found->second;
-          op->setAttribute("fillcolor", fillcolor);
-        }
-      }
-    });
-  }
-
-  for (const auto &e : operation_nodes)
-    dot_builder.update(*e.second);
-  for (const auto &e : operand_nodes)
-    dot_builder.update(*e.second);
-
-  // Dump to file
-  {
-    std::string file_name;
-    file_name += tag;
-    file_name += ".dot";
-    std::filebuf fb;
-
-    fb.open(file_name, std::ios::out);
-    std::ostream os(&fb);
-
-    dot_builder.writeDot(os);
-
-    fb.close();
-  }
-}
-
-} // namespace dot
-} // namespace dumper
-} // namespace neurun
diff --git a/runtime/neurun/core/src/dumper/dot/DotDumper.h b/runtime/neurun/core/src/dumper/dot/DotDumper.h
deleted file mode 100644
index ec4d2b967..000000000
--- a/runtime/neurun/core/src/dumper/dot/DotDumper.h
+++ /dev/null
@@ -1,60 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/Graph.h"
-
-#ifndef __NEURUN_DUMPER_DOT_DOT_DUMPER_H__
-#define __NEURUN_DUMPER_DOT_DOT_DUMPER_H__
-
-namespace neurun
-{
-namespace dumper
-{
-namespace dot
-{
-
-class DotDumper
-{
-public:
-  enum Level
-  {
-    OFF = 0,               //< Do not dump
-    ALL_BUT_CONSTANTS = 1, //< Emit all operations and operands but constants
-    ALL = 2                //< Emit all operations and operands
-  };
-
-public:
-  DotDumper(const ir::Graph &graph, Level level) : _graph(graph), _level{level} {}
-
-public:
-  /**
-   * @brief Dump to dot file as tag name if "GRAPH_DOT_DUMP" is set
-   *
-   * @param[in] tag    The name of dot file that would be created
-   * @return N/A
-   */
-  void dump(const std::string &tag);
-
-private:
-  const ir::Graph &_graph;
-  Level _level;
-};
-
-} // namespace dot
-} // namespace dumper
-} // namespace neurun
-
-#endif // __NEURUN_DUMPER_DOT_DOT_DUMPER_H__
diff --git a/runtime/neurun/core/src/dumper/dot/DotSubgraphInfo.cc b/runtime/neurun/core/src/dumper/dot/DotSubgraphInfo.cc
deleted file mode 100644
index 8cfe35900..000000000
--- a/runtime/neurun/core/src/dumper/dot/DotSubgraphInfo.cc
+++ /dev/null
@@ -1,56 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "DotSubgraphInfo.h"
-
-#include <sstream>
-
-namespace neurun
-{
-namespace dumper
-{
-namespace dot
-{
-
-DotSubgraphInfo::DotSubgraphInfo(const ir::SubgraphIndex &index, const ir::OpSequence &op_seq,
-                                 const util::Set<ir::OperandIndex> &shown_operands)
-    : _index{index}
-{
-  for (const auto &element : op_seq.operations())
-  {
-    _operations.insert(element.index);
-    for (auto o : element.node->getInputs())
-    {
-      // Must be a shown operand, not op_seq's inputs
-      if (shown_operands.contains(o) && !op_seq.getInputs().contains(o))
-      {
-        _operands.insert(o);
-      }
-    }
-    for (auto o : element.node->getOutputs())
-    {
-      // Must be a shown operand, not op_seq's inputs
-      if (shown_operands.contains(o) && !op_seq.getOutputs().contains(o))
-      {
-        _operands.insert(o);
-      }
-    }
-  }
-}
-
-} // namespace dot
-} // namespace dumper
-} // namespace neurun
diff --git a/runtime/neurun/core/src/dumper/dot/DotSubgraphInfo.h b/runtime/neurun/core/src/dumper/dot/DotSubgraphInfo.h
deleted file mode 100644
index 0aa7c6ddf..000000000
--- a/runtime/neurun/core/src/dumper/dot/DotSubgraphInfo.h
+++ /dev/null
@@ -1,59 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_CORE_DUMPER_DOT_DOT_SUBGRAPH_INFO_H__
-#define __NEURUN_CORE_DUMPER_DOT_DOT_SUBGRAPH_INFO_H__
-
-#include <unordered_set>
-
-#include "ir/Index.h"
-#include "ir/OpSequence.h"
-#include "util/Set.h"
-
-namespace neurun
-{
-namespace dumper
-{
-namespace dot
-{
-
-class DotSubgraphInfo
-{
-public:
-  DotSubgraphInfo(const ir::SubgraphIndex &index, const ir::OpSequence &op_seq,
-                  const util::Set<ir::OperandIndex> &shown_operands);
-
-  ir::SubgraphIndex index() const { return _index; }
-  std::string label() const { return _label; }
-  void label(const std::string &val) { _label = val; }
-  std::string fillcolor() const { return _fillcolor; }
-  void fillcolor(const std::string &val) { _fillcolor = val; }
-  const std::unordered_set<ir::OperationIndex> &operations() const { return _operations; }
-  const std::unordered_set<ir::OperandIndex> &operands() const { return _operands; }
-
-private:
-  ir::SubgraphIndex _index;
-  std::string _label;
-  std::string _fillcolor;
-  std::unordered_set<ir::OperationIndex> _operations;
-  std::unordered_set<ir::OperandIndex> _operands;
-};
-
-} // namespace dot
-} // namespace dumper
-} // namespace neurun
-
-#endif // __NEURUN_CORE_DUMPER_DOT_DOT_SUBGRAPH_INFO_H__
diff --git a/runtime/neurun/core/src/dumper/dot/Node.cc b/runtime/neurun/core/src/dumper/dot/Node.cc
deleted file mode 100644
index 166f0f40f..000000000
--- a/runtime/neurun/core/src/dumper/dot/Node.cc
+++ /dev/null
@@ -1,56 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "Node.h"
-
-namespace neurun
-{
-namespace dumper
-{
-namespace dot
-{
-
-const std::string Node::DEFAULT_COLORSCHEME = "x11";
-const std::string Node::DEFAULT_FILLCOLOR = "white";
-// RED, BLUE, GREEN, PURPLE, ORANGE, YELLOW, BROWN, PINK
-const std::string Node::BG_COLORS[8] = {"1", "2", "3", "4", "5", "6", "7", "8"};
-
-Node::Node(const std::string &id) : _id{id}
-{
-  // Set default values
-  _attributes["style"] = "filled";
-  _attributes["colorscheme"] = DEFAULT_COLORSCHEME;
-  _attributes["fillcolor"] = DEFAULT_FILLCOLOR;
-}
-
-void Node::setAttribute(const std::string &key, const std::string &val) { _attributes[key] = val; }
-
-std::string Node::getAttribute(const std::string &key)
-{
-  auto itr = _attributes.find(key);
-  if (itr == _attributes.end())
-  {
-    return "";
-  }
-  else
-  {
-    return itr->second;
-  }
-}
-
-} // namespace dot
-} // namespace dumper
-} // namespace neurun
diff --git a/runtime/neurun/core/src/dumper/dot/Node.h b/runtime/neurun/core/src/dumper/dot/Node.h
deleted file mode 100644
index 364cb08a4..000000000
--- a/runtime/neurun/core/src/dumper/dot/Node.h
+++ /dev/null
@@ -1,127 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/**
- * @file Node.h
- * @brief    This file contains Node class
- * @ingroup  COM_AI_RUNTIME
- *
- */
-
-#ifndef __NEURUN_DUMPER_DOT_NODE_H__
-#define __NEURUN_DUMPER_DOT_NODE_H__
-
-#include <string>
-#include <memory>
-#include <vector>
-#include <unordered_map>
-
-namespace neurun
-{
-namespace dumper
-{
-namespace dot
-{
-
-enum BGCOLORS : int
-{
-  RED,
-  BLUE,
-  GREEN,
-  PUPLE,
-  ORANGE,
-  YELLOW,
-  BROWN,
-  PINK
-};
-
-/**
- * @brief Class that represents a Node in "dot" format
- *
-*/
-class Node
-{
-public:
-  const static std::string DEFAULT_FILLCOLOR;
-  const static std::string DEFAULT_COLORSCHEME;
-  const static std::string BG_COLORS[8];
-
-public:
-  /**
-   * @brief Destroy the Node object
-   *
-  */
-  virtual ~Node() = default;
-
-  /**
-   * @brief Construct a new Node object
-   *
-   * @param id
-  */
-  Node(const std::string &id);
-
-  /**
-   * @brief return id
-   *
-   * @return id
-  */
-  std::string id() const { return _id; }
-
-  /**
-   * @brief return attributes
-   *
-   * @return const reference of attributes object
-  */
-  const std::unordered_map<std::string, std::string> &attributes() const { return _attributes; }
-  /**
-   * @brief Store an attribute with key-value pair
-   *
-   * @param[in] key attribute's key
-   * @param[in] val attribute's value that is associated with the key
-  */
-  void setAttribute(const std::string &key, const std::string &val);
-  /**
-   * @brief Get the attributte value that is associated with key
-   *
-   * @param[in] key key of the attribute
-   * @return value that is associated with the key
-  */
-  std::string getAttribute(const std::string &key);
-
-  /**
-   * @brief Add an edge in the graph, which is an outgoing edge
-   *
-   * @param[in] dotinfo A node that the new edge will be connected to
-  */
-  void addEdge(std::shared_ptr<Node> dotinfo) { _children.emplace_back(dotinfo); }
-  /**
-   * @brief Return list of edges
-   *
-   * @return Edges
-  */
-  const std::vector<std::shared_ptr<Node>> &edges() const { return _children; }
-
-private:
-  std::string _id;
-  std::unordered_map<std::string, std::string> _attributes;
-  std::vector<std::shared_ptr<Node>> _children;
-};
-
-} // namespace dot
-} // namespace dumper
-} // namespace neurun
-
-#endif // __NEURUN_DUMPER_DOT_NODE_H__
diff --git a/runtime/neurun/core/src/dumper/dot/OperandNode.cc b/runtime/neurun/core/src/dumper/dot/OperandNode.cc
deleted file mode 100644
index 76d2c704c..000000000
--- a/runtime/neurun/core/src/dumper/dot/OperandNode.cc
+++ /dev/null
@@ -1,60 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <sstream>
-
-#include "OperandNode.h"
-#include "ir/Graph.h"
-#include "ir/operand/LowerInfo.h"
-
-namespace neurun
-{
-namespace dumper
-{
-namespace dot
-{
-
-const std::string Operand::INPUT_SHAPE = "doublecircle";
-const std::string Operand::OUTPUT_SHAPE = "doublecircle";
-const std::string Operand::OPERAND_SHAPE = "ellipse";
-const std::string Operand::BG_COLOR_SCHEME = "set18";
-
-Operand::Operand(const ir::OperandIndex &index, Type type)
-    : Node{"operand" + std::to_string(index.value())}
-{
-  {
-    auto type_to_shape = [](Type type) {
-      switch (type)
-      {
-        case Type::MODEL_INPUT:
-          return INPUT_SHAPE;
-        case Type::MODEL_OUTPUT:
-          return OUTPUT_SHAPE;
-        case Type::UNDEFINED:
-        case Type::INTERNAL:
-        default:
-          return OPERAND_SHAPE;
-      }
-    };
-    setAttribute("shape", type_to_shape(type));
-  }
-
-  setAttribute("colorscheme", BG_COLOR_SCHEME);
-}
-
-} // namespace dot
-} // namespace dumper
-} // namespace neurun
diff --git a/runtime/neurun/core/src/dumper/dot/OperandNode.h b/runtime/neurun/core/src/dumper/dot/OperandNode.h
deleted file mode 100644
index 5ebd651b6..000000000
--- a/runtime/neurun/core/src/dumper/dot/OperandNode.h
+++ /dev/null
@@ -1,79 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/**
- * @file Operand.h
- * @brief    This file contains Operand
- * @ingroup  COM_AI_RUNTIME
- *
- */
-
-#ifndef __NEURUN_DUMPER_DOT_DOT_OPERAND_INFO_H__
-#define __NEURUN_DUMPER_DOT_DOT_OPERAND_INFO_H__
-
-#include <vector>
-
-#include "Node.h"
-#include "ir/Operand.h"
-#include "ir/Index.h"
-
-namespace neurun
-{
-namespace dumper
-{
-namespace dot
-{
-
-/**
- * @brief Class that represents an Operand
- *
- */
-class Operand : public Node
-{
-public:
-  enum class Type
-  {
-    UNDEFINED,
-    MODEL_INPUT,
-    MODEL_OUTPUT,
-    INTERNAL
-  };
-
-public:
-  static const std::string INPUT_SHAPE;
-  static const std::string OUTPUT_SHAPE;
-  static const std::string OPERAND_SHAPE;
-  static const std::string BG_COLOR_SCHEME;
-
-public:
-  /**
-   * @brief Construct a new Operand Node object
-   *
-   * @param[in] index Operand index
-   * @param[in] type Operand type
-   * @param[in] lower_info Operand LowerInfo
-   */
-  Operand(const ir::OperandIndex &index, Type type);
-
-private:
-  void addBackendLabel();
-};
-
-} // namespace dot
-} // namespace dumper
-} // namespace neurun
-
-#endif // __NEURUN_DUMPER_DOT_DOT_OPERAND_INFO_H__
diff --git a/runtime/neurun/core/src/dumper/dot/OperationNode.cc b/runtime/neurun/core/src/dumper/dot/OperationNode.cc
deleted file mode 100644
index ca870ba05..000000000
--- a/runtime/neurun/core/src/dumper/dot/OperationNode.cc
+++ /dev/null
@@ -1,46 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <sstream>
-
-#include "OperationNode.h"
-#include "ir/Graph.h"
-#include "ir/operation/LowerInfo.h"
-#include "backend/IConfig.h"
-#include "backend/Backend.h"
-
-namespace neurun
-{
-namespace dumper
-{
-namespace dot
-{
-
-const std::string Operation::OPERATION_SHAPE = "rect";
-const std::string Operation::BG_COLOR_SCHEME = "pastel18";
-
-Operation::Operation(const ir::OperationIndex &index, const ir::Operation &node)
-    : Node{"operation" + std::to_string(index.value())}
-{
-  setAttribute("label", std::to_string(index.value()) + " : " + node.name());
-  setAttribute("shape", OPERATION_SHAPE);
-  setAttribute("colorscheme", BG_COLOR_SCHEME);
-  setAttribute("fillcolor", DEFAULT_FILLCOLOR);
-}
-
-} // namespace dot
-} // namespace dumper
-} // namespace neurun
diff --git a/runtime/neurun/core/src/dumper/dot/OperationNode.h b/runtime/neurun/core/src/dumper/dot/OperationNode.h
deleted file mode 100644
index ba0713790..000000000
--- a/runtime/neurun/core/src/dumper/dot/OperationNode.h
+++ /dev/null
@@ -1,62 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/**
- * @file Operation.h
- * @brief    This file contains Operation
- * @ingroup  COM_AI_RUNTIME
- *
- */
-
-#ifndef __NEURUN_DUMPER_DOT_DOT_NODE_INFO_H__
-#define __NEURUN_DUMPER_DOT_DOT_NODE_INFO_H__
-
-#include "Node.h"
-#include "ir/Operation.h"
-#include "ir/Index.h"
-
-namespace neurun
-{
-namespace dumper
-{
-namespace dot
-{
-
-/**
- * @brief Class that represents an Operation
- *
- */
-class Operation : public Node
-{
-public:
-  static const std::string OPERATION_SHAPE;
-  static const std::string BG_COLOR_SCHEME;
-
-public:
-  /**
-   * @brief Construct a new Operation Node object
-   *
-   * @param[in] index operation index
-   * @param[in] node operation object
-  */
-  Operation(const ir::OperationIndex &index, const ir::Operation &node);
-};
-
-} // namespace dot
-} // namespace dumper
-} // namespace neurun
-
-#endif // __NEURUN_DUMPER_DOT_DOT_NODE_INFO_H__
diff --git a/runtime/neurun/core/src/exec/DataflowExecutor.cc b/runtime/neurun/core/src/exec/DataflowExecutor.cc
deleted file mode 100644
index e22d41031..000000000
--- a/runtime/neurun/core/src/exec/DataflowExecutor.cc
+++ /dev/null
@@ -1,176 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "DataflowExecutor.h"
-
-#include <cassert>
-
-#include "util/logging.h"
-
-namespace neurun
-{
-namespace exec
-{
-
-int64_t DataflowExecutor::calculateRank(const std::vector<ir::Element> &operations)
-{
-  int64_t rank = 0;
-  if (!_indexed_ranks)
-  {
-    return rank;
-  }
-  for (const auto &element : operations)
-  {
-    auto it = _indexed_ranks->find(element.index);
-    if (it == _indexed_ranks->end())
-    {
-      assert(element.node->opcode() == ir::OpCode::Permute);
-      // assign int32_t::max to prevent integer overflow
-      rank += std::numeric_limits<int32_t>::max();
-    }
-    else
-    {
-      rank += it->second;
-    }
-  }
-  return rank;
-}
-
-void DataflowExecutor::emplaceToReadyJobs(const uint32_t &id)
-{
-  auto &job = _waiting_jobs[id];
-  assert(job != nullptr);
-  auto &subg = _graph.subgraphs()->at(_job_to_op_seq[job->index()]);
-  auto rank = calculateRank(subg.operations());
-  _ready_jobs.emplace(rank, std::move(job));
-}
-
-void DataflowExecutor::notify(uint32_t finished_job_id)
-{
-  for (auto id : _output_info[finished_job_id])
-  {
-    assert(_input_info[id] > 0);
-    auto count = --_input_info[id];
-    if (count == 0) // No dependent jobs left, ready for execution
-    {
-      emplaceToReadyJobs(id);
-    }
-  }
-}
-bool DataflowExecutor::noWaitingJobs()
-{
-  return std::all_of(_waiting_jobs.begin(), _waiting_jobs.end(),
-                     [](const std::unique_ptr<Job> &job) { return job == nullptr; });
-}
-
-DataflowExecutor::DataflowExecutor(const ir::Graph &graph,
-                                   const std::shared_ptr<compiler::OperandContext> &operand_context,
-                                   std::unique_ptr<backend::TensorManagerSet> tensor_mgrs,
-                                   CodeMap &&code_map)
-    : ExecutorBase{graph, operand_context, std::move(tensor_mgrs)}, _code_map{std::move(code_map)}
-{
-  VERBOSE(DataflowExecutor) << "Constructing Dataflow Executor" << std::endl;
-
-  const ir::Subgraphs *subgraphs = _graph.subgraphs();
-  // Assign jobs convert SubgraphIndex to job index(uint32_t)
-  uint32_t next_job_index = 0;
-  std::unordered_map<ir::SubgraphIndex, uint32_t> subgraph_to_job;
-  subgraphs->iterate([&](const ir::SubgraphIndex &subg_index, const ir::OpSequence &) {
-    VERBOSE(DataflowExecutor) << "Create a job #" << next_job_index << " with SubgraphIndex "
-                              << subg_index.value() << std::endl;
-    _finished_jobs.emplace_back(
-        nnfw::cpp14::make_unique<Job>(next_job_index, _code_map.at(subg_index).get()));
-    subgraph_to_job[subg_index] = next_job_index++;
-  });
-
-  _waiting_jobs.resize(next_job_index);
-  _output_info.resize(next_job_index);
-  _initial_input_info.resize(next_job_index, 0);
-
-  subgraphs->iterate([&](const ir::SubgraphIndex &subg_index, const ir::OpSequence &subg) {
-    auto job_index = subgraph_to_job[subg_index];
-    for (auto output : subg.getOutputs())
-    {
-      // Update output and input info
-      subgraphs->iterate(
-          [&](const ir::SubgraphIndex &subg_cur_index, const ir::OpSequence &subg_cur) {
-            if (subg_cur.getInputs().contains(output))
-            {
-              auto dep_index = subgraph_to_job[subg_cur_index];
-              ++_initial_input_info[dep_index];
-              _output_info[job_index].push_back(dep_index);
-            }
-          });
-    }
-  });
-  for (const auto &s : subgraph_to_job)
-    _job_to_op_seq.emplace(s.second, s.first);
-
-  _input_info = _initial_input_info;
-}
-
-void DataflowExecutor::executeImpl()
-{
-  assert(noWaitingJobs());
-
-  // Execution setup
-  _waiting_jobs.swap(_finished_jobs); // Move finished jobs to waiting jobs
-
-  for (uint32_t i = 0; i < _waiting_jobs.size(); ++i)
-  {
-    if (_input_info[i] == 0)
-    {
-      emplaceToReadyJobs(i);
-    }
-  }
-  assert(!_ready_jobs.empty()); // Cannot begin if there is no initial jobs
-  bool is_profiling = util::getConfigBool(util::config::PROFILING_MODE);
-
-  _subject.notifyModelBegin(this);
-
-  while (!_ready_jobs.empty())
-  {
-    auto job = std::move((_ready_jobs.begin())->second);
-    _ready_jobs.erase(_ready_jobs.begin());
-    auto job_index = job->index();
-    VERBOSE(DataflowExecutor) << "Run job #" << job_index << std::endl;
-
-    auto subgraph_index = _job_to_op_seq[job_index];
-    auto op_seq = &_graph.subgraphs()->at(subgraph_index);
-    const backend::Backend *backend =
-        _graph.getLowerInfo()->operation.at(subgraph_index)->backend();
-
-    _subject.notifyJobBegin(this, op_seq, backend);
-
-    if (is_profiling)
-      job->fn()->runSync();
-    else
-      job->run();
-
-    _subject.notifyJobEnd(this, op_seq, backend);
-    notify(job_index);
-    _finished_jobs[job_index] = std::move(job);
-  }
-  assert(noWaitingJobs());
-
-  _subject.notifyModelEnd(this);
-
-  // Reset input info for the next execution
-  _input_info = _initial_input_info;
-}
-
-} // namespace exec
-} // namespace neurun
diff --git a/runtime/neurun/core/src/exec/DataflowExecutor.h b/runtime/neurun/core/src/exec/DataflowExecutor.h
deleted file mode 100644
index 6c12093fd..000000000
--- a/runtime/neurun/core/src/exec/DataflowExecutor.h
+++ /dev/null
@@ -1,97 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_EXEC_DATAFLOW_EXECUTOR_H__
-#define __NEURUN_EXEC_DATAFLOW_EXECUTOR_H__
-
-#include <list>
-#include <map>
-#include <unordered_map>
-
-#include "FunctionSequence.h"
-#include "Job.h"
-#include "ir/OperandIndexSequence.h"
-#include "ir/Index.h"
-#include "cpp14/memory.h"
-#include "exec/ExecutorBase.h"
-
-namespace neurun
-{
-namespace exec
-{
-
-class DataflowExecutor : public ExecutorBase
-{
-public:
-  using CodeMap = std::unordered_map<ir::SubgraphIndex, std::unique_ptr<FunctionSequence>>;
-
-protected:
-  virtual void notify(uint32_t finished_job_id);
-  bool noWaitingJobs();
-
-public:
-  /**
-   * @brief Constructs a DataflowExecutor object
-   *
-   * @param graph Graph object
-   * @param operand_context (Only for input/output operand data access)
-   * @param code_map Compiled code map
-   * @param ranks Operation ranks for ordering execution
-   */
-  DataflowExecutor(const ir::Graph &graph,
-                   const std::shared_ptr<compiler::OperandContext> &operand_context,
-                   std::unique_ptr<backend::TensorManagerSet> tensor_mgrs, CodeMap &&code_map);
-
-  void executeImpl() override;
-
-protected:
-  int64_t calculateRank(const std::vector<ir::Element> &operations);
-  void emplaceToReadyJobs(const uint32_t &id);
-
-protected:
-  CodeMap _code_map;
-  /**
-   * @brief A vector of finished jobs for current execution
-   *        After a run it has all the jobs of this execution for the next run
-   */
-  std::vector<std::unique_ptr<Job>> _finished_jobs;
-  /**
-   * @brief A vector of waiting jobs for current execution
-   *        All the jobs are moved from #_finished_jobs to it when start a run
-   */
-  std::vector<std::unique_ptr<Job>> _waiting_jobs;
-  /**
-   * @brief Jobs' output info
-   *        Used for notifying after finishing a job
-   */
-  std::vector<std::list<uint32_t>> _output_info;
-  std::vector<uint32_t> _initial_input_info;
-  std::vector<uint32_t> _input_info;
-  /**
-   * @brief A collection of jobs that are ready for execution
-   *        Jobs in it are ready to be scheduled.
-   *        Ordered by priority from `_indexed_ranks`
-   */
-  std::multimap<int64_t, std::unique_ptr<Job>, std::greater<int64_t>> _ready_jobs;
-
-  /// @brief Which job runs which op and function.
-  std::unordered_map<uint32_t, ir::SubgraphIndex> _job_to_op_seq;
-};
-
-} // namespace exec
-} // namespace neurun
-
-#endif // __NEURUN_EXEC_DATAFLOW_EXECUTOR_H__
diff --git a/runtime/neurun/core/src/exec/Execution.cc b/runtime/neurun/core/src/exec/Execution.cc
deleted file mode 100644
index bc7bbd160..000000000
--- a/runtime/neurun/core/src/exec/Execution.cc
+++ /dev/null
@@ -1,135 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "exec/Execution.h"
-
-#include "util/logging.h"
-
-namespace neurun
-{
-namespace exec
-{
-
-Execution::Execution(const std::shared_ptr<IExecutor> &executor) : _executor{executor}
-{
-  _io_desc.inputs.resize(_executor->graph().getInputs().size());
-  _io_desc.outputs.resize(_executor->graph().getOutputs().size());
-}
-
-// TODO Remove default parameter
-void Execution::setInput(const ir::IOIndex &index, const void *buffer, size_t length,
-                         ir::Layout layout)
-{
-  const auto input_index = graph().getInputs().at(index);
-  const auto info = graph().operands().at(input_index).info();
-
-  if (length < info.total_size())
-  {
-    throw std::runtime_error{"Too small length"};
-  }
-
-  _io_desc.inputs.at(index.value()) =
-      nnfw::cpp14::make_unique<InputDesc>(info, buffer, length, layout);
-}
-
-// TODO Remove default parameter
-void Execution::setInput(const ir::IOIndex &index, const ir::TypeInfo &type, const ir::Shape &shape,
-                         const void *buffer, size_t length, ir::Layout layout)
-{
-  const ir::OperandInfo info{shape, type};
-
-  if (length < info.total_size())
-  {
-    throw std::runtime_error{"Too small length"};
-  }
-
-  _io_desc.inputs.at(index.value()) =
-      nnfw::cpp14::make_unique<InputDesc>(info, buffer, length, layout);
-}
-
-// TODO Remove default parameter
-void Execution::setOutput(const ir::IOIndex &index, void *buffer, size_t length, ir::Layout layout)
-{
-  const auto output_index = graph().getOutputs().at(index);
-  const auto info = graph().operands().at(output_index).info();
-
-  if (length < info.total_size())
-  {
-    throw std::runtime_error{"Too small length"};
-  }
-
-  _io_desc.outputs.at(index.value()) =
-      nnfw::cpp14::make_unique<OutputDesc>(info, buffer, length, layout);
-}
-
-// TODO Remove default parameter
-void Execution::setOutput(const ir::IOIndex &index, const ir::TypeInfo &type,
-                          const ir::Shape &shape, void *buffer, size_t length, ir::Layout layout)
-{
-  const ir::OperandInfo info{shape, type};
-
-  if (length < info.total_size())
-  {
-    throw std::runtime_error{"Too small length"};
-  }
-
-  _io_desc.outputs.at(index.value()) =
-      nnfw::cpp14::make_unique<OutputDesc>(info, buffer, length, layout);
-}
-
-void Execution::setInputLayout(const ir::IOIndex &index, ir::Layout layout)
-{
-  const auto &input_desc = _io_desc.inputs.at(index.value());
-  _io_desc.inputs.at(index.value()) = nnfw::cpp14::make_unique<InputDesc>(
-      input_desc->info, input_desc->buffer, input_desc->size, layout);
-}
-
-void Execution::setOutputLayout(const ir::IOIndex &index, ir::Layout layout)
-{
-  const auto &output_desc = _io_desc.outputs.at(index.value());
-  _io_desc.outputs.at(index.value()) = nnfw::cpp14::make_unique<OutputDesc>(
-      output_desc->info, output_desc->buffer, output_desc->size, layout);
-}
-
-void Execution::execute()
-{
-  VERBOSE(Execution) << "Start execution" << std::endl;
-
-  _executor->execute(_io_desc);
-  finished = true;
-
-  VERBOSE(Execution) << "Execution finished" << std::endl;
-}
-
-void Execution::startExecute()
-{
-  VERBOSE(Execution) << "Create asynchronous execution thread" << std::endl;
-
-  _exec_thread = nnfw::cpp14::make_unique<std::thread>(&Execution::execute, this);
-}
-
-void Execution::waitFinish()
-{
-  VERBOSE(Execution) << "Wait to finish execution" << std::endl;
-
-  _exec_thread->join();
-  finished = true;
-}
-
-bool Execution::isFinished(void) const { return finished; }
-
-} // namespace exec
-} // namespace neurun
diff --git a/runtime/neurun/core/src/exec/ExecutionObservee.cc b/runtime/neurun/core/src/exec/ExecutionObservee.cc
deleted file mode 100644
index 3b342d703..000000000
--- a/runtime/neurun/core/src/exec/ExecutionObservee.cc
+++ /dev/null
@@ -1,64 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ExecutionObservee.h"
-
-namespace neurun
-{
-namespace exec
-{
-
-void ExecutionObservee::add(std::unique_ptr<IExecutionObserver> observer)
-{
-  _observers.emplace_back(std::move(observer));
-}
-
-void ExecutionObservee::notifyModelBegin(IExecutor *executor)
-{
-  for (auto &o : _observers)
-  {
-    o->handleBegin(executor);
-  }
-}
-
-void ExecutionObservee::notifyModelEnd(IExecutor *executor)
-{
-  for (auto &o : _observers)
-  {
-    o->handleEnd(executor);
-  }
-}
-
-void ExecutionObservee::notifyJobBegin(IExecutor *executor, const ir::OpSequence *op_seq,
-                                       const backend::Backend *backend)
-{
-  for (auto &o : _observers)
-  {
-    o->handleBegin(executor, op_seq, backend);
-  }
-}
-
-void ExecutionObservee::notifyJobEnd(IExecutor *executor, const ir::OpSequence *op_seq,
-                                     const backend::Backend *backend)
-{
-  for (auto &o : _observers)
-  {
-    o->handleEnd(executor, op_seq, backend);
-  }
-}
-
-} // namespace exec
-} // namespace neurun
diff --git a/runtime/neurun/core/src/exec/ExecutionObservee.h b/runtime/neurun/core/src/exec/ExecutionObservee.h
deleted file mode 100644
index dafeef55b..000000000
--- a/runtime/neurun/core/src/exec/ExecutionObservee.h
+++ /dev/null
@@ -1,56 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_EXEC_EXECUTION_OBSERVEE_H__
-#define __NEURUN_EXEC_EXECUTION_OBSERVEE_H__
-
-#include <list>
-
-#include "exec/ExecutionObservers.h"
-
-namespace neurun
-{
-namespace exec
-{
-
-/**
- * @brief Class that
- *
- */
-class ExecutionObservee
-{
-public:
-  /**
-   * @brief Register an observer
-   *
-   * @param observer Observer to be added
-   */
-  void add(std::unique_ptr<IExecutionObserver> observer);
-  void notifyModelBegin(IExecutor *executor);
-  void notifyModelEnd(IExecutor *executor);
-  void notifyJobBegin(IExecutor *executor, const ir::OpSequence *op_seq,
-                      const backend::Backend *backend);
-  void notifyJobEnd(IExecutor *executor, const ir::OpSequence *op_seq,
-                    const backend::Backend *backend);
-
-private:
-  std::list<std::unique_ptr<IExecutionObserver>> _observers;
-};
-
-} // namespace exec
-} // namespace neurun
-
-#endif // __NEURUN_EXEC_EXECUTION_OBSERVEE__
diff --git a/runtime/neurun/core/src/exec/ExecutionObservers.cc b/runtime/neurun/core/src/exec/ExecutionObservers.cc
deleted file mode 100644
index 071a9e228..000000000
--- a/runtime/neurun/core/src/exec/ExecutionObservers.cc
+++ /dev/null
@@ -1,130 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "exec/ExecutionObservers.h"
-
-#include <string>
-
-#include "util/logging.h"
-#include "ir/operation/Permute.h"
-#include "exec/IExecutor.h"
-#include "misc/polymorphic_downcast.h"
-#include "ir/OpSequence.h"
-
-namespace neurun
-{
-
-namespace exec
-{
-
-void ProfileObserver::handleBegin(neurun::exec::IExecutor *, const ir::OpSequence *,
-                                  const neurun::backend::Backend *backend)
-{
-  _timer = backend->config()->timer();
-  if (_timer == nullptr)
-    throw std::runtime_error("To profile backend timer() method must be implemented");
-  _timer->handleBegin();
-}
-
-void ProfileObserver::handleEnd(IExecutor *exec, const ir::OpSequence *op_seq,
-                                const backend::Backend *backend)
-{
-  _timer->handleEnd();
-  const auto timer_res = _timer->getTime();
-
-  // NOTE This assumes there is just one operation in a op_seq
-  auto node = op_seq->operations().at(0).node;
-  auto node_name = node->name();
-  VERBOSE(ProfileInfo) << "Time for " << node_name << " : " << timer_res << std::endl;
-
-  // fill ExecTime:
-  bool is_quantized = exec->graph().operands().at(node->getInputs().at(0)).typeInfo().type() ==
-                      ir::DataType::QUANT8_ASYMM;
-
-  uint32_t size = 0;
-  for (const auto &input : node->getInputs())
-  {
-    size += exec->graph().operands().at(input).info().total_size();
-  }
-  for (const auto &output : node->getOutputs())
-  {
-    size += exec->graph().operands().at(output).info().total_size();
-  }
-  if (node_name == "Permute")
-  {
-    auto *permute_node = nnfw::misc::polymorphic_downcast<const ir::operation::Permute *>(node);
-    assert(permute_node != nullptr);
-    _et->updatePermuteTime(permute_node->param().input_backend_ctx->backend,
-                           permute_node->param().output_backend_ctx->backend, is_quantized, size,
-                           timer_res);
-  }
-  else
-  {
-    _et->updateOperationExecTime(backend, node_name, is_quantized, size, timer_res);
-  }
-};
-
-ChromeTracingObserver::ChromeTracingObserver(const std::string &filepath)
-    : _ofs{filepath, std::ofstream::out}, _recorder{}, _collector{&_recorder}
-{
-}
-
-ChromeTracingObserver::~ChromeTracingObserver() { _recorder.writeToFile(_ofs); }
-
-void ChromeTracingObserver::handleBegin(IExecutor *)
-{
-  _collector.onEvent(EventCollector::Event{EventCollector::Edge::BEGIN, "runtime", "Graph"});
-}
-
-void ChromeTracingObserver::handleBegin(IExecutor *, const ir::OpSequence *op_seq,
-                                        const backend::Backend *backend)
-{
-  std::string backend_id = backend->config()->id();
-  _collector.onEvent(
-      EventCollector::Event{EventCollector::Edge::BEGIN, backend_id, subgraphTag(op_seq)});
-}
-
-void ChromeTracingObserver::handleEnd(IExecutor *, const ir::OpSequence *op_seq,
-                                      const backend::Backend *backend)
-{
-  std::string backend_id = backend->config()->id();
-  _collector.onEvent(
-      EventCollector::Event{EventCollector::Edge::END, backend_id, subgraphTag(op_seq)});
-}
-
-void ChromeTracingObserver::handleEnd(IExecutor *)
-{
-  _collector.onEvent(EventCollector::Event{EventCollector::Edge::END, "runtime", "Graph"});
-}
-
-std::string ChromeTracingObserver::subgraphTag(const ir::OpSequence *op_seq)
-{
-  if (op_seq->size() == 0)
-    return "Empty OpSequence";
-
-  auto first_op = op_seq->operations().at(0);
-  std::string tag = "$" + std::to_string(first_op.index.value());
-  tag += " " + first_op.node->name();
-  if (op_seq->size() > 1)
-  {
-    tag += " (+" + std::to_string(op_seq->size() - 1) + ")";
-  }
-  return tag;
-}
-
-} // namespace exec
-
-} // namespace neurun
diff --git a/runtime/neurun/core/src/exec/ExecutorBase.cc b/runtime/neurun/core/src/exec/ExecutorBase.cc
deleted file mode 100644
index 9692c2ba7..000000000
--- a/runtime/neurun/core/src/exec/ExecutorBase.cc
+++ /dev/null
@@ -1,145 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ExecutorBase.h"
-#include "util/logging.h"
-namespace neurun
-{
-namespace exec
-{
-
-ExecutorBase::ExecutorBase(const ir::Graph &graph,
-                           const std::shared_ptr<compiler::OperandContext> &operand_context,
-                           std::unique_ptr<backend::TensorManagerSet> tensor_mgrs)
-    : _graph{graph}, _operand_context{operand_context}, _tensor_mgrs{std::move(tensor_mgrs)},
-      _mutex()
-{
-  // DO NOTHING
-}
-
-std::unique_ptr<ISource> ExecutorBase::source(const ir::IOIndex &index, const ir::TypeInfo &type,
-                                              const void *buffer, size_t length,
-                                              ir::Layout io_layout)
-{
-  using ir::DataType;
-  switch (type.type())
-  {
-    case DataType::FLOAT32:
-      return source<float>(index, buffer, length, io_layout);
-    case DataType::INT32:
-      return source<int32_t>(index, buffer, length, io_layout);
-    case DataType::UINT32:
-      return source<uint32_t>(index, buffer, length, io_layout);
-    case DataType::BOOL8:
-    case DataType::QUANT8_ASYMM:
-    case DataType::UINT8:
-      return source<uint8_t>(index, buffer, length, io_layout);
-    case DataType::QUANT8_SYMM:
-      return source<int8_t>(index, buffer, length, io_layout);
-    default:
-      throw std::runtime_error("Not supported yet");
-  }
-}
-
-std::unique_ptr<ISink> ExecutorBase::sink(const ir::IOIndex &index, const ir::TypeInfo &type,
-                                          void *buffer, size_t length, ir::Layout io_layout)
-{
-  using ir::DataType;
-  switch (type.type())
-  {
-    case DataType::FLOAT32:
-      return sink<float>(index, buffer, length, io_layout);
-    case DataType::INT32:
-      return sink<int32_t>(index, buffer, length, io_layout);
-    case DataType::UINT32:
-      return sink<uint32_t>(index, buffer, length, io_layout);
-    case DataType::BOOL8:
-    case DataType::QUANT8_ASYMM:
-    case DataType::UINT8:
-      return sink<uint8_t>(index, buffer, length, io_layout);
-    case DataType::QUANT8_SYMM:
-      return sink<int8_t>(index, buffer, length, io_layout);
-    default:
-      throw std::runtime_error("Not supported yet");
-  }
-}
-
-void ExecutorBase::execute(const IODescription &desc)
-{
-  // For thread-safe, use mutex
-  // TODO: if all used backends on this executor are thread-safe,
-  //       do not need to use mutex (otherwise, use mutex)
-  std::lock_guard<std::mutex> lock(_mutex);
-
-  std::vector<std::unique_ptr<ISource>> sources{_graph.getInputs().size()};
-  std::vector<std::unique_ptr<ISink>> sinks{_graph.getOutputs().size()};
-
-  // Set input(s)
-  for (uint32_t n = 0; n < _graph.getInputs().size(); ++n)
-  {
-    ir::IOIndex input_index{n};
-    ir::OperandIndex index{_graph.getInputs().at(input_index)};
-
-    if (desc.inputs.at(n) == nullptr)
-    {
-      // Optional input
-      continue;
-    }
-
-    const auto operand_li = _graph.getLowerInfo()->operand.at(index).get();
-    if (operand_li->def_factors().empty())
-    {
-      // This input is not used (i.e. constant, EX. reshape's axis)
-      continue;
-    }
-
-    const auto &input = *desc.inputs.at(n);
-    sources.at(n) =
-        source(input_index, input.info.typeInfo(), input.buffer, input.size, input.layout);
-
-    auto setter = [&](::neurun::backend::operand::ITensor &tensor) { sources.at(n)->push(tensor); };
-
-    auto object = _operand_context->at(index);
-
-    object->access(setter);
-  }
-
-  executeImpl();
-
-  // Get output(s)
-  for (uint32_t n = 0; n < _graph.getOutputs().size(); ++n)
-  {
-    ir::IOIndex output_index{n};
-    // Optional output
-    if (desc.outputs.at(n) == nullptr)
-    {
-      continue;
-    }
-    const auto &output = *desc.outputs.at(n);
-    sinks.at(n) =
-        sink(output_index, output.info.typeInfo(), output.buffer, output.size, output.layout);
-
-    auto getter = [&](::neurun::backend::operand::ITensor &tensor) { sinks.at(n)->pull(tensor); };
-
-    ir::OperandIndex index{_graph.getOutputs().at(output_index)};
-    auto object = _operand_context->at(index);
-
-    object->access(getter);
-  }
-}
-
-} // namespace exec
-} // namespace neurun
diff --git a/runtime/neurun/core/src/exec/ExecutorBase.h b/runtime/neurun/core/src/exec/ExecutorBase.h
deleted file mode 100644
index a93e036a5..000000000
--- a/runtime/neurun/core/src/exec/ExecutorBase.h
+++ /dev/null
@@ -1,127 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_EXEC_EXECUTOR_BASE_H__
-#define __NEURUN_EXEC_EXECUTOR_BASE_H__
-
-#include <mutex>
-
-#include "Source.h"
-#include "exec/ExecutionObservers.h"
-#include "Sink.h"
-#include "exec/IExecutor.h"
-#include "ir/Graph.h"
-#include "ir/LowerInfoMap.h"
-#include "backend/IConfig.h"
-#include "backend/Backend.h"
-#include "compiler/OperandContext.h"
-#include "backend/ExecTime.h"
-#include "exec/IFunction.h"
-#include "backend/ITensorManager.h"
-#include "exec/ExecutionObservee.h"
-#include <list>
-
-namespace neurun
-{
-namespace exec
-{
-
-class ExecutorBase : public IExecutor
-{
-public:
-  ExecutorBase(const ir::Graph &graph,
-               const std::shared_ptr<compiler::OperandContext> &operand_context,
-               std::unique_ptr<backend::TensorManagerSet> tensor_mgrs);
-
-  virtual ~ExecutorBase() = default;
-
-  const ir::Graph &graph() final { return _graph; }
-
-  void execute(const IODescription &desc) final;
-
-  // Used only in Dataflow and Parallel Executors
-  void setIndexedRanks(std::shared_ptr<ir::OperationIndexMap<int64_t>> ranks) final
-  {
-    _indexed_ranks = std::move(ranks);
-  };
-
-  virtual void executeImpl(void) = 0;
-
-  void addObserver(std::unique_ptr<IExecutionObserver> ref) { _subject.add(std::move(ref)); };
-
-private:
-  std::unique_ptr<ISource> source(const ir::IOIndex &index, const ir::TypeInfo &type,
-                                  const void *buffer, size_t length, ir::Layout io_layout);
-  std::unique_ptr<ISink> sink(const ir::IOIndex &index, const ir::TypeInfo &type, void *buffer,
-                              size_t length, ir::Layout io_layout);
-
-  template <typename T>
-  std::unique_ptr<ISource> source(const ir::IOIndex &index, const void *buffer, size_t length,
-                                  ir::Layout io_layout)
-  {
-    const auto operand_index = _graph.getInputs().at(index);
-    const auto &operand = _graph.operands().at(operand_index);
-
-    const auto tensor = _operand_context->at(operand_index);
-    const auto tensor_layout = tensor->layout();
-
-    if (((io_layout == ir::Layout::NHWC) && (tensor_layout == ir::Layout::NCHW)) ||
-        ((io_layout == ir::Layout::NCHW) && (tensor_layout == ir::Layout::NHWC)))
-    {
-      return nnfw::cpp14::make_unique<PermutateSource<T>>(buffer, length, operand.shape(),
-                                                          io_layout);
-    }
-    // TODO Change this to return error
-    assert(io_layout != ir::Layout::UNKNOWN ||
-           (tensor_layout != ir::Layout::NCHW && tensor_layout != ir::Layout::NCHW));
-
-    return nnfw::cpp14::make_unique<CopySource<T>>(buffer, length, operand.shape());
-  }
-
-  template <typename T>
-  std::unique_ptr<ISink> sink(const ir::IOIndex &index, void *buffer, size_t length,
-                              ir::Layout io_layout)
-  {
-    const auto operand_index = _graph.getOutputs().at(index);
-    const auto &operand = _graph.operands().at(operand_index);
-    const auto tensor = _operand_context->at(operand_index);
-    const auto tensor_layout = tensor->layout();
-
-    if (((tensor_layout == ir::Layout::NCHW) && (io_layout == ir::Layout::NHWC)) ||
-        ((tensor_layout == ir::Layout::NHWC) && (io_layout == ir::Layout::NCHW)))
-    {
-      return nnfw::cpp14::make_unique<PermutateSink<T>>(buffer, length, operand.shape(), io_layout);
-    }
-    // TODO Change this to return error
-    assert(io_layout != ir::Layout::UNKNOWN ||
-           (tensor_layout != ir::Layout::NCHW && tensor_layout != ir::Layout::NCHW));
-
-    return nnfw::cpp14::make_unique<CopySink<T>>(buffer, length, operand.shape());
-  }
-
-protected:
-  ExecutionObservee _subject;
-  std::shared_ptr<ir::OperationIndexMap<int64_t>> _indexed_ranks;
-  const ir::Graph &_graph;
-  std::shared_ptr<compiler::OperandContext> _operand_context;
-  std::unique_ptr<backend::TensorManagerSet> _tensor_mgrs;
-  std::mutex _mutex;
-};
-
-} // namespace exec
-} // namespace neurun
-
-#endif // __NEURUN_EXEC_EXECUTOR_BASE_H__
diff --git a/runtime/neurun/core/src/exec/FunctionSequence.cc b/runtime/neurun/core/src/exec/FunctionSequence.cc
deleted file mode 100644
index 00214fcfa..000000000
--- a/runtime/neurun/core/src/exec/FunctionSequence.cc
+++ /dev/null
@@ -1,62 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "FunctionSequence.h"
-
-namespace neurun
-{
-namespace exec
-{
-
-void FunctionSequence::run()
-{
-  for (const auto &function : _functions)
-  {
-    function->run();
-  }
-}
-
-void FunctionSequence::runSync()
-{
-  for (const auto &function : _functions)
-  {
-    function->runSync();
-  }
-}
-
-void FunctionSequence::prepare()
-{
-  for (const auto &function : _functions)
-  {
-    function->prepare();
-  }
-}
-
-void FunctionSequence::append(std::unique_ptr<IFunction> &&function)
-{
-  _functions.push_back(std::move(function));
-}
-
-void FunctionSequence::iterate(const std::function<void(IFunction &)> &fn)
-{
-  for (const auto &func : _functions)
-  {
-    fn(*func);
-  }
-}
-
-} // namespace exec
-} // namespace neurun
diff --git a/runtime/neurun/core/src/exec/FunctionSequence.h b/runtime/neurun/core/src/exec/FunctionSequence.h
deleted file mode 100644
index 2ba5c0b08..000000000
--- a/runtime/neurun/core/src/exec/FunctionSequence.h
+++ /dev/null
@@ -1,56 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_EXEC_FUNCTION_SEQUENCE_H__
-#define __NEURUN_EXEC_FUNCTION_SEQUENCE_H__
-
-#include <memory>
-#include <vector>
-#include <functional>
-
-#include "exec/IFunction.h"
-
-namespace neurun
-{
-namespace exec
-{
-
-class FunctionSequence : public IFunction
-{
-public:
-  virtual ~FunctionSequence() = default;
-
-  void run() override;
-  void runSync() override;
-  void prepare() override;
-
-  /**
-   * @brief Appends an IFunction object to the function sequence
-   *
-   * @param function IFunction object to be appended
-   */
-  void append(std::unique_ptr<IFunction> &&function);
-
-  void iterate(const std::function<void(IFunction &)> &fn);
-
-private:
-  std::vector<std::unique_ptr<IFunction>> _functions;
-};
-
-} // namespace exec
-} // namespace neurun
-
-#endif // __NEURUN_EXEC_FUNCTION_SEQUENCE_H__
diff --git a/runtime/neurun/core/src/exec/Job.cc b/runtime/neurun/core/src/exec/Job.cc
deleted file mode 100644
index ba02daf30..000000000
--- a/runtime/neurun/core/src/exec/Job.cc
+++ /dev/null
@@ -1,33 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "Job.h"
-
-#include <cassert>
-
-#include "util/logging.h"
-
-namespace neurun
-{
-namespace exec
-{
-
-Job::Job(uint32_t index, IFunction *fn) : _index{index}, _fn{fn} {}
-
-void Job::run() { _fn->run(); }
-
-} // namespace exec
-} // namespace neurun
diff --git a/runtime/neurun/core/src/exec/Job.h b/runtime/neurun/core/src/exec/Job.h
deleted file mode 100644
index 1516b9281..000000000
--- a/runtime/neurun/core/src/exec/Job.h
+++ /dev/null
@@ -1,69 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_EXEC_JOB_H__
-#define __NEURUN_EXEC_JOB_H__
-
-#include <unordered_set>
-
-#include "exec/IFunction.h"
-#include "ir/Index.h"
-#include "ir/OperandIndexSequence.h"
-#include "backend/Backend.h"
-
-namespace neurun
-{
-namespace exec
-{
-
-class Job
-{
-public:
-  /**
-   * @brief Constructs a Job object
-   *
-   * @param index Operation index for this job
-   * @param fn compiled code to run this job
-   * @param inputs Input operand list
-   * @param outputs Output operand list
-   */
-  Job(uint32_t index, IFunction *fn);
-  /**
-   * @brief Execute the compiled code
-   */
-  void run();
-  /**
-   * @brief Return job index
-   *
-   * @return Job index
-   */
-  uint32_t index() const { return _index; }
-  /**
-   * @brief Return the function to be executed
-   *
-   * @return Pointer of the function
-   */
-  IFunction *fn() { return _fn; }
-
-private:
-  uint32_t _index;
-  IFunction *_fn;
-};
-
-} // namespace exec
-} // namespace neurun
-
-#endif // __NEURUN_EXEC_JOB_H__
diff --git a/runtime/neurun/core/src/exec/LinearExecutor.cc b/runtime/neurun/core/src/exec/LinearExecutor.cc
deleted file mode 100644
index d41dba880..000000000
--- a/runtime/neurun/core/src/exec/LinearExecutor.cc
+++ /dev/null
@@ -1,39 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "LinearExecutor.h"
-
-namespace neurun
-{
-namespace exec
-{
-
-void LinearExecutor::executeImpl()
-{
-  _subject.notifyModelBegin(this);
-  for (auto &&code : _code)
-  {
-    const auto op_seq = code.elem.op_seq;
-    const auto backend = code.elem.lower_info->backend();
-    _subject.notifyJobBegin(this, op_seq, backend);
-    code.fn->run();
-    _subject.notifyJobEnd(this, op_seq, backend);
-  }
-  _subject.notifyModelEnd(this);
-}
-
-} // namespace exec
-} // namespace neurun
diff --git a/runtime/neurun/core/src/exec/LinearExecutor.h b/runtime/neurun/core/src/exec/LinearExecutor.h
deleted file mode 100644
index baf063a12..000000000
--- a/runtime/neurun/core/src/exec/LinearExecutor.h
+++ /dev/null
@@ -1,64 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/**
- * @file  LinearExecutor.h
- * @brief This file contains LinearExecutor class to define and run execution phase
- */
-
-#ifndef __NEURUN_EXEC_EXECUTOR_H_
-#define __NEURUN_EXEC_EXECUTOR_H_
-
-#include "ExecutorBase.h"
-#include "compiler/Linear.h"
-#include "exec/FunctionSequence.h"
-#include "compiler/CodeWithInfo.h"
-
-namespace neurun
-{
-namespace exec
-{
-
-/**
- * @brief Class to handle execution phase. Simple run the sequence of operations that is sorted in
- *        topological order
- */
-class LinearExecutor final : public ExecutorBase
-{
-public:
-  /**
-   * @brief Construct a new LinearExecutor object
-   * @param[in] plan  Execution plan generated by compiled result
-   */
-  LinearExecutor(const ir::Graph &graph,
-                 const std::shared_ptr<compiler::OperandContext> &operand_context,
-                 std::unique_ptr<backend::TensorManagerSet> tensor_mgrs,
-                 std::vector<compiler::CodeWithInfo> &&code)
-      : ExecutorBase{graph, operand_context, std::move(tensor_mgrs)}, _code{std::move(code)}
-  {
-  }
-
-public:
-  void executeImpl(void) override;
-
-private:
-  std::vector<compiler::CodeWithInfo> _code;
-};
-
-} // namespace exec
-} // namespace neurun
-
-#endif // __NEURUN_EXEC_EXECUTOR_H_
diff --git a/runtime/neurun/core/src/exec/ParallelExecutor.cc b/runtime/neurun/core/src/exec/ParallelExecutor.cc
deleted file mode 100644
index c73c353d3..000000000
--- a/runtime/neurun/core/src/exec/ParallelExecutor.cc
+++ /dev/null
@@ -1,147 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ParallelExecutor.h"
-
-#include <cassert>
-
-#include "util/logging.h"
-#include "exec/IFunction.h"
-
-namespace neurun
-{
-namespace exec
-{
-
-class HookFunction : public IFunction
-{
-public:
-  HookFunction(IFunction *fn, const std::function<void()> &setup,
-               const std::function<void()> &teardown)
-      : _fn{fn}, _setup{setup}, _teardown{teardown}
-  {
-  }
-
-public:
-  void run() override
-  {
-    _setup();
-    _fn->run();
-    _teardown();
-  }
-  void runSync() override { throw("runSync is needed just for profiling in Dataflow executor"); }
-
-private:
-  IFunction *_fn;
-  std::function<void()> _setup;
-  std::function<void()> _teardown;
-};
-
-void ParallelExecutor::notify(uint32_t finished_job_id)
-{
-  std::unique_lock<std::mutex> lock{_mu_jobs};
-
-  DataflowExecutor::notify(finished_job_id);
-
-  lock.unlock();
-  _cv_jobs.notify_all();
-}
-
-ParallelExecutor::ParallelExecutor(const ir::Graph &graph,
-                                   const std::shared_ptr<compiler::OperandContext> &operand_context,
-                                   std::unique_ptr<backend::TensorManagerSet> tensor_mgrs,
-                                   CodeMap &&code_map)
-    : DataflowExecutor{graph, operand_context, std::move(tensor_mgrs), std::move(code_map)}
-{
-  VERBOSE(ParallelExecutor) << "Constructing Parallel Executor" << std::endl;
-}
-
-void ParallelExecutor::executeImpl()
-{
-  // Init scheduler
-  // TODO Consider to have distinct backend set in LowerInfoMap
-  ir::BackendSet backends;
-  for (auto &itr : _graph.getLowerInfo()->operation)
-  {
-    backends.add(itr.second->backend());
-  }
-  _scheduler = nnfw::cpp14::make_unique<ParallelScheduler>(backends);
-
-  assert(noWaitingJobs());
-
-  // Execution setup
-  _waiting_jobs.swap(_finished_jobs); // Move finished jobs to waiting jobs
-
-  for (uint32_t i = 0; i < _waiting_jobs.size(); ++i)
-  {
-    VERBOSE(ParallelExecutor) << i << ": " << _input_info[i] << std::endl;
-    if (_input_info[i] == 0)
-    {
-      emplaceToReadyJobs(i);
-    }
-  }
-  assert(!_ready_jobs.empty()); // Cannot begin if there is no initial jobs
-
-  VERBOSE(ParallelExecutor) << "INITIAL JOBS : " << _ready_jobs.size() << std::endl;
-
-  _subject.notifyModelBegin(this);
-  while (true)
-  {
-    std::unique_lock<std::mutex> lock{_mu_jobs};
-
-    if (_ready_jobs.empty())
-    {
-      _cv_jobs.wait(lock, [this] { return !_ready_jobs.empty() || noWaitingJobs(); });
-      // Check finish condition
-      if (_ready_jobs.empty() && noWaitingJobs())
-      {
-        break;
-      }
-    }
-
-    auto job = std::move(_ready_jobs.begin()->second);
-    _ready_jobs.erase(_ready_jobs.begin());
-
-    lock.unlock();
-
-    VERBOSE(ParallelExecutor) << "Assigning fn #" << job->index() << std::endl;
-
-    auto job_index = job->index();
-    auto subgraph_index = _job_to_op_seq[job_index];
-    auto op_seq = &_graph.subgraphs()->at(subgraph_index);
-    auto backend = _graph.getLowerInfo()->operation.at(subgraph_index)->backend();
-    auto setup = [&, op_seq, backend]() { _subject.notifyJobBegin(this, op_seq, backend); };
-    auto teardown = [&, job_index, op_seq, backend]() {
-      _subject.notifyJobEnd(this, op_seq, backend);
-      notify(job_index);
-    };
-
-    _scheduler->assign(nnfw::cpp14::make_unique<HookFunction>(job->fn(), setup, teardown), backend);
-    _finished_jobs[job_index] = std::move(job);
-  }
-
-  assert(noWaitingJobs());
-
-  // Wait for all the jobs done
-  _scheduler->finish();
-  _subject.notifyModelEnd(this);
-
-  // Reset input info for the next execution
-  _input_info = _initial_input_info;
-}
-
-} // namespace exec
-} // namespace neurun
diff --git a/runtime/neurun/core/src/exec/ParallelExecutor.h b/runtime/neurun/core/src/exec/ParallelExecutor.h
deleted file mode 100644
index 54377fd9e..000000000
--- a/runtime/neurun/core/src/exec/ParallelExecutor.h
+++ /dev/null
@@ -1,69 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_EXEC_PARALLEL_EXECUTOR_H__
-#define __NEURUN_EXEC_PARALLEL_EXECUTOR_H__
-
-#include <list>
-#include <queue>
-#include <unordered_map>
-
-#include "FunctionSequence.h"
-#include "Job.h"
-#include "ir/OperandIndexSequence.h"
-#include "ir/Index.h"
-#include "cpp14/memory.h"
-#include "exec/DataflowExecutor.h"
-#include "ParallelScheduler.h"
-
-namespace neurun
-{
-namespace exec
-{
-
-/**
- * @brief Class to execute Graph in parallel
- */
-class ParallelExecutor : public DataflowExecutor
-{
-protected:
-  void notify(uint32_t finished_job_id) override;
-
-public:
-  /**
-   * @brief Constructs a ParallelExecutor object
-   *
-   * @param graph Graph object
-   * @param operand_context (Only for input/output operand data access)
-   * @param code_map Compiled code map
-   * @param ranks Operation ranks for ordering execution
-   */
-  ParallelExecutor(const ir::Graph &graph,
-                   const std::shared_ptr<compiler::OperandContext> &operand_context,
-                   std::unique_ptr<backend::TensorManagerSet> tensor_mgrs, CodeMap &&code_map);
-
-  void executeImpl() override;
-
-private:
-  std::condition_variable _cv_jobs;
-  std::mutex _mu_jobs;
-  std::unique_ptr<ParallelScheduler> _scheduler;
-};
-
-} // namespace exec
-} // namespace neurun
-
-#endif // __NEURUN_EXEC_PARALLEL_EXECUTOR_H__
diff --git a/runtime/neurun/core/src/exec/ParallelScheduler.cc b/runtime/neurun/core/src/exec/ParallelScheduler.cc
deleted file mode 100644
index 5f9e9e013..000000000
--- a/runtime/neurun/core/src/exec/ParallelScheduler.cc
+++ /dev/null
@@ -1,55 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ParallelScheduler.h"
-
-#include <cassert>
-
-#include "cpp14/memory.h"
-#include "util/logging.h"
-
-namespace neurun
-{
-namespace exec
-{
-
-ParallelScheduler::ParallelScheduler(const ir::BackendSet &backends)
-{
-  assert(!backends.empty());
-
-  for (auto backend : backends)
-  {
-    _thread_pools[backend] = nnfw::cpp14::make_unique<ThreadPool>();
-  }
-}
-
-void ParallelScheduler::assign(std::unique_ptr<IFunction> &&fn, const backend::Backend *backend)
-{
-  assert(!_thread_pools.empty());
-
-  _thread_pools.at(backend)->enqueue(std::move(fn));
-}
-
-void ParallelScheduler::finish()
-{
-  for (auto &itr : _thread_pools)
-  {
-    itr.second->finish();
-  }
-}
-
-} // namespace exec
-} // namespace neurun
diff --git a/runtime/neurun/core/src/exec/ParallelScheduler.h b/runtime/neurun/core/src/exec/ParallelScheduler.h
deleted file mode 100644
index af1103750..000000000
--- a/runtime/neurun/core/src/exec/ParallelScheduler.h
+++ /dev/null
@@ -1,60 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_EXEC_PARALLEL_SCHEDULER_H__
-#define __NEURUN_EXEC_PARALLEL_SCHEDULER_H__
-
-#include <unordered_map>
-#include <memory>
-
-#include "exec/IFunction.h"
-#include "ir/BackendSet.h"
-#include "ThreadPool.h"
-
-namespace neurun
-{
-namespace exec
-{
-
-class ParallelScheduler
-{
-public:
-  /**
-   * @brief Constructs ParallelScheduler object
-   *
-   * @param backends Backend set
-   */
-  ParallelScheduler(const ir::BackendSet &backends);
-  /**
-   * @brief Assign a task to the given backend
-   *
-   * @param[in] fn Function to be assigned
-   * @param[in] fn Target backend
-   */
-  void assign(std::unique_ptr<IFunction> &&fn, const backend::Backend *backend);
-  /**
-   * @brief Block until all jobs are finished
-   */
-  void finish();
-
-private:
-  std::unordered_map<const backend::Backend *, std::unique_ptr<ThreadPool>> _thread_pools;
-};
-
-} // namespace exec
-} // namespace neurun
-
-#endif // __NEURUN_EXEC_PARALLEL_SCHEDULER_H__
diff --git a/runtime/neurun/core/src/exec/Sink.h b/runtime/neurun/core/src/exec/Sink.h
deleted file mode 100644
index bb2a6c58a..000000000
--- a/runtime/neurun/core/src/exec/Sink.h
+++ /dev/null
@@ -1,205 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_EXEC_SINK_H__
-#define __NEURUN_EXEC_SINK_H__
-
-#include <cassert>
-
-#include "cpp14/memory.h"
-#include "util/feature/nchw/Reader.h"
-#include "util/feature/nchw/View.h"
-#include "util/feature/nhwc/Reader.h"
-#include "util/feature/nhwc/View.h"
-#include "util/Utils.h"
-#include <misc/feature/IndexIterator.h>
-
-namespace neurun
-{
-namespace exec
-{
-struct ISink
-{
-  virtual ~ISink() = default;
-
-  virtual void pull(::neurun::backend::operand::ITensor &tensor) const = 0;
-};
-
-// Create second lever inheritance: the first lever is used as a reference type in use-case places
-template <typename T> class ITemplSink : public ISink
-{
-public:
-  ITemplSink(void *output_buffer, const size_t &output_size, const ir::Shape &shape,
-             const bool copy, ir::Layout io_layout)
-      : _output_buffer{reinterpret_cast<T *>(output_buffer)}, _output_size{output_size},
-        _shape{shape}, _copy{copy}, _io_layout{io_layout}
-  {
-  }
-
-protected:
-  void pullUnif(neurun::backend::operand::ITensor &tensor) const
-  {
-    assert(((_io_layout == ir::Layout::NHWC && tensor.layout() == ir::Layout::NCHW) ||
-            (_io_layout == ir::Layout::NCHW && tensor.layout() == ir::Layout::NHWC)) ||
-           _copy);
-    auto input_buffer = tensor.buffer();
-    auto rank = _shape.rank();
-
-    if (!tensor.has_padding() && rank < 4 + _copy)
-    {
-      memcpy(_output_buffer, input_buffer, _output_size);
-      return;
-    }
-
-    switch (rank)
-    {
-      case 0:
-      case 1:
-      {
-        memcpy(_output_buffer, input_buffer, _output_size);
-        break;
-      }
-      case 2:
-      {
-        const int32_t copy_len = _shape.dim(1);
-
-        for (auto i = 0; i < _shape.dim(0); ++i)
-        {
-          neurun::util::Coordinates coords{i, 0};
-          memcpy(_output_buffer + i * copy_len, input_buffer + tensor.calcOffset(coords),
-                 copy_len * sizeof(T));
-        }
-        break;
-      }
-      case 3:
-      {
-        const int32_t dim1 = _shape.dim(1);
-        const int32_t dim2 = _shape.dim(2);
-
-        for (auto i = 0; i < _shape.dim(0); ++i)
-        {
-          for (auto j = 0; j < _shape.dim(1); ++j)
-          {
-            neurun::util::Coordinates coords{i, j, 0};
-            memcpy(_output_buffer + i * dim1 * dim2 + j * dim2,
-                   input_buffer + tensor.calcOffset(coords), dim2 * sizeof(T));
-          }
-        }
-        break;
-      }
-      case 4:
-      {
-        if (_copy)
-        {
-          const int32_t dim1 = _shape.dim(1);
-          const int32_t dim2 = _shape.dim(2);
-          const int32_t dim3 = _shape.dim(3);
-
-          for (auto i = 0; i < _shape.dim(0); ++i)
-          {
-            for (auto j = 0; j < _shape.dim(1); ++j)
-            {
-              for (auto k = 0; k < _shape.dim(2); ++k)
-              {
-                neurun::util::Coordinates coords{i, j, k, 0};
-                memcpy(_output_buffer + i * dim1 * dim2 * dim3 + j * dim2 * dim3 + k * dim3,
-                       input_buffer + tensor.calcOffset(coords), dim3 * sizeof(T));
-              }
-            }
-          }
-        }
-        else
-        {
-          const auto shape = _shape.asFeature(_io_layout);
-
-          if (_io_layout == ir::Layout::NHWC)
-          {
-            const util::feature::nchw::Reader<T> from(&tensor);
-            util::feature::nhwc::View<T> into(shape, _output_buffer, _output_size);
-            ::nnfw::misc::feature::iterate(shape)
-                << [&](uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) {
-                     const auto value = from.at(batch, ch, row, col);
-                     into.at(batch, row, col, ch) = value;
-                   };
-          }
-          else if (_io_layout == ir::Layout::NCHW)
-          {
-            const util::feature::nhwc::Reader<T> from(&tensor);
-            util::feature::nchw::View<T> into(shape, _output_buffer, _output_size);
-            ::nnfw::misc::feature::iterate(shape)
-                << [&](uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) {
-                     const auto value = from.at(batch, row, col, ch);
-                     into.at(batch, ch, row, col) = value;
-                   };
-          }
-          else
-          {
-            throw std::runtime_error("Wrong Layout");
-          }
-        }
-        break;
-      }
-      default:
-        throw std::runtime_error("NYI");
-        break;
-    }
-  }
-
-private:
-  T *_output_buffer;
-  const size_t _output_size;
-  const ir::Shape _shape;
-  const bool _copy;
-  const ir::Layout _io_layout;
-};
-
-template <typename T> class PermutateSink final : public ITemplSink<T>
-{
-public:
-  PermutateSink(void *output_buffer, const size_t &output_size, const ir::Shape &shape,
-                ir::Layout io_layout)
-      : ITemplSink<T>(output_buffer, output_size, shape, false, io_layout)
-  {
-  }
-
-public:
-  void pull(neurun::backend::operand::ITensor &tensor) const override
-  {
-    ITemplSink<T>::pullUnif(tensor);
-  }
-};
-
-// Only supports NHWC format front-end(NNAPI) now
-template <typename T> class CopySink final : public ITemplSink<T>
-{
-public:
-  CopySink(void *output_buffer, const size_t &output_size, const ir::Shape &shape,
-           ir::Layout io_layout = ir::Layout::UNKNOWN)
-      : ITemplSink<T>(output_buffer, output_size, shape, true, io_layout)
-  {
-  }
-
-public:
-  void pull(neurun::backend::operand::ITensor &tensor) const override
-  {
-    ITemplSink<T>::pullUnif(tensor);
-  }
-};
-
-} // namespace exec
-} // namespace neurun
-
-#endif // __NEURUN_EXEC_SINK_H__
diff --git a/runtime/neurun/core/src/exec/Source.h b/runtime/neurun/core/src/exec/Source.h
deleted file mode 100644
index fd52dd546..000000000
--- a/runtime/neurun/core/src/exec/Source.h
+++ /dev/null
@@ -1,211 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_EXEC_SOURCE_H__
-#define __NEURUN_EXEC_SOURCE_H__
-
-#include <cassert>
-
-#include "cpp14/memory.h"
-#include "util/feature/nchw/Reader.h"
-#include "util/feature/nchw/View.h"
-#include "util/feature/nhwc/Reader.h"
-#include "util/feature/nhwc/View.h"
-#include "util/Utils.h"
-#include <misc/feature/IndexIterator.h>
-#include <ir/Layout.h>
-#include "ir/Shape.h"
-
-namespace neurun
-{
-namespace exec
-{
-
-struct ISource
-{
-  virtual ~ISource() = default;
-
-  virtual void push(::neurun::backend::operand::ITensor &tensor) const = 0;
-};
-
-// Create second lever inheritance: the first lever is used as a reference type in use-case places
-template <typename T> class ITemplSource : public ISource
-{
-public:
-  ITemplSource(const void *input_buffer, const size_t &input_size, const ir::Shape &shape,
-               const bool copy, ir::Layout io_layout)
-      : _input_buffer{reinterpret_cast<const T *>(input_buffer)}, _input_size{input_size},
-        _shape{shape}, _copy(copy), _io_layout{io_layout}
-  {
-  }
-
-  virtual void push(::neurun::backend::operand::ITensor &tensor) const = 0;
-
-protected:
-  void pushUnif(neurun::backend::operand::ITensor &tensor) const
-  {
-    assert(((_io_layout == ir::Layout::NHWC && tensor.layout() == ir::Layout::NCHW) ||
-            (_io_layout == ir::Layout::NCHW && tensor.layout() == ir::Layout::NHWC)) ||
-           _copy);
-    auto output_buffer = tensor.buffer();
-    auto rank = _shape.rank();
-
-    if (!tensor.has_padding() && rank < 4 + _copy)
-    {
-      memcpy(output_buffer, _input_buffer, _input_size);
-      return;
-    }
-
-    switch (rank)
-    {
-      case 0:
-      case 1:
-      {
-        memcpy(output_buffer, _input_buffer, _input_size);
-        break;
-      }
-      case 2:
-      {
-        const int32_t copy_len = _shape.dim(1);
-
-        for (auto i = 0; i < _shape.dim(0); ++i)
-        {
-          neurun::util::Coordinates coords{i, 0};
-          memcpy(output_buffer + tensor.calcOffset(coords), _input_buffer + i * copy_len,
-                 copy_len * sizeof(T));
-        }
-        break;
-      }
-      case 3:
-      {
-        const int32_t dim1 = _shape.dim(1);
-        const int32_t dim2 = _shape.dim(2);
-
-        for (auto i = 0; i < _shape.dim(0); ++i)
-        {
-          for (auto j = 0; j < _shape.dim(1); ++j)
-          {
-            neurun::util::Coordinates coords{i, j, 0};
-            memcpy(output_buffer + tensor.calcOffset(coords),
-                   _input_buffer + i * dim1 * dim2 + j * dim2, dim2 * sizeof(T));
-          }
-        }
-        break;
-      }
-      case 4:
-      {
-        if (_copy)
-        {
-          const int32_t dim1 = _shape.dim(1);
-          const int32_t dim2 = _shape.dim(2);
-          const int32_t dim3 = _shape.dim(3);
-          for (auto i = 0; i < _shape.dim(0); ++i)
-          {
-            for (auto j = 0; j < _shape.dim(1); ++j)
-            {
-              for (auto k = 0; k < _shape.dim(2); ++k)
-              {
-                neurun::util::Coordinates coords{i, j, k, 0};
-                memcpy(output_buffer + tensor.calcOffset(coords),
-                       _input_buffer + i * dim1 * dim2 * dim3 + j * dim2 * dim3 + k * dim3,
-                       dim3 * sizeof(T));
-              }
-            }
-          }
-        }
-        else
-        {
-          const auto shape = _shape.asFeature(_io_layout);
-
-          if (_io_layout == ir::Layout::NCHW)
-          {
-            const util::feature::nchw::Reader<T> from(shape, _input_buffer, _input_size);
-            util::feature::nhwc::View<T> into(&tensor);
-            ::nnfw::misc::feature::iterate(shape)
-                << [&](uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) {
-                     const auto value = from.at(batch, ch, row, col);
-                     into.at(batch, row, col, ch) = value;
-                   };
-          }
-          else if (_io_layout == ir::Layout::NHWC)
-          {
-            const util::feature::nhwc::Reader<T> from(shape, _input_buffer, _input_size);
-            util::feature::nchw::View<T> into(&tensor);
-            ::nnfw::misc::feature::iterate(shape)
-                << [&](uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) {
-                     const auto value = from.at(batch, row, col, ch);
-                     into.at(batch, ch, row, col) = value;
-                   };
-          }
-          else
-          {
-            throw std::runtime_error("Wrong Layout");
-          }
-        }
-
-        break;
-      }
-      default:
-        throw std::runtime_error("NYI");
-        break;
-    }
-  }
-
-private:
-  const T *_input_buffer;
-  const size_t _input_size;
-  const ir::Shape _shape;
-  const bool _copy;
-  const ir::Layout _io_layout;
-};
-
-template <typename T> class PermutateSource final : public ITemplSource<T>
-{
-public:
-  PermutateSource(const void *input_buffer, const size_t &input_size, const ir::Shape &shape,
-                  ir::Layout io_layout)
-      : ITemplSource<T>(input_buffer, input_size, shape, false, io_layout)
-  {
-  }
-
-public:
-  void push(neurun::backend::operand::ITensor &tensor) const override
-  {
-    // do NHWC_TO_NCHW or NCHW_TO_NHWC permutation
-    ITemplSource<T>::pushUnif(tensor);
-  }
-};
-
-template <typename T> class CopySource final : public ITemplSource<T>
-{
-public:
-  CopySource(const void *input_buffer, const size_t &input_size, const ir::Shape &shape,
-             ir::Layout io_layout = ir::Layout::UNKNOWN)
-      : ITemplSource<T>(input_buffer, input_size, shape, true, io_layout)
-  {
-  }
-
-public:
-  void push(neurun::backend::operand::ITensor &tensor) const override
-  {
-    ITemplSource<T>::pushUnif(tensor);
-  }
-};
-
-} // namespace exec
-} // namespace neurun
-
-#endif // __NEURUN_EXEC_SOURCE_H__
diff --git a/runtime/neurun/core/src/exec/ThreadPool.cc b/runtime/neurun/core/src/exec/ThreadPool.cc
deleted file mode 100644
index d8c706e30..000000000
--- a/runtime/neurun/core/src/exec/ThreadPool.cc
+++ /dev/null
@@ -1,65 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ThreadPool.h"
-
-#include <cassert>
-
-namespace neurun
-{
-namespace exec
-{
-
-ThreadPool::ThreadPool(uint32_t num_threads)
-{
-  assert(num_threads >= 1);
-
-  for (uint32_t i = 0; i < num_threads; i++)
-  {
-    _threads.emplace_back(std::ref(_worker));
-  }
-}
-
-ThreadPool::~ThreadPool()
-{
-  if (!_threads.empty())
-  {
-    _worker.terminate();
-    join();
-  }
-}
-
-void ThreadPool::enqueue(std::unique_ptr<IFunction> &&fn) { _worker.enqueue(std::move(fn)); }
-
-uint32_t ThreadPool::numJobsInQueue() { return _worker.numJobsInQueue(); }
-
-void ThreadPool::join()
-{
-  for (auto &thread : _threads)
-  {
-    thread.join();
-  }
-  _threads.clear();
-}
-
-void ThreadPool::finish()
-{
-  _worker.finish();
-  join();
-}
-
-} // namespace exec
-} // namespace neurun
diff --git a/runtime/neurun/core/src/exec/ThreadPool.h b/runtime/neurun/core/src/exec/ThreadPool.h
deleted file mode 100644
index a1a027617..000000000
--- a/runtime/neurun/core/src/exec/ThreadPool.h
+++ /dev/null
@@ -1,73 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_EXEC_THREAD_POOL_H__
-#define __NEURUN_EXEC_THREAD_POOL_H__
-
-#include <thread>
-#include <memory>
-#include <vector>
-
-#include "WorkQueue.h"
-
-namespace neurun
-{
-namespace exec
-{
-
-class ThreadPool
-{
-public:
-  /**
-   * @brief Coustruct ThreadPool object
-   *
-   * @param num_threads Number of threads
-   */
-  ThreadPool(uint32_t num_threads = 1);
-  /**
-   * @brief Destroy ThreadPool object
-   */
-  ~ThreadPool();
-  /**
-   * @brief Enqueue a function
-   *
-   * @param fn A function to be queued
-   */
-  void enqueue(std::unique_ptr<IFunction> &&fn);
-  /**
-   * @brief Get number of jobs in worker's queue
-   *
-   * @return Number of jobs
-   */
-  uint32_t numJobsInQueue();
-
-  /**
-   * @brief Block until all jobs are finished
-   */
-  void finish();
-
-private:
-  void join();
-
-private:
-  WorkQueue _worker;
-  std::vector<std::thread> _threads;
-};
-
-} // namespace exec
-} // namespace neurun
-
-#endif // __NEURUN_EXEC_THREAD_POOL_H__
diff --git a/runtime/neurun/core/src/exec/WorkQueue.cc b/runtime/neurun/core/src/exec/WorkQueue.cc
deleted file mode 100644
index 6712554ac..000000000
--- a/runtime/neurun/core/src/exec/WorkQueue.cc
+++ /dev/null
@@ -1,104 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "WorkQueue.h"
-
-#include <cassert>
-
-namespace neurun
-{
-namespace exec
-{
-
-WorkQueue::~WorkQueue()
-{
-  {
-    std::unique_lock<std::mutex> lock(_mu);
-    _state = State::FORCE_FINISHING;
-  }
-  _cv.notify_all();
-}
-
-void WorkQueue::operator()()
-{
-  while (true)
-  {
-    std::unique_ptr<IFunction> fn = nullptr;
-
-    {
-      std::unique_lock<std::mutex> lock{_mu};
-      _cv.wait(lock, [this] {
-        return (_state == State::FORCE_FINISHING) || (_state == State::FINISHING) ||
-               (_state == State::ONLINE && !_functions.empty());
-      });
-
-      if (_state == State::FORCE_FINISHING)
-      {
-        assert(_functions.empty() && "Terminating with unfinished jobs");
-        return;
-      }
-      else if (_state == State::FINISHING && _functions.empty())
-      {
-        return;
-      }
-      else
-      {
-        assert(((_state == State::FINISHING) || (_state == State::ONLINE)) && !_functions.empty());
-        fn = std::move(_functions.front());
-        _functions.pop();
-      }
-    }
-
-    assert(fn);
-    fn->run();
-  }
-}
-
-void WorkQueue::enqueue(std::unique_ptr<IFunction> &&fn)
-{
-  {
-    std::unique_lock<std::mutex> lock{_mu};
-    _functions.emplace(std::move(fn));
-  }
-  _cv.notify_one();
-}
-
-void WorkQueue::terminate()
-{
-  {
-    std::unique_lock<std::mutex> lock{_mu};
-    _state = State::FORCE_FINISHING;
-  }
-  _cv.notify_all();
-}
-
-void WorkQueue::finish()
-{
-  {
-    std::unique_lock<std::mutex> lock{_mu};
-    _state = State::FINISHING;
-  }
-  _cv.notify_all();
-}
-
-uint32_t WorkQueue::numJobsInQueue()
-{
-  std::unique_lock<std::mutex> lock{_mu};
-  return _functions.size();
-}
-
-} // namespace exec
-} // namespace neurun
diff --git a/runtime/neurun/core/src/exec/WorkQueue.h b/runtime/neurun/core/src/exec/WorkQueue.h
deleted file mode 100644
index cdbadfb8f..000000000
--- a/runtime/neurun/core/src/exec/WorkQueue.h
+++ /dev/null
@@ -1,87 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_EXEC_WORK_QUEUE_H__
-#define __NEURUN_EXEC_WORK_QUEUE_H__
-
-#include <condition_variable>
-#include <memory>
-#include <mutex>
-#include <queue>
-
-#include "exec/IFunction.h"
-
-namespace neurun
-{
-namespace exec
-{
-
-class WorkQueue
-{
-public:
-  enum class State
-  {
-    ONLINE,
-    FINISHING,
-    FORCE_FINISHING
-  };
-
-public:
-  /**
-   * @brief Create WorkQueue object
-   */
-  WorkQueue() = default;
-  /**
-   * @brief Destroy WorkQueue object
-   */
-  ~WorkQueue();
-  /**
-   * @brief Thread entry function
-   */
-  void operator()();
-  /**
-   * @brief Push the given Task to the job queue
-   *
-   * @param fn Function to be executed(a job)
-   */
-  void enqueue(std::unique_ptr<IFunction> &&fn);
-  /**
-   * @brief Flag as terminating so all the worker threads can terminate
-   */
-  void terminate();
-  /**
-   * @brief Flag as terminating so all the worker threads can terminate
-   */
-  void finish();
-  /**
-   * @brief Check if it has pending jobs. Even if this returns fals, WorkQueue threads may be still
-   * running
-   *
-   * @return true if the job queue not empty otherwise false
-   */
-  uint32_t numJobsInQueue();
-
-private:
-  State _state{State::ONLINE};
-  std::queue<std::unique_ptr<IFunction>> _functions;
-  std::mutex _mu;
-  std::condition_variable _cv;
-};
-
-} // namespace exec
-} // namespace neurun
-
-#endif // __NEURUN_EXEC_WORK_QUEUE_H__
diff --git a/runtime/neurun/core/src/exec/interp/Buffer.h b/runtime/neurun/core/src/exec/interp/Buffer.h
deleted file mode 100644
index d60b59a2f..000000000
--- a/runtime/neurun/core/src/exec/interp/Buffer.h
+++ /dev/null
@@ -1,94 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/**
- * @file  Buffer.h
- * @brief This file contains Buffer interface and InternalBuffer, ExternalBuffer class
- */
-#ifndef __NEURUN_EXEC_INTERP_BUFFER_H__
-#define __NEURUN_EXEC_INTERP_BUFFER_H__
-
-#include <cpp14/memory.h>
-
-#include "ir/Data.h"
-
-namespace neurun
-{
-namespace exec
-{
-namespace interp
-{
-
-/**
- * @brief Interface for writable data area
- */
-class Buffer : public ir::Data
-{
-public:
-  /**
-   * @brief   Return writable pointer for data area
-   * @return  Writable pointer
-   */
-  virtual uint8_t *baseWritable(void) const = 0;
-};
-
-/**
- * @brief Class for internally allocated data area
- */
-class InternalBuffer final : public Buffer
-{
-public:
-  InternalBuffer(size_t size) : _base{nnfw::cpp14::make_unique<uint8_t[]>(size)}, _size{size}
-  {
-    // DO NOTHING
-  }
-
-public:
-  size_t size(void) const override { return _size; }
-  const uint8_t *base(void) const override { return _base.get(); }
-  uint8_t *baseWritable(void) const override { return _base.get(); }
-
-private:
-  std::unique_ptr<uint8_t[]> _base;
-  size_t _size;
-};
-
-/**
- * @brief Class for data area from outside
- */
-class ExternalBuffer final : public Buffer
-{
-public:
-  ExternalBuffer(uint8_t *base, size_t size) : _base{base}, _size{size}
-  {
-    // DO NOTHING
-  }
-
-public:
-  size_t size(void) const override { return _size; }
-  const uint8_t *base(void) const override { return _base; }
-  uint8_t *baseWritable(void) const override { return _base; }
-
-private:
-  uint8_t *_base;
-  size_t _size;
-};
-
-} // namespace interp
-} // namespace exec
-} // namespace neurun
-
-#endif // __NEURUN_EXEC_INTERP_BUFFER_H__
diff --git a/runtime/neurun/core/src/exec/interp/ExecEnv.h b/runtime/neurun/core/src/exec/interp/ExecEnv.h
deleted file mode 100644
index 0f7d45e2a..000000000
--- a/runtime/neurun/core/src/exec/interp/ExecEnv.h
+++ /dev/null
@@ -1,165 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/**
- * @file  ExecEnv.h
- * @brief This file contains ExecEnv to access interpreter tensor and execution status
- */
-#ifndef __NEURUN_EXEC_INTERP_EXEC_ENV_H_
-#define __NEURUN_EXEC_INTERP_EXEC_ENV_H_
-
-#include <unordered_set>
-
-#include "ir/Graph.h"
-#include "Tensor.h"
-
-namespace neurun
-{
-namespace exec
-{
-namespace interp
-{
-
-/**
- * @brief Class to gather interpreter execution environment
- *        Each interpreter instance own execution environment
- */
-class ExecEnv
-{
-public:
-  /**
-   * @brief Construct a new Exec Env object (deleted)
-   */
-  ExecEnv(void) = delete;
-  /**
-   * @brief Construct a new ExecEnv object
-   * @param[in] graph Graph to execute by interpreter
-   */
-  explicit ExecEnv(const ir::Graph &graph) : _graph(graph)
-  {
-    // DO NOTHING
-  }
-
-public:
-  /**
-   * @brief   Return graph to execute
-   * @return  Graph
-   */
-  const ir::Graph &graph(void) const { return _graph; }
-  /**
-   * @brief     Assign tensor to environment which have allocated or assigned buffer
-   * @param[in] index   Tensor index
-   * @param[in] tensor  Tensor
-   */
-  void assignTensor(const ir::OperandIndex index, std::shared_ptr<ITensor> tensor)
-  {
-    assert(tensor->bufferRO() != nullptr);
-    _tensors.emplace(index, tensor);
-  }
-
-  /**
-   * @brief     Return tensor pointer in environment
-   * @param[in] index Tensor index
-   * @return    Tensor pointer
-   */
-  const ITensor *tensorAt(const ir::OperandIndex index) const { return _tensors.at(index).get(); }
-
-  /**
-   * @brief     Check environment contains tensor
-   * @param[in] index Tensor index
-   * @return    @c true if environment contain tensor, otherwise @c false
-   */
-  bool contains(const ir::OperandIndex index) const
-  {
-    return (_tensors.find(index) != _tensors.end());
-  }
-
-  /**
-   * @brief     Allocate tensor using operand info
-   * @param[in] index     Tensor index
-   * @param[in] info      Operand info
-   * @note      If already allocated, just return
-   * @TODO      More smart allocation policy
-   */
-  void allocateIfNeeded(const ir::OperandIndex index, const ir::OperandInfo &info)
-  {
-    // already allocated, or constant
-    if (contains(index))
-    {
-      return;
-    }
-
-    auto tensor = std::make_shared<Tensor>(info);
-    tensor->setBuffer(std::make_shared<InternalBuffer>(tensor->total_size()));
-    assignTensor(index, tensor);
-    _buffers.insert(index);
-  }
-
-  /**
-   * @brief     Allocate read-only tensor and share data with other tensor
-   * @param[in] index           Tensor index
-   * @param[in] info            Operand info
-   * @param[in] index_to_share  Tensor index that have data to share
-   */
-  void allocateAndShareIfNeeded(const ir::OperandIndex index, const ir::OperandInfo &info,
-                                const ir::OperandIndex index_to_share)
-  {
-    if (!contains(index_to_share))
-    {
-      throw std::runtime_error{"Cannot find tensor to share data"};
-    }
-
-    // already allocated
-    if (contains(index))
-    {
-      return;
-    }
-    else
-    {
-      auto tensor = std::make_shared<ROTensor>(info);
-      tensor->setData(tensorAt(index_to_share)->shareData());
-      assignTensor(index, tensor);
-      _buffers.insert(index);
-    }
-  }
-
-  /**
-   * @brief     Free buffer if allocated by allocateIfNeed
-   * @param[in] index Tensor index
-   * @note      If allocated by outside, just return
-   */
-  void freeIfAllocated(const ir::OperandIndex index)
-  {
-    if (_buffers.find(index) != _buffers.end())
-    {
-      _tensors.at(index)->releaseData();
-    }
-  }
-
-private:
-  const ir::Graph &_graph;
-  // Tensor map to use in interpreter
-  // It should map tensors that have allocated or assigned buffer pointer
-  std::unordered_map<ir::OperandIndex, std::shared_ptr<ITensor>> _tensors;
-  // Tensors allocated by allocateIfNeed (buffer)
-  std::unordered_set<ir::OperandIndex> _buffers;
-};
-
-} // namespace interp
-} // namespace exec
-} // namespace neurun
-
-#endif // __NEURUN_EXEC_INTERP_EXEC_ENV_H_
diff --git a/runtime/neurun/core/src/exec/interp/ExecManager.cc b/runtime/neurun/core/src/exec/interp/ExecManager.cc
deleted file mode 100644
index 92f182c06..000000000
--- a/runtime/neurun/core/src/exec/interp/ExecManager.cc
+++ /dev/null
@@ -1,125 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ExecManager.h"
-#include "ExecEnv.h"
-#include "Interpreter.h"
-
-#include "util/logging.h"
-
-#include <cpp14/memory.h>
-
-namespace neurun
-{
-namespace exec
-{
-namespace interp
-{
-
-void ExecManager::execute(const IODescription &desc)
-{
-  /************************************************************************
-   * Prepare execution model (submodel)
-     It may execute divided model
-     but now consider model inference is done at interpreter
-   ***********************************************************************/
-  ir::OperandIndexMap<std::shared_ptr<ITensor>> tensor_map;
-
-  for (uint32_t n = 0; n < _graph.getInputs().size(); n++)
-  {
-    ir::IOIndex index{n};
-    const auto input_index = _graph.getInputs().at(index);
-    const auto &input = *desc.inputs.at(n);
-
-    auto input_tensor = std::make_shared<ROTensor>(input.info);
-    input_tensor->setData(std::make_shared<const ir::ExternalData>(
-        reinterpret_cast<const uint8_t *>(input.buffer), input.size));
-    tensor_map[input_index] = input_tensor;
-  }
-
-  for (uint32_t n = 0; n < _graph.getOutputs().size(); n++)
-  {
-    ir::IOIndex index{n};
-    const auto output_index = _graph.getOutputs().at(index);
-    const auto &output = *desc.outputs.at(n);
-
-    auto output_tensor = std::make_shared<Tensor>(output.info);
-    output_tensor->setBuffer(
-        std::make_shared<ExternalBuffer>(reinterpret_cast<uint8_t *>(output.buffer), output.size));
-    tensor_map[output_index] = output_tensor;
-  }
-
-  /************************************************************************
-   * Prepare execution environment
-     Execution environment will be assigned to invoked interpreter instance
-   ***********************************************************************/
-
-  std::unique_ptr<ExecEnv> interp_env = nnfw::cpp14::make_unique<ExecEnv>(_graph);
-
-  // Assign input tensor into interpreter execution environment
-  for (auto index : _graph.getInputs())
-  {
-    if (tensor_map.find(index) != tensor_map.end())
-    {
-      VERBOSE(INTERPRETER) << "Assign input tensor. operand index:" << index.value() << std::endl;
-      interp_env->assignTensor(index, tensor_map.at(index));
-    }
-  }
-
-  // Assign output tensor into interpreter execution environment
-  for (auto index : _graph.getOutputs())
-  {
-    if (tensor_map.find(index) != tensor_map.end())
-    {
-      VERBOSE(INTERPRETER) << "Assign output tensor. operand index: " << index.value() << std::endl;
-      interp_env->assignTensor(index, tensor_map.at(index));
-    }
-  }
-
-  // Allocate constant tensor
-  _graph.operands().iterate([&](const ir::OperandIndex &ind, const ir::Operand &obj) {
-    if (obj.isConstant())
-    {
-      VERBOSE(INTERPRETER) << "Allocate and assign constant tensor. operand index:" << ind.value()
-                           << std::endl;
-
-      auto const_tensor = std::make_shared<ROTensor>(obj.info());
-      // Assume that interpreter's tensor layout is same with model (NHWC)
-      const_tensor->setData(
-          std::make_shared<ir::ExternalData>(obj.data().base(), obj.info().total_size()));
-      interp_env->assignTensor(ind, const_tensor);
-    }
-  });
-
-  /*****************************************************************************
-   * Invoke interpreter
-   ****************************************************************************/
-
-  Interpreter interp(std::move(interp_env));
-  interp.run();
-
-  /*****************************************************************************
-   * Invoked interpreter run is finished
-   ****************************************************************************/
-
-  // If interpreter execute submodel
-  //  1. Get tensor output of submodel into tensor_map to save result
-  //  2. Generate new ExecEnv for next interpretation
-}
-
-} // namespace interp
-} // namespace exec
-} // namespace neurun
diff --git a/runtime/neurun/core/src/exec/interp/ExecManager.h b/runtime/neurun/core/src/exec/interp/ExecManager.h
deleted file mode 100644
index f952abf02..000000000
--- a/runtime/neurun/core/src/exec/interp/ExecManager.h
+++ /dev/null
@@ -1,71 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/**
- * @file  ExecManager.h
- * @brief This file contains ExecManager class\n
- *        to manage interpreter execution and environment
- */
-#ifndef __NEURUN_EXEC_INTERP_EXEC_MANAGER_H_
-#define __NEURUN_EXEC_INTERP_EXEC_MANAGER_H_
-
-#include "ir/Graph.h"
-#include "exec/IExecutor.h"
-#include "Tensor.h"
-
-namespace neurun
-{
-namespace exec
-{
-namespace interp
-{
-
-/**
- * @brief Class to execute model using interpreter
- */
-class ExecManager final : public IExecutor
-{
-public:
-  explicit ExecManager(const ir::Graph &graph) : _graph(graph)
-  {
-    // DO NOTHING
-  }
-
-public:
-  /**
-   * @brief   Return graph object
-   * @return  Graph object
-   */
-  const ir::Graph &graph() final { return _graph; }
-  void setIndexedRanks(std::shared_ptr<ir::OperationIndexMap<int64_t>>) override{
-      // Not implemented
-  };
-  /**
-   * @brief  Start execution
-   * @note   It should be called after setting input and output buffer
-   */
-  void execute(const IODescription &desc) final;
-
-private:
-  const ir::Graph &_graph;
-  ir::OperandIndexMap<std::shared_ptr<ITensor>> _tensor_map;
-};
-
-} // namespace interp
-} // namespace exec
-} // namespace neurun
-
-#endif // __NEURUN_EXEC_INTERP_EXEC_MANAGER_H_
diff --git a/runtime/neurun/core/src/exec/interp/Interpreter.cc b/runtime/neurun/core/src/exec/interp/Interpreter.cc
deleted file mode 100644
index 8373419f6..000000000
--- a/runtime/neurun/core/src/exec/interp/Interpreter.cc
+++ /dev/null
@@ -1,210 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "Interpreter.h"
-
-#include <stack>
-#include <unordered_set>
-
-#include "Registration.h"
-
-#include "ir/OperandIndexMap.h"
-#include "util/logging.h"
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace exec
-{
-namespace interp
-{
-
-// TODO more structured execution kernel implementation
-// TODO use cker for execution
-// TODO divide tensor prepare and execution
-// TODO introduce memory manager (buffer allocate and free)
-class OperationExecutor : ir::OperationVisitor
-{
-public:
-  OperationExecutor(ExecEnv *env) : _env{env}
-  {
-    _kernels[ir::OpCode::Add] = getAdd();
-    _kernels[ir::OpCode::Sub] = getSub();
-    _kernels[ir::OpCode::Mul] = getMul();
-    _kernels[ir::OpCode::Conv2D] = getConv2D();
-    _kernels[ir::OpCode::MaxPool2D] = getMaxPool2D();
-    _kernels[ir::OpCode::Concat] = getConcat();
-    _kernels[ir::OpCode::Gather] = getGather();
-    _kernels[ir::OpCode::AvgPool2D] = getAvgPool2D();
-    _kernels[ir::OpCode::FullyConnected] = getFullyConnected();
-    _kernels[ir::OpCode::InstanceNorm] = getInstanceNorm();
-    _kernels[ir::OpCode::Softmax] = getSoftMax();
-    _kernels[ir::OpCode::Reshape] = getReshape();
-    _kernels[ir::OpCode::DepthwiseConv2D] = getDepthwiseConv();
-    _kernels[ir::OpCode::TransposeConv] = getTransposeConv();
-    _kernels[ir::OpCode::Logistic] = getLogistic();
-    _kernels[ir::OpCode::Pad] = getPad();
-    _kernels[ir::OpCode::ReLU] = getReLU();
-    _kernels[ir::OpCode::ReLU1] = getReLU1();
-    _kernels[ir::OpCode::ReLU6] = getReLU6();
-    _kernels[ir::OpCode::Tanh] = getTanh();
-  }
-
-  void execute(const ir::OperationIndex &idx)
-  {
-    const auto nodeName = _env->graph().operations().at(idx).name();
-    VERBOSE(INTERPRETER) << "Prepare output operands and execute " << nodeName
-                         << " operation (id: " << idx.value() << ")" << std::endl;
-    _env->graph().operations().at(idx).accept(*this);
-  }
-
-private:
-#define OP(InternalName)                                        \
-  void visit(const ir::operation::InternalName &node) override  \
-  {                                                             \
-    if (_kernels[ir::OpCode::InternalName]->prepare != nullptr) \
-    {                                                           \
-      _kernels[ir::OpCode::InternalName]->prepare(_env, node);  \
-    }                                                           \
-    _kernels[ir::OpCode::InternalName]->invoke(_env, node);     \
-  }
-#include "ir/Operations.lst"
-#undef OP
-
-private:
-  ExecEnv *_env;
-  std::unordered_map<ir::OpCode, OpKernel *> _kernels;
-};
-
-void Interpreter::run()
-{
-  VERBOSE(INTERPRETER) << "Interpreter is invoked " << std::endl;
-
-  // operand_stack: save operands prepared to use
-  std::stack<ir::OperandIndex> operand_stack;
-
-  // Note: We should push input first, then constant.
-  //       We use use-def for find operators ready to execution,
-  //       but Use-Def cannot handle parameters (maybe constant, but not always)
-  // Note: If all model inputs are constant, it may not work (depend on tensors' order).
-  //       But that scenario may not exist
-  for (auto ind : _env->graph().getInputs())
-  {
-    VERBOSE(INTERPRETER) << "Input: Push to operand stack " << ind.value() << std::endl;
-
-    operand_stack.push(ind);
-  }
-
-  _env->graph().operands().iterate([&](const ir::OperandIndex &ind, const ir::Operand &obj) {
-    if (obj.isConstant())
-    {
-      VERBOSE(INTERPRETER) << "Constant: Push to operand stack " << ind.value() << std::endl;
-
-      operand_stack.push(ind);
-    }
-  });
-
-  // Execution
-  std::unordered_set<ir::OperandIndex> ready_check;
-  std::unordered_set<ir::OperationIndex> executed;
-  OperationExecutor executor{_env.get()};
-  while (!operand_stack.empty())
-  {
-    const auto current_operand_index = operand_stack.top();
-    operand_stack.pop();
-    VERBOSE(INTERPRETER) << "Poped operand " << current_operand_index.value()
-                         << " is checked ready to use" << std::endl;
-
-    assert(ready_check.find(current_operand_index) == ready_check.end());
-    ready_check.insert(current_operand_index);
-
-    // Find prepared operations by scan use of current operand
-    std::stack<ir::OperationIndex> operation_stack;
-    auto use_operators = std::list<ir::OperationIndex>(
-        _env->graph().operands().at(current_operand_index).getUses().list());
-    // Remove operation index duplication
-    // If one operation uses same operand tensor for multiple input,
-    // use-list have duplicated operation index
-    use_operators.unique();
-    for (auto use_operator : use_operators)
-    {
-      // Assumption: all parameters are ready to use
-      bool operator_ready = true;
-      for (auto input_index : _env->graph().operations().at(use_operator).getInputs())
-      {
-        if (ready_check.find(input_index) == ready_check.end())
-        {
-          operator_ready = false;
-          break;
-        }
-      }
-
-      if (operator_ready)
-      {
-        VERBOSE(INTERPRETER) << "Ready to execute operation " << use_operator.value() << std::endl;
-        operation_stack.push(use_operator);
-      }
-    }
-
-    while (!operation_stack.empty())
-    {
-      const auto current_operation_index = operation_stack.top();
-      operation_stack.pop();
-      VERBOSE(INTERPRETER) << "Poped operation: " << current_operation_index.value() << "("
-                           << _env->graph().operations().at(current_operation_index).name() << ")"
-                           << std::endl;
-
-      // execution
-      // 1. Prepare output tensor
-      // 2. Call operation kernel
-      executor.execute(current_operation_index);
-      executed.insert(current_operation_index);
-
-      // 3. Push each output into operand stack
-      const auto def_operands = _env->graph().operations().at(current_operation_index).getOutputs();
-      for (auto def_operand : def_operands)
-      {
-        VERBOSE(INTERPRETER) << "Buffer: Push to operand stack " << def_operand.value()
-                             << std::endl;
-        operand_stack.push(def_operand);
-      }
-
-      // 4. Free if lifetime of buffer operands used by input is finished
-      for (auto input_index : _env->graph().operations().at(current_operation_index).getInputs())
-      {
-        const auto use_operators = _env->graph().operands().at(input_index).getUses();
-        bool dead_buffer = true;
-        for (auto use_operator : use_operators.list())
-        {
-          if (executed.find(use_operator) == executed.end())
-          {
-            dead_buffer = false;
-            break;
-          }
-        }
-
-        if (dead_buffer)
-        {
-          _env->freeIfAllocated(input_index);
-        }
-      }
-    }
-  }
-}
-
-} // namespace interp
-} // namespace exec
-} // namespace neurun
diff --git a/runtime/neurun/core/src/exec/interp/Interpreter.h b/runtime/neurun/core/src/exec/interp/Interpreter.h
deleted file mode 100644
index 1b73592b3..000000000
--- a/runtime/neurun/core/src/exec/interp/Interpreter.h
+++ /dev/null
@@ -1,67 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/**
- * @file  Interpreter.h
- * @brief This file contains Interpreter class for interpretation
- */
-#ifndef __NEURUN_EXEC_INTERP_INTERPRETER_H__
-#define __NEURUN_EXEC_INTERP_INTERPRETER_H__
-
-#include "ExecEnv.h"
-
-namespace neurun
-{
-namespace exec
-{
-namespace interp
-{
-
-/**
- * @brief Class for interpretation
- */
-class Interpreter
-{
-
-public:
-  /**
-   * @brief Construct a new Interpreter object (deleted)
-   */
-  Interpreter() = delete;
-  /**
-   * @brief     Construct a new Interpreter object
-   * @param[in] env Execution environment variable for interpreter object
-   */
-  Interpreter(std::unique_ptr<ExecEnv> env) : _env{std::move(env)}
-  {
-    // DO NOTHING
-  }
-
-public:
-  /**
-   * @brief Run interpreter until there is no operation to execute
-   */
-  void run();
-
-private:
-  std::unique_ptr<ExecEnv> _env;
-};
-
-} // namespace interp
-} // namespace exec
-} // namespace neurun
-
-#endif // __NEURUN_EXEC_INTERP_INTERPRETER_H__
diff --git a/runtime/neurun/core/src/exec/interp/Registration.h b/runtime/neurun/core/src/exec/interp/Registration.h
deleted file mode 100644
index 3ebe3bc9f..000000000
--- a/runtime/neurun/core/src/exec/interp/Registration.h
+++ /dev/null
@@ -1,63 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_EXEC_INTERP_REGISTRATION_H__
-#define __NEURUN_EXEC_INTERP_REGISTRATION_H__
-
-#include "ExecEnv.h"
-
-#include "ir/Operation.h"
-
-namespace neurun
-{
-namespace exec
-{
-namespace interp
-{
-
-struct OpKernel
-{
-  std::function<void(ExecEnv *, const ir::Operation &)> prepare;
-  std::function<void(const ExecEnv *, const ir::Operation &)> invoke;
-};
-
-// Defined in operations/ directory
-OpKernel *getAdd();
-OpKernel *getSub();
-OpKernel *getMul();
-OpKernel *getConv2D();
-OpKernel *getMaxPool2D();
-OpKernel *getConcat();
-OpKernel *getGather();
-OpKernel *getAvgPool2D();
-OpKernel *getFullyConnected();
-OpKernel *getInstanceNorm();
-OpKernel *getSoftMax();
-OpKernel *getDepthwiseConv();
-OpKernel *getReshape();
-OpKernel *getTransposeConv();
-OpKernel *getLogistic();
-OpKernel *getPad();
-OpKernel *getReLU();
-OpKernel *getReLU1();
-OpKernel *getReLU6();
-OpKernel *getTanh();
-
-} // namespace interp
-} // namespace exec
-} // namespace neurun
-
-#endif // __NEURUN_EXEC_INTERP_REGISTRATION_H__
diff --git a/runtime/neurun/core/src/exec/interp/Tensor.cc b/runtime/neurun/core/src/exec/interp/Tensor.cc
deleted file mode 100644
index 5c1da3587..000000000
--- a/runtime/neurun/core/src/exec/interp/Tensor.cc
+++ /dev/null
@@ -1,59 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "Tensor.h"
-
-#define NO_USE(a) (void)(a)
-
-namespace neurun
-{
-namespace exec
-{
-namespace interp
-{
-
-void ITensor::access(const std::function<void(backend::operand::ITensor &tensor)> &fn)
-{
-  fn(*this);
-}
-
-size_t ROTensor::calcOffset(const neurun::util::Coordinates &coords) const
-{
-  NO_USE(coords);
-  throw std::runtime_error("offset_element_in_bytes is not supported for cpu::Tensor now.");
-}
-
-size_t Tensor::calcOffset(const neurun::util::Coordinates &coords) const
-{
-  NO_USE(coords);
-  throw std::runtime_error("offset_element_in_bytes is not supported for cpu::Tensor now.");
-}
-
-ir::Layout ROTensor::layout() const
-{
-  // TODO Changes to return frontend layout
-  return ir::Layout::NHWC;
-}
-
-ir::Layout Tensor::layout() const
-{
-  // TODO Changes to return frontend layout
-  return ir::Layout::NHWC;
-}
-
-} // namespace interp
-} // namespace exec
-} // namespace neurun
diff --git a/runtime/neurun/core/src/exec/interp/Tensor.h b/runtime/neurun/core/src/exec/interp/Tensor.h
deleted file mode 100644
index c53fd46a6..000000000
--- a/runtime/neurun/core/src/exec/interp/Tensor.h
+++ /dev/null
@@ -1,180 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/**
- * @file  Tensor.h
- * @brief This file contains ITensor interface, ROTensor class, and Tensor class
- */
-#ifndef __NEURUN_EXEC_INTERP_TENSOR_H__
-#define __NEURUN_EXEC_INTERP_TENSOR_H__
-
-#include "Buffer.h"
-
-#include "ir/OperandInfo.h"
-#include "backend/operand/ITensor.h"
-#include "ir/Layout.h"
-
-namespace neurun
-{
-namespace exec
-{
-namespace interp
-{
-
-/**
- * @brief Interface to handle Tensor in interpreter
- */
-class ITensor : public backend::operand::ITensor
-{
-public:
-  virtual ~ITensor() = default;
-
-public:
-  virtual uint8_t *buffer() const = 0;
-  /**
-   * @brief   Return shared pointer for buffer
-   * @return  Buffer shared pointer
-   */
-  virtual std::shared_ptr<const Buffer> shareBuffer() const = 0;
-  /**
-   * @brief   Return read-only buffer pointer
-   * @return  Read-only buffer pointer
-   */
-  virtual const uint8_t *bufferRO() const = 0;
-  /**
-   * @brief   Return shared pointer for data
-   * @return  Data shared pointer
-   */
-  virtual std::shared_ptr<const ir::Data> shareData() const = 0;
-  /**
-   * @brief     Set internal/external buffer
-   * @param[in] buffer  Buffer pointer
-   */
-  virtual void setBuffer(std::shared_ptr<const Buffer> buffer) = 0;
-  /**
-   * @brief     Set data reference (including constant, input)
-   * @param[in] data  Data pointer
-   */
-  virtual void setData(std::shared_ptr<const ir::Data> data) = 0;
-  virtual void releaseData() = 0;
-
-  virtual size_t total_size() const = 0;
-  virtual size_t dimension(size_t index) const = 0;
-  virtual size_t num_dimensions() const = 0;
-  virtual size_t calcOffset(const util::Coordinates &coords) const = 0;
-
-  virtual bool has_padding() const = 0;
-  /**
-   * @brief   Return data type of tensor
-   * @return  Data type of tensor
-   */
-  virtual ir::DataType data_type() const = 0;
-  /**
-   * @brief   Return TensorInfo
-   * @return  TensorInfo
-   */
-  virtual const ir::OperandInfo &tensorInfo() const = 0;
-  /**
-   * @brief   Return number of elements
-   * @return  Number of elements
-   */
-  virtual uint64_t num_elements() const = 0;
-  void access(const std::function<void(backend::operand::ITensor &tensor)> &fn) final;
-};
-
-/**
- * @brief Class to handle tensor in interpreter as read-only
- */
-class ROTensor final : public ITensor
-{
-public:
-  ROTensor() = delete;
-  ROTensor(const ir::OperandInfo &info) : _info(info)
-  {
-    // DO NOTHING
-  }
-
-public:
-  uint8_t *buffer() const override { throw std::runtime_error{"Read only tensor"}; }
-  std::shared_ptr<const Buffer> shareBuffer() const override
-  {
-    throw std::runtime_error{"Read only tensor"};
-  }
-  const uint8_t *bufferRO() const override { return _data->base(); }
-  std::shared_ptr<const ir::Data> shareData() const override { return _data; }
-  void setBuffer(std::shared_ptr<const Buffer> buffer) override { _data = buffer; }
-  void setData(std::shared_ptr<const ir::Data> data) override { _data = data; }
-  void releaseData() override { _data = nullptr; }
-
-  size_t total_size() const override { return _info.total_size(); }
-  size_t dimension(size_t index) const override { return _info.shape().dim(index); }
-  size_t num_dimensions() const override { return _info.shape().rank(); }
-  size_t calcOffset(const util::Coordinates &coords) const override;
-  ir::Layout layout() const override;
-  bool has_padding() const override { return false; }
-  ir::DataType data_type() const override { return _info.typeInfo().type(); }
-  const ir::OperandInfo &tensorInfo() const override { return _info; }
-  uint64_t num_elements() const override { return _info.shape().num_elements(); };
-
-private:
-  const ir::OperandInfo _info;
-  std::shared_ptr<const ir::Data> _data{nullptr};
-};
-
-/**
- * @brief Class to handle tensor in interpreter as writable
- */
-class Tensor final : public ITensor
-{
-public:
-  Tensor() = delete;
-  Tensor(const ir::OperandInfo &info) : _info(info)
-  {
-    // DO NOTHING
-  }
-
-public:
-  uint8_t *buffer() const override { return _buffer->baseWritable(); }
-  std::shared_ptr<const Buffer> shareBuffer() const override { return _buffer; };
-  const uint8_t *bufferRO() const override { return _buffer->base(); }
-  std::shared_ptr<const ir::Data> shareData() const override { return _buffer; }
-  void setBuffer(std::shared_ptr<const Buffer> buffer) override { _buffer = buffer; }
-  void setData(std::shared_ptr<const ir::Data>) override
-  {
-    throw std::runtime_error{"Passed data may read-only"};
-  }
-  void releaseData() override { _buffer = nullptr; }
-
-  size_t total_size() const override { return _info.total_size(); }
-  size_t dimension(size_t index) const override { return _info.shape().dim(index); }
-  size_t num_dimensions() const override { return _info.shape().rank(); }
-  size_t calcOffset(const util::Coordinates &coords) const override;
-  ir::Layout layout() const override;
-  bool has_padding() const override { return false; }
-  ir::DataType data_type() const override { return _info.typeInfo().type(); }
-  const ir::OperandInfo &tensorInfo() const override { return _info; }
-  uint64_t num_elements() const override { return _info.shape().num_elements(); };
-
-private:
-  const ir::OperandInfo _info;
-  std::shared_ptr<const Buffer> _buffer{nullptr};
-};
-
-} // namespace interp
-} // namespace exec
-} // namespace neurun
-
-#endif // __NEURUN_EXEC_INTERP_TENSOR_H__
diff --git a/runtime/neurun/core/src/exec/interp/operations/AvgPool2D.cc b/runtime/neurun/core/src/exec/interp/operations/AvgPool2D.cc
deleted file mode 100644
index bd396491f..000000000
--- a/runtime/neurun/core/src/exec/interp/operations/AvgPool2D.cc
+++ /dev/null
@@ -1,129 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <cker/operation/AveragePool.h>
-
-#include "OperationUtil.h"
-
-#include "exec/interp/Registration.h"
-#include "ir/operation/AvgPool2D.h"
-#include "util/Utils.h"
-#include "util/Padding.h"
-#include "util/ShapeInference.h"
-#include "misc/polymorphic_downcast.h"
-
-namespace neurun
-{
-namespace exec
-{
-namespace interp
-{
-namespace avgpool2d
-{
-
-void prepareAvgPool2D(ExecEnv *env, const ir::Operation &node)
-{
-  const auto in_index = node.getInputs().at(0);
-  const auto out_index = node.getOutputs().at(0);
-
-  const auto in_tensor = env->tensorAt(in_index);
-  UNUSED_RELEASE(in_tensor);
-
-  assert(in_tensor->num_dimensions() == 4);
-
-  const auto output_info = env->graph().operands().at(out_index).info();
-  if (output_info.total_size() == 0)
-  {
-    // Handle unspecified output shape
-    const auto &avgpool_node =
-        nnfw::misc::polymorphic_downcast<const ir::operation::AvgPool2D &>(node);
-    const auto infered_output_shapes =
-        shape_inference::inferAvgPoolShape(in_tensor->tensorInfo().shape(), avgpool_node.param());
-    env->allocateIfNeeded(out_index, {infered_output_shapes[0], output_info.typeInfo()});
-  }
-  else
-  {
-    env->allocateIfNeeded(out_index, output_info);
-  }
-
-  auto out_tensor = env->tensorAt(out_index);
-  UNUSED_RELEASE(out_tensor);
-
-  // Handle same ifm & ofm data type only
-  assert(in_tensor->data_type() == out_tensor->data_type());
-  assert(out_tensor->num_dimensions() == 4);
-}
-
-void invoke(const ITensor *in_tensor, const ITensor *out_tensor,
-            const ir::operation::AvgPool2D::Param &param)
-{
-  // TODO Support NCHW frontend
-  const auto ifm_shape = in_tensor->tensorInfo().shape().asFeature(ir::Layout::NHWC);
-  const auto ofm_shape = out_tensor->tensorInfo().shape().asFeature(ir::Layout::NHWC);
-  const auto padding = neurun::util::calculatePadding(param.padding, ifm_shape, ofm_shape,
-                                                      param.stride, param.kw, param.kh);
-  // Calculate
-  nnfw::cker::PoolParams cker_param;
-  calculateActivationRange(param.activation, &cker_param.float_activation_min,
-                           &cker_param.float_activation_max);
-  cker_param.filter_width = param.kw;
-  cker_param.filter_height = param.kh;
-  cker_param.padding_values.width = padding.left;
-  cker_param.padding_values.height = padding.top;
-  cker_param.stride_width = param.stride.horizontal;
-  cker_param.stride_height = param.stride.vertical;
-
-  const auto in_shape = convertShape(in_tensor->tensorInfo().shape());
-  const auto out_shape = convertShape(out_tensor->tensorInfo().shape());
-  const float *in_ptr = reinterpret_cast<const float *>(in_tensor->bufferRO());
-  float *out_ptr = reinterpret_cast<float *>(out_tensor->buffer());
-
-  nnfw::cker::AveragePool(cker_param, in_shape, in_ptr, out_shape, out_ptr);
-}
-
-void invokeAvgPool2D(const ExecEnv *env, const ir::Operation &node)
-{
-  const auto &avgpool_node =
-      nnfw::misc::polymorphic_downcast<const ir::operation::AvgPool2D &>(node);
-
-  const auto in_index = node.getInputs().at(0);
-  const auto out_index = node.getOutputs().at(0);
-
-  // Check lhs shape is same with rhs (with broadcast)
-  const auto in_tensor = env->tensorAt(in_index);
-  const auto out_tensor = env->tensorAt(out_index);
-
-  const auto data_type = in_tensor->data_type();
-  if (data_type == ir::DataType::FLOAT32)
-  {
-    invoke(in_tensor, out_tensor, avgpool_node.param());
-  }
-  else
-  {
-    throw std::runtime_error{"NYI: Support float only"};
-  }
-}
-} // namespace avgpool2d
-
-OpKernel *getAvgPool2D()
-{
-  static OpKernel kernel = {avgpool2d::prepareAvgPool2D, avgpool2d::invokeAvgPool2D};
-  return &kernel;
-}
-
-} // namespace interp
-} // namespace exec
-} // namespace neurun
diff --git a/runtime/neurun/core/src/exec/interp/operations/BinaryArithmeticOps.cc b/runtime/neurun/core/src/exec/interp/operations/BinaryArithmeticOps.cc
deleted file mode 100644
index 16469b9db..000000000
--- a/runtime/neurun/core/src/exec/interp/operations/BinaryArithmeticOps.cc
+++ /dev/null
@@ -1,202 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <cker/operation/BinaryArithmeticOps.h>
-
-#include "OperationUtil.h"
-
-#include "exec/interp/Registration.h"
-#include "ir/operation/Add.h"
-#include "ir/operation/Sub.h"
-#include "ir/operation/Mul.h"
-#include "misc/polymorphic_downcast.h"
-
-namespace neurun
-{
-namespace exec
-{
-namespace interp
-{
-namespace
-{
-
-enum class OpType
-{
-  ADD,
-  SUB,
-  MUL
-};
-
-template <typename node_type> void prepareAdd(ExecEnv *env, const ir::Operation &node)
-{
-  const auto &add_node = nnfw::misc::polymorphic_downcast<const node_type &>(node);
-
-  const auto lhs_index = node.getInputs().at(add_node.LHS);
-  const auto rhs_index = node.getInputs().at(add_node.RHS);
-  const auto out_index = node.getOutputs().at(0);
-
-  const auto lhs_tensor = env->tensorAt(lhs_index);
-  const auto rhs_tensor = env->tensorAt(rhs_index);
-
-  // Check shape and type lhs is same with rhs
-  // TODO Util function to compare TensorInfo
-  if (lhs_tensor->data_type() != rhs_tensor->data_type())
-  {
-    throw std::runtime_error{"Interp(Add): Different input types"};
-  }
-
-  bool try_broadcast = (lhs_tensor->tensorInfo().shape() != rhs_tensor->tensorInfo().shape());
-  if (try_broadcast)
-  {
-    bool success = true;
-    auto out_shape = calcBroadcastShape(lhs_tensor->tensorInfo().shape(),
-                                        rhs_tensor->tensorInfo().shape(), success);
-    if (!success)
-    {
-      throw std::runtime_error{"Interp(Add): Fail to brodcasting"};
-    }
-
-    auto output_info = ir::OperandInfo(out_shape, lhs_tensor->tensorInfo().typeInfo());
-    // We can handle already allocated (ex. model output)
-    env->allocateIfNeeded(out_index, output_info);
-  }
-  else
-  {
-    // Output's shape and type is same with input
-    auto output_info = lhs_tensor->tensorInfo();
-    // We can handle already allocated (ex. model output)
-    env->allocateIfNeeded(out_index, output_info);
-  }
-
-  auto out_tensor = env->tensorAt(out_index);
-  // Check shape and type lhs is same with output
-  // TODO Util function to compare TensorInfo
-  if (lhs_tensor->data_type() != out_tensor->data_type())
-  {
-    throw std::runtime_error{"Interp(Add): Invalid output type"};
-  }
-}
-
-inline void setActivationParams(float min, float max, nnfw::cker::BinaryArithmeticOpParam *params)
-{
-  params->float_activation_min = min;
-  params->float_activation_max = max;
-}
-
-inline void setActivationParams(int32_t min, int32_t max,
-                                nnfw::cker::BinaryArithmeticOpParam *params)
-{
-  params->quantized_activation_min = min;
-  params->quantized_activation_max = max;
-}
-
-template <typename raw_type, typename param_type, OpType op_type>
-void invoke(const ITensor *lhs_tensor, const ITensor *rhs_tensor, const ITensor *out_tensor,
-            const param_type &param)
-{
-  const auto lhs_buffer = lhs_tensor->bufferRO();
-  const auto rhs_buffer = rhs_tensor->bufferRO();
-  auto out_buffer = out_tensor->buffer();
-
-  nnfw::cker::BinaryArithmeticOpParam cker_param;
-  raw_type activation_min, activation_max;
-  calculateActivationRange(param.activation, &activation_min, &activation_max);
-  setActivationParams(activation_min, activation_max, &cker_param);
-  const raw_type *lhs_ptr = reinterpret_cast<const raw_type *>(lhs_buffer);
-  const raw_type *rhs_ptr = reinterpret_cast<const raw_type *>(rhs_buffer);
-  raw_type *out_ptr = reinterpret_cast<raw_type *>(out_buffer);
-
-  // Calculate
-  const std::function<raw_type(const raw_type &, const raw_type &)> fn_add =
-      [](const raw_type &a, const raw_type &b) { return a + b; };
-  const std::function<raw_type(const raw_type &, const raw_type &)> fn_sub =
-      [](const raw_type &a, const raw_type &b) { return a - b; };
-  const std::function<raw_type(const raw_type &, const raw_type &)> fn_mul =
-      [](const raw_type &a, const raw_type &b) { return a * b; };
-
-  const std::function<raw_type(const raw_type &, const raw_type &)> fn =
-      (op_type == OpType::ADD) ? fn_add : ((op_type == OpType::SUB) ? fn_sub : fn_mul);
-
-  if (lhs_tensor->tensorInfo().shape() != rhs_tensor->tensorInfo().shape())
-  {
-    const auto lhs_shape = convertExtendShape(lhs_tensor->tensorInfo().shape());
-    const auto rhs_shape = convertExtendShape(rhs_tensor->tensorInfo().shape());
-    const auto out_shape = convertExtendShape(out_tensor->tensorInfo().shape());
-    nnfw::cker::BroadcastBinaryArithmeticOpSlow(cker_param, lhs_shape, lhs_ptr, rhs_shape, rhs_ptr,
-                                                out_shape, out_ptr, fn);
-    return;
-  }
-
-  const auto lhs_shape = convertShape(lhs_tensor->tensorInfo().shape());
-  const auto rhs_shape = convertShape(rhs_tensor->tensorInfo().shape());
-  const auto out_shape = convertShape(out_tensor->tensorInfo().shape());
-  nnfw::cker::BinaryArithmeticOp(cker_param, lhs_shape, lhs_ptr, rhs_shape, rhs_ptr, out_shape,
-                                 out_ptr, fn);
-}
-
-template <typename node_type, typename param_type, OpType op_type>
-void invokeAdd(const ExecEnv *env, const ir::Operation &node)
-{
-  const auto &arithmetic_node = nnfw::misc::polymorphic_downcast<const node_type &>(node);
-
-  const auto lhs_index = node.getInputs().at(arithmetic_node.LHS);
-  const auto rhs_index = node.getInputs().at(arithmetic_node.RHS);
-  const auto out_index = node.getOutputs().at(0);
-  const auto lhs_tensor = env->tensorAt(lhs_index);
-  const auto rhs_tensor = env->tensorAt(rhs_index);
-  const auto out_tensor = env->tensorAt(out_index);
-  const auto data_type = lhs_tensor->data_type();
-
-  if (data_type == ir::DataType::INT32)
-  {
-    invoke<int32_t, param_type, op_type>(lhs_tensor, rhs_tensor, out_tensor,
-                                         arithmetic_node.param());
-  }
-  else if (data_type == ir::DataType::FLOAT32)
-  {
-    invoke<float, param_type, op_type>(lhs_tensor, rhs_tensor, out_tensor, arithmetic_node.param());
-  }
-  else
-  {
-    throw std::runtime_error{"NYI: Unsupported data type"};
-  }
-}
-} // namespace add
-
-OpKernel *getAdd()
-{
-  static OpKernel kernel = {prepareAdd<ir::operation::Add>,
-                            invokeAdd<ir::operation::Add, ir::operation::Add::Param, OpType::ADD>};
-  return &kernel;
-}
-
-OpKernel *getSub()
-{
-  static OpKernel kernel = {prepareAdd<ir::operation::Sub>,
-                            invokeAdd<ir::operation::Sub, ir::operation::Sub::Param, OpType::SUB>};
-  return &kernel;
-}
-
-OpKernel *getMul()
-{
-  static OpKernel kernel = {prepareAdd<ir::operation::Mul>,
-                            invokeAdd<ir::operation::Mul, ir::operation::Mul::Param, OpType::MUL>};
-  return &kernel;
-}
-
-} // namespace interp
-} // namespace exec
-} // namespace neurun
diff --git a/runtime/neurun/core/src/exec/interp/operations/Concat.cc b/runtime/neurun/core/src/exec/interp/operations/Concat.cc
deleted file mode 100644
index a127e5f30..000000000
--- a/runtime/neurun/core/src/exec/interp/operations/Concat.cc
+++ /dev/null
@@ -1,150 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <cker/operation/Concatenation.h>
-
-#include "OperationUtil.h"
-
-#include "exec/interp/Registration.h"
-#include "ir/operation/Concat.h"
-#include "misc/polymorphic_downcast.h"
-
-namespace neurun
-{
-namespace exec
-{
-namespace interp
-{
-namespace concat
-{
-
-void prepareConcat(ExecEnv *env, const ir::Operation &node)
-{
-  const auto &concat_node = nnfw::misc::polymorphic_downcast<const ir::operation::Concat &>(node);
-
-  const auto first_index = node.getInputs().at(0);
-  const auto out_index = node.getOutputs().at(0);
-
-  const auto first_tensor = env->tensorAt(first_index);
-  uint32_t out_axis_dimension = 0;
-  const int32_t axis_raw = concat_node.param().axis;
-  const uint32_t axis = (axis_raw < 0) ? (axis_raw + first_tensor->num_dimensions()) : axis_raw;
-
-  // All inputs shape should be same except axis dimension
-  // All inputs type should be same
-  for (auto input : node.getInputs())
-  {
-    assert(first_tensor->num_dimensions() == env->tensorAt(input)->num_dimensions());
-    assert(first_tensor->data_type() == env->tensorAt(input)->data_type());
-    for (uint32_t i = 0; i < first_tensor->num_dimensions(); i++)
-    {
-      if (i == axis)
-      {
-        out_axis_dimension += env->tensorAt(input)->dimension(i);
-        continue;
-      }
-      assert(first_tensor->dimension(i) == env->tensorAt(input)->dimension(i));
-    }
-  }
-
-  // Make output tensor info using first input tensor info, and accumulated axis dimension value
-  auto out_shape = first_tensor->tensorInfo().shape();
-  out_shape.dim(axis) = out_axis_dimension;
-  env->allocateIfNeeded(out_index,
-                        ir::OperandInfo{out_shape, first_tensor->tensorInfo().typeInfo()});
-
-  auto out_tensor = env->tensorAt(out_index);
-  UNUSED_RELEASE(out_tensor);
-
-  // Output shape should be same with input except axis dimension
-  // Output type should be same with input
-  assert(first_tensor->data_type() == out_tensor->data_type());
-  for (uint32_t i = 0; i < first_tensor->num_dimensions(); i++)
-  {
-    if (i == axis)
-    {
-      continue;
-    }
-    assert(first_tensor->dimension(i) == out_tensor->dimension(i));
-  }
-}
-
-void invoke(const std::vector<const ITensor *> in_tensors, const ITensor *out_tensor, uint32_t axis)
-{
-  const uint32_t count = in_tensors.size();
-
-  // Calculate
-  nnfw::cker::ConcatenationParams cker_param;
-  cker_param.axis = (int8_t)axis;
-  cker_param.inputs_count = count;
-
-  const auto out_shape = convertShape(out_tensor->tensorInfo().shape());
-
-  std::vector<nnfw::cker::Shape> in_shapes;
-  std::vector<const nnfw::cker::Shape *> in_shape_ptrs;
-  in_shapes.reserve(count);
-  in_shape_ptrs.reserve(count);
-  std::vector<const float *> in_ptrs;
-  for (uint32_t i = 0; i < count; i++)
-  {
-    in_shapes.push_back(convertShape(in_tensors[i]->tensorInfo().shape()));
-    in_shape_ptrs.push_back(&in_shapes[i]);
-    in_ptrs.push_back(reinterpret_cast<const float *>(in_tensors[i]->bufferRO()));
-  }
-
-  auto out_buffer = out_tensor->buffer();
-  float *out_ptr = reinterpret_cast<float *>(out_buffer);
-
-  nnfw::cker::Concatenation<float>(cker_param, in_shape_ptrs.data(), in_ptrs.data(), out_shape,
-                                   out_ptr);
-}
-
-void invokeConcat(const ExecEnv *env, const ir::Operation &node)
-{
-  const auto &concat_node = nnfw::misc::polymorphic_downcast<const ir::operation::Concat &>(node);
-  const int32_t axis_raw = concat_node.param().axis;
-
-  std::vector<const ITensor *> in_tensors;
-  for (const auto &e : concat_node.getInputs())
-  {
-    in_tensors.emplace_back(env->tensorAt(e));
-  }
-
-  const auto out_index = node.getOutputs().at(0);
-  const auto out_tensor = env->tensorAt(out_index);
-  const uint32_t axis = (axis_raw < 0) ? (axis_raw + out_tensor->num_dimensions()) : axis_raw;
-
-  const auto data_type = in_tensors[0]->data_type();
-  if (data_type == ir::DataType::FLOAT32)
-  {
-    invoke(in_tensors, out_tensor, axis);
-  }
-  else
-  {
-    throw std::runtime_error{"NYI: Support float32 only"};
-  }
-}
-} // namespace concat
-
-OpKernel *getConcat()
-{
-  static OpKernel kernel = {concat::prepareConcat, concat::invokeConcat};
-  return &kernel;
-}
-
-} // namespace interp
-} // namespace exec
-} // namespace neurun
diff --git a/runtime/neurun/core/src/exec/interp/operations/Conv2D.cc b/runtime/neurun/core/src/exec/interp/operations/Conv2D.cc
deleted file mode 100644
index 5242247a4..000000000
--- a/runtime/neurun/core/src/exec/interp/operations/Conv2D.cc
+++ /dev/null
@@ -1,152 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <cker/operation/Conv.h>
-
-#include "OperationUtil.h"
-
-#include "exec/interp/Registration.h"
-#include "ir/operation/Conv2D.h"
-#include "util/Utils.h"
-#include "util/Padding.h"
-#include "util/ShapeInference.h"
-#include "misc/polymorphic_downcast.h"
-
-namespace neurun
-{
-namespace exec
-{
-namespace interp
-{
-namespace conv2d
-{
-
-void prepareConv2D(ExecEnv *env, const ir::Operation &node)
-{
-  const auto in_index = node.getInputs().at(ir::operation::Conv2D::INPUT);
-  const auto kernel_index = node.getInputs().at(ir::operation::Conv2D::KERNEL);
-  const auto bias_index = node.getInputs().at(ir::operation::Conv2D::BIAS);
-  const auto out_index = node.getOutputs().at(0);
-
-  const auto in_tensor = env->tensorAt(in_index);
-  const auto kernel_tensor = env->tensorAt(kernel_index);
-  const auto bias_tensor = env->tensorAt(bias_index);
-
-  assert(in_tensor->num_dimensions() == 4);
-  assert(kernel_tensor->num_dimensions() == 4);
-  assert(bias_tensor->num_dimensions() == 1);
-
-  UNUSED_RELEASE(in_tensor);
-  UNUSED_RELEASE(kernel_tensor);
-  UNUSED_RELEASE(bias_tensor);
-
-  const auto output_info = env->graph().operands().at(out_index).info();
-  if (output_info.total_size() == 0)
-  {
-    // Handle unspecified output shape
-    const auto &conv_node = nnfw::misc::polymorphic_downcast<const ir::operation::Conv2D &>(node);
-    const auto infered_output_shapes = shape_inference::inferConv2DShape(
-        in_tensor->tensorInfo().shape(), kernel_tensor->tensorInfo().shape(), conv_node.param());
-    env->allocateIfNeeded(out_index, {infered_output_shapes[0], output_info.typeInfo()});
-  }
-  else
-  {
-    env->allocateIfNeeded(out_index, output_info);
-  }
-
-  auto out_tensor = env->tensorAt(out_index);
-  UNUSED_RELEASE(out_tensor);
-
-  // Handle same ifm & ofm data type only
-  assert(in_tensor->data_type() == out_tensor->data_type());
-  assert(out_tensor->num_dimensions() == 4);
-}
-
-void invoke(const ITensor *ifm_tensor, const ITensor *ker_tensor, const ITensor *bias_tensor,
-            const ITensor *ofm_tensor, const ir::operation::Conv2D::Param &param)
-{
-  // TODO Support NCHW frontned
-  const auto ifm_shape = ifm_tensor->tensorInfo().shape().asFeature(ir::Layout::NHWC);
-  const auto ofm_shape = ofm_tensor->tensorInfo().shape().asFeature(ir::Layout::NHWC);
-  // Kernel format is [depth_out, kernel_height, kernel_width, depth_in].
-  const auto &ker_shape = ker_tensor->tensorInfo().shape();
-  const auto ker_height = ker_shape.dim(1);
-  const auto ker_width = ker_shape.dim(2);
-  const auto padding = neurun::util::calculatePadding(param.padding, ifm_shape, ofm_shape,
-                                                      param.stride, ker_width, ker_height);
-
-  // Calculate
-  float activation_min, activation_max;
-  calculateActivationRange(param.activation, &activation_min, &activation_max);
-
-  nnfw::cker::ConvParams cker_param;
-  cker_param.padding_values.width = padding.left;
-  cker_param.padding_values.height = padding.top;
-  cker_param.stride_width = param.stride.horizontal;
-  cker_param.stride_height = param.stride.vertical;
-  cker_param.dilation_width_factor = 1;
-  cker_param.dilation_height_factor = 1;
-  cker_param.float_activation_min = activation_min;
-  cker_param.float_activation_max = activation_max;
-
-  const auto cker_ifm_shape = convertShape(ifm_tensor->tensorInfo().shape());
-  const auto cker_ker_shape = convertShape(ker_tensor->tensorInfo().shape());
-  const auto cker_bias_shape = convertShape(bias_tensor->tensorInfo().shape());
-  const auto cker_ofm_shape = convertShape(ofm_tensor->tensorInfo().shape());
-  const float *ifm_ptr = reinterpret_cast<const float *>(ifm_tensor->bufferRO());
-  const float *ker_ptr = reinterpret_cast<const float *>(ker_tensor->bufferRO());
-  const float *bias_ptr = reinterpret_cast<const float *>(bias_tensor->bufferRO());
-  float *ofm_ptr = reinterpret_cast<float *>(ofm_tensor->buffer());
-
-  nnfw::cker::Conv(cker_param, cker_ifm_shape, ifm_ptr, cker_ker_shape, ker_ptr, cker_bias_shape,
-                   bias_ptr, cker_ofm_shape, ofm_ptr);
-}
-
-void invokeConv2D(const ExecEnv *env, const ir::Operation &node)
-{
-  const auto &conv_node = nnfw::misc::polymorphic_downcast<const ir::operation::Conv2D &>(node);
-
-  const auto ifm_index = node.getInputs().at(ir::operation::Conv2D::INPUT);
-  const auto ker_index = node.getInputs().at(ir::operation::Conv2D::KERNEL);
-  const auto bias_index = node.getInputs().at(ir::operation::Conv2D::BIAS);
-  const auto ofm_index = node.getOutputs().at(0);
-
-  const auto ifm_tensor = env->tensorAt(ifm_index);
-  const auto ker_tensor = env->tensorAt(ker_index);
-  const auto bias_tensor = env->tensorAt(bias_index);
-  const auto ofm_tensor = env->tensorAt(ofm_index);
-
-  const auto data_type = ifm_tensor->data_type();
-  if (data_type == ir::DataType::FLOAT32)
-  {
-    invoke(ifm_tensor, ker_tensor, bias_tensor, ofm_tensor, conv_node.param());
-  }
-  else
-  {
-    throw std::runtime_error{"NYI: Support float32 only"};
-  }
-}
-} // namespace conv2d
-
-OpKernel *getConv2D()
-{
-  static OpKernel kernel = {conv2d::prepareConv2D, conv2d::invokeConv2D};
-  return &kernel;
-}
-
-} // namespace interp
-} // namespace exec
-} // namespace neurun
diff --git a/runtime/neurun/core/src/exec/interp/operations/DepthwiseConv.cc b/runtime/neurun/core/src/exec/interp/operations/DepthwiseConv.cc
deleted file mode 100644
index 1d3649f48..000000000
--- a/runtime/neurun/core/src/exec/interp/operations/DepthwiseConv.cc
+++ /dev/null
@@ -1,159 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <cker/operation/DepthwiseConv.h>
-#include <misc/polymorphic_downcast.h>
-
-#include "OperationUtil.h"
-
-#include "exec/interp/Registration.h"
-#include "ir/operation/DepthwiseConv2D.h"
-#include "util/Padding.h"
-#include "util/Utils.h"
-#include "util/ShapeInference.h"
-
-namespace neurun
-{
-namespace exec
-{
-namespace interp
-{
-
-namespace
-{
-
-void prepareDepthwiseConv(ExecEnv *env, const ir::Operation &node)
-{
-  const auto in_index = node.getInputs().at(ir::operation::DepthwiseConv2D::INPUT);
-  const auto kernel_index = node.getInputs().at(ir::operation::DepthwiseConv2D::KERNEL);
-  const auto bias_index = node.getInputs().at(ir::operation::DepthwiseConv2D::BIAS);
-  const auto out_index = node.getOutputs().at(0);
-
-  const auto in_tensor = env->tensorAt(in_index);
-  const auto kernel_tensor = env->tensorAt(kernel_index);
-  const auto bias_tensor = env->tensorAt(bias_index);
-
-  assert(in_tensor->num_dimensions() == 4);
-  assert(kernel_tensor->num_dimensions() == 4);
-  assert(bias_tensor->num_dimensions() == 1);
-
-  UNUSED_RELEASE(in_tensor);
-  UNUSED_RELEASE(kernel_tensor);
-  UNUSED_RELEASE(bias_tensor);
-
-  // TODO handle unspecified output shape:
-  //      calculate output shape using ifm shape, kernel shape, padding, stride
-  const auto output_info = env->graph().operands().at(out_index).info();
-  if (output_info.total_size() == 0)
-  {
-    // Handle unspecified output shape
-    const auto &depth_conv_node =
-        nnfw::misc::polymorphic_downcast<const ir::operation::DepthwiseConv2D &>(node);
-    const auto infered_output_shapes = shape_inference::inferDepthwiseConv2DShape(
-        in_tensor->tensorInfo().shape(), kernel_tensor->tensorInfo().shape(),
-        depth_conv_node.param());
-    env->allocateIfNeeded(out_index, {infered_output_shapes[0], output_info.typeInfo()});
-  }
-  else
-  {
-    env->allocateIfNeeded(out_index, output_info);
-  }
-
-  auto out_tensor = env->tensorAt(out_index);
-  UNUSED_RELEASE(out_tensor);
-
-  // Handle same ifm & ofm data type only
-  assert(in_tensor->data_type() == out_tensor->data_type());
-  assert(out_tensor->num_dimensions() == 4);
-}
-
-void invoke(const ITensor *ifm_tensor, const ITensor *ker_tensor, const ITensor *bias_tensor,
-            const ITensor *ofm_tensor, const ir::operation::DepthwiseConv2D::Param &param)
-{
-  // TODO Support NCHW frontend
-  const auto ifm_shape = ifm_tensor->tensorInfo().shape().asFeature(ir::Layout::NHWC);
-  const auto ofm_shape = ofm_tensor->tensorInfo().shape().asFeature(ir::Layout::NHWC);
-  // Kernel format is [1, kernel_height, kernel_width, depth_out].
-  const auto &ker_shape = ker_tensor->tensorInfo().shape();
-  const auto ker_height = ker_shape.dim(1);
-  const auto ker_width = ker_shape.dim(2);
-  const auto padding = neurun::util::calculatePadding(param.padding, ifm_shape, ofm_shape,
-                                                      param.stride, ker_width, ker_height);
-
-  // Calculate
-  float activation_min, activation_max;
-  calculateActivationRange(param.activation, &activation_min, &activation_max);
-
-  nnfw::cker::DepthwiseConvParams cker_param;
-  cker_param.padding_values.width = padding.left;
-  cker_param.padding_values.height = padding.top;
-  cker_param.depth_multiplier = param.multiplier;
-  cker_param.stride_width = param.stride.horizontal;
-  cker_param.stride_height = param.stride.vertical;
-  cker_param.dilation_width_factor = 1;
-  cker_param.dilation_height_factor = 1;
-  cker_param.float_activation_min = activation_min;
-  cker_param.float_activation_max = activation_max;
-
-  const auto cker_ifm_shape = convertShape(ifm_tensor->tensorInfo().shape());
-  const auto cker_ker_shape = convertShape(ker_tensor->tensorInfo().shape());
-  const auto cker_bias_shape = convertShape(bias_tensor->tensorInfo().shape());
-  const auto cker_ofm_shape = convertShape(ofm_tensor->tensorInfo().shape());
-  const float *ifm_ptr = reinterpret_cast<const float *>(ifm_tensor->bufferRO());
-  const float *ker_ptr = reinterpret_cast<const float *>(ker_tensor->bufferRO());
-  const float *bias_ptr = reinterpret_cast<const float *>(bias_tensor->bufferRO());
-  float *ofm_ptr = reinterpret_cast<float *>(ofm_tensor->buffer());
-
-  nnfw::cker::DepthwiseConv(cker_param, cker_ifm_shape, ifm_ptr, cker_ker_shape, ker_ptr,
-                            cker_bias_shape, bias_ptr, cker_ofm_shape, ofm_ptr);
-}
-
-void invokeDepthwiseConv(const ExecEnv *env, const ir::Operation &node)
-{
-  const auto &conv_node = static_cast<const ir::operation::DepthwiseConv2D &>(node);
-
-  const auto ifm_index = node.getInputs().at(ir::operation::DepthwiseConv2D::INPUT);
-  const auto ker_index = node.getInputs().at(ir::operation::DepthwiseConv2D::KERNEL);
-  const auto bias_index = node.getInputs().at(ir::operation::DepthwiseConv2D::BIAS);
-  const auto ofm_index = node.getOutputs().at(0);
-
-  const auto ifm_tensor = env->tensorAt(ifm_index);
-  const auto ker_tensor = env->tensorAt(ker_index);
-  const auto bias_tensor = env->tensorAt(bias_index);
-  const auto ofm_tensor = env->tensorAt(ofm_index);
-
-  const auto data_type = ifm_tensor->data_type();
-  if (data_type == ir::DataType::FLOAT32)
-  {
-    invoke(ifm_tensor, ker_tensor, bias_tensor, ofm_tensor, conv_node.param());
-  }
-  else
-  {
-    throw std::runtime_error{"NYI: Support float32 only"};
-  }
-}
-
-} // namespace
-
-OpKernel *getDepthwiseConv()
-{
-  static OpKernel kernel = {prepareDepthwiseConv, invokeDepthwiseConv};
-  return &kernel;
-}
-
-} // namespace interp
-} // namespace exec
-} // namespace neurun
diff --git a/runtime/neurun/core/src/exec/interp/operations/FullyConnected.cc b/runtime/neurun/core/src/exec/interp/operations/FullyConnected.cc
deleted file mode 100644
index 9c1c5d4e2..000000000
--- a/runtime/neurun/core/src/exec/interp/operations/FullyConnected.cc
+++ /dev/null
@@ -1,137 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <cker/operation/FullyConnected.h>
-
-#include "OperationUtil.h"
-
-#include "exec/interp/Registration.h"
-#include "ir/operation/FullyConnected.h"
-#include "misc/polymorphic_downcast.h"
-
-namespace neurun
-{
-namespace exec
-{
-namespace interp
-{
-namespace fc
-{
-
-void prepareFC(ExecEnv *env, const ir::Operation &node)
-{
-  const auto in_index = node.getInputs().at(ir::operation::FullyConnected::INPUT);
-  const auto kernel_index = node.getInputs().at(ir::operation::FullyConnected::WEIGHT);
-  const auto bias_index = node.getInputs().at(ir::operation::FullyConnected::BIAS);
-  const auto out_index = node.getOutputs().at(0);
-
-  const auto in_tensor = env->tensorAt(in_index);
-  const auto kernel_tensor = env->tensorAt(kernel_index);
-  const auto bias_tensor = env->tensorAt(bias_index);
-
-  UNUSED_RELEASE(in_tensor);
-  UNUSED_RELEASE(kernel_tensor);
-  UNUSED_RELEASE(bias_tensor);
-
-  assert(in_tensor->num_dimensions() >= 2);
-  assert(kernel_tensor->num_dimensions() == 2);
-  assert(bias_tensor->num_dimensions() == 1);
-
-  const auto input_size_with_batch = in_tensor->num_elements();
-  const auto num_units = kernel_tensor->dimension(0);
-  const auto input_size = kernel_tensor->dimension(1);
-  const auto batch_size = input_size_with_batch / input_size;
-  assert(input_size_with_batch % input_size == 0);
-  assert(num_units == bias_tensor->dimension(0));
-
-  // Make output tensor info
-  ir::Shape output_shape(2);
-  output_shape.dim(0) = batch_size;
-  output_shape.dim(1) = num_units;
-  const ir::OperandInfo out_info{output_shape, in_tensor->tensorInfo().typeInfo()};
-  env->allocateIfNeeded(out_index, out_info);
-
-  auto out_tensor = env->tensorAt(out_index);
-  UNUSED_RELEASE(out_tensor);
-
-  // Handle same ifm & ofm data type only
-  assert(in_tensor->data_type() == out_tensor->data_type());
-  assert(out_tensor->num_dimensions() == 2);
-  assert(out_tensor->dimension(0) == batch_size);
-  assert(out_tensor->dimension(1) == num_units);
-}
-
-void invoke(const ITensor *ifm_tensor, const ITensor *ker_tensor, const ITensor *bias_tensor,
-            const ITensor *ofm_tensor, const ir::operation::FullyConnected::Param &param)
-{
-  const auto ifm_buffer = ifm_tensor->bufferRO();
-  const auto ker_buffer = ker_tensor->bufferRO();
-  const auto bias_buffer = bias_tensor->bufferRO();
-  auto ofm_buffer = ofm_tensor->buffer();
-
-  // Calculate
-  nnfw::cker::FullyConnectedParams cker_param;
-  calculateActivationRange(param.activation, &cker_param.float_activation_min,
-                           &cker_param.float_activation_max);
-  const auto cker_ifm_shape = convertExtendShape(ifm_tensor->tensorInfo().shape());
-  const auto cker_ker_shape = convertExtendShape(ker_tensor->tensorInfo().shape());
-  const auto cker_bias_shape = convertExtendShape(bias_tensor->tensorInfo().shape());
-  const auto cker_ofm_shape = convertExtendShape(ofm_tensor->tensorInfo().shape());
-  const float *ifm_ptr = reinterpret_cast<const float *>(ifm_buffer);
-  const float *ker_ptr = reinterpret_cast<const float *>(ker_buffer);
-  const float *bias_ptr = reinterpret_cast<const float *>(bias_buffer);
-  float *ofm_ptr = reinterpret_cast<float *>(ofm_buffer);
-
-  nnfw::cker::FullyConnected(cker_param, cker_ifm_shape, ifm_ptr, cker_ker_shape, ker_ptr,
-                             cker_bias_shape, bias_ptr, cker_ofm_shape, ofm_ptr);
-}
-
-void invokeFC(const ExecEnv *env, const ir::Operation &node)
-{
-  const auto &conv_node =
-      nnfw::misc::polymorphic_downcast<const ir::operation::FullyConnected &>(node);
-
-  const auto ifm_index = node.getInputs().at(ir::operation::FullyConnected::INPUT);
-  const auto ker_index = node.getInputs().at(ir::operation::FullyConnected::WEIGHT);
-  const auto bias_index = node.getInputs().at(ir::operation::FullyConnected::BIAS);
-  const auto ofm_index = node.getOutputs().at(0);
-
-  const auto ifm_tensor = env->tensorAt(ifm_index);
-  const auto ker_tensor = env->tensorAt(ker_index);
-  const auto bias_tensor = env->tensorAt(bias_index);
-  const auto ofm_tensor = env->tensorAt(ofm_index);
-
-  const auto data_type = ifm_tensor->data_type();
-  if (data_type == ir::DataType::FLOAT32)
-  {
-    invoke(ifm_tensor, ker_tensor, bias_tensor, ofm_tensor, conv_node.param());
-  }
-  else
-  {
-    throw std::runtime_error{"NYI: Support float only"};
-  }
-}
-} // namespace fc
-
-OpKernel *getFullyConnected()
-{
-  static OpKernel kernel = {fc::prepareFC, fc::invokeFC};
-  return &kernel;
-}
-
-} // namespace interp
-} // namespace exec
-} // namespace neurun
diff --git a/runtime/neurun/core/src/exec/interp/operations/Gather.cc b/runtime/neurun/core/src/exec/interp/operations/Gather.cc
deleted file mode 100644
index 8b64d1937..000000000
--- a/runtime/neurun/core/src/exec/interp/operations/Gather.cc
+++ /dev/null
@@ -1,141 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <cker/operation/Gather.h>
-
-#include "OperationUtil.h"
-
-#include "exec/interp/Registration.h"
-#include "ir/operation/Gather.h"
-#include "misc/polymorphic_downcast.h"
-
-namespace neurun
-{
-namespace exec
-{
-namespace interp
-{
-namespace
-{
-
-void prepareGather(ExecEnv *env, const ir::Operation &node)
-{
-  const auto input_index = node.getInputs().at(ir::operation::Gather::INPUT);
-  const auto indices_index = node.getInputs().at(ir::operation::Gather::INDICES);
-  const auto output_index = node.getOutputs().at(0);
-
-  const auto input_tensor = env->tensorAt(input_index);
-  const auto indices_tensor = env->tensorAt(indices_index);
-
-  // TODO handle unspecified output shape:
-  //      calculate output shape using ifm shape, kernel shape, padding, stride
-  const auto output_info = env->graph().operands().at(output_index).info();
-  if (output_info.total_size() == 0)
-  {
-    throw std::runtime_error{"Interp(Gather): NYI for unspecified output shape"};
-  }
-  else
-  {
-    env->allocateIfNeeded(output_index, output_info);
-  }
-
-  if (indices_tensor->data_type() != ir::DataType::INT32)
-  {
-    throw std::runtime_error{"Interp(Gather): Invalid indices data type"};
-  }
-
-  auto output_tensor = env->tensorAt(output_index);
-  auto output_rank = input_tensor->num_dimensions() + indices_tensor->num_dimensions() - 1;
-
-  if (output_rank != output_tensor->num_dimensions())
-  {
-    throw std::runtime_error{"Interp(Gather): Invalid output rank"};
-  }
-  if (output_tensor->data_type() != input_tensor->data_type())
-  {
-    throw std::runtime_error{"Interp(Gather): Invalid output data type"};
-  }
-
-  if (input_tensor->data_type() == ir::DataType::QUANT8_ASYMM &&
-      input_tensor->tensorInfo().typeInfo() != output_tensor->tensorInfo().typeInfo())
-  {
-    throw std::runtime_error{
-        "Interp(Gather): Cannot handle different I/O QUANT8_ASYMM scale/offset"};
-  }
-}
-
-template <typename raw_type>
-void invoke(const ITensor *input_tensors, const ITensor *indices_tensors,
-            const ITensor *output_tensor, uint32_t axis)
-{
-  // Calculate
-  nnfw::cker::GatherParams cker_param;
-  cker_param.axis = (int8_t)axis;
-
-  const auto cker_input_shapes = convertShape(input_tensors->tensorInfo().shape());
-  const auto cker_indices_shape = convertShape(indices_tensors->tensorInfo().shape());
-  const auto cker_output_shape = convertShape(output_tensor->tensorInfo().shape());
-  const raw_type *input_ptr = reinterpret_cast<const raw_type *>(input_tensors->bufferRO());
-  const int32_t *indices_ptr = reinterpret_cast<const int32_t *>(indices_tensors->bufferRO());
-  raw_type *output_ptr = reinterpret_cast<raw_type *>(output_tensor->buffer());
-
-  nnfw::cker::Gather<raw_type>(cker_param, cker_input_shapes, input_ptr, cker_indices_shape,
-                               indices_ptr, cker_output_shape, output_ptr);
-}
-
-void invokeGather(const ExecEnv *env, const ir::Operation &node)
-{
-  const auto &gather_node = nnfw::misc::polymorphic_downcast<const ir::operation::Gather &>(node);
-  const int32_t axis_raw = gather_node.param().axis;
-
-  const auto input_index = node.getInputs().at(ir::operation::Gather::INPUT);
-  const auto indices_index = node.getInputs().at(ir::operation::Gather::INDICES);
-  const auto output_index = node.getOutputs().at(0);
-
-  const auto input_tensor = env->tensorAt(input_index);
-  const auto indices_tensor = env->tensorAt(indices_index);
-  const auto output_tensor = env->tensorAt(output_index);
-  const uint32_t axis = (axis_raw < 0) ? (axis_raw + input_tensor->num_dimensions()) : axis_raw;
-
-  const auto data_type = input_tensor->data_type();
-
-  switch (data_type)
-  {
-    case ir::DataType::FLOAT32:
-      invoke<float>(input_tensor, indices_tensor, output_tensor, axis);
-      break;
-    case ir::DataType::INT32:
-      invoke<int32_t>(input_tensor, indices_tensor, output_tensor, axis);
-      break;
-    case ir::DataType::QUANT8_ASYMM:
-      invoke<uint8_t>(input_tensor, indices_tensor, output_tensor, axis);
-      break;
-    default:
-      throw std::runtime_error{"Interp(Gather): NYI - Not supported type"};
-  }
-}
-
-} // namespace concat
-
-OpKernel *getGather()
-{
-  static OpKernel kernel = {prepareGather, invokeGather};
-  return &kernel;
-}
-
-} // namespace interp
-} // namespace exec
-} // namespace neurun
diff --git a/runtime/neurun/core/src/exec/interp/operations/InstanceNorm.cc b/runtime/neurun/core/src/exec/interp/operations/InstanceNorm.cc
deleted file mode 100644
index d1623d53c..000000000
--- a/runtime/neurun/core/src/exec/interp/operations/InstanceNorm.cc
+++ /dev/null
@@ -1,124 +0,0 @@
-/*
- * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <cker/operation/InstanceNorm.h>
-
-#include "OperationUtil.h"
-
-#include "exec/interp/Registration.h"
-#include "ir/operation/InstanceNorm.h"
-#include "misc/polymorphic_downcast.h"
-
-namespace neurun
-{
-namespace exec
-{
-namespace interp
-{
-namespace instancenorm
-{
-
-void prepareInstanceNorm(ExecEnv *env, const ir::Operation &node)
-{
-  const auto &instancenorm_node =
-      nnfw::misc::polymorphic_downcast<const ir::operation::InstanceNorm &>(node);
-
-  const auto input_index = node.getInputs().at(instancenorm_node.INPUT);
-  const auto output_index = node.getOutputs().at(0);
-  const auto input_tensor = env->tensorAt(input_index);
-
-  if (input_tensor->num_dimensions() != 4)
-  {
-    throw std::runtime_error{"Interp(InstanceNorm): Input should be 4D-tensor"};
-  }
-
-  // Output shape should be same with input
-  env->allocateIfNeeded(output_index, input_tensor->tensorInfo());
-
-  auto output_tensor = env->tensorAt(output_index);
-  UNUSED_RELEASE(output_tensor);
-
-  // Handle same ifm & ofm data type only
-  assert(input_tensor->data_type() == output_tensor->data_type());
-  assert(input_tensor->tensorInfo().shape() == output_tensor->tensorInfo().shape());
-}
-
-inline void setActivationParams(float min, float max, nnfw::cker::InstanceNormParams *params)
-{
-  params->float_activation_min = min;
-  params->float_activation_max = max;
-}
-
-void invoke(const ITensor *input_tensor, const ITensor *gamma_tensor, const ITensor *beta_tensor,
-            const ITensor *output_tensor, const ir::operation::InstanceNorm::Param &param)
-{
-  // Calculate
-  float activation_min, activation_max;
-  calculateActivationRange(param.activation, &activation_min, &activation_max);
-
-  nnfw::cker::InstanceNormParams cker_param;
-  cker_param.epsilon = param.epsilon;
-  cker_param.float_activation_min = activation_min;
-  cker_param.float_activation_max = activation_max;
-
-  const auto cker_input_shape = convertShape(input_tensor->tensorInfo().shape());
-  const auto cker_gamma_shape = convertShape(gamma_tensor->tensorInfo().shape());
-  const auto cker_beta_shape = convertShape(beta_tensor->tensorInfo().shape());
-  const auto cker_output_shape = convertShape(output_tensor->tensorInfo().shape());
-  const float *input_ptr = reinterpret_cast<const float *>(input_tensor->bufferRO());
-  const float *gamma_ptr = reinterpret_cast<const float *>(gamma_tensor->bufferRO());
-  const float *beta_ptr = reinterpret_cast<const float *>(beta_tensor->bufferRO());
-  float *output_ptr = reinterpret_cast<float *>(output_tensor->buffer());
-
-  nnfw::cker::InstanceNorm(cker_param, cker_input_shape, input_ptr, cker_gamma_shape, gamma_ptr,
-                           cker_beta_shape, beta_ptr, cker_output_shape, output_ptr);
-}
-
-void invokeInstanceNorm(const ExecEnv *env, const ir::Operation &node)
-{
-  const auto &instancenorm_node =
-      nnfw::misc::polymorphic_downcast<const ir::operation::InstanceNorm &>(node);
-
-  const auto input_index = node.getInputs().at(instancenorm_node.INPUT);
-  const auto gamma_index = node.getInputs().at(instancenorm_node.GAMMA);
-  const auto beta_index = node.getInputs().at(instancenorm_node.BETA);
-  const auto out_index = node.getOutputs().at(0);
-  const auto input_tensor = env->tensorAt(input_index);
-  const auto gamma_tensor = env->tensorAt(gamma_index);
-  const auto beta_tensor = env->tensorAt(beta_index);
-  const auto out_tensor = env->tensorAt(out_index);
-  const auto data_type = input_tensor->data_type();
-
-  if (data_type == ir::DataType::FLOAT32)
-  {
-    invoke(input_tensor, gamma_tensor, beta_tensor, out_tensor, instancenorm_node.param());
-  }
-  else
-  {
-    throw std::runtime_error{"NYI: Unsupported data type"};
-  }
-}
-} // namespace instancenorm
-
-OpKernel *getInstanceNorm()
-{
-  static OpKernel kernel = {instancenorm::prepareInstanceNorm, instancenorm::invokeInstanceNorm};
-  return &kernel;
-}
-
-} // namespace interp
-} // namespace exec
-} // namespace neurun
diff --git a/runtime/neurun/core/src/exec/interp/operations/Logistic.cc b/runtime/neurun/core/src/exec/interp/operations/Logistic.cc
deleted file mode 100644
index 2fc68ffd2..000000000
--- a/runtime/neurun/core/src/exec/interp/operations/Logistic.cc
+++ /dev/null
@@ -1,102 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <cker/operation/Logistic.h>
-
-#include "OperationUtil.h"
-
-#include "exec/interp/Registration.h"
-#include "ir/operation/Logistic.h"
-
-namespace neurun
-{
-namespace exec
-{
-namespace interp
-{
-namespace
-{
-
-void prepareLogistic(ExecEnv *env, const ir::Operation &node)
-{
-  const auto input_index = node.getInputs().at(0);
-  const auto output_index = node.getOutputs().at(0);
-
-  const auto input_tensor = env->tensorAt(input_index);
-
-  const auto output_info = env->graph().operands().at(output_index).info();
-
-  // Check shape and type lhs is same with rhs
-  // TODO Util function to compare TensorInfo
-  if (output_info.total_size() == 0)
-  {
-    throw std::runtime_error{"Interp(TConv): NYI unspecified output shape"};
-  }
-  else
-  {
-    env->allocateIfNeeded(output_index, output_info);
-  }
-
-  const auto output_tensor = env->tensorAt(output_index);
-  if (input_tensor->data_type() != output_tensor->data_type())
-  {
-    throw std::runtime_error{"Interp(Logistic): Invalid output type"};
-  }
-}
-
-void invoke(const ITensor *input_tensor, const ITensor *output_tensor)
-{
-  const auto input_buffer = input_tensor->bufferRO();
-  auto output_buffer = output_tensor->buffer();
-
-  const auto cker_input_shape = convertShape(input_tensor->tensorInfo().shape());
-  const auto cker_output_shape = convertShape(output_tensor->tensorInfo().shape());
-  const float *input_ptr = reinterpret_cast<const float *>(input_buffer);
-  float *output_ptr = reinterpret_cast<float *>(output_buffer);
-
-  nnfw::cker::Logistic(cker_input_shape, input_ptr, cker_output_shape, output_ptr);
-}
-
-void invokeLogistic(const ExecEnv *env, const ir::Operation &node)
-{
-  const auto input_index = node.getInputs().at(0);
-  const auto output_index = node.getOutputs().at(0);
-
-  const auto input_tensor = env->tensorAt(input_index);
-  const auto output_tensor = env->tensorAt(output_index);
-
-  const auto data_type = input_tensor->data_type();
-
-  if (data_type == ir::DataType::FLOAT32)
-  {
-    invoke(input_tensor, output_tensor);
-  }
-  else
-  {
-    throw std::runtime_error{"Interp(Logistic): NYI - Unsupported data type"};
-  }
-}
-} // namespace
-
-OpKernel *getLogistic()
-{
-  static OpKernel kernel = {prepareLogistic, invokeLogistic};
-  return &kernel;
-}
-
-} // namespace interp
-} // namespace exec
-} // namespace neurun
diff --git a/runtime/neurun/core/src/exec/interp/operations/MaxPool2D.cc b/runtime/neurun/core/src/exec/interp/operations/MaxPool2D.cc
deleted file mode 100644
index 3e1711d8e..000000000
--- a/runtime/neurun/core/src/exec/interp/operations/MaxPool2D.cc
+++ /dev/null
@@ -1,128 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <cker/operation/MaxPool.h>
-
-#include "OperationUtil.h"
-
-#include "exec/interp/Registration.h"
-#include "ir/operation/MaxPool2D.h"
-#include "util/Utils.h"
-#include "util/Padding.h"
-#include "util/ShapeInference.h"
-#include "misc/polymorphic_downcast.h"
-
-namespace neurun
-{
-namespace exec
-{
-namespace interp
-{
-namespace
-{
-
-void prepareMaxPool2D(ExecEnv *env, const ir::Operation &node)
-{
-  const auto in_index = node.getInputs().at(0);
-  const auto out_index = node.getOutputs().at(0);
-
-  const auto in_tensor = env->tensorAt(in_index);
-
-  assert(in_tensor->num_dimensions() == 4);
-  UNUSED_RELEASE(in_tensor);
-
-  const auto output_info = env->graph().operands().at(out_index).info();
-  if (output_info.total_size() == 0)
-  {
-    // Handle unspecified output shape
-    const auto &maxpool_node =
-        nnfw::misc::polymorphic_downcast<const ir::operation::MaxPool2D &>(node);
-    const auto infered_output_shapes =
-        shape_inference::inferMaxPoolShape(in_tensor->tensorInfo().shape(), maxpool_node.param());
-    env->allocateIfNeeded(out_index, {infered_output_shapes[0], output_info.typeInfo()});
-  }
-  else
-  {
-    env->allocateIfNeeded(out_index, output_info);
-  }
-
-  auto out_tensor = env->tensorAt(out_index);
-  UNUSED_RELEASE(out_tensor);
-
-  // Handle same ifm & ofm data type only
-  assert(in_tensor->data_type() == out_tensor->data_type());
-  assert(out_tensor->num_dimensions() == 4);
-}
-
-void invoke(const ITensor *in_tensor, const ITensor *out_tensor,
-            const ir::operation::MaxPool2D::Param &param)
-{
-  // TODO support NCHW frontend
-  const auto ifm_shape = in_tensor->tensorInfo().shape().asFeature(ir::Layout::NHWC);
-  const auto ofm_shape = out_tensor->tensorInfo().shape().asFeature(ir::Layout::NHWC);
-  const auto padding = neurun::util::calculatePadding(param.padding, ifm_shape, ofm_shape,
-                                                      param.stride, param.kw, param.kh);
-  // Calculate
-  nnfw::cker::PoolParams cker_param;
-  calculateActivationRange(param.activation, &cker_param.float_activation_min,
-                           &cker_param.float_activation_max);
-  cker_param.filter_width = param.kw;
-  cker_param.filter_height = param.kh;
-  cker_param.padding_values.width = padding.left;
-  cker_param.padding_values.height = padding.top;
-  cker_param.stride_width = param.stride.horizontal;
-  cker_param.stride_height = param.stride.vertical;
-
-  const auto in_shape = convertShape(in_tensor->tensorInfo().shape());
-  const auto out_shape = convertShape(out_tensor->tensorInfo().shape());
-  const float *in_ptr = reinterpret_cast<const float *>(in_tensor->bufferRO());
-  float *out_ptr = reinterpret_cast<float *>(out_tensor->buffer());
-
-  nnfw::cker::MaxPool(cker_param, in_shape, in_ptr, out_shape, out_ptr);
-}
-
-void invokeMaxPool2D(const ExecEnv *env, const ir::Operation &node)
-{
-  const auto &maxpool_node =
-      nnfw::misc::polymorphic_downcast<const ir::operation::MaxPool2D &>(node);
-
-  const auto in_index = node.getInputs().at(0);
-  const auto out_index = node.getOutputs().at(0);
-
-  const auto in_tensor = env->tensorAt(in_index);
-  const auto out_tensor = env->tensorAt(out_index);
-
-  const auto data_type = in_tensor->data_type();
-  if (data_type == ir::DataType::FLOAT32)
-  {
-    invoke(in_tensor, out_tensor, maxpool_node.param());
-  }
-  else
-  {
-    throw std::runtime_error{"NYI: Support float32 only"};
-  }
-}
-} // namespace
-
-OpKernel *getMaxPool2D()
-{
-  static OpKernel kernel = {prepareMaxPool2D, invokeMaxPool2D};
-  return &kernel;
-}
-
-} // namespace interp
-} // namespace exec
-} // namespace neurun
diff --git a/runtime/neurun/core/src/exec/interp/operations/OperationUtil.h b/runtime/neurun/core/src/exec/interp/operations/OperationUtil.h
deleted file mode 100644
index 5f4146bb8..000000000
--- a/runtime/neurun/core/src/exec/interp/operations/OperationUtil.h
+++ /dev/null
@@ -1,177 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_EXEC_INTERP_OPERATIONS_OPERATION_UTILS_H_
-#define __NEURUN_EXEC_INTERP_OPERATIONS_OPERATION_UTILS_H_
-
-#include "ir/Shape.h"
-#include "ir/InternalType.h"
-
-#include <cker/Shape.h>
-
-namespace neurun
-{
-namespace exec
-{
-namespace interp
-{
-
-inline nnfw::cker::Shape convertShape(const ir::Shape &shape)
-{
-  auto dimensions = std::vector<uint32_t>(shape.dims().begin(), shape.dims().end());
-
-  std::vector<int32_t> raw_shape;
-  raw_shape.resize(4);
-
-  for (uint32_t i = 0; i < 4; ++i)
-  {
-    if (i >= dimensions.size())
-    {
-      raw_shape[i] = 1;
-    }
-    else
-    {
-      raw_shape[i] = dimensions[i];
-    }
-  }
-
-  return nnfw::cker::GetShape(raw_shape);
-}
-
-inline nnfw::cker::Shape convertExtendShape(const ir::Shape &shape)
-{
-  auto dimensions = std::vector<uint32_t>(shape.dims().begin(), shape.dims().end());
-
-  std::vector<int32_t> raw_shape;
-  raw_shape.resize(4);
-  uint32_t start = 4 - dimensions.size();
-
-  for (uint32_t i = 0; i < 4; ++i)
-  {
-    if (i < start)
-    {
-      raw_shape[i] = 1;
-    }
-    else
-    {
-      raw_shape[i] = dimensions[i - start];
-    }
-  }
-
-  return nnfw::cker::GetShape(raw_shape);
-}
-
-template <typename T>
-void calculateActivationRange(ir::Activation activation, T *activation_min, T *activation_max)
-{
-  if (activation == ir::Activation::RELU)
-  {
-    *activation_min = 0;
-    *activation_max = std::numeric_limits<T>::max();
-  }
-  else if (activation == ir::Activation::RELU6)
-  {
-    *activation_min = 0;
-    *activation_max = 6;
-  }
-  else if (activation == ir::Activation::RELU1)
-  {
-    *activation_min = -1;
-    *activation_max = 1;
-  }
-  else if (activation == ir::Activation::NONE)
-  {
-    *activation_min = std::numeric_limits<T>::lowest();
-    *activation_max = std::numeric_limits<T>::max();
-  }
-  else
-  {
-    throw std::runtime_error{"Unsupported activation type"};
-  }
-}
-
-inline ir::Shape calcBroadcastShape(const ir::Shape &lhs, const ir::Shape &rhs, bool &success)
-{
-  int lhs_rank = lhs.rank();
-  int rhs_rank = rhs.rank();
-
-  int out_rank = (lhs_rank > rhs_rank ? lhs_rank : rhs_rank);
-  ir::Shape out_shape(out_rank);
-
-  int lhs_idim = lhs_rank - 1;
-  int rhs_idim = rhs_rank - 1;
-  success = true;
-  for (int out_idim = out_rank - 1; out_idim >= 0; out_idim--)
-  {
-    if (lhs_idim == -1 && rhs_idim == -1)
-    {
-      // invalid result
-      success = false;
-      break;
-    }
-
-    if (lhs_idim == -1)
-    {
-      out_shape.dim(out_idim) = rhs.dim(rhs_idim);
-      rhs_idim--;
-    }
-    else if (rhs_idim == -1)
-    {
-      out_shape.dim(out_idim) = lhs.dim(lhs_idim);
-      lhs_idim--;
-    }
-    else
-    {
-      if (lhs.dim(lhs_idim) == rhs.dim(rhs_idim))
-      {
-        out_shape.dim(out_idim) = lhs.dim(lhs_idim);
-        lhs_idim--;
-        rhs_idim--;
-      }
-      else if (lhs.dim(lhs_idim) == 1)
-      {
-        out_shape.dim(out_idim) = rhs.dim(rhs_idim);
-        lhs_idim--;
-        rhs_idim--;
-      }
-      else if (rhs.dim(rhs_idim) == 1)
-      {
-        out_shape.dim(out_idim) = lhs.dim(lhs_idim);
-        lhs_idim--;
-        rhs_idim--;
-      }
-      else
-      {
-        // invalid result
-        success = false;
-        break;
-      }
-    }
-  }
-
-  if (lhs_idim != -1 || rhs_idim != -1)
-  {
-    // invalid result
-    success = false;
-  }
-  return out_shape;
-}
-
-} // namespace interp
-} // namespace exec
-} // namespace neurun
-
-#endif // __NEURUN_EXEC_INTERP_OPERATIONS_OPERATION_UTILS_H_
diff --git a/runtime/neurun/core/src/exec/interp/operations/Pad.cc b/runtime/neurun/core/src/exec/interp/operations/Pad.cc
deleted file mode 100644
index 0c8267a90..000000000
--- a/runtime/neurun/core/src/exec/interp/operations/Pad.cc
+++ /dev/null
@@ -1,109 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <cker/operation/Pad.h>
-
-#include "OperationUtil.h"
-
-#include "exec/interp/Registration.h"
-#include "ir/operation/Pad.h"
-
-namespace neurun
-{
-namespace exec
-{
-namespace interp
-{
-namespace
-{
-
-void preparePad(ExecEnv *env, const ir::Operation &node)
-{
-  const auto input_index = node.getInputs().at(ir::operation::Pad::INPUT);
-  const auto output_index = node.getOutputs().at(0);
-
-  const auto input_tensor = env->tensorAt(input_index);
-
-  const auto output_info = env->graph().operands().at(output_index).info();
-
-  // Check shape and type lhs is same with rhs
-  // TODO Util function to compare TensorInfo
-  if (output_info.total_size() == 0)
-  {
-    throw std::runtime_error{"Interp(Pad): NYI unspecified output shape"};
-  }
-  else
-  {
-    env->allocateIfNeeded(output_index, output_info);
-  }
-
-  const auto output_tensor = env->tensorAt(output_index);
-  if (input_tensor->data_type() != output_tensor->data_type())
-  {
-    throw std::runtime_error{"Interp(Pad): Invalid output type"};
-  }
-}
-
-void invoke(const ITensor *input_tensor, const ITensor *pad_tensor, const ITensor *output_tensor)
-{
-  const auto input_buffer = input_tensor->bufferRO();
-  const auto pad_buffer = pad_tensor->bufferRO();
-  auto output_buffer = output_tensor->buffer();
-
-  int32_t pad_rank = pad_tensor->dimension(0);
-
-  const auto cker_input_shape = convertShape(input_tensor->tensorInfo().shape());
-  const auto cker_output_shape = convertShape(output_tensor->tensorInfo().shape());
-  const float *input_ptr = reinterpret_cast<const float *>(input_buffer);
-  const int32_t *pad_ptr = reinterpret_cast<const int32_t *>(pad_buffer);
-  float *output_ptr = reinterpret_cast<float *>(output_buffer);
-
-  nnfw::cker::Pad(pad_ptr, pad_rank, cker_input_shape, input_ptr, cker_output_shape, output_ptr,
-                  nullptr);
-}
-
-void invokePad(const ExecEnv *env, const ir::Operation &node)
-{
-  const auto input_index = node.getInputs().at(ir::operation::Pad::INPUT);
-  const auto pad_index = node.getInputs().at(ir::operation::Pad::PAD);
-  const auto output_index = node.getOutputs().at(0);
-
-  const auto input_tensor = env->tensorAt(input_index);
-  const auto pad_tensor = env->tensorAt(pad_index);
-  const auto output_tensor = env->tensorAt(output_index);
-
-  const auto data_type = input_tensor->data_type();
-
-  if (data_type == ir::DataType::FLOAT32)
-  {
-    invoke(input_tensor, pad_tensor, output_tensor);
-  }
-  else
-  {
-    throw std::runtime_error{"Interp(Pad): NYI - Unsupported data type"};
-  }
-}
-} // namespace
-
-OpKernel *getPad()
-{
-  static OpKernel kernel = {preparePad, invokePad};
-  return &kernel;
-}
-
-} // namespace interp
-} // namespace exec
-} // namespace neurun
diff --git a/runtime/neurun/core/src/exec/interp/operations/Reshape.cc b/runtime/neurun/core/src/exec/interp/operations/Reshape.cc
deleted file mode 100644
index a160232de..000000000
--- a/runtime/neurun/core/src/exec/interp/operations/Reshape.cc
+++ /dev/null
@@ -1,66 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "exec/interp/Registration.h"
-
-namespace neurun
-{
-namespace exec
-{
-namespace interp
-{
-namespace
-{
-
-void prepare(ExecEnv *env, const ir::Operation &node)
-{
-  const auto in_index = node.getInputs().at(0);
-  const auto out_index = node.getOutputs().at(0);
-
-  // Unspecified shape is not supported in operation node spec now
-  const auto output_info = env->graph().operands().at(out_index).info();
-  env->allocateAndShareIfNeeded(out_index, output_info, in_index);
-
-  assert(output_info.total_size() == env->graph().operands().at(in_index).info().total_size());
-}
-
-void invoke(const ExecEnv *env, const ir::Operation &node)
-{
-  const auto in_index = node.getInputs().at(0);
-  const auto out_index = node.getOutputs().at(0);
-
-  if (env->tensorAt(in_index)->bufferRO() == env->tensorAt(out_index)->bufferRO())
-  {
-    // Same data
-    return;
-  }
-
-  const auto output_info = env->graph().operands().at(out_index).info();
-  memcpy(env->tensorAt(out_index)->buffer(), env->tensorAt(in_index)->bufferRO(),
-         output_info.total_size());
-}
-
-} // namespace {anonymous}
-
-OpKernel *getReshape()
-{
-  static OpKernel kernel = {prepare, invoke};
-  return &kernel;
-}
-
-} // namespace interp
-} // namespace exec
-} // namespace neurun
diff --git a/runtime/neurun/core/src/exec/interp/operations/SoftMax.cc b/runtime/neurun/core/src/exec/interp/operations/SoftMax.cc
deleted file mode 100644
index 91d98889f..000000000
--- a/runtime/neurun/core/src/exec/interp/operations/SoftMax.cc
+++ /dev/null
@@ -1,163 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <cker/operation/SoftMax.h>
-
-#include "OperationUtil.h"
-
-#include "exec/interp/Registration.h"
-#include "ir/operation/Softmax.h"
-#include "misc/polymorphic_downcast.h"
-
-namespace neurun
-{
-namespace exec
-{
-namespace interp
-{
-namespace
-{
-
-void Softmax2D(const float *in, const int input_size, const int batch_size, const float beta,
-               float *out)
-{
-  assert(input_size > 0);
-
-  // For each batch
-  for (int b = 0; b < batch_size; b++)
-  {
-    // Find the max coeff.
-    float max_coeff = in[0];
-    for (int i = 1; i < input_size; i++)
-    {
-      if (in[i] > max_coeff)
-        max_coeff = in[i];
-    }
-
-    // Compute the normalized sum of exps.
-    float exp_sum = 0.0;
-    for (int i = 0; i < input_size; i++)
-    {
-      out[i] = std::exp((in[i] - max_coeff) * beta);
-      exp_sum += out[i];
-    }
-
-    // Divide by the sum of exps.
-    float reciprocal_sum_exp = 1.f / exp_sum;
-    for (int i = 0; i < input_size; i++)
-    {
-      out[i] *= reciprocal_sum_exp;
-    }
-
-    // Advance in and out pointers for the next batch.
-    in += input_size;
-    out += input_size;
-  }
-}
-
-void prepareSoftMax(ExecEnv *env, const ir::Operation &node)
-{
-  const auto in_index = node.getInputs().at(0);
-  const auto out_index = node.getOutputs().at(0);
-
-  const auto in_tensor = env->tensorAt(in_index);
-  UNUSED_RELEASE(in_tensor);
-
-  assert((in_tensor->num_dimensions() == 4) || (in_tensor->num_dimensions() == 2));
-
-  // Output shape should be same with input
-  // Output type is pre-defined in model
-  const auto output_shape = env->graph().operands().at(in_index).info().shape();
-  const auto output_type = env->graph().operands().at(out_index).info().typeInfo();
-
-  const ir::OperandInfo output_info{output_shape, output_type};
-  env->allocateIfNeeded(out_index, output_info);
-
-  auto out_tensor = env->tensorAt(out_index);
-  UNUSED_RELEASE(out_tensor);
-
-  // Check output shape is same with input
-  assert(out_tensor->num_dimensions() == out_tensor->num_dimensions());
-  for (uint32_t i = 0; i < in_tensor->num_dimensions(); i++)
-  {
-    assert(in_tensor->dimension(i) == out_tensor->dimension(i));
-  }
-}
-
-void invoke(const ITensor *in_tensor, const ITensor *out_tensor,
-            const ir::operation::Softmax::Param &param)
-{
-  const float *in_ptr = reinterpret_cast<const float *>(in_tensor->bufferRO());
-  float *out_ptr = reinterpret_cast<float *>(out_tensor->buffer());
-
-  float beta = param.beta;
-
-  if (in_tensor->num_dimensions() == 2)
-  {
-    uint32_t batch_size = in_tensor->dimension(0);
-    uint32_t input_size = in_tensor->dimension(1);
-
-    Softmax2D(in_ptr, input_size, batch_size, beta, out_ptr);
-  }
-  else if (in_tensor->num_dimensions() == 4)
-  {
-    const auto in_shape = convertShape(in_tensor->tensorInfo().shape());
-    const auto out_shape = convertShape(out_tensor->tensorInfo().shape());
-
-    nnfw::cker::SoftmaxParams cker_param;
-    cker_param.beta = beta;
-
-    nnfw::cker::Softmax(cker_param, in_shape, in_ptr, out_shape, out_ptr);
-  }
-  else
-  {
-    throw std::runtime_error{"Unsuported input dimension: support 2D or 4D"};
-  }
-}
-
-void invokeSoftMax(const ExecEnv *env, const ir::Operation &node)
-{
-  const auto &softmax_node = nnfw::misc::polymorphic_downcast<const ir::operation::Softmax &>(node);
-
-  const auto in_index = node.getInputs().at(0);
-  const auto out_index = node.getOutputs().at(0);
-
-  const auto in_tensor = env->tensorAt(in_index);
-  const auto out_tensor = env->tensorAt(out_index);
-
-  const auto in_data_type = in_tensor->data_type();
-  const auto out_data_type = out_tensor->data_type();
-  if ((in_data_type == ir::DataType::FLOAT32) && (out_data_type == ir::DataType::FLOAT32))
-  {
-    invoke(in_tensor, out_tensor, softmax_node.param());
-  }
-  else
-  {
-    throw std::runtime_error{"NYI: Support float32 only"};
-  }
-}
-
-} // namespace
-
-OpKernel *getSoftMax()
-{
-  static OpKernel kernel = {prepareSoftMax, invokeSoftMax};
-  return &kernel;
-}
-
-} // namespace interp
-} // namespace exec
-} // namespace neurun
diff --git a/runtime/neurun/core/src/exec/interp/operations/TransposeConv.cc b/runtime/neurun/core/src/exec/interp/operations/TransposeConv.cc
deleted file mode 100644
index 70b72c88d..000000000
--- a/runtime/neurun/core/src/exec/interp/operations/TransposeConv.cc
+++ /dev/null
@@ -1,145 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <cker/operation/TransposeConv.h>
-#include <misc/polymorphic_downcast.h>
-
-#include "OperationUtil.h"
-
-#include "exec/interp/Registration.h"
-#include "ir/operation/TransposeConv.h"
-#include "util/Padding.h"
-
-namespace neurun
-{
-namespace exec
-{
-namespace interp
-{
-namespace
-{
-
-void prepareTransposeConv(ExecEnv *env, const ir::Operation &node)
-{
-  const auto ifm_index = node.getInputs().at(ir::operation::TransposeConv::INPUT);
-  const auto ker_index = node.getInputs().at(ir::operation::TransposeConv::KERNEL);
-  const auto ofm_shape_index = node.getInputs().at(ir::operation::TransposeConv::OUTPUT_SHAPE);
-  const auto ofm_index = node.getOutputs().at(0);
-
-  const auto ifm_tensor = env->tensorAt(ifm_index);
-  const auto ker_tensor = env->tensorAt(ker_index);
-  const auto ofm_shape_tensor = env->tensorAt(ofm_shape_index);
-
-  assert(ifm_tensor->num_dimensions() == 4);
-  assert(ker_tensor->num_dimensions() == 4);
-  assert(ofm_shape_tensor->num_dimensions() == 1);
-
-  UNUSED_RELEASE(ifm_tensor);
-  UNUSED_RELEASE(ker_tensor);
-  UNUSED_RELEASE(ofm_shape_tensor);
-
-  const auto output_info = env->graph().operands().at(ofm_index).info();
-  if (output_info.total_size() == 0)
-  {
-    // TODO: Handle unspecified output shape
-    throw std::runtime_error{"Interp(TConv): NYI unspecified output shape"};
-  }
-  else
-  {
-    env->allocateIfNeeded(ofm_index, output_info);
-  }
-
-  auto ofm_tensor = env->tensorAt(ofm_index);
-  UNUSED_RELEASE(ofm_tensor);
-
-  // Handle same ifm & ofm data type only
-  if (ifm_tensor->data_type() != ofm_tensor->data_type())
-  {
-    throw std::runtime_error{"Interp(TConv): Different I/O data dype"};
-  }
-
-  if (ofm_tensor->num_dimensions() != 4)
-  {
-    throw std::runtime_error{"Interp(TConv): Invalid output rank"};
-  }
-}
-
-void invoke(const ITensor *ifm_tensor, const ITensor *ker_tensor, const ITensor *ofm_tensor,
-            const ir::operation::TransposeConv::Param &param)
-{
-  const auto ifm_shape = ifm_tensor->tensorInfo().shape().asFeature(ir::Layout::NHWC);
-  const auto ofm_shape = ofm_tensor->tensorInfo().shape().asFeature(ir::Layout::NHWC);
-  // Kernel format is [depth_out, kernel_height, kernel_width, depth_in].
-  const auto ker_shape = ker_tensor->tensorInfo().shape();
-  const auto ker_height = ker_shape.dim(1);
-  const auto ker_width = ker_shape.dim(2);
-  const auto padding = neurun::util::calculatePadding(param.padding, ofm_shape, ifm_shape,
-                                                      param.stride, ker_width, ker_height);
-
-  nnfw::cker::TransposeConvParams cker_param;
-  cker_param.padding_values.width = padding.left;
-  cker_param.padding_values.height = padding.top;
-  cker_param.stride_width = param.stride.horizontal;
-  cker_param.stride_height = param.stride.vertical;
-  cker_param.dilation_width_factor = 1;
-  cker_param.dilation_height_factor = 1;
-
-  const auto cker_ifm_shape = convertShape(ifm_tensor->tensorInfo().shape());
-  const auto cker_ker_shape = convertShape(ker_tensor->tensorInfo().shape());
-  const auto cker_ofm_shape = convertShape(ofm_tensor->tensorInfo().shape());
-  const float *ifm_ptr = reinterpret_cast<const float *>(ifm_tensor->bufferRO());
-  const float *ker_ptr = reinterpret_cast<const float *>(ker_tensor->bufferRO());
-  float *ofm_ptr = reinterpret_cast<float *>(ofm_tensor->buffer());
-
-  nnfw::cker::TransposeConv(cker_param, cker_ifm_shape, ifm_ptr, cker_ker_shape, ker_ptr,
-                            cker_ofm_shape, ofm_ptr);
-}
-
-void invokeTransposeConv(const ExecEnv *env, const ir::Operation &node)
-{
-  const auto &tconv_node =
-      nnfw::misc::polymorphic_downcast<const ir::operation::TransposeConv &>(node);
-
-  const auto ifm_index = node.getInputs().at(ir::operation::TransposeConv::INPUT);
-  const auto ker_index = node.getInputs().at(ir::operation::TransposeConv::KERNEL);
-  const auto ofm_index = node.getOutputs().at(0);
-
-  const auto ifm_tensor = env->tensorAt(ifm_index);
-  const auto ker_tensor = env->tensorAt(ker_index);
-  const auto ofm_tensor = env->tensorAt(ofm_index);
-
-  const auto data_type = ifm_tensor->data_type();
-  if (data_type == ir::DataType::FLOAT32)
-  {
-    invoke(ifm_tensor, ker_tensor, ofm_tensor, tconv_node.param());
-  }
-  else
-  {
-    throw std::runtime_error{"Interp(TConv): Support float32 only"};
-  }
-}
-
-} // namespace transposeconv
-
-OpKernel *getTransposeConv()
-{
-  static OpKernel kernel = {prepareTransposeConv, invokeTransposeConv};
-  return &kernel;
-}
-
-} // namespace interp
-} // namespace exec
-} // namespace neurun
diff --git a/runtime/neurun/core/src/exec/interp/operations/UnaryActivations.cc b/runtime/neurun/core/src/exec/interp/operations/UnaryActivations.cc
deleted file mode 100644
index 116806fc4..000000000
--- a/runtime/neurun/core/src/exec/interp/operations/UnaryActivations.cc
+++ /dev/null
@@ -1,156 +0,0 @@
-/*
- * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <cmath>
-
-#include "OperationUtil.h"
-
-#include "exec/interp/Registration.h"
-
-#include "ir/operation/ReLU.h"
-#include "ir/operation/ReLU1.h"
-#include "ir/operation/ReLU6.h"
-#include "ir/operation/Tanh.h"
-
-namespace neurun
-{
-namespace exec
-{
-namespace interp
-{
-namespace
-{
-
-enum class ActivationType
-{
-  ReLU,
-  ReLU1,
-  ReLU6,
-  Tanh
-};
-
-void prepare(ExecEnv *env, const ir::Operation &node)
-{
-  const auto input_index = node.getInputs().at(0);
-  const auto output_index = node.getOutputs().at(0);
-
-  const auto input_tensor = env->tensorAt(input_index);
-
-  const auto output_info = env->graph().operands().at(output_index).info();
-  if (output_info.total_size() == 0)
-  {
-    // Output's shape and type is same with input
-    auto input_info = input_tensor->tensorInfo();
-    // We can handle already allocated (ex. model output)
-    env->allocateIfNeeded(output_index, input_info);
-  }
-  else
-  {
-    env->allocateIfNeeded(output_index, output_info);
-  }
-
-  const auto output_tensor = env->tensorAt(output_index);
-  // Check shape and type lhs is same with output
-  // TODO Util function to compare TensorInfo
-  if (input_tensor->data_type() != output_tensor->data_type())
-  {
-    throw std::runtime_error{"Interp(Activations): Invalid output type"};
-  }
-}
-
-template <ActivationType act_type>
-void evalFloat(const float *input_ptr, float *output_ptr, uint64_t num_elements)
-{
-  std::function<float(const float &)> fn = [](const float &) { return std::nanf(""); };
-  switch (act_type)
-  {
-    case ActivationType::ReLU:
-      fn = [](const float &in) { return std::max(0.f, in); };
-      break;
-    case ActivationType::ReLU1:
-      fn = [](const float &in) { return std::min(std::max(-1.f, in), 1.f); };
-      break;
-    case ActivationType::ReLU6:
-      fn = [](const float &in) { return std::min(std::max(0.f, in), 6.f); };
-      break;
-    case ActivationType::Tanh:
-      fn = [](const float &in) { return std::tanh(in); };
-      break;
-    default:
-      throw std::runtime_error{"Interp(Activations): NYI - Unsupported activation"};
-      break;
-  }
-
-  const float *input_end = input_ptr + num_elements;
-  for (; input_ptr < input_end; input_ptr++, output_ptr++)
-  {
-    *output_ptr = fn(*input_ptr);
-  }
-}
-
-template <ActivationType act_type> void invoke(const ExecEnv *env, const ir::Operation &node)
-{
-  const auto input_index = node.getInputs().at(0);
-  const auto output_index = node.getOutputs().at(0);
-
-  // Check lhs shape is same with rhs (with broadcast)
-  const auto input_tensor = env->tensorAt(input_index);
-  const auto output_tensor = env->tensorAt(output_index);
-
-  const auto data_type = input_tensor->data_type();
-  if (data_type == ir::DataType::FLOAT32)
-  {
-    uint64_t elements = input_tensor->num_elements();
-    const float *input_start = reinterpret_cast<const float *>(input_tensor->bufferRO());
-    float *out = reinterpret_cast<float *>(output_tensor->buffer());
-
-    evalFloat<act_type>(input_start, out, elements);
-  }
-  else
-  {
-    throw std::runtime_error{"Interp(ReLU6): NYI - Support float only"};
-  }
-}
-
-} // namespace
-
-OpKernel *getReLU()
-{
-  static OpKernel kernel = {prepare, invoke<ActivationType::ReLU>};
-  return &kernel;
-}
-
-OpKernel *getReLU1()
-{
-  static OpKernel kernel = {prepare, invoke<ActivationType::ReLU1>};
-  return &kernel;
-}
-
-OpKernel *getReLU6()
-{
-  static OpKernel kernel = {prepare, invoke<ActivationType::ReLU6>};
-  return &kernel;
-}
-
-OpKernel *getTanh()
-{
-  static OpKernel kernel = {prepare, invoke<ActivationType::Tanh>};
-  return &kernel;
-}
-
-} // namespace interp
-} // namespace exec
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/Graph.cc b/runtime/neurun/core/src/ir/Graph.cc
deleted file mode 100644
index a84ebb68b..000000000
--- a/runtime/neurun/core/src/ir/Graph.cc
+++ /dev/null
@@ -1,551 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/Graph.h"
-
-#include <algorithm>
-#include <bitset>
-#include <sstream>
-
-#include "util/logging.h"
-#include "verifier/Verifier.h"
-#include "cpp14/memory.h"
-#include "ir/operation/LowerInfo.h"
-#include "ir/operand/LowerInfo.h"
-#include "ir/operand/PermuteFactor.h"
-#include "ir/GraphIterator.h"
-#include "operand/Shape4DConvert.h"
-#include "compiler/BackendResolver.h"
-#include "backend/IConfig.h"
-#include "pass/ConstantInsertionPass.h"
-#include "pass/PermutationInsertionPass.h"
-#include "pass/PermutationEliminationPass.h"
-#include "pass/PermutationOperationPass.h"
-
-namespace neurun
-{
-namespace ir
-{
-
-Graph::Graph() = default;
-
-Graph::~Graph(void) = default;
-
-OperandIndex Graph::addOperand(const Shape &shape, const TypeInfo &type)
-{
-  return _operands.emplace(shape, type);
-}
-
-OperationIndex Graph::addOperation(std::unique_ptr<Operation> &&node)
-{
-  assert(isBuildingPhase());
-  return _operations.push(std::move(node));
-}
-
-void Graph::setOperandValue(const OperandIndex &ind, std::unique_ptr<Data> &&data)
-{
-  assert(isBuildingPhase());
-  assert(_operands.exist(ind));
-  _operands.at(ind).data(std::move(data));
-}
-
-void Graph::addInput(const OperandIndex &ind)
-{
-  assert(isBuildingPhase());
-  _inputs.append(ind);
-}
-
-void Graph::addOutput(const OperandIndex &ind)
-{
-  assert(isBuildingPhase());
-  _outputs.append(ind);
-}
-
-void Graph::finishBuilding(void)
-{
-  assert(isBuildingPhase());
-  _phase = Phase::MODEL;
-
-  // Initialize operand use-def
-  initializeUseDef();
-
-  // Call graph verifications for the MODEL phase
-  {
-    assert(verifier::DAGChecker().verify(*this));
-    assert(verifier::EdgeConsistencyChecker().verify(*this));
-  }
-}
-
-void Graph::lower(void)
-{
-  assert(_phase == Phase::MODEL);
-
-  _op_seqs = nnfw::cpp14::make_unique<Subgraphs>();
-
-  // Lower
-  {
-    // operand::LowerInfo holder
-    OperandIndexMap<std::unique_ptr<operand::LowerInfo>> operands_lower_info;
-
-    _operands.iterate([&](const OperandIndex &index, const Operand &object) {
-      operands_lower_info[index] =
-          nnfw::cpp14::make_unique<operand::LowerInfo>(operand::asShape4D(object.shape()));
-    });
-
-    _lower_info_map = nnfw::cpp14::make_unique<LowerInfoMap>();
-
-    // Make subgraphs while checking whether a node can be merged into a op_seq.
-    makeSubgraphs(operands_lower_info);
-
-    _op_seqs->iterate([&](const SubgraphIndex &, OpSequence &subg) {
-      assert(subg.operations().size() > 0);
-      std::reverse(std::begin(subg.operations()), std::end(subg.operations()));
-    });
-
-    _op_seqs->dump("merged and sorted operations without permutation");
-
-    pass::ConstantInsertionPass ci_pass(*this);
-    ci_pass.run();
-
-    // Set LowerInfo for each operand from the operand::LowerInfo holder
-    manipulateLowerInfo(operands_lower_info);
-
-    dumpLowerInfo();
-  }
-
-  // Run Permutation Passes
-  {
-    pass::PermutationOperationPass po_pass(*this);
-    po_pass.run();
-
-    pass::PermutationInsertionPass pi_pass(*this);
-    pi_pass.run();
-    // Implemented code no longer works.
-    // pass::PermutationEliminationPass pe_pass(*this);
-    // pe_pass.run();
-
-    // TODO merge perm subgraphs if possible
-    _op_seqs->dump("merged and sorted operations with permutation");
-  }
-
-  // Graph verifications for the LOWERED phase
-  {
-    assert(verifier::DAGChecker().verify(*this));
-    assert(verifier::EdgeConsistencyChecker().verify(*this));
-  }
-}
-
-void Graph::initializeUseDef()
-{
-  operations().iterate([&](const OperationIndex &index, const Operation &node) -> void {
-    auto outputs = node.getOutputs();
-    for (auto output : outputs)
-    {
-      operands().at(output).appendDef(index);
-    }
-
-    auto inputs = node.getInputs();
-    for (auto input : inputs)
-    {
-      operands().at(input).appendUse(index);
-    }
-  });
-}
-
-const operation::LowerInfo *Graph::getLowerInfo(const SubgraphIndex &subg_index) const
-{
-  if (!_lower_info_map)
-    return nullptr;
-  auto itr = _lower_info_map->operation.find(subg_index);
-  if (itr == _lower_info_map->operation.end())
-    return nullptr;
-  return itr->second.get();
-}
-
-void Graph::setLowerInfo(const SubgraphIndex &subg_index,
-                         std::unique_ptr<operation::LowerInfo> &&lower_info)
-{
-  assert(_lower_info_map);
-  _lower_info_map->operation.insert(std::make_pair(subg_index, std::move(lower_info)));
-}
-
-void Graph::removeLowerInfo(const SubgraphIndex &subg_index)
-{
-  auto &subg_lower_info = _lower_info_map->operation;
-  assert(subg_lower_info.find(subg_index) != subg_lower_info.end());
-  for (auto it = subg_lower_info.begin(); it != subg_lower_info.end(); ++it)
-  {
-    if (it->first == subg_index)
-    {
-      subg_lower_info.erase(it);
-      break;
-    }
-  }
-}
-
-const operand::LowerInfo *Graph::getLowerInfo(const OperandIndex &index) const
-{
-  if (!_lower_info_map)
-    return nullptr;
-  auto itr = _lower_info_map->operand.find(index);
-  if (itr == _lower_info_map->operand.end())
-    return nullptr;
-  return itr->second.get();
-}
-
-operand::LowerInfo *Graph::getLowerInfo(const OperandIndex &index)
-{
-  if (!_lower_info_map)
-    return nullptr;
-  auto itr = _lower_info_map->operand.find(index);
-  if (itr == _lower_info_map->operand.end())
-    return nullptr;
-  return itr->second.get();
-}
-
-void Graph::setLowerInfo(const OperandIndex &index,
-                         std::unique_ptr<operand::LowerInfo> &&lower_info)
-{
-  assert(_lower_info_map);
-  _lower_info_map->operand.insert(std::make_pair(index, std::move(lower_info)));
-}
-
-void Graph::removeLowerInfo(const OperandIndex &index) { _lower_info_map->operand.erase(index); }
-
-void Graph::makeSubgraphs(OperandIndexMap<std::unique_ptr<operand::LowerInfo>> &operands_lower_info)
-{
-  // if SUBG_MAX_NODE == 0, no limit on nodes of a op_seq
-  const int subg_max_node = util::getConfigInt(util::config::SUBG_MAX_NODE);
-  assert(subg_max_node >= 0);
-
-  bool is_profiling = util::getConfigBool(util::config::PROFILING_MODE);
-  OpSequence *subg = nullptr;
-  SubgraphIndex subg_index;
-
-  // NOTE: The below method appends nodes while making one op_seq if needed. If something better
-  // ways, happy to update this code.
-  PostDfsConstIterator{}.iterate(*this, [&](const OperationIndex &node_index,
-                                            const Operation &node) {
-    // LowerInfo for in/output operands
-    auto backend = _backend_resolver->getBackend(node_index);
-
-    // TODO How to get frontend layout of this node from IR
-    auto frontend_layout = Layout::NHWC;
-    auto backend_layout = frontend_layout;
-
-    // The layout of each backend should be set at another place
-    // TODO Change setting layout of each backend at another place
-    // TODO Remove getting id of backend
-    if (backend->config()->id() == "acl_cl" || backend->config()->id() == "acl_neon")
-    {
-      const std::string acl_layout_str = util::getConfigString(util::config::ACL_LAYOUT);
-      if (acl_layout_str == "NHWC")
-      {
-        backend_layout = Layout::NHWC;
-      }
-      else if (acl_layout_str == "NCHW")
-      {
-        backend_layout = Layout::NCHW;
-      }
-    }
-    else if (backend->config()->id() == "srcn")
-    {
-      const std::string ncnn_layout_str = util::getConfigString(util::config::NCNN_LAYOUT);
-      if (ncnn_layout_str == "NHWC")
-      {
-        backend_layout = Layout::NHWC;
-      }
-      else if (ncnn_layout_str == "NCHW")
-      {
-        backend_layout = Layout::NCHW;
-      }
-    }
-    else if (backend->config()->id() == "cpu")
-    {
-      backend_layout = Layout::NHWC;
-    }
-
-    for (auto operand : node.getInputs())
-    {
-      auto &&lower_info = operands_lower_info.at(operand);
-      lower_info->addUsePermuteFactor(operand::PermuteFactor{backend, backend_layout});
-    }
-    for (auto operand : node.getOutputs())
-    {
-      auto &&lower_info = operands_lower_info.at(operand);
-      lower_info->addDefPermuteFactor(operand::PermuteFactor{backend, backend_layout});
-    }
-
-    bool new_subg =
-        (subg == nullptr ||
-         (subg_max_node != 0 && subg->operations().size() >= static_cast<size_t>(subg_max_node)));
-
-    // for profiling each op_seq must contain just one node,
-    // so that we can measure a node separately
-    if (new_subg || is_profiling || !mergeable(subg_index, node_index, backend_layout))
-    {
-      auto new_subg_index = appendFreshSingleOpSubgraph(node_index, node, frontend_layout);
-
-      // OpSequence LowerInfo
-      setLowerInfo(new_subg_index,
-                   nnfw::cpp14::make_unique<operation::LowerInfo>(backend, backend_layout));
-
-      subg_index = new_subg_index;
-      subg = &(_op_seqs->at(new_subg_index));
-
-      VERBOSE(Lower) << "SUBG#" << subg_index.value() << " is created for "
-                     << "NODE#" << node_index.value() << "(" << node.name() << ")" << std::endl;
-    }
-    else
-    {
-      subg->appendOperation(node_index, node);
-      subg->setInputs(node.getInputs());
-
-      VERBOSE(Lower) << "SUBG#" << subg_index.value() << " merges "
-                     << "NODE#" << node_index.value() << "(" << node.name() << ")" << std::endl;
-    }
-  });
-}
-
-void Graph::manipulateLowerInfo(
-    OperandIndexMap<std::unique_ptr<operand::LowerInfo>> &operands_lower_info)
-{
-  const auto default_backend = backend::BackendManager::get().getDefault();
-  for (auto index : _inputs)
-  {
-    // Pick just any one from the uses, here the first one is chosen
-    // For the other uses, Permute operations will be inserted later
-    auto &&lower_info = operands_lower_info.at(index);
-    assert(lower_info->use_factors().size() > 0);
-    lower_info->addDefPermuteFactor(*lower_info->use_factors().begin());
-  }
-  for (auto index : _outputs)
-  {
-    auto &&lower_info = operands_lower_info.at(index);
-    if (_operands.at(index).isConstant())
-    {
-      lower_info->addDefPermuteFactor(operand::PermuteFactor{
-          default_backend,
-          Layout::NHWC // TODO Get frontend layout of this node from IR
-      });
-    }
-  }
-
-  // Set LowerInfo for each operand from the operand::LowerInfo holder
-  _operands.iterate([&](const OperandIndex &index, Operand &) {
-    setLowerInfo(index, std::move(operands_lower_info[index]));
-  });
-}
-
-void Graph::dumpLowerInfo()
-{
-  if (::neurun::util::logging::ctx.enabled() == false)
-    return;
-
-  std::map<uint32_t, std::string> dumps;
-
-  _operands.iterate([&](const OperandIndex &index, Operand &object) {
-    std::stringstream sstream;
-    if (!getLowerInfo(index)->def_factors().empty() || !getLowerInfo(index)->use_factors().empty())
-    {
-      auto factors_to_string = [](const operand::PermuteFactorSet &factors) {
-        std::string str;
-        for (auto factor : factors)
-        {
-          str += factor.backend()->config()->id();
-          str += "(" + to_string(factor.layout()) + ")";
-          str += " ";
-        }
-        return "{ " + str + "}";
-      };
-
-      auto operation_index_to_string = [](const OperationIndexList &operations) {
-        std::string str;
-        for (auto op : operations.list())
-        {
-          str += std::to_string(op.value());
-          str += " ";
-        }
-        return "{ " + str + "}";
-      };
-
-      const auto lower_info = getLowerInfo(index);
-      const auto &shape = object.shape();
-      const auto &lower_shape = lower_info->shape();
-      std::string def_ops = operation_index_to_string(object.getDef());
-      std::string use_ops = operation_index_to_string(object.getUses());
-      std::string def_layouts = factors_to_string(lower_info->def_factors());
-      std::string use_layouts = factors_to_string(lower_info->use_factors());
-      sstream << "Operand #" << index.value() << " LowerInfo" << std::endl;
-      sstream << "  - Shape           : { " << (shape.rank() > 0 ? shape.dim(0) : 0) << " "
-              << (shape.rank() > 1 ? shape.dim(1) : 0) << " "
-              << (shape.rank() > 2 ? shape.dim(2) : 0) << " "
-              << (shape.rank() > 3 ? shape.dim(3) : 0) << " "
-              << "}" << std::endl;
-      sstream << "  - Def Operations  : " << def_ops << std::endl;
-      sstream << "  - Use Operations  : " << use_ops << std::endl;
-      sstream << "  - Lower Info" << std::endl;
-      sstream << "    - 4D Shape (NHWC) : { " << lower_shape.n() << " " << lower_shape.h() << " "
-              << lower_shape.w() << " " << lower_shape.c() << " "
-              << "}" << std::endl;
-      sstream << "    - Def Backends    : " << def_layouts << std::endl;
-      sstream << "    - Use Backends    : " << use_layouts << std::endl;
-    }
-    dumps.emplace(index.value(), sstream.str());
-  });
-
-  for (const auto &e : dumps)
-  {
-    if (!e.second.empty())
-    {
-      VERBOSE(Lower) << e.second;
-    }
-  }
-}
-
-bool Graph::mergeable(const SubgraphIndex &subg_index, const OperationIndex &node_index,
-                      Layout layout)
-{
-  // Are they mergeable?
-  // 1. the same backend id and layout?
-  // 2. Is op_seq or node branched?
-  // 3. if 1 is true, the subg and a node are connected?
-  const auto &subg = _op_seqs->at(subg_index);
-  const auto &node = _operations.at(node_index);
-
-  // The same backend id and layout?
-  {
-    const auto subg_backend_layout = getLowerInfo(subg_index)->layout();
-    const auto &subg_backend_id = getLowerInfo(subg_index)->backend()->config()->id();
-    const auto &node_backend_id = _backend_resolver->getBackend(node_index)->config()->id();
-    VERBOSE(Lower) << "SUBG#" << subg_index.value() << " { " << subg_backend_id << "("
-                   << to_string(subg_backend_layout) << ") } "
-                   << " NODE#" << node_index.value() << " (" << node.name() << ") { "
-                   << node_backend_id << "(" << to_string(layout) << ") } " << std::endl;
-    if (subg_backend_id != node_backend_id || subg_backend_layout != layout)
-      return false;
-  }
-
-  // Branched?
-  {
-    std::unordered_set<OperationIndex> branched_set;
-
-    // Check for branching up
-    const auto &inputs = subg.getInputs();
-    for (const auto &input : inputs)
-    {
-      const auto &input_obj = _operands.at(input);
-      for (const auto &def : input_obj.getDef().list())
-      {
-        branched_set.insert(def);
-        if (branched_set.size() > 1)
-        {
-          return false;
-        }
-      }
-    }
-    branched_set.clear();
-
-    // Check for branching down
-    const auto &outputs = node.getOutputs();
-    for (const auto &output : outputs)
-    {
-      const auto &output_obj = _operands.at(output);
-      for (const auto &use : output_obj.getUses().list())
-      {
-        branched_set.insert(use);
-        if (branched_set.size() > 1)
-        {
-          return false;
-        }
-      }
-    }
-  }
-
-  // Connected?
-  // an input of one node is an output of the other node? or vice-versa?
-  {
-    const auto &node_inputs = node.getInputs();
-    const auto &node_outputs = node.getOutputs();
-
-    // subg's operations are in order so that we just check the first and the last
-    std::vector<Element> subg_ops{subg.operations()[0]};
-    if (subg.operations().size() > 1)
-      subg_ops.emplace_back(subg.operations()[subg.operations().size() - 1]);
-
-    for (const auto &elem : subg_ops)
-    {
-      const auto &n_index = elem.index;
-      const auto &n = *elem.node;
-
-      // node's output == subg's input?
-      const auto &n_inputs = n.getInputs();
-      for (auto input : n_inputs)
-      {
-        if (node_outputs.contains(input))
-        {
-          VERBOSE(Lower) << "SUBG#" << subg_index.value() << " 's NODE#" << n_index.value() << "("
-                         << n.name() << ") is connected to NODE#" << node_index.value() << "("
-                         << node.name() << ")" << std::endl;
-          return true;
-        }
-      }
-
-      // node's input == subg's output?
-      const auto &n_outputs = n.getOutputs();
-      for (auto output : n_outputs)
-      {
-        if (node_inputs.contains(output))
-        {
-          VERBOSE(Lower) << "SUBG#" << subg_index.value() << " 's NODE#" << n_index.value() << " ("
-                         << n.name() << ") is connected to NODE#" << node_index.value()
-                         << std::endl;
-          return true;
-        }
-      }
-    }
-
-    VERBOSE(Lower) << "SUBG#" << subg_index.value() << " is not connected to NODE#"
-                   << node_index.value() << "(" << node.name() << ")" << std::endl;
-  }
-
-  return false;
-}
-
-SubgraphIndex Graph::appendFreshSingleOpSubgraph(const OperationIndex &node_index,
-                                                 const Operation &node, Layout layout)
-{
-  // Create a fresh op_seq with one operation, and append it to subgraphs
-  // Create a fresh op_seq
-  auto subg = nnfw::cpp14::make_unique<OpSequence>(layout);
-
-  // Add an operation
-  subg->appendOperation(node_index, node);
-
-  // Update input/output
-  subg->setOutputs(node.getOutputs());
-  subg->setInputs(node.getInputs());
-
-  return _op_seqs->emplace(std::move(subg));
-}
-
-void Graph::setBackendResolver(std::unique_ptr<compiler::BackendResolver> &&br)
-{
-  _backend_resolver = std::move(br);
-}
-
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/GraphIterator.cc b/runtime/neurun/core/src/ir/GraphIterator.cc
deleted file mode 100644
index ce20787ae..000000000
--- a/runtime/neurun/core/src/ir/GraphIterator.cc
+++ /dev/null
@@ -1,84 +0,0 @@
-/*
- * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "GraphIterator.h"
-
-#include "ir/OperationIndexMap.h"
-#include "ir/Graph.h"
-
-namespace neurun
-{
-namespace ir
-{
-
-// Explicit instantiations to have implementation in the source file.
-
-template class DefaultIterator<true>;
-template class DefaultIterator<false>;
-
-template class PostDfsIterator<true>;
-template class PostDfsIterator<false>;
-
-//
-// Graph::DefaultIterator
-//
-
-template <bool is_const>
-void DefaultIterator<is_const>::iterate(GraphRef graph, const IterFn &fn) const
-{
-  graph.operations().iterate(
-      [&](const OperationIndex &index, NodeRef node) -> void { fn(index, node); });
-}
-
-//
-// Graph::PostDfsIterator
-//
-
-template <bool is_const>
-void PostDfsIterator<is_const>::iterate(GraphRef graph, const IterFn &fn) const
-{
-  assert(!graph.isBuildingPhase()); // Restrict iteration condition
-
-  OperationIndexMap<bool> visited;
-  graph.operations().iterate([&](const OperationIndex &index, NodeRef) { visited[index] = false; });
-
-  std::function<void(const OperationIndex &, NodeRef)> dfs_recursive =
-      [&](const OperationIndex &index, NodeRef node) -> void {
-    if (visited[index])
-      return;
-    visited[index] = true;
-
-    for (auto output : node.getOutputs())
-    {
-      const auto &operand = graph.operands().at(output);
-      for (const auto &use : operand.getUses().list())
-      {
-        dfs_recursive(use, graph.operations().at(use));
-      }
-    }
-
-    fn(index, node);
-  };
-
-  graph.operations().iterate(dfs_recursive);
-
-  // All of the operations(nodes) must have been visited.
-  assert(std::all_of(visited.begin(), visited.end(),
-                     [](const std::pair<const OperationIndex, bool> &v) { return v.second; }));
-}
-
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/GraphIterator.h b/runtime/neurun/core/src/ir/GraphIterator.h
deleted file mode 100644
index a5bf1c323..000000000
--- a/runtime/neurun/core/src/ir/GraphIterator.h
+++ /dev/null
@@ -1,74 +0,0 @@
-/*
- * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_GRAPH_ITERATOR_H__
-#define __NEURUN_IR_GRAPH_ITERATOR_H__
-
-#include <type_traits>
-
-#include "ir/Index.h"
-
-namespace neurun
-{
-namespace ir
-{
-
-class Graph;
-class Operation;
-
-template <bool is_const> class Iterator
-{
-public:
-  using GraphRef = typename std::conditional<is_const, const Graph &, Graph &>::type;
-  using IndexRef = const OperationIndex &;
-  using NodeRef = typename std::conditional<is_const, const Operation &, Operation &>::type;
-  using IterFn = std::function<void(IndexRef, NodeRef)>;
-
-public:
-  virtual ~Iterator() = default;
-  virtual void iterate(GraphRef graph, const IterFn &fn) const = 0;
-};
-
-template <bool is_const = false> class DefaultIterator final : public Iterator<is_const>
-{
-public:
-  using GraphRef = typename Iterator<is_const>::GraphRef;
-  using IndexRef = typename Iterator<is_const>::IndexRef;
-  using NodeRef = typename Iterator<is_const>::NodeRef;
-  using IterFn = typename Iterator<is_const>::IterFn;
-
-public:
-  void iterate(GraphRef graph, const IterFn &fn) const;
-};
-using DefaultConstIterator = DefaultIterator<true>;
-
-template <bool is_const = false> class PostDfsIterator final : public Iterator<is_const>
-{
-public:
-  using GraphRef = typename Iterator<is_const>::GraphRef;
-  using IndexRef = typename Iterator<is_const>::IndexRef;
-  using NodeRef = typename Iterator<is_const>::NodeRef;
-  using IterFn = typename Iterator<is_const>::IterFn;
-
-public:
-  void iterate(GraphRef graph, const IterFn &fn) const;
-};
-using PostDfsConstIterator = PostDfsIterator<true>;
-
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_GRAPH_ITERATOR_H__
diff --git a/runtime/neurun/core/src/ir/LayoutSet.cc b/runtime/neurun/core/src/ir/LayoutSet.cc
deleted file mode 100644
index 025ba45dc..000000000
--- a/runtime/neurun/core/src/ir/LayoutSet.cc
+++ /dev/null
@@ -1,66 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "LayoutSet.h"
-
-namespace neurun
-{
-namespace ir
-{
-
-LayoutSet::LayoutSet(std::initializer_list<Layout> layouts)
-{
-  for (auto layout : layouts)
-  {
-    _set.insert(layout);
-  }
-}
-
-LayoutSet LayoutSet::operator|(const LayoutSet &other) const
-{
-  auto ret = *this;
-  for (auto layout : other)
-  {
-    ret.add(layout);
-  }
-  return ret;
-}
-
-LayoutSet LayoutSet::operator&(const LayoutSet &other) const
-{
-  LayoutSet ret;
-  for (auto layout : other)
-  {
-    if (contains(layout))
-    {
-      ret.add(layout);
-    }
-  }
-  return ret;
-}
-
-LayoutSet LayoutSet::operator-(const LayoutSet &other) const
-{
-  auto ret = *this;
-  for (auto layout : other)
-  {
-    ret.remove(layout);
-  }
-  return ret;
-}
-
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/LayoutSet.h b/runtime/neurun/core/src/ir/LayoutSet.h
deleted file mode 100644
index e38ef3ce2..000000000
--- a/runtime/neurun/core/src/ir/LayoutSet.h
+++ /dev/null
@@ -1,58 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_IR_LAYOUT_SET_H__
-#define __NEURUN_IR_LAYOUT_SET_H__
-
-#include <initializer_list>
-#include <unordered_set>
-
-#include "ir/Layout.h"
-
-namespace neurun
-{
-namespace ir
-{
-
-class LayoutSet
-{
-public:
-  LayoutSet() = default;
-  LayoutSet(std::initializer_list<Layout> layouts);
-
-public:
-  void add(const Layout &layout) { _set.insert(layout); }
-  void remove(const Layout &layout) { _set.erase(layout); }
-  uint32_t size() const { return static_cast<uint32_t>(_set.size()); }
-  bool contains(const Layout &layout) const { return _set.find(layout) != _set.end(); }
-
-public:
-  LayoutSet operator|(const LayoutSet &other) const; // Union
-  LayoutSet operator&(const LayoutSet &other) const; // Intersect
-  LayoutSet operator-(const LayoutSet &other) const; // Minus
-
-public:
-  std::unordered_set<Layout>::const_iterator begin() const { return _set.begin(); }
-  std::unordered_set<Layout>::const_iterator end() const { return _set.end(); }
-
-private:
-  std::unordered_set<Layout> _set;
-};
-
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_IR_LAYOUT_SET_H__
diff --git a/runtime/neurun/core/src/ir/OpCode.cc b/runtime/neurun/core/src/ir/OpCode.cc
deleted file mode 100644
index e6552a275..000000000
--- a/runtime/neurun/core/src/ir/OpCode.cc
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/OpCode.h"
-
-#include <unordered_map>
-
-namespace neurun
-{
-namespace ir
-{
-
-const char *toString(OpCode opcode)
-{
-  static const std::unordered_map<OpCode, const char *> map{{OpCode::Invalid, "Invalid"},
-#define OP(Name) {OpCode::Name, #Name},
-#include "ir/Operations.lst"
-#undef OP
-                                                            {OpCode::COUNT, "COUNT"}};
-  return map.at(opcode);
-}
-
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/OpSequence.cc b/runtime/neurun/core/src/ir/OpSequence.cc
deleted file mode 100644
index 13a6cbe27..000000000
--- a/runtime/neurun/core/src/ir/OpSequence.cc
+++ /dev/null
@@ -1,83 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/OpSequence.h"
-#include "ir/OperationVisitor.h"
-#include <sstream>
-
-namespace neurun
-{
-namespace ir
-{
-
-OpSequence::OpSequence(Layout layout) : _layout{layout}
-{
-  // DO NOTHING
-}
-
-void OpSequence::accept(OperationVisitor &v) const { v.visit(*this); }
-
-// TODO: Impl Dumper instead of this method
-std::string OpSequence::getStr() const
-{
-  // "  OpSequence IN(xx,xx,xx) -> { op0, op1, op2 } -> OUT(yy,yy,yy)"
-  std::stringstream ss;
-  ss << "  OpSequence IN(";
-  for (const auto &index : getInputs())
-  {
-    ss << " " << index.value();
-  }
-  ss << " ) -> {";
-  for (const auto &elem : _operations)
-  {
-    ss << " " << elem.index.value() << "(" << elem.node->name() << ")";
-  }
-  ss << " } -> OUT(";
-  for (const auto &index : getOutputs())
-  {
-    ss << " " << index.value();
-  }
-  ss << " )";
-  return ss.str();
-}
-
-void OpSequence::remove(const OperationIndex &index)
-{
-  assert(exist(index));
-  for (auto it = _operations.cbegin(); it != _operations.cend(); ++it)
-  {
-    if (it->index == index)
-    {
-      _operations.erase(it);
-      break;
-    }
-  }
-}
-
-bool OpSequence::exist(const OperationIndex &index) const
-{
-  for (const auto &element : _operations)
-  {
-    if (element.index == index)
-    {
-      return true;
-    }
-  }
-  return false;
-}
-
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/Operand.cc b/runtime/neurun/core/src/ir/Operand.cc
deleted file mode 100644
index 335dd17b9..000000000
--- a/runtime/neurun/core/src/ir/Operand.cc
+++ /dev/null
@@ -1,70 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/Operand.h"
-
-namespace neurun
-{
-namespace ir
-{
-
-size_t Operand::operandSize(void) const
-{
-  const uint32_t ranks = shape().rank();
-  int32_t elements = 1;
-
-  for (uint32_t rank = 0; rank < ranks; rank++)
-  {
-    elements *= shape().dim(rank);
-  }
-
-  DataType type = typeInfo().type();
-  size_t element_size = sizeOfDataType(type);
-
-  // Value of type is matched with OperandCode enum in NeuralNetworks.h
-  return element_size * elements;
-}
-
-void Operand::appendUse(const OperationIndex &idx) { _uses.append(idx); }
-
-void Operand::removeUse(const OperationIndex &idx) { _uses.remove(idx); }
-
-void Operand::appendDef(const OperationIndex &idx)
-{
-  assert(!isConstant());
-  assert(_def.size() == 0);
-
-  _def.append(idx);
-}
-
-void Operand::removeDef(const OperationIndex &idx)
-{
-  assert(_def.contains(idx));
-
-  _def.remove(idx);
-}
-
-void Operand::parent_info(std::unique_ptr<operand::ParentInfo> &&parent_info)
-{
-  _parent_info = std::move(parent_info);
-}
-
-const operand::ParentInfo *Operand::parent_info() const { return _parent_info.get(); }
-
-operand::ParentInfo *Operand::parent_info() { return _parent_info.get(); }
-
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/OperandIndexSequence.cc b/runtime/neurun/core/src/ir/OperandIndexSequence.cc
deleted file mode 100644
index 302444125..000000000
--- a/runtime/neurun/core/src/ir/OperandIndexSequence.cc
+++ /dev/null
@@ -1,58 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/OperandIndexSequence.h"
-
-#include <algorithm>
-
-namespace neurun
-{
-namespace ir
-{
-
-OperandIndexSequence::OperandIndexSequence(std::initializer_list<OperandIndex> list) : _set(list)
-{
-  // DO NOTHING
-}
-
-OperandIndexSequence::OperandIndexSequence(std::initializer_list<int32_t> list)
-{
-  for (auto val : list)
-  {
-    _set.emplace_back(static_cast<uint32_t>(val));
-  }
-}
-
-OperandIndexSequence::OperandIndexSequence(std::initializer_list<uint32_t> list)
-{
-  for (auto val : list)
-  {
-    _set.emplace_back(val);
-  }
-}
-
-bool OperandIndexSequence::contains(const OperandIndex &index) const
-{
-  return std::find(_set.begin(), _set.end(), index) != _set.end();
-}
-
-void OperandIndexSequence::replace(const OperandIndex &from, const OperandIndex &to)
-{
-  std::replace(_set.begin(), _set.end(), from, to);
-}
-
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/Operation.cc b/runtime/neurun/core/src/ir/Operation.cc
deleted file mode 100644
index 3e4b606f2..000000000
--- a/runtime/neurun/core/src/ir/Operation.cc
+++ /dev/null
@@ -1,55 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/Operation.h"
-
-#include <cassert>
-
-namespace neurun
-{
-namespace ir
-{
-
-Operation::Operation(OperandConstraint input_constr, const OperandIndexSequence &inputs,
-                     const OperandIndexSequence &outputs)
-    : _input_constr{input_constr}, _inputs{inputs}, _outputs{outputs}
-{
-}
-
-Operation::Operation(OperandConstraint input_constr) : _input_constr{input_constr} {}
-
-Operation::~Operation() = default;
-
-void Operation::setInputs(const OperandIndexSequence &indexes)
-{
-  assert(_input_constr.check(indexes.size()));
-  _inputs = indexes;
-}
-
-void Operation::setOutputs(const OperandIndexSequence &indexes) { _outputs = indexes; }
-
-void Operation::replaceInput(const OperandIndex &from, const OperandIndex &to)
-{
-  _inputs.replace(from, to);
-}
-
-void Operation::replaceOutput(const OperandIndex &from, const OperandIndex &to)
-{
-  _outputs.replace(from, to);
-}
-
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/OperationIndexList.cc b/runtime/neurun/core/src/ir/OperationIndexList.cc
deleted file mode 100644
index 261cc5ce6..000000000
--- a/runtime/neurun/core/src/ir/OperationIndexList.cc
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/OperationIndexList.h"
-
-#include <algorithm>
-
-namespace neurun
-{
-namespace ir
-{
-
-OperationIndexList::OperationIndexList(std::initializer_list<OperationIndex> list) : _list(list)
-{
-  // DO NOTHING
-}
-
-bool OperationIndexList::contains(const OperationIndex &index) const
-{
-  return std::find(_list.begin(), _list.end(), index) != _list.end();
-}
-
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/Shape.cc b/runtime/neurun/core/src/ir/Shape.cc
deleted file mode 100644
index 2679f83c6..000000000
--- a/runtime/neurun/core/src/ir/Shape.cc
+++ /dev/null
@@ -1,85 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/Shape.h"
-#include "util/Utils.h"
-
-#include <cassert>
-#include <functional>
-#include <numeric>
-
-namespace neurun
-{
-namespace ir
-{
-
-FeatureShape Shape::asFeature(Layout layout) const
-{
-  assert(rank() == 4);
-
-  if (layout == Layout::NHWC)
-  {
-    // Feature Map in NHWC layout
-    //  - Dimension(0) -> Batch
-    //  - Dimension(1) -> Height
-    //  - Dimension(2) -> Width
-    //  - Dimension(3) -> Depth
-    const auto batch = dim(0);
-    const auto depth = dim(3);
-    const auto height = dim(1);
-    const auto width = dim(2);
-
-    return {batch, depth, height, width};
-  }
-  else if (layout == Layout::NCHW)
-  {
-    // Feature Map in NHWC layout
-    //  - Dimension(0) -> Batch
-    //  - Dimension(1) -> Depth
-    //  - Dimension(2) -> Height
-    //  - Dimension(3) -> Width
-    const auto batch = dim(0);
-    const auto depth = dim(1);
-    const auto height = dim(2);
-    const auto width = dim(3);
-
-    return {batch, depth, height, width};
-  }
-  else
-  {
-    throw std::runtime_error("Wrong Layout");
-  }
-}
-
-// Extended dimension is filled with 1.
-void Shape::extendRank(int to_rank)
-{
-  assert(to_rank - rank() >= 0);
-  _dimensions.insert(_dimensions.cbegin(), to_rank - rank(), 1);
-}
-
-uint64_t Shape::num_elements() const
-{
-  // All of the nodes must have non-negative dimension
-  assert(std::all_of(_dimensions.begin(), _dimensions.end(),
-                     [](const int32_t &v) { return (v >= 0); }));
-
-  return std::accumulate(_dimensions.cbegin(), _dimensions.cend(), UINT64_C(1),
-                         std::multiplies<uint64_t>());
-}
-
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/Subgraphs.cc b/runtime/neurun/core/src/ir/Subgraphs.cc
deleted file mode 100644
index 780fc8c28..000000000
--- a/runtime/neurun/core/src/ir/Subgraphs.cc
+++ /dev/null
@@ -1,87 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/Subgraphs.h"
-#include "util/logging.h"
-#include "cpp14/memory.h"
-
-#include <cassert>
-#include <string>
-
-namespace neurun
-{
-namespace ir
-{
-
-SubgraphIndex Subgraphs::emplace(const OperationIndex &index, const Operation &node, Layout layout)
-{
-  std::unique_ptr<OpSequence> subg = nnfw::cpp14::make_unique<OpSequence>(layout);
-  subg->appendOperation(index, node);
-  return push(std::move(subg));
-}
-
-SubgraphIndex Subgraphs::emplace(std::unique_ptr<OpSequence> &&subg)
-{
-  return push(std::move(subg));
-}
-
-bool Subgraphs::containsOperation(const OperationIndex &operation_index) const
-{
-  return findOperation(operation_index).valid();
-}
-
-SubgraphIndex Subgraphs::getOperation(const OperationIndex &operation_index) const
-{
-  SubgraphIndex ret = findOperation(operation_index);
-  assert(ret.valid());
-  return ret;
-}
-
-// TODO: Extract this into external helper function
-void Subgraphs::dump(const std::string &msg) const
-{
-  VERBOSE(Subgraphs) << "Subgraphs(" << msg << ")" << std::endl;
-  iterate([&](const SubgraphIndex &idx, const OpSequence &subg) {
-    VERBOSE(Subgraphs) << idx.value() << "] " << subg.getStr() << std::endl;
-  });
-}
-
-void Subgraphs::removeFromSubgraph(const OperationIndex &operation_index)
-{
-  const auto subg_index = findOperation(operation_index);
-  auto &subg = at(subg_index);
-  subg.remove(operation_index);
-  if (subg.size() == 0)
-  {
-    remove(subg_index);
-  }
-}
-
-SubgraphIndex Subgraphs::findOperation(const OperationIndex &operation_index) const
-{
-  SubgraphIndex ret;
-  iterate([&](const SubgraphIndex &index, const OpSequence &object) {
-    for (const auto &elem : object.operations())
-    {
-      if (elem.index == operation_index)
-        ret = index;
-    }
-  });
-  return ret;
-}
-
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/TypeInfo.cc b/runtime/neurun/core/src/ir/TypeInfo.cc
deleted file mode 100644
index 280146b51..000000000
--- a/runtime/neurun/core/src/ir/TypeInfo.cc
+++ /dev/null
@@ -1,47 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/TypeInfo.h"
-
-namespace neurun
-{
-namespace ir
-{
-
-bool operator==(const TypeInfo &lhs, const TypeInfo &rhs)
-{
-  if (lhs.type() != rhs.type())
-  {
-    return false;
-  }
-
-  if (lhs.offset() != rhs.offset())
-  {
-    return false;
-  }
-
-  if (lhs.scale() != rhs.scale())
-  {
-    return false;
-  }
-
-  return true;
-}
-
-bool operator!=(const TypeInfo &lhs, const TypeInfo &rhs) { return !(lhs == rhs); }
-
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/dumper/Dumper.cc b/runtime/neurun/core/src/ir/dumper/Dumper.cc
deleted file mode 100644
index ddfd1a47a..000000000
--- a/runtime/neurun/core/src/ir/dumper/Dumper.cc
+++ /dev/null
@@ -1,633 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "Dumper.h"
-
-#include <string>
-
-#include "util/logging.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace dumper
-{
-
-using namespace operation;
-
-void Dumper::visit(const Abs &node)
-{
-  VERBOSE(LIR) << "* Abs" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(Abs::Input::INPUT).value() << ")"
-               << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const Add &node)
-{
-  VERBOSE(LIR) << "* Add" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(Add::Input::LHS).value() << ", "
-               << node.getInputs().at(Add::Input::RHS).value() << ")" << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const ArgMax &node)
-{
-  VERBOSE(LIR) << "* ArgMax" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(ArgMax::Input::INPUT).value() << ")"
-               << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const AvgPool2D &node)
-{
-  VERBOSE(LIR) << "* AvgPool2D(Implicit)" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : IFM(" << node.getInputs().at(AvgPool2D::Input::INPUT).value() << ")"
-               << std::endl;
-  VERBOSE(LIR) << "  - Output : OFM(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const Cast &node)
-{
-  VERBOSE(LIR) << "* Cast" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(Cast::Input::INPUT).value() << ")"
-               << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const Comparison &node)
-{
-  VERBOSE(LIR) << "* Comparison" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(Comparison::Input::INPUT0).value()
-               << ", " << node.getInputs().at(Comparison::Input::INPUT1).value() << ")"
-               << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const Concat &node)
-{
-  VERBOSE(LIR) << "* Concat" << std::endl;
-  std::string inputs;
-  for (auto i : node.getInputs())
-  {
-    inputs += std::to_string(i.value()) + ",";
-  }
-  VERBOSE(LIR) << "  - Inputs : IFM(" << inputs << ")" << std::endl;
-  VERBOSE(LIR) << "  - Output : OFM(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const Conv2D &node)
-{
-  std::string padding_type =
-      node.param().padding.type == PaddingType::EXPLICIT ? "Explicit" : "Implicit";
-  VERBOSE(LIR) << "* Conv2D(" << padding_type << ")" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : IFM(" << node.getInputs().at(Conv2D::Input::INPUT).value()
-               << ") Kernel(" << node.getInputs().at(Conv2D::Input::KERNEL).value() << ") Bias("
-               << node.getInputs().at(Conv2D::Input::BIAS).value() << ")" << std::endl;
-  VERBOSE(LIR) << "  - Output : OFM(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const DepthToSpace &node)
-{
-  VERBOSE(LIR) << "* DepthToSpace" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(DepthToSpace::Input::INPUT).value()
-               << ")" << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const DepthwiseConv2D &node)
-{
-  std::string padding_type =
-      node.param().padding.type == PaddingType::EXPLICIT ? "Explicit" : "Implicit";
-  VERBOSE(LIR) << "* DepthwiseConv2D(" << padding_type << ")" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : IFM(" << node.getInputs().at(DepthwiseConv2D::Input::INPUT).value()
-               << ") Kernel(" << node.getInputs().at(DepthwiseConv2D::Input::KERNEL).value()
-               << ") Bias(" << node.getInputs().at(DepthwiseConv2D::Input::BIAS).value() << ")"
-               << std::endl;
-  VERBOSE(LIR) << "  - Output : OFM(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const Dequantize &node)
-{
-  VERBOSE(LIR) << "* Dequantize" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(Dequantize::Input::INPUT).value()
-               << ")" << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const Div &node)
-{
-  VERBOSE(LIR) << "* Div" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(Div::Input::LHS).value() << ", "
-               << node.getInputs().at(Div::Input::RHS).value() << ")" << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const EmbeddingLookup &node)
-{
-  VERBOSE(LIR) << "* EmbeddingLookup" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Lookups("
-               << node.getInputs().at(EmbeddingLookup::Input::LOOKUPS).value() << ") VALUES("
-               << node.getInputs().at(EmbeddingLookup::Input::VALUES).value() << ")" << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const Exp &node)
-{
-  VERBOSE(LIR) << "* Exp" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(Exp::Input::INPUT).value() << ")"
-               << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const Floor &node)
-{
-  VERBOSE(LIR) << "* Floor" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(Floor::Input::INPUT).value() << ")"
-               << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const FullyConnected &node)
-{
-  VERBOSE(LIR) << "* FullyConnected" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : IFM(" << node.getInputs().at(FullyConnected::Input::INPUT).value()
-               << ") Weight(" << node.getInputs().at(FullyConnected::Input::WEIGHT).value()
-               << ") Bias(" << node.getInputs().at(FullyConnected::Input::BIAS).value() << ")"
-               << std::endl;
-  VERBOSE(LIR) << "  - Output : OFM(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const Gather &node)
-{
-  VERBOSE(LIR) << "* Gather" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(Gather::Input::INPUT).value()
-               << ") Indices(" << node.getInputs().at(Gather::Input::INDICES).value() << ")"
-               << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const HashtableLookup &node)
-{
-  VERBOSE(LIR) << "* HashTableLookup" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Lookups("
-               << node.getInputs().at(HashtableLookup::Input::LOOKUPS).value() << ") Keys("
-               << node.getInputs().at(HashtableLookup::Input::KEYS).value() << ") Values("
-               << node.getInputs().at(HashtableLookup::Input::VALUES).value() << ")" << std::endl;
-  VERBOSE(LIR) << "  - Outputs : Output("
-               << node.getInputs().at(HashtableLookup::Output::OUTPUT).value() << ") Hits("
-               << node.getInputs().at(HashtableLookup::Output::HITS).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const InstanceNorm &node)
-{
-  VERBOSE(LIR) << "* InstanceNorm" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : IFM(" << node.getInputs().at(InstanceNorm::Input::INPUT).value()
-               << ") Gamma(" << node.getInputs().at(InstanceNorm::Input::GAMMA).value() << ") Beta("
-               << node.getInputs().at(InstanceNorm::Input::BETA).value() << ") Epsilon("
-               << node.param().epsilon << ")" << std::endl;
-  VERBOSE(LIR) << "  - Output : OFM(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const L2Normalization &node)
-{
-  VERBOSE(LIR) << "* L2Normalization" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input("
-               << node.getInputs().at(L2Normalization::Input::INPUT).value() << ")" << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const L2Pool2D &node)
-{
-  VERBOSE(LIR) << "* L2Pool2D" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(L2Pool2D::Input::INPUT).value()
-               << ")" << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const LocalResponseNormalization &node)
-{
-  VERBOSE(LIR) << "* LocalResponseNormalization" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input("
-               << node.getInputs().at(LocalResponseNormalization::Input::INPUT).value() << ")"
-               << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const LSTM &node)
-{
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(LSTM::Input::INPUT).value()
-               << ") Input To Input Weights("
-               << node.getInputs().at(LSTM::Input::INPUT_TO_INPUT_WEIGHTS).value()
-               << ") Input To Forget Weights("
-               << node.getInputs().at(LSTM::Input::INPUT_TO_FORGET_WEIGHTS).value()
-               << ") Input To Cell Weights("
-               << node.getInputs().at(LSTM::Input::INPUT_TO_CELL_WEIGHTS).value()
-               << ") Input To Output Weights("
-               << node.getInputs().at(LSTM::Input::INPUT_TO_OUTPUT_WEIGHTS).value()
-               << ") Recurrent To Input Weights("
-               << node.getInputs().at(LSTM::Input::RECURRENT_TO_INPUT_WEIGHTS).value()
-               << ") Recurrent To Forget Weights("
-               << node.getInputs().at(LSTM::Input::RECURRENT_TO_FORGET_WEIGHTS).value()
-               << ") Recurrent To Cell Weights("
-               << node.getInputs().at(LSTM::Input::RECURRENT_TO_CELL_WEIGHTS).value()
-               << ") Recurrent To Output Weights("
-               << node.getInputs().at(LSTM::Input::RECURRENT_TO_OUTPUT_WEIGHTS).value()
-               << ") Cell To Input Weights("
-               << node.getInputs().at(LSTM::Input::CELL_TO_INPUT_WEIGHTS).value()
-               << ") Cell To Forget Weights("
-               << node.getInputs().at(LSTM::Input::CELL_TO_FORGET_WEIGHTS).value()
-               << ") Cell To OUTPUT Weights("
-               << node.getInputs().at(LSTM::Input::CELL_TO_OUTPUT_WEIGHTS).value()
-               << ") Input Gate Bias(" << node.getInputs().at(LSTM::Input::INPUT_GATE_BIAS).value()
-               << ") Forget Gate Bias("
-               << node.getInputs().at(LSTM::Input::FORGET_GATE_BIAS).value() << ") Cell Bias("
-               << node.getInputs().at(LSTM::Input::CELL_BIAS).value() << ") Output Gate Bias("
-               << node.getInputs().at(LSTM::Input::OUTPUT_GATE_BIAS).value()
-               << ") Projection Weights("
-               << node.getInputs().at(LSTM::Input::PROJECTION_WEIGHTS).value()
-               << ") Projection Bias(" << node.getInputs().at(LSTM::Input::PROJECTION_BIAS).value()
-               << ") Output State In(" << node.getInputs().at(LSTM::Input::OUTPUT_STATE_IN).value()
-               << ") Cell State In(" << node.getInputs().at(LSTM::Input::CELL_STATE_IN).value()
-               << ")" << std::endl;
-  VERBOSE(LIR) << "  - Output : Scratch Buffer("
-               << node.getOutputs().at(LSTM::Output::SCRATCH_BUFFER).value()
-               << ") Output State Out("
-               << node.getInputs().at(LSTM::Output::OUTPUT_STATE_OUT).value() << ") Cell State Out("
-               << node.getInputs().at(LSTM::Output::CELL_STATE_OUT).value() << ") Output("
-               << node.getInputs().at(LSTM::Output::OUTPUT).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const LogicalAnd &node)
-{
-  VERBOSE(LIR) << "* LogicalAnd" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(LogicalAnd::Input::INPUT0).value()
-               << ", " << node.getInputs().at(LogicalAnd::Input::INPUT1).value() << ")"
-               << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const LogicalNot &node)
-{
-  VERBOSE(LIR) << "* LogicalNot" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(LogicalNot::Input::INPUT).value()
-               << ")" << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const LogicalOr &node)
-{
-  VERBOSE(LIR) << "* LogicalOr" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(LogicalOr::Input::INPUT0).value()
-               << ", " << node.getInputs().at(LogicalOr::Input::INPUT1).value() << ")" << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const Logistic &node)
-{
-  VERBOSE(LIR) << "* Logistic" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(Logistic::Input::INPUT).value()
-               << ")" << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const MaxPool2D &node)
-{
-  std::string padding_type =
-      node.param().padding.type == PaddingType::EXPLICIT ? "Explicit" : "Implicit";
-  VERBOSE(LIR) << "* MaxPool2D(" << padding_type << ")" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : IFM(" << node.getInputs().at(MaxPool2D::Input::INPUT).value() << ")"
-               << std::endl;
-  VERBOSE(LIR) << "  - Output : OFM(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const Mean &node)
-{
-  VERBOSE(LIR) << "* Mean" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(Mean::Input::INPUT).value() << ")"
-               << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const Mul &node)
-{
-  VERBOSE(LIR) << "* Mul" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(Mul::Input::LHS).value() << ", "
-               << node.getInputs().at(Mul::Input::RHS).value() << ")" << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const Neg &node)
-{
-  VERBOSE(LIR) << "* Neg" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(Neg::Input::INPUT).value() << ")"
-               << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const Pack &node)
-{
-  VERBOSE(LIR) << "* Pack" << std::endl;
-  std::string inputs;
-  const auto &input_indices = node.getInputs();
-  for (auto it = std::begin(input_indices); it != std::end(input_indices); ++it)
-  {
-    inputs += std::to_string(it->value());
-    if (std::next(it) != std::end(input_indices))
-      inputs += ", ";
-  }
-  VERBOSE(LIR) << "  - Inputs : Inputs(" << inputs << ")" << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const Permute &node)
-{
-  std::string permute_type = "Unknown";
-  switch (node.getPermuteType())
-  {
-    case Permute::Type::COPY:
-      permute_type = "Copy";
-      break;
-    case Permute::Type::NHWC_TO_NCHW:
-      permute_type = "NHWC to NCHW";
-      break;
-    case Permute::Type::NCHW_TO_NHWC:
-      permute_type = "NCHW to NHWC";
-      break;
-  }
-
-  VERBOSE(LIR) << "* Permute(" + permute_type + ")" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(0).value() << ")" << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const PReLU &node)
-{
-  VERBOSE(LIR) << "* PReLU" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(PReLU::Input::INPUT).value()
-               << ") Alpha(" << node.getInputs().at(PReLU::Input::ALPHA).value() << ")"
-               << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const ReduceMax &node)
-{
-  VERBOSE(LIR) << "* ReduceMax" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(ReduceMax::Input::INPUT).value()
-               << ")" << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const ReduceMin &node)
-{
-  VERBOSE(LIR) << "* ReduceMin" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(ReduceMin::Input::INPUT).value()
-               << ")" << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const ReduceSum &node)
-{
-  VERBOSE(LIR) << "* ReduceSum" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(ReduceSum::Input::INPUT).value()
-               << ")" << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const ReLU &node)
-{
-  VERBOSE(LIR) << "* ReLU" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(ReLU::Input::INPUT).value() << ")"
-               << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const ReLU1 &node)
-{
-  VERBOSE(LIR) << "* ReLU1" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(ReLU1::Input::INPUT).value() << ")"
-               << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const ReLU6 &node)
-{
-  VERBOSE(LIR) << "* ReLU6" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(ReLU6::Input::INPUT).value() << ")"
-               << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const Reshape &node)
-{
-  VERBOSE(LIR) << "* Reshape" << std::endl;
-  // TODO The shape index should be "node.getInputs().at(1).value()" but not valid for now
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(Reshape::Input::INPUT).value()
-               << ") Shape("
-               << "?"
-               << ")" << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const ResizeBilinear &node)
-{
-  VERBOSE(LIR) << "* ResizeBilinear" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(ResizeBilinear::Input::INPUT).value()
-               << ")" << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const RNN &node)
-{
-  VERBOSE(LIR) << "* RNN" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(RNN::Input::INPUT).value()
-               << ") Weights" << node.getInputs().at(RNN::Input::WEIGHTS).value()
-               << ") Recurrent Weights"
-               << node.getInputs().at(RNN::Input::RECURRENT_WEIGHTS).value() << ") Bias"
-               << node.getInputs().at(RNN::Input::BIAS).value() << ") Hidden State"
-               << node.getInputs().at(RNN::Input::HIDDEN_STATE_IN).value() << ")" << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(RNN::Output::OUTPUT).value()
-               << ") Hidden State" << node.getInputs().at(RNN::Output::HIDDEN_STATE_OUT).value()
-               << ")" << std::endl;
-}
-
-void Dumper::visit(const RSQRT &node)
-{
-  VERBOSE(LIR) << "* RSQRT" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(RSQRT::Input::INPUT).value() << ")"
-               << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const Softmax &node)
-{
-  VERBOSE(LIR) << "* Softmax" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(Softmax::Input::INPUT).value() << ")"
-               << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const SpaceToDepth &node)
-{
-  VERBOSE(LIR) << "* SpaceToDepth" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(SpaceToDepth::Input::INPUT).value()
-               << ")" << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const Split &node)
-{
-  VERBOSE(LIR) << "* Split" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(Split::Input::INPUT).value() << ")"
-               << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const SQRT &node)
-{
-  VERBOSE(LIR) << "* SQRT" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(SQRT::Input::INPUT).value() << ")"
-               << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const SquaredDifference &node)
-{
-  VERBOSE(LIR) << "* SquaredDifference" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input("
-               << node.getInputs().at(SquaredDifference::Input::LHS).value() << ", "
-               << node.getInputs().at(SquaredDifference::Input::RHS).value() << ")" << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const Squeeze &node)
-{
-  VERBOSE(LIR) << "* Squeeze" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(Squeeze::Input::INPUT).value() << ")"
-               << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const Slice &node)
-{
-  VERBOSE(LIR) << "* Slice" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(Slice::Input::INPUT).value() << ")"
-               << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const StridedSlice &node)
-{
-  VERBOSE(LIR) << "* StridedSlice" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(StridedSlice::Input::INPUT).value()
-               << ")" << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const Sub &node)
-{
-  VERBOSE(LIR) << "* Sub" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(Sub::Input::LHS).value() << ", "
-               << node.getInputs().at(Sub::Input::RHS).value() << ")" << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const Tanh &node)
-{
-  VERBOSE(LIR) << "* TanH" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(Tanh::Input::INPUT).value() << ")"
-               << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const TopKV2 &node)
-{
-  VERBOSE(LIR) << "* TopKV2" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(TopKV2::Input::INPUT).value() << ")"
-               << std::endl;
-  VERBOSE(LIR) << "  - Outputs : Values("
-               << node.getOutputs().at(TopKV2::Output::OUTPUT_VALUES).value() << ") Indices("
-               << node.getOutputs().at(TopKV2::Output::OUTPUT_INDICES).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const TransposeConv &node)
-{
-  std::string padding_type =
-      node.param().padding.type == PaddingType::EXPLICIT ? "Explicit" : "Implicit";
-  VERBOSE(LIR) << "* TransposeConv(" << padding_type << ")" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Output Shape("
-               << node.getInputs().at(TransposeConv::Input::OUTPUT_SHAPE).value() << ") KERNEL("
-               << node.getInputs().at(TransposeConv::Input::KERNEL).value() << ") IFM("
-               << node.getInputs().at(TransposeConv::Input::INPUT).value() << ")" << std::endl;
-  VERBOSE(LIR) << "  - Output : OFM(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const Transpose &node)
-{
-  VERBOSE(LIR) << "* Transpose" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(Transpose::Input::INPUT).value()
-               << ")" << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const Unpack &node)
-{
-  VERBOSE(LIR) << "* Unpack" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(Unpack::Input::INPUT).value() << ")"
-               << std::endl;
-  std::string outputs;
-  const auto &output_indices = node.getOutputs();
-  for (auto it = std::begin(output_indices); it != std::end(output_indices); ++it)
-  {
-    outputs += std::to_string(it->value());
-    if (std::next(it) != std::end(output_indices))
-      outputs += ", ";
-  }
-  VERBOSE(LIR) << "  - Outputs : Outputs(" << outputs << ")" << std::endl;
-}
-
-void Dumper::visit(const Min &node)
-{
-  VERBOSE(LIR) << "* Min" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(Min::Input::LHS).value() << ", "
-               << node.getInputs().at(Min::Input::RHS).value() << ")" << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const Max &node)
-{
-  VERBOSE(LIR) << "* Max" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : Input(" << node.getInputs().at(Max::Input::LHS).value() << ", "
-               << node.getInputs().at(Max::Input::RHS).value() << ")" << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-void Dumper::visit(const OneHot &node)
-{
-  VERBOSE(LIR) << "* OneHot" << std::endl;
-  VERBOSE(LIR) << "  - Inputs : "
-               << "Indices(" << node.getInputs().at(OneHot::Input::INDICES).value() << ") "
-               << "Depth(" << node.getInputs().at(OneHot::Input::DEPTH).value() << ") "
-               << "OnValue(" << node.getInputs().at(OneHot::Input::ON_VALUE).value() << ") "
-               << "OffValue(" << node.getInputs().at(OneHot::Input::OFF_VALUE).value() << ") "
-               << "Axis(" << node.param().axis << ") " << std::endl;
-  VERBOSE(LIR) << "  - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl;
-}
-
-} // namespace dumper
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/dumper/Dumper.h b/runtime/neurun/core/src/ir/dumper/Dumper.h
deleted file mode 100644
index 458f1c81f..000000000
--- a/runtime/neurun/core/src/ir/dumper/Dumper.h
+++ /dev/null
@@ -1,102 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_GRAPH_DUMPER_H__
-#define __NEURUN_GRAPH_DUMPER_H__
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace dumper
-{
-
-class Dumper : public OperationVisitor
-{
-public:
-  Dumper() = default;
-
-public:
-  void visit(const operation::Abs &) override;
-  void visit(const operation::Add &node) override;
-  void visit(const operation::ArgMax &) override;
-  void visit(const operation::AvgPool2D &node) override;
-  void visit(const operation::Cast &) override;
-  void visit(const operation::Comparison &) override;
-  void visit(const operation::Concat &node) override;
-  void visit(const operation::Conv2D &node) override;
-  void visit(const operation::DepthToSpace &) override;
-  void visit(const operation::DepthwiseConv2D &node) override;
-  void visit(const operation::Dequantize &) override;
-  void visit(const operation::Div &) override;
-  void visit(const operation::EmbeddingLookup &) override;
-  void visit(const operation::Exp &) override;
-  void visit(const operation::Floor &) override;
-  void visit(const operation::FullyConnected &node) override;
-  void visit(const operation::Gather &) override;
-  void visit(const operation::HashtableLookup &) override;
-  void visit(const operation::InstanceNorm &) override;
-  void visit(const operation::L2Normalization &) override;
-  void visit(const operation::L2Pool2D &) override;
-  void visit(const operation::LocalResponseNormalization &) override;
-  void visit(const operation::LogicalAnd &) override;
-  void visit(const operation::LogicalNot &) override;
-  void visit(const operation::LogicalOr &) override;
-  void visit(const operation::Logistic &) override;
-  void visit(const operation::LSTM &) override;
-  void visit(const operation::MaxPool2D &node) override;
-  void visit(const operation::Mean &) override;
-  void visit(const operation::Mul &) override;
-  void visit(const operation::Neg &) override;
-  void visit(const operation::Pack &) override;
-  void visit(const operation::Permute &node) override;
-  void visit(const operation::PReLU &) override;
-  void visit(const operation::ReduceMax &) override;
-  void visit(const operation::ReduceMin &) override;
-  void visit(const operation::ReduceSum &) override;
-  void visit(const operation::ReLU &) override;
-  void visit(const operation::ReLU1 &) override;
-  void visit(const operation::ReLU6 &) override;
-  void visit(const operation::Reshape &node) override;
-  void visit(const operation::ResizeBilinear &) override;
-  void visit(const operation::RNN &) override;
-  void visit(const operation::RSQRT &) override;
-  void visit(const operation::Softmax &node) override;
-  void visit(const operation::SpaceToDepth &) override;
-  void visit(const operation::Split &) override;
-  void visit(const operation::SQRT &) override;
-  void visit(const operation::SquaredDifference &) override;
-  void visit(const operation::Squeeze &) override;
-  void visit(const operation::Slice &) override;
-  void visit(const operation::StridedSlice &) override;
-  void visit(const operation::Sub &) override;
-  void visit(const operation::Tanh &) override;
-  void visit(const operation::TopKV2 &) override;
-  void visit(const operation::TransposeConv &) override;
-  void visit(const operation::Transpose &) override;
-  void visit(const operation::Unpack &) override;
-  void visit(const operation::Min &) override;
-  void visit(const operation::Max &) override;
-  void visit(const operation::OneHot &) override;
-};
-
-} // namespace dumper
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_GRAPH_DUMPER_H__
diff --git a/runtime/neurun/core/src/ir/operand/Shape4DConvert.h b/runtime/neurun/core/src/ir/operand/Shape4DConvert.h
deleted file mode 100644
index feffee89f..000000000
--- a/runtime/neurun/core/src/ir/operand/Shape4DConvert.h
+++ /dev/null
@@ -1,57 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_GRAPH_OPERAND_SHAPE4D_CONVERT_H__
-#define __NEURUN_GRAPH_OPERAND_SHAPE4D_CONVERT_H__
-
-#include "ir/operand/LowerInfo.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operand
-{
-
-inline LowerInfo::Shape4D asShape4D(const Shape &shape)
-{
-  switch (shape.rank())
-  {
-    case 0u:
-      return LowerInfo::Shape4D(1, 1, 1, 1);
-
-    case 1u:
-      return LowerInfo::Shape4D(1, 1, 1, shape.dim(0));
-
-    case 2u:
-      return LowerInfo::Shape4D(1, 1, shape.dim(0), shape.dim(1));
-
-    case 3u:
-      return LowerInfo::Shape4D(1, shape.dim(0), shape.dim(1), shape.dim(2));
-
-    case 4u:
-      return LowerInfo::Shape4D(shape.dim(0), shape.dim(1), shape.dim(2), shape.dim(3));
-
-    default:
-      throw "Unsupported rank > 4";
-  }
-}
-
-} // namespace operand
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_GRAPH_OPERAND_SHAPE4D_CONVERT_H__
diff --git a/runtime/neurun/core/src/ir/operation/Abs.cc b/runtime/neurun/core/src/ir/operation/Abs.cc
deleted file mode 100644
index 9506f83d2..000000000
--- a/runtime/neurun/core/src/ir/operation/Abs.cc
+++ /dev/null
@@ -1,39 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/Abs.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void Abs::accept(OperationVisitor &v) const { v.visit(*this); }
-
-Abs::Abs(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/Add.cc b/runtime/neurun/core/src/ir/operation/Add.cc
deleted file mode 100644
index a7c40c37a..000000000
--- a/runtime/neurun/core/src/ir/operation/Add.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/Add.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void Add::accept(OperationVisitor &v) const { v.visit(*this); }
-
-Add::Add(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-         const Param &param)
-    : Operation{OperandConstraint::createExact(2u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/ArgMax.cc b/runtime/neurun/core/src/ir/operation/ArgMax.cc
deleted file mode 100644
index 200abc7dd..000000000
--- a/runtime/neurun/core/src/ir/operation/ArgMax.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/ArgMax.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void ArgMax::accept(OperationVisitor &v) const { v.visit(*this); }
-
-ArgMax::ArgMax(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-               const Param &param)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/AvgPool2D.cc b/runtime/neurun/core/src/ir/operation/AvgPool2D.cc
deleted file mode 100644
index 21ec052eb..000000000
--- a/runtime/neurun/core/src/ir/operation/AvgPool2D.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/AvgPool2D.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void AvgPool2D::accept(OperationVisitor &v) const { v.visit(*this); }
-
-AvgPool2D::AvgPool2D(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-                     const Param &param)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/BatchToSpaceND.cc b/runtime/neurun/core/src/ir/operation/BatchToSpaceND.cc
deleted file mode 100644
index 042144c12..000000000
--- a/runtime/neurun/core/src/ir/operation/BatchToSpaceND.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/BatchToSpaceND.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void BatchToSpaceND::accept(OperationVisitor &v) const { v.visit(*this); }
-
-BatchToSpaceND::BatchToSpaceND(const OperandIndexSequence &inputs,
-                               const OperandIndexSequence &outputs)
-    : Operation{OperandConstraint::createExact(2u), inputs, outputs}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/Cast.cc b/runtime/neurun/core/src/ir/operation/Cast.cc
deleted file mode 100644
index 095225eca..000000000
--- a/runtime/neurun/core/src/ir/operation/Cast.cc
+++ /dev/null
@@ -1,39 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/Cast.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void Cast::accept(OperationVisitor &v) const { v.visit(*this); }
-
-Cast::Cast(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/Comparison.cc b/runtime/neurun/core/src/ir/operation/Comparison.cc
deleted file mode 100644
index 995d56764..000000000
--- a/runtime/neurun/core/src/ir/operation/Comparison.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/Comparison.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void Comparison::accept(OperationVisitor &v) const { v.visit(*this); }
-
-Comparison::Comparison(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-                       const Param &param)
-    : Operation{OperandConstraint::createExact(2u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/Concat.cc b/runtime/neurun/core/src/ir/operation/Concat.cc
deleted file mode 100644
index 1772da1fc..000000000
--- a/runtime/neurun/core/src/ir/operation/Concat.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/Concat.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void Concat::accept(OperationVisitor &v) const { v.visit(*this); }
-
-Concat::Concat(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-               const Param &param)
-    : Operation{OperandConstraint::createAtLeast(1u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/Conv2D.cc b/runtime/neurun/core/src/ir/operation/Conv2D.cc
deleted file mode 100644
index 505e916a9..000000000
--- a/runtime/neurun/core/src/ir/operation/Conv2D.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/Conv2D.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void Conv2D::accept(OperationVisitor &v) const { v.visit(*this); }
-
-Conv2D::Conv2D(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-               const Param &param)
-    : Operation{OperandConstraint::createExact(3u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/Custom.cc b/runtime/neurun/core/src/ir/operation/Custom.cc
deleted file mode 100644
index 67f36d588..000000000
--- a/runtime/neurun/core/src/ir/operation/Custom.cc
+++ /dev/null
@@ -1,46 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/Custom.h"
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void Custom::accept(OperationVisitor &v) const { v.visit(*this); }
-
-Custom::Custom(OperandConstraint input_constr, const OperandIndexSequence &inputs,
-               const OperandIndexSequence &outputs, std::string id, const Userdata &userdata)
-    : Operation{input_constr, inputs, outputs}, _id(std::move(id)), _userdata(userdata)
-{
-}
-
-const std::string &Custom::id() const { return _id; }
-
-const Custom::Userdata &Custom::userdata() const { return _userdata; }
-
-Custom::~Custom() { delete[] _userdata.data; }
-
-std::string Custom::name() const { return id(); }
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/DepthToSpace.cc b/runtime/neurun/core/src/ir/operation/DepthToSpace.cc
deleted file mode 100644
index fd1d1f1aa..000000000
--- a/runtime/neurun/core/src/ir/operation/DepthToSpace.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/DepthToSpace.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void DepthToSpace::accept(OperationVisitor &v) const { v.visit(*this); }
-
-DepthToSpace::DepthToSpace(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-                           const Param &param)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/DepthwiseConv2D.cc b/runtime/neurun/core/src/ir/operation/DepthwiseConv2D.cc
deleted file mode 100644
index ed76594a3..000000000
--- a/runtime/neurun/core/src/ir/operation/DepthwiseConv2D.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/DepthwiseConv2D.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void DepthwiseConv2D::accept(OperationVisitor &v) const { v.visit(*this); }
-
-DepthwiseConv2D::DepthwiseConv2D(const OperandIndexSequence &inputs,
-                                 const OperandIndexSequence &outputs, const Param &param)
-    : Operation{OperandConstraint::createExact(3u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/Dequantize.cc b/runtime/neurun/core/src/ir/operation/Dequantize.cc
deleted file mode 100644
index e99a59cb7..000000000
--- a/runtime/neurun/core/src/ir/operation/Dequantize.cc
+++ /dev/null
@@ -1,39 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/Dequantize.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void Dequantize::accept(OperationVisitor &v) const { v.visit(*this); }
-
-Dequantize::Dequantize(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/Div.cc b/runtime/neurun/core/src/ir/operation/Div.cc
deleted file mode 100644
index 484406ff3..000000000
--- a/runtime/neurun/core/src/ir/operation/Div.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/Div.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void Div::accept(OperationVisitor &v) const { v.visit(*this); }
-
-Div::Div(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-         const Param &param)
-    : Operation{OperandConstraint::createExact(2u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/EmbeddingLookup.cc b/runtime/neurun/core/src/ir/operation/EmbeddingLookup.cc
deleted file mode 100644
index 206e6bfaa..000000000
--- a/runtime/neurun/core/src/ir/operation/EmbeddingLookup.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/EmbeddingLookup.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void EmbeddingLookup::accept(OperationVisitor &v) const { v.visit(*this); }
-
-EmbeddingLookup::EmbeddingLookup(const OperandIndexSequence &inputs,
-                                 const OperandIndexSequence &outputs)
-    : Operation{OperandConstraint::createExact(2u), inputs, outputs}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/Exp.cc b/runtime/neurun/core/src/ir/operation/Exp.cc
deleted file mode 100644
index 3c0e0cf9b..000000000
--- a/runtime/neurun/core/src/ir/operation/Exp.cc
+++ /dev/null
@@ -1,39 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/Exp.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void Exp::accept(OperationVisitor &v) const { v.visit(*this); }
-
-Exp::Exp(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/Floor.cc b/runtime/neurun/core/src/ir/operation/Floor.cc
deleted file mode 100644
index 75373cd41..000000000
--- a/runtime/neurun/core/src/ir/operation/Floor.cc
+++ /dev/null
@@ -1,39 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/Floor.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void Floor::accept(OperationVisitor &v) const { v.visit(*this); }
-
-Floor::Floor(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/FullyConnected.cc b/runtime/neurun/core/src/ir/operation/FullyConnected.cc
deleted file mode 100644
index 9560c0593..000000000
--- a/runtime/neurun/core/src/ir/operation/FullyConnected.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/FullyConnected.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void FullyConnected::accept(OperationVisitor &v) const { v.visit(*this); }
-
-FullyConnected::FullyConnected(const OperandIndexSequence &inputs,
-                               const OperandIndexSequence &outputs, const Param &param)
-    : Operation{OperandConstraint::createExact(3u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/Gather.cc b/runtime/neurun/core/src/ir/operation/Gather.cc
deleted file mode 100644
index f98cef9ae..000000000
--- a/runtime/neurun/core/src/ir/operation/Gather.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/Gather.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void Gather::accept(OperationVisitor &v) const { v.visit(*this); }
-
-Gather::Gather(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-               const Param &param)
-    : Operation{OperandConstraint::createExact(2u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/HashtableLookup.cc b/runtime/neurun/core/src/ir/operation/HashtableLookup.cc
deleted file mode 100644
index ecb9d3195..000000000
--- a/runtime/neurun/core/src/ir/operation/HashtableLookup.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/HashtableLookup.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void HashtableLookup::accept(OperationVisitor &v) const { v.visit(*this); }
-
-HashtableLookup::HashtableLookup(const OperandIndexSequence &inputs,
-                                 const OperandIndexSequence &outputs)
-    : Operation{OperandConstraint::createExact(3u), inputs, outputs}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/InstanceNorm.cc b/runtime/neurun/core/src/ir/operation/InstanceNorm.cc
deleted file mode 100644
index 69e47abd4..000000000
--- a/runtime/neurun/core/src/ir/operation/InstanceNorm.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/InstanceNorm.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void InstanceNorm::accept(OperationVisitor &v) const { v.visit(*this); }
-
-InstanceNorm::InstanceNorm(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-                           const Param &param)
-    : Operation{OperandConstraint::createExact(3u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/L2Normalization.cc b/runtime/neurun/core/src/ir/operation/L2Normalization.cc
deleted file mode 100644
index 67085989e..000000000
--- a/runtime/neurun/core/src/ir/operation/L2Normalization.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/L2Normalization.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void L2Normalization::accept(OperationVisitor &v) const { v.visit(*this); }
-
-L2Normalization::L2Normalization(const OperandIndexSequence &inputs,
-                                 const OperandIndexSequence &outputs, const Param &param)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/L2Pool2D.cc b/runtime/neurun/core/src/ir/operation/L2Pool2D.cc
deleted file mode 100644
index 0815cb5ab..000000000
--- a/runtime/neurun/core/src/ir/operation/L2Pool2D.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/L2Pool2D.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void L2Pool2D::accept(OperationVisitor &v) const { v.visit(*this); }
-
-L2Pool2D::L2Pool2D(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-                   const Param &param)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/LSTM.cc b/runtime/neurun/core/src/ir/operation/LSTM.cc
deleted file mode 100644
index 58e2aa32e..000000000
--- a/runtime/neurun/core/src/ir/operation/LSTM.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/LSTM.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void LSTM::accept(OperationVisitor &v) const { v.visit(*this); }
-
-LSTM::LSTM(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-           const Param &param)
-    : Operation{OperandConstraint::createExact(23u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/LocalResponseNormalization.cc b/runtime/neurun/core/src/ir/operation/LocalResponseNormalization.cc
deleted file mode 100644
index dcba7f1cb..000000000
--- a/runtime/neurun/core/src/ir/operation/LocalResponseNormalization.cc
+++ /dev/null
@@ -1,41 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/LocalResponseNormalization.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void LocalResponseNormalization::accept(OperationVisitor &v) const { v.visit(*this); }
-
-LocalResponseNormalization::LocalResponseNormalization(const OperandIndexSequence &inputs,
-                                                       const OperandIndexSequence &outputs,
-                                                       const Param &param)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/LogicalAnd.cc b/runtime/neurun/core/src/ir/operation/LogicalAnd.cc
deleted file mode 100644
index 51f4f0ee0..000000000
--- a/runtime/neurun/core/src/ir/operation/LogicalAnd.cc
+++ /dev/null
@@ -1,39 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/LogicalAnd.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void LogicalAnd::accept(OperationVisitor &v) const { v.visit(*this); }
-
-LogicalAnd::LogicalAnd(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs)
-    : Operation{OperandConstraint::createExact(2u), inputs, outputs}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/LogicalNot.cc b/runtime/neurun/core/src/ir/operation/LogicalNot.cc
deleted file mode 100644
index 48c25142a..000000000
--- a/runtime/neurun/core/src/ir/operation/LogicalNot.cc
+++ /dev/null
@@ -1,39 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/LogicalNot.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void LogicalNot::accept(OperationVisitor &v) const { v.visit(*this); }
-
-LogicalNot::LogicalNot(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/LogicalOr.cc b/runtime/neurun/core/src/ir/operation/LogicalOr.cc
deleted file mode 100644
index 663b7deb5..000000000
--- a/runtime/neurun/core/src/ir/operation/LogicalOr.cc
+++ /dev/null
@@ -1,39 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/LogicalOr.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void LogicalOr::accept(OperationVisitor &v) const { v.visit(*this); }
-
-LogicalOr::LogicalOr(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs)
-    : Operation{OperandConstraint::createExact(2u), inputs, outputs}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/Logistic.cc b/runtime/neurun/core/src/ir/operation/Logistic.cc
deleted file mode 100644
index 3ed2f3453..000000000
--- a/runtime/neurun/core/src/ir/operation/Logistic.cc
+++ /dev/null
@@ -1,39 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/Logistic.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void Logistic::accept(OperationVisitor &v) const { v.visit(*this); }
-
-Logistic::Logistic(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/LowerInfo.cc b/runtime/neurun/core/src/ir/operation/LowerInfo.cc
deleted file mode 100644
index 6133be3f8..000000000
--- a/runtime/neurun/core/src/ir/operation/LowerInfo.cc
+++ /dev/null
@@ -1,34 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/LowerInfo.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-LowerInfo::LowerInfo(const backend::Backend *backend, Layout layout)
-    : _permute_factor{backend, layout}
-{
-  // DO NOTHING
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/Max.cc b/runtime/neurun/core/src/ir/operation/Max.cc
deleted file mode 100644
index be4bdd365..000000000
--- a/runtime/neurun/core/src/ir/operation/Max.cc
+++ /dev/null
@@ -1,39 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/Max.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void Max::accept(OperationVisitor &v) const { v.visit(*this); }
-
-Max::Max(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs)
-    : Operation{OperandConstraint::createExact(2u), inputs, outputs}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/MaxPool2D.cc b/runtime/neurun/core/src/ir/operation/MaxPool2D.cc
deleted file mode 100644
index 8f1b70cd6..000000000
--- a/runtime/neurun/core/src/ir/operation/MaxPool2D.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/MaxPool2D.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void MaxPool2D::accept(OperationVisitor &v) const { v.visit(*this); }
-
-MaxPool2D::MaxPool2D(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-                     const Param &param)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/Mean.cc b/runtime/neurun/core/src/ir/operation/Mean.cc
deleted file mode 100644
index 016b5dd85..000000000
--- a/runtime/neurun/core/src/ir/operation/Mean.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/Mean.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void Mean::accept(OperationVisitor &v) const { v.visit(*this); }
-
-Mean::Mean(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-           const Param &param)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/Min.cc b/runtime/neurun/core/src/ir/operation/Min.cc
deleted file mode 100644
index a864405dc..000000000
--- a/runtime/neurun/core/src/ir/operation/Min.cc
+++ /dev/null
@@ -1,39 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/Min.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void Min::accept(OperationVisitor &v) const { v.visit(*this); }
-
-Min::Min(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs)
-    : Operation{OperandConstraint::createExact(2u), inputs, outputs}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/Mul.cc b/runtime/neurun/core/src/ir/operation/Mul.cc
deleted file mode 100644
index 0b2d67a9d..000000000
--- a/runtime/neurun/core/src/ir/operation/Mul.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/Mul.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void Mul::accept(OperationVisitor &v) const { v.visit(*this); }
-
-Mul::Mul(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-         const Param &param)
-    : Operation{OperandConstraint::createExact(2u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/Neg.cc b/runtime/neurun/core/src/ir/operation/Neg.cc
deleted file mode 100644
index 65922d57c..000000000
--- a/runtime/neurun/core/src/ir/operation/Neg.cc
+++ /dev/null
@@ -1,39 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/Neg.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void Neg::accept(OperationVisitor &v) const { v.visit(*this); }
-
-Neg::Neg(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/OneHot.cc b/runtime/neurun/core/src/ir/operation/OneHot.cc
deleted file mode 100644
index 0ba3c9d60..000000000
--- a/runtime/neurun/core/src/ir/operation/OneHot.cc
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/OneHot.h"
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void OneHot::accept(OperationVisitor &v) const { v.visit(*this); }
-
-OneHot::OneHot(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-               const Param &param)
-    : Operation{OperandConstraint::createExact(4u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/PReLU.cc b/runtime/neurun/core/src/ir/operation/PReLU.cc
deleted file mode 100644
index b8555ccbd..000000000
--- a/runtime/neurun/core/src/ir/operation/PReLU.cc
+++ /dev/null
@@ -1,39 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/PReLU.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void PReLU::accept(OperationVisitor &v) const { v.visit(*this); }
-
-PReLU::PReLU(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs)
-    : Operation{OperandConstraint::createExact(2u), inputs, outputs}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/Pack.cc b/runtime/neurun/core/src/ir/operation/Pack.cc
deleted file mode 100644
index 412c744ea..000000000
--- a/runtime/neurun/core/src/ir/operation/Pack.cc
+++ /dev/null
@@ -1,33 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#include "ir/operation/Pack.h"
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-void Pack::accept(OperationVisitor &v) const { v.visit(*this); }
-Pack::Pack(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-           const Param &param)
-    : Operation{OperandConstraint::createAtLeast(3u), inputs, outputs}, _param{param}
-{
-}
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/Pad.cc b/runtime/neurun/core/src/ir/operation/Pad.cc
deleted file mode 100644
index a08be12a6..000000000
--- a/runtime/neurun/core/src/ir/operation/Pad.cc
+++ /dev/null
@@ -1,38 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/Pad.h"
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void Pad::accept(OperationVisitor &v) const { v.visit(*this); }
-
-Pad::Pad(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-         const Param &param)
-    : Operation{OperandConstraint::createExact(2u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/Permute.cc b/runtime/neurun/core/src/ir/operation/Permute.cc
deleted file mode 100644
index ec3d969c8..000000000
--- a/runtime/neurun/core/src/ir/operation/Permute.cc
+++ /dev/null
@@ -1,44 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/Permute.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void Permute::accept(OperationVisitor &v) const { v.visit(*this); }
-
-Permute::Permute(const OperandIndex &input, const OperandIndex &output,
-                 const backend::BackendContext *input_backend_ctx,
-                 const backend::BackendContext *output_backend_ctx, Type type, DataType data_type)
-    : Operation{OperandConstraint::createExact(1u)}, _param{input_backend_ctx, output_backend_ctx},
-      _type{type}, _dataType{data_type}
-{
-  setInputs({input});
-  setOutputs({output});
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/RNN.cc b/runtime/neurun/core/src/ir/operation/RNN.cc
deleted file mode 100644
index 8db5cbceb..000000000
--- a/runtime/neurun/core/src/ir/operation/RNN.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/RNN.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void RNN::accept(OperationVisitor &v) const { v.visit(*this); }
-
-RNN::RNN(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-         const Param &param)
-    : Operation{OperandConstraint::createExact(5u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/RSQRT.cc b/runtime/neurun/core/src/ir/operation/RSQRT.cc
deleted file mode 100644
index ec13b20ec..000000000
--- a/runtime/neurun/core/src/ir/operation/RSQRT.cc
+++ /dev/null
@@ -1,39 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/RSQRT.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void RSQRT::accept(OperationVisitor &v) const { v.visit(*this); }
-
-RSQRT::RSQRT(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/ReLU.cc b/runtime/neurun/core/src/ir/operation/ReLU.cc
deleted file mode 100644
index 6b3f7e72d..000000000
--- a/runtime/neurun/core/src/ir/operation/ReLU.cc
+++ /dev/null
@@ -1,39 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/ReLU.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void ReLU::accept(OperationVisitor &v) const { v.visit(*this); }
-
-ReLU::ReLU(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/ReLU1.cc b/runtime/neurun/core/src/ir/operation/ReLU1.cc
deleted file mode 100644
index d7b4e1b11..000000000
--- a/runtime/neurun/core/src/ir/operation/ReLU1.cc
+++ /dev/null
@@ -1,39 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/ReLU1.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void ReLU1::accept(OperationVisitor &v) const { v.visit(*this); }
-
-ReLU1::ReLU1(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/ReLU6.cc b/runtime/neurun/core/src/ir/operation/ReLU6.cc
deleted file mode 100644
index 245eb923f..000000000
--- a/runtime/neurun/core/src/ir/operation/ReLU6.cc
+++ /dev/null
@@ -1,39 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/ReLU6.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void ReLU6::accept(OperationVisitor &v) const { v.visit(*this); }
-
-ReLU6::ReLU6(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/ReduceMax.cc b/runtime/neurun/core/src/ir/operation/ReduceMax.cc
deleted file mode 100644
index b7ef2c5a9..000000000
--- a/runtime/neurun/core/src/ir/operation/ReduceMax.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/ReduceMax.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void ReduceMax::accept(OperationVisitor &v) const { v.visit(*this); }
-
-ReduceMax::ReduceMax(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-                     const Param &param)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/ReduceMin.cc b/runtime/neurun/core/src/ir/operation/ReduceMin.cc
deleted file mode 100644
index 84d7e0cc5..000000000
--- a/runtime/neurun/core/src/ir/operation/ReduceMin.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/ReduceMin.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void ReduceMin::accept(OperationVisitor &v) const { v.visit(*this); }
-
-ReduceMin::ReduceMin(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-                     const Param &param)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/ReduceSum.cc b/runtime/neurun/core/src/ir/operation/ReduceSum.cc
deleted file mode 100644
index 7e3b19bd6..000000000
--- a/runtime/neurun/core/src/ir/operation/ReduceSum.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/ReduceSum.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void ReduceSum::accept(OperationVisitor &v) const { v.visit(*this); }
-
-ReduceSum::ReduceSum(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-                     const Param &param)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/Reshape.cc b/runtime/neurun/core/src/ir/operation/Reshape.cc
deleted file mode 100644
index bae37e12f..000000000
--- a/runtime/neurun/core/src/ir/operation/Reshape.cc
+++ /dev/null
@@ -1,39 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/Reshape.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void Reshape::accept(OperationVisitor &v) const { v.visit(*this); }
-
-Reshape::Reshape(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/ResizeBilinear.cc b/runtime/neurun/core/src/ir/operation/ResizeBilinear.cc
deleted file mode 100644
index 55ae4815d..000000000
--- a/runtime/neurun/core/src/ir/operation/ResizeBilinear.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/ResizeBilinear.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void ResizeBilinear::accept(OperationVisitor &v) const { v.visit(*this); }
-
-ResizeBilinear::ResizeBilinear(const OperandIndexSequence &inputs,
-                               const OperandIndexSequence &outputs, const Param &param)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/SQRT.cc b/runtime/neurun/core/src/ir/operation/SQRT.cc
deleted file mode 100644
index 6c6daa3a0..000000000
--- a/runtime/neurun/core/src/ir/operation/SQRT.cc
+++ /dev/null
@@ -1,39 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/SQRT.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void SQRT::accept(OperationVisitor &v) const { v.visit(*this); }
-
-SQRT::SQRT(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/Slice.cc b/runtime/neurun/core/src/ir/operation/Slice.cc
deleted file mode 100644
index 88014d1e4..000000000
--- a/runtime/neurun/core/src/ir/operation/Slice.cc
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/Slice.h"
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void Slice::accept(OperationVisitor &v) const { v.visit(*this); }
-
-Slice::Slice(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-             const Param &param)
-    : Operation{OperandConstraint::createExact(3u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/Softmax.cc b/runtime/neurun/core/src/ir/operation/Softmax.cc
deleted file mode 100644
index 6b3a6b164..000000000
--- a/runtime/neurun/core/src/ir/operation/Softmax.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/Softmax.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void Softmax::accept(OperationVisitor &v) const { v.visit(*this); }
-
-Softmax::Softmax(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-                 const Param &param)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/SpaceToBatchND.cc b/runtime/neurun/core/src/ir/operation/SpaceToBatchND.cc
deleted file mode 100644
index a07453504..000000000
--- a/runtime/neurun/core/src/ir/operation/SpaceToBatchND.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/SpaceToBatchND.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void SpaceToBatchND::accept(OperationVisitor &v) const { v.visit(*this); }
-
-SpaceToBatchND::SpaceToBatchND(const OperandIndexSequence &inputs,
-                               const OperandIndexSequence &outputs)
-    : Operation{OperandConstraint::createExact(3u), inputs, outputs}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/SpaceToDepth.cc b/runtime/neurun/core/src/ir/operation/SpaceToDepth.cc
deleted file mode 100644
index ca16bd92f..000000000
--- a/runtime/neurun/core/src/ir/operation/SpaceToDepth.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/SpaceToDepth.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void SpaceToDepth::accept(OperationVisitor &v) const { v.visit(*this); }
-
-SpaceToDepth::SpaceToDepth(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-                           const Param &param)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/Split.cc b/runtime/neurun/core/src/ir/operation/Split.cc
deleted file mode 100644
index a4b15a9b2..000000000
--- a/runtime/neurun/core/src/ir/operation/Split.cc
+++ /dev/null
@@ -1,33 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#include "ir/operation/Split.h"
-#include <cassert>
-#include "ir/OperationVisitor.h"
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-void Split::accept(OperationVisitor &v) const { v.visit(*this); }
-Split::Split(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-             const Param &param)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}, _param{param}
-{
-}
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/SquaredDifference.cc b/runtime/neurun/core/src/ir/operation/SquaredDifference.cc
deleted file mode 100644
index 141fb7560..000000000
--- a/runtime/neurun/core/src/ir/operation/SquaredDifference.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/SquaredDifference.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void SquaredDifference::accept(OperationVisitor &v) const { v.visit(*this); }
-
-SquaredDifference::SquaredDifference(const OperandIndexSequence &inputs,
-                                     const OperandIndexSequence &outputs)
-    : Operation{OperandConstraint::createExact(2u), inputs, outputs}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/Squeeze.cc b/runtime/neurun/core/src/ir/operation/Squeeze.cc
deleted file mode 100644
index 22ee5763d..000000000
--- a/runtime/neurun/core/src/ir/operation/Squeeze.cc
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/Squeeze.h"
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void Squeeze::accept(OperationVisitor &v) const { v.visit(*this); }
-
-Squeeze::Squeeze(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-                 const Param &param)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}, _param(param)
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/StridedSlice.cc b/runtime/neurun/core/src/ir/operation/StridedSlice.cc
deleted file mode 100644
index f764dccc0..000000000
--- a/runtime/neurun/core/src/ir/operation/StridedSlice.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/StridedSlice.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void StridedSlice::accept(OperationVisitor &v) const { v.visit(*this); }
-
-StridedSlice::StridedSlice(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-                           const Param &param)
-    : Operation{OperandConstraint::createExact(3u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/Sub.cc b/runtime/neurun/core/src/ir/operation/Sub.cc
deleted file mode 100644
index 7d83e3d74..000000000
--- a/runtime/neurun/core/src/ir/operation/Sub.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/Sub.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void Sub::accept(OperationVisitor &v) const { v.visit(*this); }
-
-Sub::Sub(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-         const Param &param)
-    : Operation{OperandConstraint::createExact(2u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/Tanh.cc b/runtime/neurun/core/src/ir/operation/Tanh.cc
deleted file mode 100644
index a3125e947..000000000
--- a/runtime/neurun/core/src/ir/operation/Tanh.cc
+++ /dev/null
@@ -1,39 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/Tanh.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void Tanh::accept(OperationVisitor &v) const { v.visit(*this); }
-
-Tanh::Tanh(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/TopKV2.cc b/runtime/neurun/core/src/ir/operation/TopKV2.cc
deleted file mode 100644
index 6fabd34a3..000000000
--- a/runtime/neurun/core/src/ir/operation/TopKV2.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/TopKV2.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void TopKV2::accept(OperationVisitor &v) const { v.visit(*this); }
-
-TopKV2::TopKV2(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-               const Param &param)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/Transpose.cc b/runtime/neurun/core/src/ir/operation/Transpose.cc
deleted file mode 100644
index 74239b0f6..000000000
--- a/runtime/neurun/core/src/ir/operation/Transpose.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/Transpose.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void Transpose::accept(OperationVisitor &v) const { v.visit(*this); }
-
-Transpose::Transpose(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-                     const Param &param)
-    : Operation{OperandConstraint::createExact(2u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/TransposeConv.cc b/runtime/neurun/core/src/ir/operation/TransposeConv.cc
deleted file mode 100644
index 30664e974..000000000
--- a/runtime/neurun/core/src/ir/operation/TransposeConv.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ir/operation/TransposeConv.h"
-
-#include <cassert>
-
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-
-void TransposeConv::accept(OperationVisitor &v) const { v.visit(*this); }
-
-TransposeConv::TransposeConv(const OperandIndexSequence &inputs,
-                             const OperandIndexSequence &outputs, const Param &param)
-    : Operation{OperandConstraint::createExact(3u), inputs, outputs}, _param{param}
-{
-}
-
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/operation/Unpack.cc b/runtime/neurun/core/src/ir/operation/Unpack.cc
deleted file mode 100644
index 7c2c24892..000000000
--- a/runtime/neurun/core/src/ir/operation/Unpack.cc
+++ /dev/null
@@ -1,33 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#include "ir/operation/Unpack.h"
-#include "ir/OperationVisitor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace operation
-{
-void Unpack::accept(OperationVisitor &v) const { v.visit(*this); }
-Unpack::Unpack(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs,
-               const Param &param)
-    : Operation{OperandConstraint::createExact(1u), inputs, outputs}, _param{param}
-{
-}
-} // namespace operation
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/pass/ConstantInsertionPass.cc b/runtime/neurun/core/src/ir/pass/ConstantInsertionPass.cc
deleted file mode 100644
index 8f8ebff1b..000000000
--- a/runtime/neurun/core/src/ir/pass/ConstantInsertionPass.cc
+++ /dev/null
@@ -1,104 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ConstantInsertionPass.h"
-
-#include "backend/Backend.h"
-#include <ir/Graph.h>
-#include "ir/operand/Shape4DConvert.h"
-#include <util/Utils.h>
-
-namespace neurun
-{
-namespace ir
-{
-namespace pass
-{
-
-void ConstantInsertionPass::callback(const OperationIndex &node_index, Operation &node)
-{
-  const auto &subgraph_index = _graph.subgraphs().getOperation(node_index);
-  const auto subg_lower_info = _graph.getLowerInfo(subgraph_index);
-  const auto backend = subg_lower_info->backend();
-  const auto layout = subg_lower_info->layout();
-  const auto factor = operand::PermuteFactor{backend, layout};
-
-  for (const auto input : node.getInputs())
-  {
-    auto &object = _graph.operands().at(input);
-
-    if (object.isConstant())
-    {
-      const auto key = ReplaceKey{input, factor};
-      if (_replace_operands_map.count(key) == 0)
-      {
-        auto new_object = object;
-        // TODO Remove const_case
-        const_cast<std::list<OperationIndex> &>(new_object.getDef().list()).clear();
-        const_cast<std::list<OperationIndex> &>(new_object.getUses().list()).clear();
-        const auto new_index = _graph.operands().emplace(new_object);
-        _replace_operands_map[key] = new_index;
-
-        _graph.setLowerInfo(new_index, nnfw::cpp14::make_unique<operand::LowerInfo>(
-                                           operand::asShape4D(new_object.shape())));
-        _graph.getLowerInfo(new_index)->addDefPermuteFactor(factor);
-      }
-
-      const auto replaced_input = _replace_operands_map[key];
-      // Update op_seq
-      if (_graph.subgraphs().at(subgraph_index).getInputs().contains(input))
-      {
-        _graph.subgraphs().at(subgraph_index).replaceInput(input, replaced_input);
-      }
-
-      // Update node
-      node.replaceInput(input, replaced_input);
-
-      // Update operand
-      auto &replaced_object = _graph.operands().at(replaced_input);
-      replaced_object.appendUse(node_index);
-
-      // Update lower_info
-      auto replaced_lower_info = _graph.getLowerInfo(replaced_input);
-      replaced_lower_info->addUsePermuteFactor(factor);
-
-      // Remove this node from def and uses of origin operand
-      if (object.getDef().contains(node_index))
-      {
-        object.removeDef(node_index);
-      }
-      object.removeUse(node_index);
-
-      // Remove origin operand
-      if (object.getDef().size() == 0 && object.getUses().size() == 0)
-      {
-        _graph.removeOperand(input);
-        _graph.removeLowerInfo(input);
-      }
-    }
-  }
-
-  // Now this runtime does not support the node making output as constant
-  for (const auto &output : node.getOutputs())
-  {
-    UNUSED_RELEASE(output);
-    assert(!_graph.operands().at(output).isConstant());
-  }
-}
-
-} // namespace pass
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/pass/ConstantInsertionPass.h b/runtime/neurun/core/src/ir/pass/ConstantInsertionPass.h
deleted file mode 100644
index 40476b20e..000000000
--- a/runtime/neurun/core/src/ir/pass/ConstantInsertionPass.h
+++ /dev/null
@@ -1,75 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_GRAPH_PASS_CONSTANT_INSERTION_PASS_H__
-#define __NEURUN_GRAPH_PASS_CONSTANT_INSERTION_PASS_H__
-
-#include <ir/operand/PermuteFactor.h>
-#include <ir/Index.h>
-#include "OperationPass.h"
-#include <unordered_map>
-#include <utility>
-
-namespace neurun
-{
-namespace ir
-{
-namespace pass
-{
-
-class ConstantInsertionPass : public OperationPass
-{
-public:
-  using OperationPass::OperationPass;
-
-public:
-  std::string id() final { return "ConstantInsertionPass"; }
-
-public:
-  void callback(const OperationIndex &index, Operation &node) final;
-
-private:
-  struct ReplaceKey
-  {
-    OperandIndex index;
-    operand::PermuteFactor factor;
-
-    bool operator==(const ReplaceKey &other) const
-    {
-      return index == other.index && factor == other.factor;
-    }
-  };
-
-  /**
-   * @brief Structure that provides hash function of ReplaceKey
-   */
-  struct KeyHasher
-  {
-    std::size_t operator()(const ReplaceKey &key) const noexcept
-    {
-      using std::hash;
-      return hash<OperandIndex>()(key.index) ^ (hash<operand::PermuteFactor>()(key.factor) << 1);
-    }
-  };
-
-  std::unordered_map<ReplaceKey, OperandIndex, KeyHasher> _replace_operands_map;
-};
-
-} // namespace pass
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_GRAPH_PASS_CONSTANT_INSERTION_PASS_H__
diff --git a/runtime/neurun/core/src/ir/pass/OperandPass.cc b/runtime/neurun/core/src/ir/pass/OperandPass.cc
deleted file mode 100644
index f31d0d850..000000000
--- a/runtime/neurun/core/src/ir/pass/OperandPass.cc
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "OperandPass.h"
-
-#include "ir/Graph.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace pass
-{
-
-void OperandPass::run()
-{
-  _graph.operands().iterate(
-      [&](const OperandIndex &index, Operand &object) { callback(index, object); });
-}
-
-} // namespace pass
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/pass/OperandPass.h b/runtime/neurun/core/src/ir/pass/OperandPass.h
deleted file mode 100644
index c9fbf541d..000000000
--- a/runtime/neurun/core/src/ir/pass/OperandPass.h
+++ /dev/null
@@ -1,53 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_GRAPH_PASS_OPERAND_PASS_H__
-#define __NEURUN_GRAPH_PASS_OPERAND_PASS_H__
-
-#include "Pass.h"
-#include "ir/Index.h"
-
-namespace neurun
-{
-namespace ir
-{
-class Operand;
-} // namespace ir
-} // namespace neurun
-
-namespace neurun
-{
-namespace ir
-{
-namespace pass
-{
-
-class OperandPass : public Pass
-{
-public:
-  using Pass::Pass;
-
-public:
-  std::string id() override = 0;
-  void run() override final;
-  virtual void callback(const OperandIndex &i, Operand &o) = 0;
-};
-
-} // namespace pass
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_GRAPH_PASS_OPERAND_PASS_H__
diff --git a/runtime/neurun/core/src/ir/pass/OperationPass.cc b/runtime/neurun/core/src/ir/pass/OperationPass.cc
deleted file mode 100644
index c9438ee39..000000000
--- a/runtime/neurun/core/src/ir/pass/OperationPass.cc
+++ /dev/null
@@ -1,38 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "OperationPass.h"
-
-#include "ir/Index.h"
-#include "ir/Operation.h"
-#include "ir/Graph.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace pass
-{
-
-void OperationPass::run()
-{
-  _graph.operations().iterate(
-      [&](const OperationIndex &index, Operation &node) { callback(index, node); });
-}
-
-} // namespace pass
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/pass/OperationPass.h b/runtime/neurun/core/src/ir/pass/OperationPass.h
deleted file mode 100644
index 4b7de7109..000000000
--- a/runtime/neurun/core/src/ir/pass/OperationPass.h
+++ /dev/null
@@ -1,76 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/**
- * @file  OperationPass.h
- * @brief This file contains OperationPass class
- */
-
-#ifndef __NEURUN_GRAPH_PASS_OPERATION_PASS_H__
-#define __NEURUN_GRAPH_PASS_OPERATION_PASS_H__
-
-#include "Pass.h"
-#include "ir/Index.h"
-
-namespace neurun
-{
-namespace ir
-{
-class Operation;
-} // namespace ir
-} // namespace neurun
-
-namespace neurun
-{
-namespace ir
-{
-namespace pass
-{
-
-/**
- * @brief  Class to iterate over operations and calls callback() method
- */
-class OperationPass : public Pass
-{
-public:
-  using Pass::Pass;
-
-public:
-  /**
-   * @brief Returns string id for this pass. Same with class name.
-   *
-   * @return string id
-   */
-  std::string id() override = 0;
-
-  /**
-   * @brief Be called for all nodes of graph.
-   * @param index is the index of a node in graph
-   * @param node is the node in graph
-   */
-  virtual void callback(const OperationIndex &index, Operation &node) = 0;
-
-  /**
-   * @brief Run the pass
-   */
-  void run() final;
-};
-
-} // namespace pass
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_GRAPH_PASS_OPERATION_PASS_H__
diff --git a/runtime/neurun/core/src/ir/pass/Pass.h b/runtime/neurun/core/src/ir/pass/Pass.h
deleted file mode 100644
index 0aa0f36a6..000000000
--- a/runtime/neurun/core/src/ir/pass/Pass.h
+++ /dev/null
@@ -1,55 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_GRAPH_PASS_PASS_H__
-#define __NEURUN_GRAPH_PASS_PASS_H__
-
-#include <string>
-
-namespace neurun
-{
-namespace ir
-{
-class Graph;
-} // namespace ir
-} // namespace neurun
-
-namespace neurun
-{
-namespace ir
-{
-namespace pass
-{
-
-class Pass
-{
-public:
-  Pass(Graph &graph) : _graph{graph} {}
-  virtual ~Pass() = default;
-
-public:
-  virtual std::string id() = 0;
-  virtual void run() = 0;
-
-protected:
-  Graph &_graph;
-};
-
-} // namespace pass
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_GRAPH_PASS_PASS_H__
diff --git a/runtime/neurun/core/src/ir/pass/PermutationEliminationPass.cc b/runtime/neurun/core/src/ir/pass/PermutationEliminationPass.cc
deleted file mode 100644
index 71f3d7e82..000000000
--- a/runtime/neurun/core/src/ir/pass/PermutationEliminationPass.cc
+++ /dev/null
@@ -1,195 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "PermutationEliminationPass.h"
-
-#include "ir/Operand.h"
-#include "ir/operand/LowerInfo.h"
-#include "ir/Graph.h"
-#include "backend/IConfig.h"
-#include "util/logging.h"
-#include "compiler/BackendResolver.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace pass
-{
-void PermutationEliminationPass::callback(const OperandIndex &inp_index, Operand &object)
-{
-  if (_graph.getInputs().contains(inp_index))
-  {
-    eliminateInput(inp_index, object);
-  }
-  else if (_graph.getOutputs().contains(inp_index))
-  {
-    eliminateOutput(inp_index, object);
-  }
-}
-
-void PermutationEliminationPass::eliminateInput(const OperandIndex &inp_index, Operand &object)
-{
-  auto &model_inputs = _graph.getInputs();
-
-  // get uses of the model's given input
-  auto uses = object.getUses();
-
-  // input must be used just by permutation
-  if (uses.size() != 1)
-  {
-    return;
-  }
-
-  for (auto input_use : uses.list())
-  {
-    auto &perm_operation = _graph.operations().at(input_use);
-    auto perm_inputs = perm_operation.getInputs();
-
-    auto perm_outputs = perm_operation.getOutputs();
-
-    if (!isPermuteLayerToEliminate(perm_inputs, perm_outputs, true))
-    {
-      return;
-    }
-
-    assert(perm_inputs.at(0) == inp_index);
-
-    VERBOSE(PermutationEliminationPass::EliminateInput) << "remove NHWC_TO_NCHW permutation\n";
-
-    // set model's new input, which was output of permutation
-    model_inputs.replace(inp_index, perm_outputs.at(0));
-
-    // remove model's input, which is also input of permutation
-    _graph.removeOperand(inp_index);
-
-    // remove permutation operation
-    assert(_graph.subgraphs().containsOperation(input_use));
-    auto subg_idx = _graph.subgraphs().getOperation(input_use);
-    _graph.subgraphs().remove(subg_idx);
-    _graph.operations().remove(input_use);
-
-    VERBOSE(PermutationEliminationPass::EliminateInput)
-        << inp_index.value() << " is model's input and is removed. New input is "
-        << perm_outputs.at(0).value() << "\n"
-        << input_use.value() << " is removed permutation operation\n";
-  }
-}
-
-void PermutationEliminationPass::eliminateOutput(const OperandIndex &out_index, Operand &object)
-{
-  auto &model_outputs = _graph.getOutputs();
-
-  // get defs of the model's given output
-  auto defs = object.getDef();
-
-  // output must use just permutation
-  if (defs.size() != 1)
-  {
-    return;
-  }
-
-  for (auto output_def : defs.list())
-  {
-    auto &perm_operation = _graph.operations().at(output_def);
-    auto perm_outputs = perm_operation.getOutputs();
-
-    auto perm_inputs = perm_operation.getInputs();
-    if (!isPermuteLayerToEliminate(perm_inputs, perm_outputs, false))
-    {
-      return;
-    }
-
-    assert(perm_outputs.at(0) == out_index);
-
-    VERBOSE(PermutationEliminationPass::EliminateOutput) << "remove NCHW_TO_NHWC permutation\n";
-
-    // Update operations' output that is used by permute operand
-    for (auto perm_input_index : perm_inputs)
-    {
-      auto &perm_input_operand = _graph.operands().at(perm_input_index);
-      perm_input_operand.removeUse(output_def);
-    }
-
-    // set model's new output, which was input of permutation
-    model_outputs.replace(out_index, perm_inputs.at(0));
-
-    // remove model's output, which is also output of permutation
-    _graph.removeOperand(out_index);
-
-    // remove permutation operation
-    assert(_graph.subgraphs().containsOperation(output_def));
-    auto subg_idx = _graph.subgraphs().getOperation(output_def);
-    _graph.subgraphs().remove(subg_idx);
-    _graph.operations().remove(output_def);
-
-    VERBOSE(PermutationEliminationPass::EliminateOutput)
-        << out_index.value() << " is model's output and is removed. New output is "
-        << perm_inputs.at(0).value() << "\n"
-        << output_def.value() << " is removed permutation operation\n";
-  }
-}
-
-bool PermutationEliminationPass::isPermuteLayerToEliminate(const OperandIndexSequence &inp_indexes,
-                                                           const OperandIndexSequence &out_indexes,
-                                                           bool is_for_model_input)
-{
-  auto input_def_factors = _graph.getLowerInfo(inp_indexes.at(0))->def_factors();
-  auto output_def_factors = _graph.getLowerInfo(out_indexes.at(0))->def_factors();
-
-  auto input_layout = input_def_factors.getOnlyElement().layout();
-  auto output_layout = output_def_factors.getOnlyElement().layout();
-
-  if (input_def_factors.size() != 1 || output_def_factors.size() != 1)
-  {
-    return false;
-  }
-
-  // all operands' factor must be the same
-  for (auto index : inp_indexes)
-  {
-    auto op_factor_set = _graph.getLowerInfo(index)->def_factors();
-    if (op_factor_set.size() != 1 ||
-        input_layout != _graph.getLowerInfo(index)->def_factors().getOnlyElement().layout())
-    {
-      return false;
-    }
-  }
-  // all operands' factor must be the same
-  for (auto index : out_indexes)
-  {
-    auto op_factor_set = _graph.getLowerInfo(index)->def_factors();
-    if (op_factor_set.size() != 1 ||
-        output_layout != _graph.getLowerInfo(index)->def_factors().getOnlyElement().layout())
-    {
-      return false;
-    }
-  }
-
-  if (is_for_model_input)
-  {
-    // check if this is NHWC_TO_NCHW permutation: must have single input, which is model's input
-    return (inp_indexes.size() == 1 && input_layout == Layout::NHWC &&
-            output_layout == Layout::NCHW);
-  }
-
-  // check if this is NCHW_TO_NHWC permutation: must have single output, which is model's output
-  return (out_indexes.size() == 1 && input_layout == Layout::NCHW && output_layout == Layout::NHWC);
-}
-
-} // namespace pass
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/pass/PermutationEliminationPass.h b/runtime/neurun/core/src/ir/pass/PermutationEliminationPass.h
deleted file mode 100644
index 4431eabbc..000000000
--- a/runtime/neurun/core/src/ir/pass/PermutationEliminationPass.h
+++ /dev/null
@@ -1,86 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_GRAPH_PASS_PERMUTATION_ELIMINATION_PASS_H__
-#define __NEURUN_GRAPH_PASS_PERMUTATION_ELIMINATION_PASS_H__
-
-#include "OperandPass.h"
-#include "ir/Operand.h"
-#include "ir/OperandIndexSequence.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace pass
-{
-
-class PermutationEliminationPass : public OperandPass
-{
-public:
-  using OperandPass::OperandPass;
-
-public:
-  std::string id() override { return "PermutationEliminationPass"; }
-
-  void callback(const OperandIndex &index, Operand &object) override;
-
-private:
-  /**
-   * @brief Remove Permute operation that permutates input
-   *
-   * Note: This function aslo removes model's input and
-   * sets output of permutation as model's new input
-   *
-   * @param inp_index is the target operand index for the elimination
-   * @param object is the target operand object for the elimination
-   *
-   * @return
-   */
-  void eliminateInput(const OperandIndex &inp_index, Operand &object);
-
-  /**
-   * @brief Remove Permute operation that permutates output of a model
-   *
-   * Note: This function aslo removes model's output and
-   * sets input of permutation as model's new output
-   *
-   * @param out_index is the target operand index for the elimination
-   * @param object is the target operand object for the elimination
-   *
-   * @return
-   */
-  void eliminateOutput(const OperandIndex &out_index, Operand &object);
-
-  /**
-   * @brief Determine if passed operands are permute layer's input and output, that must be
-   * eliminated
-   *
-   * @param inp_index indexes of the input operand to operation
-   * @param out_index indexes of the output operand to operation
-   * @param is_for_model_input checking for model's input or output
-   *
-   * @return if it is permutation layer
-   */
-  bool isPermuteLayerToEliminate(const OperandIndexSequence &inp_indexes,
-                                 const OperandIndexSequence &out_indexes, bool is_for_model_input);
-};
-
-} // namespace pass
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_GRAPH_PASS_PERMUTATION_ELIMINATION_PASS_H__
diff --git a/runtime/neurun/core/src/ir/pass/PermutationInsertionPass.cc b/runtime/neurun/core/src/ir/pass/PermutationInsertionPass.cc
deleted file mode 100644
index 052e3026a..000000000
--- a/runtime/neurun/core/src/ir/pass/PermutationInsertionPass.cc
+++ /dev/null
@@ -1,209 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "PermutationInsertionPass.h"
-
-#include <cassert>
-#include <utility>
-#include <unordered_map>
-
-#include "ir/Operand.h"
-#include "ir/operation/LowerInfo.h"
-#include "ir/Graph.h"
-#include "backend/IConfig.h"
-#include "util/logging.h"
-#include "cpp14/memory.h"
-#include "ir/operation/Permute.h"
-#include "ir/operand/Shape4DConvert.h"
-#include "compiler/BackendResolver.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace pass
-{
-
-void PermutationInsertionPass::callback(const OperandIndex &index, Operand &object)
-{
-  auto &&operand_li = _graph.getLowerInfo(index);
-  assert(operand_li);
-
-  // NOTE Later, constants also will have Def
-  // Ignore constants
-  if (operand_li->def_factors().size() == 0)
-  {
-    return;
-  }
-
-  std::list<OperationIndex> permute_indexes;
-
-  // Build a map for all necessary type of operands
-  std::unordered_map<operand::PermuteFactor, OperandIndex> factor_to_index;
-  {
-    assert(operand_li->def_factors().size() == 1);
-    for (auto factor : operand_li->def_factors())
-    {
-      factor_to_index.emplace(factor, index);
-    }
-
-    auto insert_set = operand_li->use_factors() - operand_li->def_factors();
-    for (auto factor : insert_set)
-    {
-      const auto permute_operation_index = insertPermute(index, factor);
-      permute_indexes.push_back(permute_operation_index);
-      VERBOSE(PermutationInsertionPass) << "Insert 'Permute' operation for operand "
-                                        << index.value() << std::endl;
-      const auto &permute_operation = _graph.operations().at(permute_operation_index);
-      const auto permuted_operand_index = permute_operation.getOutputs().at(0);
-      factor_to_index.emplace(factor, permuted_operand_index);
-    }
-  }
-
-  // Update operations' input that uses this operand
-  {
-    std::list<OperationIndex> remove_list;
-
-    auto uses = object.getUses();
-    for (auto use : uses.list())
-    {
-      // If permute operation, ignore it
-      if (std::find(permute_indexes.begin(), permute_indexes.end(), use) != permute_indexes.end())
-        continue;
-
-      auto &operation = _graph.operations().at(use);
-      assert(_graph.subgraphs().containsOperation(use));
-      auto subg_index = _graph.subgraphs().getOperation(use);
-      auto subg_li = _graph.getLowerInfo(subg_index);
-      assert(subg_li);
-      const auto subg_layout = subg_li->layout();
-      const backend::Backend *backend = subg_li->backend();
-      assert(backend);
-      auto use_node_inputs = operation.getInputs();
-      assert(use_node_inputs.contains(index));
-
-      auto new_index = factor_to_index.at({backend, subg_layout});
-      if (index != new_index)
-      {
-        // Update from op_seq
-        _graph.subgraphs().at(subg_index).replaceInput(index, new_index);
-
-        // Update from operation
-        operation.replaceInput(index, new_index);
-
-        // Update from operand
-        remove_list.push_back(
-            use); // Removal should be done in another loop since we are in the loop
-        _graph.operands().at(new_index).appendUse(use);
-      }
-    }
-
-    for (auto &operation : remove_list)
-    {
-      object.removeUse(operation);
-    }
-  }
-}
-
-OperationIndex PermutationInsertionPass::insertPermute(const OperandIndex &operand_index,
-                                                       const operand::PermuteFactor &factor)
-{
-  assert(!_graph.isBuildingPhase());
-
-  auto &operand = _graph.operands().at(operand_index);
-
-  // Generate output operand and permute operation
-  auto out_operand_index = _graph.addOperand(operand.shape(), operand.typeInfo());
-  // change model output if operand_index is model output index
-  auto &model_outputs = _graph.getOutputs();
-  if (model_outputs.contains(operand_index))
-  {
-    model_outputs.replace(operand_index, out_operand_index);
-  }
-
-  // Find Permute information
-  auto input_backend = _graph.getLowerInfo(operand_index)->def_factors().getOnlyElement().backend();
-  auto output_backend = factor.backend();
-  // NOTE Permute may not have specific layout because the layout of input and output may be
-  // different.
-  const auto permute_node_layout = Layout::UNKNOWN;
-  const auto permute_node_backend = backend::BackendManager::get().getDefault();
-  const operand::PermuteFactor permute_node_factor{permute_node_backend, permute_node_layout};
-
-  // Update LowerInfo of input operand
-  auto operand_lower_info = _graph.getLowerInfo(operand_index);
-  operand_lower_info->removeUsePermuteFactor(factor);
-  operand_lower_info->addUsePermuteFactor(permute_node_factor);
-
-  // Update LowerInfo of output operand
-  auto out_operand_li =
-      nnfw::cpp14::make_unique<operand::LowerInfo>(operand::asShape4D(operand.shape()));
-
-  // The input and output factors of all nodes will be the same except Permute. So Tensor's
-  // allocators allocates memory using only the information of def permutation factor now.
-  // TODO Change param to permute_node_factor
-  out_operand_li->addDefPermuteFactor(factor);
-  out_operand_li->addUsePermuteFactor(factor);
-  _graph.setLowerInfo(out_operand_index, std::move(out_operand_li));
-
-  auto input_backend_ctx = _graph.backend_resolver()->getBackendContext(input_backend);
-  auto output_backend_ctx = _graph.backend_resolver()->getBackendContext(output_backend);
-
-  // Insert permute operation to the graph
-  const auto input_layout =
-      _graph.getLowerInfo(operand_index)->def_factors().getOnlyElement().layout();
-  const auto output_layout = factor.layout();
-  using Permute = operation::Permute;
-  const auto permute_type = [&]() {
-    if (input_layout == Layout::NHWC && output_layout == Layout::NCHW)
-    {
-      return Permute::Type::NHWC_TO_NCHW;
-    }
-    else if (input_layout == Layout::NCHW && output_layout == Layout::NHWC)
-    {
-      return Permute::Type::NCHW_TO_NHWC;
-    }
-    else
-    {
-      return Permute::Type::COPY;
-    }
-  }();
-  auto insert_node = nnfw::cpp14::make_unique<Permute>(
-      operand_index, out_operand_index, input_backend_ctx, output_backend_ctx, permute_type);
-
-  auto node_index = _graph.operations().push(std::move(insert_node));
-  const auto &node = _graph.operations().at(node_index);
-
-  // OpSequence
-  {
-    auto subg_index = _graph.subgraphs().emplace(node_index, node, permute_node_layout);
-    auto &subg = _graph.subgraphs().at(subg_index);
-    subg.setInputs(node.getInputs());
-    subg.setOutputs(node.getOutputs());
-    _graph.setLowerInfo(subg_index, nnfw::cpp14::make_unique<operation::LowerInfo>(
-                                        permute_node_backend, permute_node_layout));
-  }
-
-  // Update Use/Def info
-  {
-    _graph.operands().at(operand_index).appendUse(node_index);
-    _graph.operands().at(out_operand_index).appendDef(node_index);
-  }
-  return node_index;
-}
-} // namespace pass
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/pass/PermutationInsertionPass.h b/runtime/neurun/core/src/ir/pass/PermutationInsertionPass.h
deleted file mode 100644
index 4065fc6ac..000000000
--- a/runtime/neurun/core/src/ir/pass/PermutationInsertionPass.h
+++ /dev/null
@@ -1,59 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_GRAPH_PASS_PERMUTATION_INSERTION_PASS_H__
-#define __NEURUN_GRAPH_PASS_PERMUTATION_INSERTION_PASS_H__
-
-#include "OperandPass.h"
-#include "ir/Operand.h" //for OperationIndex
-#include "backend/BackendManager.h"
-#include "ir/operand/PermuteFactor.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace pass
-{
-
-class PermutationInsertionPass : public OperandPass
-{
-public:
-  using OperandPass::OperandPass;
-
-public:
-  std::string id() override { return "PermutationInsertionPass"; }
-  void callback(const OperandIndex &index, Operand &object) override;
-
-  /**
-   * @brief Insert Permute operation that has given operand as input
-   *
-   * @param operand_index is the target operand index for the insertion
-   * @param factor is the output operand's backend type and layout
-   *
-   * @return OperationIndex
-   */
-  OperationIndex insertPermute(const OperandIndex &operand_index,
-                               const operand::PermuteFactor &factor);
-
-private:
-};
-
-} // namespace pass
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_GRAPH_PASS_PERMUTATION_INSERTION_PASS_H__
diff --git a/runtime/neurun/core/src/ir/pass/PermutationOperationPass.cc b/runtime/neurun/core/src/ir/pass/PermutationOperationPass.cc
deleted file mode 100644
index 41a1ad903..000000000
--- a/runtime/neurun/core/src/ir/pass/PermutationOperationPass.cc
+++ /dev/null
@@ -1,230 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "PermutationOperationPass.h"
-
-#include "backend/Backend.h"
-#include "backend/IConfig.h"
-#include "ir/Graph.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace pass
-{
-
-void PermutationOperationPass::callback(const OperationIndex &, Operation &node)
-{
-  node.accept(*this);
-};
-
-void PermutationOperationPass::changeToKeepLayout(const Operation &node)
-{
-  const auto &output_ind = node.getOutputs().at(0);
-  const auto &output_obj = _graph.operands().at(output_ind);
-
-  assert(output_obj.getDef().size() == 1);
-  const auto &node_index = output_obj.getDef().list().front();
-  const auto &subg_index = _graph.subgraphs().getOperation(node_index);
-
-  const auto frontend_layout = _graph.subgraphs().at(subg_index).getLayout();
-  const auto backend_layout = _graph.getLowerInfo(subg_index)->layout();
-
-  if (frontend_layout == backend_layout)
-  {
-    return;
-  }
-
-  // CPU supports only NHWC now
-  if (_graph.getLowerInfo(subg_index)->backend()->config()->id() != "cpu")
-  {
-    // TODO Change backend of this node
-    assert(frontend_layout == Layout::NHWC || backend_layout == Layout::UNKNOWN);
-  }
-
-  // Divide op_seq based on target operation
-  {
-    auto &above_subg = _graph.subgraphs().at(subg_index);
-
-    // Create new op_seq and move information from existing op_seq to new op_seq if target
-    // node is the end of op_seq
-    auto it = above_subg.begin();
-    // Find iterator of target node in op_seq
-    while ((it++)->index != node_index)
-      ;
-    if (it != above_subg.end())
-    {
-      const auto &below_subg_index =
-          _graph.subgraphs().emplace(it->index, *it->node, above_subg.getLayout());
-      auto &below_subg = _graph.subgraphs().at(below_subg_index);
-      below_subg.setInputs(it->node->getInputs());
-      below_subg.setOutputs(it->node->getOutputs());
-
-      std::vector<OperationIndex> remove_list;
-      remove_list.emplace_back(it->index);
-      while (++it != above_subg.end())
-      {
-        below_subg.appendOperation(it->index, *it->node);
-        below_subg.setOutputs(it->node->getOutputs());
-        remove_list.emplace_back(it->index);
-      }
-
-      above_subg.setOutputs(node.getOutputs());
-      for (const auto &index : remove_list)
-      {
-        above_subg.remove(index);
-      }
-
-      const auto subg_li = _graph.getLowerInfo(subg_index);
-      _graph.setLowerInfo(below_subg_index, nnfw::cpp14::make_unique<operation::LowerInfo>(
-                                                subg_li->backend(), subg_li->layout()));
-    }
-  }
-
-  // Remove target operation from op_seq and insert the target operation to new op_seq
-  {
-    const auto backend = _graph.getLowerInfo(subg_index)->backend();
-
-    // Remove target operation from subraph
-    _graph.subgraphs().removeFromSubgraph(node_index);
-
-    if (!_graph.subgraphs().exist(subg_index))
-    {
-      // Remove lowerinfo for op_seq of target operation if the op_seq does not exist
-      _graph.removeLowerInfo(subg_index);
-    }
-    else
-    {
-      // Update op_seq of target operation if the op_seq exists
-      auto &above_subg = _graph.subgraphs().at(subg_index);
-      const auto last_node = (--above_subg.end())->node;
-      above_subg.setOutputs(last_node->getOutputs());
-    }
-
-    // Create new op_seq and set information to the op_seq
-    auto new_subg_index = _graph.subgraphs().emplace(node_index, node, frontend_layout);
-    auto &new_subg = _graph.subgraphs().at(new_subg_index);
-    new_subg.setInputs(node.getInputs());
-    new_subg.setOutputs(node.getOutputs());
-    _graph.setLowerInfo(new_subg_index,
-                        nnfw::cpp14::make_unique<operation::LowerInfo>(backend, frontend_layout));
-  }
-
-  // Change PermuteFactors of operands of target node
-  {
-    const auto &subg_index = _graph.subgraphs().getOperation(node_index);
-    const auto subg_li = _graph.getLowerInfo(subg_index);
-    const auto backend = subg_li->backend();
-    const operand::PermuteFactor removed_factor{backend, backend_layout};
-    const operand::PermuteFactor new_factor{backend, frontend_layout};
-    for (const auto &input : node.getInputs())
-    {
-      bool canRemove = true;
-      for (const auto &use : _graph.operands().at(input).getUses().list())
-      {
-        if (use != node_index)
-        {
-          const auto &use_subg_index = _graph.subgraphs().getOperation(use);
-          auto use_subg_li = _graph.getLowerInfo(use_subg_index);
-          if (use_subg_li->backend() == backend && use_subg_li->layout() == backend_layout)
-          {
-            canRemove = false;
-            break;
-          }
-        }
-      }
-
-      auto lower_info = _graph.getLowerInfo(input);
-      if (canRemove)
-      {
-        lower_info->removeUsePermuteFactor(removed_factor);
-      }
-      lower_info->addUsePermuteFactor(new_factor);
-
-      // Whether if node's input is an input of model or a constant
-      if (_graph.operands().at(input).getDef().size() == 0)
-      {
-        assert(_graph.getInputs().contains(input) || _graph.operands().at(input).isConstant());
-        lower_info->removeDefPermuteFactor(removed_factor);
-        lower_info->addDefPermuteFactor(new_factor);
-      }
-    }
-
-    for (const auto &output : node.getOutputs())
-    {
-      auto lower_info = _graph.getLowerInfo(output);
-      lower_info->removeDefPermuteFactor(removed_factor);
-      lower_info->addDefPermuteFactor(new_factor);
-
-      // Whether if node's output is an output of model
-      if (_graph.operands().at(output).getUses().size() == 0)
-      {
-        assert(_graph.getOutputs().contains(output));
-        lower_info->removeUsePermuteFactor(removed_factor);
-        lower_info->addUsePermuteFactor(new_factor);
-      }
-    }
-  }
-}
-
-void PermutationOperationPass::visit(const operation::FullyConnected &node)
-{
-  const auto &input_ind = node.getInputs().at(operation::FullyConnected::Input::INPUT);
-  const auto &input_obj = _graph.operands().at(input_ind);
-  const auto &input_shape = input_obj.shape();
-
-  if (input_shape.rank() == 4)
-  {
-    changeToKeepLayout(node);
-  }
-}
-
-void PermutationOperationPass::visit(const operation::Gather &node)
-{
-  const auto &input_ind = node.getInputs().at(operation::Gather::Input::INPUT);
-  const auto &input_obj = _graph.operands().at(input_ind);
-  const auto &input_shape = input_obj.shape();
-
-  const auto &output_ind = node.getOutputs().at(0);
-  const auto &output_obj = _graph.operands().at(output_ind);
-  const auto &output_shape = output_obj.shape();
-
-  if (input_shape.rank() >= 4 || output_shape.rank() >= 4)
-  {
-    changeToKeepLayout(node);
-  }
-}
-
-void PermutationOperationPass::visit(const operation::Reshape &node)
-{
-  const auto &input_ind = node.getInputs().at(operation::Reshape::Input::INPUT);
-  const auto &input_obj = _graph.operands().at(input_ind);
-  const auto &input_shape = input_obj.shape();
-
-  const auto &output_ind = node.getOutputs().at(0);
-  const auto &output_obj = _graph.operands().at(output_ind);
-  const auto &output_shape = output_obj.shape();
-
-  if (input_shape.rank() >= 4 || output_shape.rank() >= 4)
-  {
-    changeToKeepLayout(node);
-  }
-}
-
-} // namespace pass
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/pass/PermutationOperationPass.h b/runtime/neurun/core/src/ir/pass/PermutationOperationPass.h
deleted file mode 100644
index 896e0176a..000000000
--- a/runtime/neurun/core/src/ir/pass/PermutationOperationPass.h
+++ /dev/null
@@ -1,54 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_GRAPH_PASS_PERMUTATION_OPERATION_PASS_H__
-#define __NEURUN_GRAPH_PASS_PERMUTATION_OPERATION_PASS_H__
-
-#include "ir/OperationVisitor.h"
-#include "OperationPass.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace pass
-{
-
-class PermutationOperationPass : public OperationPass, public OperationVisitor
-{
-public:
-  using OperationPass::OperationPass;
-
-public:
-  std::string id() final { return "PermutationOperationPass"; }
-
-public:
-  void callback(const OperationIndex &i, Operation &n) final;
-
-public:
-  void visit(const operation::FullyConnected &) final;
-  void visit(const operation::Gather &) final;
-  void visit(const operation::Reshape &) final;
-
-private:
-  void changeToKeepLayout(const Operation &);
-};
-
-} // namespace pass
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_GRAPH_PASS_PERMUTATION_OPERATION_PASS_H__
diff --git a/runtime/neurun/core/src/ir/verifier/Verifier.cc b/runtime/neurun/core/src/ir/verifier/Verifier.cc
deleted file mode 100644
index 7bd8ac512..000000000
--- a/runtime/neurun/core/src/ir/verifier/Verifier.cc
+++ /dev/null
@@ -1,96 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "Verifier.h"
-
-#include "ir/Graph.h"
-#include "ir/OperationIndexMap.h"
-
-#include "util/logging.h"
-
-namespace neurun
-{
-namespace ir
-{
-namespace verifier
-{
-
-//
-// DAGChecker
-//
-
-bool DAGChecker::verify(const Graph &graph) const
-{
-  auto &operations = graph.operations();
-  bool cyclic = false;
-
-  OperationIndexMap<bool> visited;
-  operations.iterate(
-      [&](const OperationIndex &index, const Operation &) { visited[index] = false; });
-  OperationIndexMap<bool> on_stack = visited; // Copy from visited
-
-  std::function<void(const OperationIndex &index, const Operation &)> dfs_recursive =
-      [&](const OperationIndex &index, const Operation &node) -> void {
-    if (on_stack[index])
-      cyclic = true;
-    if (visited[index])
-      return;
-    visited[index] = true;
-    on_stack[index] = true;
-
-    for (auto output : node.getOutputs())
-    {
-      const auto &operand = graph.operands().at(output);
-      for (const auto &use : operand.getUses().list())
-      {
-        dfs_recursive(use, graph.operations().at(use));
-      }
-    }
-
-    on_stack[index] = false;
-  };
-
-  operations.iterate(dfs_recursive);
-
-  return !cyclic;
-}
-
-//
-// EdgeConsistencyVerifier
-//
-
-bool EdgeConsistencyChecker::verify(const Graph &graph) const
-{
-  auto &operations = graph.operations();
-  uint32_t mismatches = 0;
-  operations.iterate([&](const OperationIndex &index, const Operation &node) {
-    for (auto operand_index : node.getInputs())
-    {
-      auto &operand = graph.operands().at(operand_index);
-      mismatches += (operand.getUses().contains(index) ? 0 : 1);
-    }
-    for (auto operand_index : node.getOutputs())
-    {
-      auto &operand = graph.operands().at(operand_index);
-      mismatches += (operand.getDef().contains(index) ? 0 : 1);
-    }
-  });
-  return mismatches == 0;
-}
-
-} // namespace verifier
-} // namespace ir
-} // namespace neurun
diff --git a/runtime/neurun/core/src/ir/verifier/Verifier.h b/runtime/neurun/core/src/ir/verifier/Verifier.h
deleted file mode 100644
index 0993a239e..000000000
--- a/runtime/neurun/core/src/ir/verifier/Verifier.h
+++ /dev/null
@@ -1,68 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_GRAPH_VERIFIER_VERIFIER_H__
-#define __NEURUN_GRAPH_VERIFIER_VERIFIER_H__
-
-namespace neurun
-{
-namespace ir
-{
-class Graph;
-} // namespace ir
-} // namespace neurun
-
-namespace neurun
-{
-namespace ir
-{
-namespace verifier
-{
-
-struct IVerifier
-{
-  virtual ~IVerifier() = default;
-  virtual bool verify(const Graph &graph) const = 0;
-};
-
-} // namespace verifier
-} // namespace ir
-} // namespace neurun
-
-namespace neurun
-{
-namespace ir
-{
-namespace verifier
-{
-
-class DAGChecker : public IVerifier
-{
-public:
-  bool verify(const Graph &graph) const override;
-};
-
-class EdgeConsistencyChecker : public IVerifier
-{
-public:
-  bool verify(const Graph &graph) const override;
-};
-
-} // namespace verifier
-} // namespace ir
-} // namespace neurun
-
-#endif // __NEURUN_GRAPH_VERIFIER_VERIFIER_H__
diff --git a/runtime/neurun/core/src/library_info.cc b/runtime/neurun/core/src/library_info.cc
deleted file mode 100644
index 601d09185..000000000
--- a/runtime/neurun/core/src/library_info.cc
+++ /dev/null
@@ -1,17 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-volatile const char info[] = "library information : runtime=neurun";
diff --git a/runtime/neurun/core/src/util/ConfigSource.cc b/runtime/neurun/core/src/util/ConfigSource.cc
deleted file mode 100644
index 7d57ec178..000000000
--- a/runtime/neurun/core/src/util/ConfigSource.cc
+++ /dev/null
@@ -1,116 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "util/ConfigSource.h"
-#include "util/GeneralConfigSource.h"
-#include "util/EnvConfigSource.h"
-
-#include <array>
-#include <algorithm>
-#include <cassert>
-
-#include "cpp14/memory.h"
-
-namespace neurun
-{
-namespace util
-{
-
-static std::unique_ptr<IConfigSource> _source;
-
-void config_source(std::unique_ptr<IConfigSource> &&source) { _source = std::move(source); }
-
-static IConfigSource *config_source()
-{
-  if (!_source)
-  {
-#ifdef ENVVAR_FOR_DEFAULT_CONFIG
-    // Default ConfigSource is EnvConfigSource
-    _source = nnfw::cpp14::make_unique<EnvConfigSource>();
-#else
-    _source = nnfw::cpp14::make_unique<GeneralConfigSource>();
-#endif // ENVVAR_FOR_DEFAULT_CONFIG
-  }
-  return _source.get();
-}
-
-static std::string getConfigOrDefault(const std::string &key)
-{
-  static std::unordered_map<std::string, std::string> defaults;
-  if (defaults.empty())
-  {
-#define CONFIG(Name, Type, Default)               \
-  {                                               \
-    auto name = std::string{#Name};               \
-    defaults.emplace(name, std::string{Default}); \
-  }
-
-#include "util/Config.lst"
-
-#undef CONFIG
-  }
-
-  // Treat empty string and absence of the value to be the same
-  auto ret = config_source()->get(key);
-  if (ret.empty())
-  {
-    auto itr = defaults.find(key);
-    if (itr != defaults.end())
-    {
-      // Return the default value if exists
-      ret = itr->second;
-    }
-  }
-
-  return ret;
-}
-
-bool getConfigBool(const std::string &key)
-{
-  auto raw = getConfigOrDefault(key);
-  static const std::array<std::string, 5> false_list{"0", "OFF", "FALSE", "N", "NO"};
-  auto false_found = std::find(false_list.begin(), false_list.end(), raw);
-
-  return (false_found == false_list.end());
-}
-
-int getConfigInt(const std::string &key)
-{
-  auto raw = getConfigOrDefault(key);
-  return std::stoi(raw);
-}
-
-std::string getConfigString(const std::string &key) { return getConfigOrDefault(key); }
-
-} // namespace util
-} // namespace neurun
-
-namespace neurun
-{
-namespace util
-{
-namespace config
-{
-
-#define CONFIG(Name, Type, Default) const char *Name = #Name;
-
-#include "util/Config.lst"
-
-#undef CONFIG
-
-} // namespace config
-} // namespace util
-} // namespace neurun
diff --git a/runtime/neurun/core/src/util/EnvConfigSource.cc b/runtime/neurun/core/src/util/EnvConfigSource.cc
deleted file mode 100644
index 52a6bf2a4..000000000
--- a/runtime/neurun/core/src/util/EnvConfigSource.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "util/EnvConfigSource.h"
-
-#include <cstdlib>
-
-namespace neurun
-{
-namespace util
-{
-
-std::string EnvConfigSource::get(const std::string &key) const
-{
-  const char *value = std::getenv(key.c_str());
-  if (value != nullptr)
-  {
-    return value;
-  }
-  else
-  {
-    return GeneralConfigSource::get(key);
-  }
-}
-
-} // namespace util
-} // namespace neurun
diff --git a/runtime/neurun/core/src/util/EventCollectorGlobal.cc b/runtime/neurun/core/src/util/EventCollectorGlobal.cc
deleted file mode 100644
index 6c3594f5f..000000000
--- a/runtime/neurun/core/src/util/EventCollectorGlobal.cc
+++ /dev/null
@@ -1,86 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "util/EventCollectorGlobal.h"
-
-#include <cassert>
-#include <fstream>
-
-#include "util/ConfigSource.h"
-
-namespace neurun
-{
-namespace util
-{
-
-EventCollectorGlobal::EventCollectorGlobal() : _recorder{}, _collector{&_recorder}
-{
-  // DO NOTHING
-}
-
-EventCollectorGlobal::~EventCollectorGlobal()
-{
-  auto path = util::getConfigString(util::config::TRACE_FILEPATH);
-  if (!path.empty())
-  {
-    // TODO Need better way for saved file path than just appending ".global" to the trace file path
-    std::ofstream ofs{path + ".global"};
-    _recorder.writeToFile(ofs);
-  }
-}
-
-EventCollectorGlobal &EventCollectorGlobal::get()
-{
-  static EventCollectorGlobal instance;
-  return instance;
-}
-
-EventDurationBlock::EventDurationBlock(const std::string &tag) : _tag{tag}
-{
-  auto &glob = EventCollectorGlobal::get();
-  glob.collector().onEvent(EventCollector::Event{EventCollector::Edge::BEGIN, "0", _tag});
-}
-EventDurationBlock::~EventDurationBlock()
-{
-  auto &glob = EventCollectorGlobal::get();
-  glob.collector().onEvent(EventCollector::Event{EventCollector::Edge::END, "0", _tag});
-}
-
-EventDurationManual::EventDurationManual(const std::string &tag) : _tag{tag}, _pair{true} {}
-
-EventDurationManual::~EventDurationManual()
-{
-  // Check if it has called begin-end pair
-  assert(_pair);
-}
-
-void EventDurationManual::begin()
-{
-  _pair = false;
-  auto &glob = EventCollectorGlobal::get();
-  glob.collector().onEvent(EventCollector::Event{EventCollector::Edge::BEGIN, "0", _tag});
-}
-
-void EventDurationManual::end()
-{
-  assert(!_pair);
-  _pair = true;
-  auto &glob = EventCollectorGlobal::get();
-  glob.collector().onEvent(EventCollector::Event{EventCollector::Edge::END, "0", _tag});
-}
-
-} // namespace util
-} // namespace neurun
diff --git a/runtime/neurun/core/src/util/GeneralConfigSource.cc b/runtime/neurun/core/src/util/GeneralConfigSource.cc
deleted file mode 100644
index 084e4c109..000000000
--- a/runtime/neurun/core/src/util/GeneralConfigSource.cc
+++ /dev/null
@@ -1,45 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "util/GeneralConfigSource.h"
-#include "util/logging.h"
-
-namespace neurun
-{
-namespace util
-{
-
-std::string GeneralConfigSource::get(const std::string &key) const
-{
-  auto itr = _map.find(key);
-  if (itr == _map.end())
-  {
-    return "";
-  }
-  else
-  {
-    return itr->second;
-  }
-}
-
-void GeneralConfigSource::set(const std::string &key, const std::string &val)
-{
-  VERBOSE(GeneralConfigSource) << key << " : " << val << std::endl;
-  _map[key] = val;
-}
-
-} // namespace util
-} // namespace neurun
diff --git a/runtime/neurun/core/src/util/Padding.cc b/runtime/neurun/core/src/util/Padding.cc
deleted file mode 100644
index 2e2202b58..000000000
--- a/runtime/neurun/core/src/util/Padding.cc
+++ /dev/null
@@ -1,119 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "util/Padding.h"
-#include "util/Utils.h"
-
-#include <algorithm>
-#include <stdexcept>
-
-namespace neurun
-{
-namespace util
-{
-
-ir::ExplicitPadding validPadding(void)
-{
-  //
-  // ANEURALNETWORKS_PADDING_VALID
-  //
-  // VALID padding. No padding.
-  //
-  // When the input size is not evenly divisible by the filter size,
-  // the input at the end that could not fill the whole filter tile
-  // will simply be ignored.
-  //
-  ir::ExplicitPadding padding;
-
-  padding.top = 0;
-  padding.bottom = 0;
-  padding.left = 0;
-  padding.right = 0;
-
-  return padding;
-}
-
-ir::ExplicitPadding samePaddingUsingIFM(const ir::FeatureShape &ifm_shape, const ir::Stride &stride,
-                                        uint32_t kw, uint32_t kh)
-{
-  ir::ExplicitPadding padding;
-
-  // ANEURALNETWORKS_PADDING_SAME (from NNAPI spec)
-  //
-  // SAME padding. Padding on both ends are the "same":
-  //
-  // padding_to_beginning = total_padding / 2
-  // padding_to_end = (total_padding + 1)/2.
-  //
-  const int32_t vertical_expected_output = (ifm_shape.H + stride.vertical - 1) / stride.vertical;
-  const int32_t horizontal_expected_output =
-      (ifm_shape.W + stride.horizontal - 1) / stride.horizontal;
-
-  const int32_t vertical_needed_input = (vertical_expected_output - 1) * stride.vertical + kh;
-  const int32_t vertical_total_padding = std::max(0, vertical_needed_input - ifm_shape.H);
-
-  const int32_t horizontal_needed_input = (horizontal_expected_output - 1) * stride.horizontal + kw;
-  const int32_t horizontal_total_padding = std::max(0, horizontal_needed_input - ifm_shape.W);
-
-  padding.top = vertical_total_padding / 2;
-  padding.bottom = (vertical_total_padding + 1) / 2;
-  padding.left = horizontal_total_padding / 2;
-  padding.right = (horizontal_total_padding + 1) / 2;
-
-  return padding;
-}
-
-ir::ExplicitPadding samePadding(const ir::FeatureShape &ifm_shape,
-                                const ir::FeatureShape &ofm_shape, const ir::Stride &stride,
-                                uint32_t kw, uint32_t kh)
-{
-  const int32_t vertical_expected_output = (ifm_shape.H + stride.vertical - 1) / stride.vertical;
-  const int32_t horizontal_expected_output =
-      (ifm_shape.W + stride.horizontal - 1) / stride.horizontal;
-  assert(vertical_expected_output == ofm_shape.H);
-  assert(horizontal_expected_output == ofm_shape.W);
-
-  UNUSED_RELEASE(ofm_shape);
-  UNUSED_RELEASE(vertical_expected_output);
-  UNUSED_RELEASE(horizontal_expected_output);
-
-  return samePaddingUsingIFM(ifm_shape, stride, kw, kh);
-}
-
-ir::ExplicitPadding calculatePadding(const ir::Padding &padding, const ir::FeatureShape &ifm_shape,
-                                     const ir::FeatureShape &ofm_shape, const ir::Stride &stride,
-                                     uint32_t kw, uint32_t kh)
-{
-  if (padding.type == ir::PaddingType::EXPLICIT)
-  {
-    return padding.param;
-  }
-  else if (padding.type == ir::PaddingType::SAME)
-  {
-    return samePadding(ifm_shape, ofm_shape, stride, kw, kh);
-  }
-  else if (padding.type == ir::PaddingType::VALID)
-  {
-    return validPadding();
-  }
-  else
-  {
-    throw std::runtime_error{"Cannot handle padding type"};
-  }
-}
-
-} // namespace util
-} // namespace neurun
diff --git a/runtime/neurun/core/src/util/ShapeInference.cc b/runtime/neurun/core/src/util/ShapeInference.cc
deleted file mode 100644
index 6fa29e7d8..000000000
--- a/runtime/neurun/core/src/util/ShapeInference.cc
+++ /dev/null
@@ -1,200 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "util/Utils.h"
-#include "ir/InternalType.h"
-#include "ir/Shape.h"
-#include "ir/operation/AvgPool2D.h"
-#include "ir/operation/MaxPool2D.h"
-#include "util/ShapeInference.h"
-
-namespace neurun
-{
-namespace shape_inference
-{
-
-//
-// Helper functions
-//
-
-namespace
-{
-
-template <typename T, typename U>
-typename std::enable_if<std::is_integral<T>::value && std::is_integral<U>::value,
-                        typename std::common_type<T, U>::type>::type
-ceil_div(T dividend, U divisor)
-{
-  assert(dividend > 0 && divisor > 0 && "this implementations is for positive numbers only");
-  return (dividend + divisor - 1) / divisor;
-}
-
-// Calculate the result of broadcast of two shapes
-ir::Shape broadcastShapes(const ir::Shape &lhs_shape, const ir::Shape &rhs_shape)
-{
-  ir::Shape out_shape;
-  auto max_rank = std::max(lhs_shape.rank(), rhs_shape.rank());
-
-  for (int idx = 0; idx < max_rank; ++idx)
-  {
-    // Go over operands dimensions from right to left
-    int lhs_idx = lhs_shape.rank() - idx - 1;
-    int rhs_idx = rhs_shape.rank() - idx - 1;
-
-    int32_t lhs_dim = lhs_idx >= 0 ? lhs_shape.dim(lhs_idx) : 1;
-    int32_t rhs_dim = rhs_idx >= 0 ? rhs_shape.dim(rhs_idx) : 1;
-
-    if (lhs_dim != 1 && rhs_dim != 1 && lhs_dim != rhs_dim)
-      throw std::runtime_error("Incompatible shapes for broadcast");
-
-    out_shape.prepend(std::max(lhs_dim, rhs_dim));
-  }
-
-  return out_shape;
-}
-
-// Calculate output height and width of convolution-like operation
-std::pair<int, int> calcConvLikeHeightAndWidth(const int in_h, const int in_w, const int ker_h,
-                                               const int ker_w, const ir::Padding pad,
-                                               const ir::Stride stride)
-{
-  int32_t out_h = 0, out_w = 0;
-
-  switch (pad.type)
-  {
-    case ir::PaddingType::SAME:
-      out_h = ceil_div(in_h, stride.vertical);
-      out_w = ceil_div(in_w, stride.horizontal);
-      break;
-    case ir::PaddingType::VALID:
-      out_h = ceil_div(in_h - ker_h + 1, stride.vertical);
-      out_w = ceil_div(in_w - ker_w + 1, stride.horizontal);
-      break;
-    case ir::PaddingType::EXPLICIT:
-      out_h = (in_h + pad.param.top + pad.param.bottom - ker_h) / stride.vertical + 1;
-      out_w = (in_w + pad.param.left + pad.param.right - ker_w) / stride.horizontal + 1;
-      break;
-    default:
-      assert(false);
-  }
-
-  return {out_h, out_w};
-}
-
-} // namespace
-
-//
-// Shape inference
-//
-
-Shapes inferEltwiseShape(const ir::Shape &lhs_shape, const ir::Shape &rhs_shape)
-{
-  return {broadcastShapes(lhs_shape, rhs_shape)};
-}
-
-Shapes inferAvgPoolShape(const ir::Shape &in_shape, const ir::operation::AvgPool2D::Param &param,
-                         const ir::Layout layout)
-{
-  assert(layout == ir::Layout::NHWC);
-  auto ifm_shape = in_shape.asFeature(layout);
-  const auto out_h_w = calcConvLikeHeightAndWidth(ifm_shape.H, ifm_shape.W, param.kh, param.kw,
-                                                  param.padding, param.stride);
-  // Pooling don't change number of channels and batch size
-  return {ir::Shape{ifm_shape.N, out_h_w.first, out_h_w.second, ifm_shape.C}};
-}
-
-Shapes inferConcatShape(const Shapes &in_shapes, const ir::operation::Concat::Param &param)
-{
-  const int32_t concat_axis = param.axis;
-  const auto &first_in_shape = in_shapes[0];
-
-  // Check that all shapes are equal except for concat axis dimension
-  for (const auto &in_shape : in_shapes)
-  {
-    assert(in_shape.rank() == first_in_shape.rank());
-    for (int64_t dim_idx = 0; dim_idx < in_shape.rank(); ++dim_idx)
-      assert(dim_idx == concat_axis || in_shape.dim(dim_idx) == first_in_shape.dim(dim_idx));
-  }
-
-  // Calculate output shape
-  ir::Shape out_shape(first_in_shape);
-  out_shape.dim(concat_axis) = 0;
-  for (const auto &in_shape : in_shapes)
-    out_shape.dim(concat_axis) += in_shape.dim(concat_axis);
-  return {out_shape};
-}
-
-Shapes inferMaxPoolShape(const ir::Shape &in_shape, const ir::operation::MaxPool2D::Param &param,
-                         const ir::Layout layout)
-{
-  assert(layout == ir::Layout::NHWC);
-  auto ifm_shape = in_shape.asFeature(layout);
-  const auto out_h_w = calcConvLikeHeightAndWidth(ifm_shape.H, ifm_shape.W, param.kh, param.kw,
-                                                  param.padding, param.stride);
-  // Pooling don't change number of channels and batch size
-  return {ir::Shape{ifm_shape.N, out_h_w.first, out_h_w.second, ifm_shape.C}};
-}
-
-Shapes inferConv2DShape(const ir::Shape &in_shape, const ir::Shape &ker_shape,
-                        const ir::operation::Conv2D::Param &param, ir::Layout layout)
-{
-  assert(layout == ir::Layout::NHWC);
-  auto ifm_shape = in_shape.asFeature(layout);
-
-  // Kernel format is [depth_out, kernel_height, kernel_width, depth_in]
-  auto kf_shape = ker_shape.asFeature(layout);
-  assert(ifm_shape.C == kf_shape.C);
-
-  const auto out_h_w = calcConvLikeHeightAndWidth(ifm_shape.H, ifm_shape.W, kf_shape.H, kf_shape.W,
-                                                  param.padding, param.stride);
-
-  return {ir::Shape{ifm_shape.N, out_h_w.first, out_h_w.second, kf_shape.N}};
-}
-
-Shapes inferDepthwiseConv2DShape(const ir::Shape &in_shape, const ir::Shape &ker_shape,
-                                 const ir::operation::DepthwiseConv2D::Param &param,
-                                 ir::Layout layout)
-{
-  assert(layout == ir::Layout::NHWC);
-  auto ifm_shape = in_shape.asFeature(layout);
-
-  // Kernel format is [1, kernel_height, kernel_width, depth_out]
-  auto kf_shape = ker_shape.asFeature(layout);
-  assert(kf_shape.C == static_cast<int32_t>(ifm_shape.C * param.multiplier));
-  assert(kf_shape.N == 1);
-
-  const auto out_h_w = calcConvLikeHeightAndWidth(ifm_shape.H, ifm_shape.W, kf_shape.H, kf_shape.W,
-                                                  param.padding, param.stride);
-
-  return {ir::Shape{ifm_shape.N, out_h_w.first, out_h_w.second, kf_shape.C}};
-}
-
-Shapes inferFullyConnectedShape(const ir::Shape &in_shape, const ir::Shape &ker_shape)
-{
-  assert(in_shape.rank() >= 2);
-  assert(ker_shape.rank() == 2);
-
-  const auto input_size_with_batch = in_shape.num_elements();
-  const auto num_units = ker_shape.dim(0);
-  const auto input_size = ker_shape.dim(1);
-  const auto batch_size = input_size_with_batch / input_size;
-  assert(input_size_with_batch % input_size == 0);
-
-  return {{ir::Shape({static_cast<int32_t>(batch_size), num_units})}};
-}
-
-} // namespace shape_inference
-} // namespace neurun
diff --git a/runtime/neurun/core/src/util/Utils.cc b/runtime/neurun/core/src/util/Utils.cc
deleted file mode 100644
index 1e24e28d4..000000000
--- a/runtime/neurun/core/src/util/Utils.cc
+++ /dev/null
@@ -1,68 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "util/Utils.h"
-
-#include <cassert>
-
-namespace neurun
-{
-namespace util
-{
-
-const char *to_string(const ir::PaddingType type)
-{
-  assert((type == ir::PaddingType::EXPLICIT) || (type == ir::PaddingType::SAME) ||
-         (type == ir::PaddingType::VALID));
-
-  switch (type)
-  {
-    case ir::PaddingType::EXPLICIT:
-      return "Padding::EXPLICIT";
-    case ir::PaddingType::SAME:
-      return "Padding::SAME";
-    case ir::PaddingType::VALID:
-      return "Padding::VALID";
-  }
-
-  return nullptr;
-}
-
-Coordinates convertCoordinates(const Coordinates &from_coordinates, ir::Layout from_layout,
-                               ir::Layout to_layout)
-{
-  assert(from_coordinates.size() == 4);
-  Coordinates to{from_coordinates};
-  if (from_layout == ir::Layout::NHWC && to_layout == ir::Layout::NCHW)
-  {
-    to.set(0, from_coordinates[0]);
-    to.set(1, from_coordinates[3]);
-    to.set(2, from_coordinates[1]);
-    to.set(3, from_coordinates[2]);
-  }
-  else if (from_layout == ir::Layout::NCHW && to_layout == ir::Layout::NHWC)
-  {
-    to.set(0, from_coordinates[0]);
-    to.set(1, from_coordinates[2]);
-    to.set(2, from_coordinates[3]);
-    to.set(3, from_coordinates[1]);
-  }
-
-  return to;
-}
-
-} // namespace util
-} // namespace neurun
diff --git a/runtime/neurun/core/src/util/logging.cc b/runtime/neurun/core/src/util/logging.cc
deleted file mode 100644
index c23e2b53c..000000000
--- a/runtime/neurun/core/src/util/logging.cc
+++ /dev/null
@@ -1,7 +0,0 @@
-#include "util/logging.h"
-
-neurun::util::logging::Context &neurun::util::logging::Context::get() noexcept
-{
-  static Context ctx;
-  return ctx;
-}
diff --git a/runtime/neurun/frontend/CMakeLists.txt b/runtime/neurun/frontend/CMakeLists.txt
deleted file mode 100644
index 5ea6cdadd..000000000
--- a/runtime/neurun/frontend/CMakeLists.txt
+++ /dev/null
@@ -1 +0,0 @@
-add_subdirectories()
diff --git a/runtime/neurun/frontend/base_loader/CMakeLists.txt b/runtime/neurun/frontend/base_loader/CMakeLists.txt
deleted file mode 100644
index 358fc2646..000000000
--- a/runtime/neurun/frontend/base_loader/CMakeLists.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-if(NOT BUILD_TFLITE_LOADER AND NOT BUILD_CIRCLE_LOADER)
-  return()
-endif(NOT BUILD_TFLITE_LOADER AND NOT BUILD_CIRCLE_LOADER)
-
-add_library(base_loader INTERFACE)
-target_include_directories(base_loader INTERFACE ${CMAKE_CURRENT_SOURCE_DIR}/include)
-target_link_libraries(base_loader INTERFACE neurun_core nnfw_lib_cpp14)
diff --git a/runtime/neurun/frontend/base_loader/include/base_loader.h b/runtime/neurun/frontend/base_loader/include/base_loader.h
deleted file mode 100644
index ae1562f6c..000000000
--- a/runtime/neurun/frontend/base_loader/include/base_loader.h
+++ /dev/null
@@ -1,1278 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __BASE_LOADER_BASE_LOADER_H__
-#define __BASE_LOADER_BASE_LOADER_H__
-
-#include "ir/Graph.h"
-#include "ir/Operations.Include.h"
-
-#include <map>
-#include <cpp14/memory.h>
-#include <fstream>
-#include <limits>
-
-namespace neurun
-{
-namespace base_loader
-{
-
-template <typename LoaderDomain, typename SpecificLoader> class BaseLoader
-{
-  using Verifier = typename LoaderDomain::Verifier;
-  using ActivationFunctionType = typename LoaderDomain::ActivationFunctionType;
-  using Buffer = typename LoaderDomain::Buffer;
-  using BuiltinOperator = typename LoaderDomain::BuiltinOperator;
-  using CustomOptionsFormat = typename LoaderDomain::CustomOptionsFormat;
-  using Model = typename LoaderDomain::Model;
-  using Operator = typename LoaderDomain::Operator;
-  using Padding = typename LoaderDomain::Padding;
-  using Pool2DOptions = typename LoaderDomain::Pool2DOptions;
-  using SubGraph = typename LoaderDomain::SubGraph;
-  using Tensor = typename LoaderDomain::Tensor;
-  using TensorType = typename LoaderDomain::TensorType;
-
-public:
-  /**
-   * @brief Construct a new Loader object
-   *
-   * @param graph reference on graph
-   */
-  explicit BaseLoader(ir::Graph &graph) : _graph(graph), _model{nullptr} {}
-
-  /**
-   * @brief Load a model from file
-   *
-   * @param file_path
-   */
-  void loadFromFile(const char *file_path);
-
-protected:
-  ~BaseLoader() = default;
-
-  void loadModel();
-
-  // Helper functions
-  ir::Activation convertActivation(ActivationFunctionType type);
-  ir::DataType tensorTypeToDataType(TensorType type);
-
-  // Create operands form tflite::Tensor
-  ir::OperandIndex loadOperand(const Tensor *tensor);
-  void loadOperationIO(const Operator *op, ir::OperandIndexSequence &inputs,
-                       ir::OperandIndexSequence &outputs);
-  // Create operations from Operator
-  void loadOperation(const Operator *op);
-  // Load Strides and Paddings from options to param
-  template <typename Param, typename OptionsType>
-  void loadStridesAndPaddings(Param &param, const OptionsType *options);
-  // Load Pool2D param
-  template <typename Param> void loadPool2D(Param &param, const Pool2DOptions *options);
-
-  // Operations
-  void loadConv2D(const Operator *op);
-  void loadDepthwiseConv2D(const Operator *op);
-  void loadTransposeConv(const Operator *op);
-  void loadAvgPool2D(const Operator *op);
-  void loadReshape(const Operator *op);
-  void loadSoftmax(const Operator *op);
-  void loadMaxPool2D(const Operator *op);
-  void loadConcatenation(const Operator *op);
-  void loadInstanceNorm(const Operator *op);
-  void loadFC(const Operator *op);
-  void loadAdd(const Operator *op);
-  void loadSub(const Operator *op);
-  void loadMul(const Operator *op);
-  void loadDiv(const Operator *op);
-  void loadPack(const Operator *op);
-  void loadRelu(const Operator *op);
-  void loadRelu6(const Operator *op);
-  void loadResizeBilinear(const Operator *op);
-  void loadRsqrt(const Operator *op);
-  void loadSqrt(const Operator *op);
-  void loadSquaredDifference(const Operator *op);
-  void loadTanh(const Operator *op);
-  void loadTranspose(const Operator *op);
-  void loadMean(const Operator *op);
-  void loadReduceMax(const Operator *op);
-  void loadPad(const Operator *op);
-  void loadLogistic(const Operator *op);
-  void loadExp(const Operator *op);
-  void loadGather(const Operator *op);
-  void loadCustom(const Operator *op);
-  void loadSpaceToBatchND(const Operator *op);
-  void loadBatchToSpaceND(const Operator *op);
-  void loadReduceSum(const Operator *op);
-  void loadSqueeze(const Operator *op);
-  void loadPrelu(const Operator *op);
-  void loadSplit(const Operator *op);
-  void loadSlice(const Operator *op);
-  void loadStridedSlice(const Operator *op);
-  void loadUnpack(const Operator *op);
-  void loadMinimum(const Operator *op);
-  void loadMaximum(const Operator *op);
-  void loadCast(const Operator *op);
-  void loadComparison(const Operator *op);
-  void loadOneHot(const Operator *op);
-
-protected:
-  // Buffer for loading (if needed)
-  std::vector<char> _buffer;
-  // Reference on loadable Graph
-  ir::Graph &_graph;
-  const Model *_model;
-  // Maps Tensor indices to neurun Operands.
-  std::vector<ir::OperandIndex> _tensor_to_operand;
-  // Verifier
-  std::unique_ptr<Verifier> _verifier;
-};
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::BaseLoader::loadFromFile(const char *file_path)
-{
-  std::ifstream stream(file_path, std::fstream::in | std::fstream::binary);
-
-  if (!stream)
-  {
-    std::string msg = "Failed to open file `";
-    msg += file_path;
-    msg += "`";
-    throw std::runtime_error{msg};
-  }
-
-  stream.seekg(0, stream.end);
-  auto size = stream.tellg();
-  stream.seekg(0, stream.beg);
-
-  _buffer.resize(size);
-  stream.read(_buffer.data(), size);
-
-  stream.close();
-
-  // Prepare verifier
-  _verifier = nnfw::cpp14::make_unique<Verifier>(
-      reinterpret_cast<const std::uint8_t *>(_buffer.data()), _buffer.size());
-
-  loadModel();
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-ir::Activation BaseLoader<LoaderDomain, SpecificLoader>::BaseLoader::convertActivation(
-    const ActivationFunctionType type)
-{
-  switch (type)
-  {
-    case ActivationFunctionType::ActivationFunctionType_NONE:
-      return ir::Activation::NONE;
-    case ActivationFunctionType::ActivationFunctionType_RELU:
-      return ir::Activation::RELU;
-    case ActivationFunctionType::ActivationFunctionType_RELU_N1_TO_1:
-      return ir::Activation::RELU1;
-    case ActivationFunctionType::ActivationFunctionType_RELU6:
-      return ir::Activation::RELU6;
-    case ActivationFunctionType::ActivationFunctionType_TANH:
-      return ir::Activation::TANH;
-    default:
-      throw std::runtime_error(std::string("Unsupported activation type: ")
-                                   .append(EnumNameActivationFunctionType(type)));
-  }
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-ir::DataType
-BaseLoader<LoaderDomain, SpecificLoader>::BaseLoader::tensorTypeToDataType(const TensorType type)
-{
-  switch (type)
-  {
-    case TensorType::TensorType_FLOAT32:
-      return ir::DataType::FLOAT32;
-    case TensorType::TensorType_INT32:
-      return ir::DataType::INT32;
-    case TensorType::TensorType_BOOL:
-      return ir::DataType::BOOL8;
-    case TensorType::TensorType_UINT8:
-      return ir::DataType::UINT8;
-    default:
-      throw std::runtime_error(
-          std::string("Unsupported tensor type: ").append(EnumNameTensorType(type)));
-  }
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-ir::OperandIndex BaseLoader<LoaderDomain, SpecificLoader>::loadOperand(const Tensor *tensor)
-{
-  ir::Shape shape;
-  // Shape
-  const auto *tensor_shape = tensor->shape();
-  for (const auto &dim : *tensor_shape)
-  {
-    shape.append(dim);
-  }
-  // Type
-  ir::DataType data_type = tensorTypeToDataType(tensor->type());
-  // Quantization
-  auto q_params = tensor->quantization();
-  float scale = 0.0;
-  long zero_point = 0;
-  if (q_params != nullptr)
-  {
-    if (q_params->scale())
-    {
-      if (q_params->scale()->size() != 1)
-      {
-        throw std::runtime_error("Only 1 scale for a tensor is supported.");
-      }
-      scale = q_params->scale()->Get(0);
-    }
-
-    if (q_params->zero_point())
-    {
-      if (q_params->zero_point()->size() != 1)
-      {
-        throw std::runtime_error("Only 1 zero_point value for a tensor is supported.");
-      }
-      zero_point = q_params->zero_point()->Get(0);
-      // zero_point is long while TypeInfo.zero_point is defined as int32_t.
-      assert(zero_point >= std::numeric_limits<int32_t>::min());
-      assert(zero_point <= std::numeric_limits<int32_t>::max());
-    }
-    auto details = q_params->details_as_CustomQuantization();
-    if (details != nullptr)
-      throw std::runtime_error("Custom Quantization is not supported");
-
-    if (q_params->scale() && q_params->zero_point())
-    {
-      data_type = ir::DataType::QUANT8_ASYMM;
-    }
-  }
-  // Create TypeInfo
-  ir::TypeInfo type_info(data_type, scale, zero_point);
-  // Create operand
-  const auto operand_index = _graph.addOperand(shape, type_info);
-
-  // Constant tensors are indicated by non-empty data.
-  const auto *data = _model->buffers()->Get(tensor->buffer())->data();
-  if (data != nullptr)
-  {
-    auto ptr = nnfw::cpp14::make_unique<ir::CachedData>(data->data(), data->size());
-    _graph.setOperandValue(operand_index, std::move(ptr));
-  }
-
-  // Name unused
-  // auto name = tensor->name();
-  // Variablie
-  if (tensor->is_variable())
-    throw std::runtime_error("Variable tensor not supported!");
-
-  return operand_index;
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadOperationIO(const Operator *op,
-                                                               ir::OperandIndexSequence &inputs,
-                                                               ir::OperandIndexSequence &outputs)
-{
-  for (const std::int32_t idx : *op->inputs())
-  {
-    inputs.append(_tensor_to_operand[idx]);
-  }
-
-  for (const std::int32_t idx : *op->outputs())
-  {
-    outputs.append(_tensor_to_operand[idx]);
-  }
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-template <typename Param, typename OptionsType>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadStridesAndPaddings(Param &param,
-                                                                      const OptionsType *options)
-{
-  // Strides
-  param.stride.vertical = options->stride_w();
-  param.stride.horizontal = options->stride_h();
-  // Paddings
-  if (options->padding() == Padding::Padding_SAME)
-    param.padding.type = ir::PaddingType::SAME;
-  if (options->padding() == Padding::Padding_VALID)
-    param.padding.type = ir::PaddingType::VALID;
-  // param paddings indexes unused
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-template <typename Param>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadPool2D(Param &param,
-                                                          const Pool2DOptions *options)
-{
-  // Strides and Paddings
-  loadStridesAndPaddings(param, options);
-  // Filter width and height
-  // Strides
-  param.kw = options->filter_width();
-  param.kh = options->filter_height();
-  // Activation
-  param.activation = convertActivation(options->fused_activation_function());
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadConv2D(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  ir::operation::Conv2D::Param param;
-  const auto *options = op->builtin_options_as_Conv2DOptions();
-  param.activation = convertActivation(options->fused_activation_function());
-  loadStridesAndPaddings(param, options);
-  // Dilation h/w factor unused
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::Conv2D(inputs, outputs, param));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadDepthwiseConv2D(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  ir::operation::DepthwiseConv2D::Param param;
-  const auto *options = op->builtin_options_as_DepthwiseConv2DOptions();
-  param.activation = convertActivation(options->fused_activation_function());
-  loadStridesAndPaddings(param, options);
-  // Multiplier
-  param.multiplier = options->depth_multiplier();
-  // Dilation h/w factor unused
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::DepthwiseConv2D(inputs, outputs, param));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadTransposeConv(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  ir::operation::TransposeConv::Param param;
-  const auto *options = op->builtin_options_as_TransposeConvOptions();
-  loadStridesAndPaddings(param, options);
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::TransposeConv(inputs, outputs, param));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadAvgPool2D(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  ir::operation::AvgPool2D::Param param;
-  const auto *options = op->builtin_options_as_Pool2DOptions();
-
-  loadPool2D(param, options);
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::AvgPool2D(inputs, outputs, param));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadReshape(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  // const auto *options = op->builtin_options_as_ReshapeOptions();
-  // No params
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::Reshape(inputs, outputs));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadSoftmax(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  ir::operation::Softmax::Param param;
-  const auto *options = op->builtin_options_as_SoftmaxOptions();
-  // Beta
-  param.beta = options->beta();
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::Softmax(inputs, outputs, param));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadMaxPool2D(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  ir::operation::MaxPool2D::Param param;
-  const auto *options = op->builtin_options_as_Pool2DOptions();
-
-  loadPool2D(param, options);
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::MaxPool2D(inputs, outputs, param));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadConcatenation(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  ir::operation::Concat::Param param;
-  const auto *options = op->builtin_options_as_ConcatenationOptions();
-  // Axis
-  param.axis = options->axis();
-  param.rank = _graph.operands().at(outputs.at(0)).shape().rank();
-  // activation unused
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::Concat(inputs, outputs, param));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadInstanceNorm(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  ir::operation::InstanceNorm::Param param;
-  const auto *options = op->builtin_options_as_InstanceNormOptions();
-
-  param.activation = convertActivation(options->fused_activation_function());
-  // Use default value 1e-5 if value of epsilon is zero
-  param.epsilon = options->epsilon() == 0.f ? 1e-5 : options->epsilon();
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::InstanceNorm(inputs, outputs, param));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadFC(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  const auto &input_operand = _graph.operands().at(inputs.at(ir::operation::FullyConnected::INPUT));
-  auto &weights_operand = _graph.operands().at(inputs.at(ir::operation::FullyConnected::WEIGHT));
-  if (input_operand.typeInfo().type() == ir::DataType::FLOAT32 &&
-      weights_operand.typeInfo().type() == ir::DataType::QUANT8_ASYMM)
-  {
-    weights_operand.type(ir::DataType::QUANT8_SYMM);
-  }
-
-  ir::operation::FullyConnected::Param param;
-  const auto *options = op->builtin_options_as_FullyConnectedOptions();
-
-  param.activation = convertActivation(options->fused_activation_function());
-  // weights_format unused
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::FullyConnected(inputs, outputs, param));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadAdd(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  ir::operation::Add::Param param;
-  const auto *options = op->builtin_options_as_AddOptions();
-
-  param.activation = convertActivation(options->fused_activation_function());
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::Add(inputs, outputs, param));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadSub(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  ir::operation::Sub::Param param;
-  const auto *options = op->builtin_options_as_SubOptions();
-
-  param.activation = convertActivation(options->fused_activation_function());
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::Sub(inputs, outputs, param));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadMul(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  ir::operation::Mul::Param param;
-  const auto *options = op->builtin_options_as_MulOptions();
-
-  param.activation = convertActivation(options->fused_activation_function());
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::Mul(inputs, outputs, param));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadDiv(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  ir::operation::Div::Param param;
-  const auto *options = op->builtin_options_as_DivOptions();
-
-  param.activation = convertActivation(options->fused_activation_function());
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::Div(inputs, outputs, param));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadPack(const Operator *op)
-{
-  // This runtime_error will be removed if the one of backend supports this operation
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  ir::operation::Pack::Param param;
-  const auto *options = op->builtin_options_as_PackOptions();
-  param.num = options->values_count();
-  param.axis = options->axis();
-  param.rank = _graph.operands().at(outputs.at(0)).shape().rank();
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::Pack(inputs, outputs, param));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadRelu(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::ReLU(inputs, outputs));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadRelu6(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::ReLU6(inputs, outputs));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadResizeBilinear(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-  auto input = inputs.at(0);
-  auto size = inputs.at(1);
-
-  // FIXME Handle ResizeBilinearOptions.
-  if (!_graph.operands().at(size).isConstant())
-    throw std::runtime_error("ResizeBilinear: non-constant 'size' is not supported.");
-
-  std::vector<std::int32_t> size_v = _graph.operands().at(size).template asVector<std::int32_t>();
-
-  ir::operation::ResizeBilinear::Param param;
-  param.height_out = size_v[0];
-  param.width_out = size_v[1];
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::ResizeBilinear({input}, outputs, param));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadRsqrt(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::RSQRT(inputs, outputs));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadSqrt(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::SQRT(inputs, outputs));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadSquaredDifference(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::SquaredDifference(inputs, outputs));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadTanh(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::Tanh(inputs, outputs));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadTranspose(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-  auto input = inputs.at(0);
-  auto perm = inputs.at(1);
-
-  if (!_graph.operands().at(perm).isConstant())
-    throw std::runtime_error("Transpose: non-constant 'perm' is not supported.");
-
-  ir::operation::Transpose::Param param;
-  param.perm = _graph.operands().at(perm).template asVector<int>();
-  param.rank = _graph.operands().at(inputs.at(0)).shape().rank();
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::Transpose({input}, outputs, param));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadMean(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-  auto input = inputs.at(0);
-  auto axes = inputs.at(1);
-
-  if (!_graph.operands().at(axes).isConstant())
-    throw std::runtime_error("Mean: non-constant 'axes' is not supported.");
-
-  ir::operation::Mean::Param param;
-  param.axes = _graph.operands().at(axes).template asVector<int>();
-  param.keep_dims = op->builtin_options_as_ReducerOptions()->keep_dims();
-  param.rank = _graph.operands().at(inputs.at(0)).shape().rank();
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::Mean({input}, outputs, param));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadReduceMax(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-  auto input = inputs.at(0);
-  auto axes = inputs.at(1);
-
-  // FIXME Handle ReducerOptions.
-  if (!_graph.operands().at(axes).isConstant())
-    throw std::runtime_error("ReduceSum: non-constant 'axes' is not supported.");
-
-  ir::operation::ReduceMax::Param param;
-  param.axes = _graph.operands().at(axes).template asVector<int>();
-  param.keep_dims = op->builtin_options_as_ReducerOptions()->keep_dims();
-  param.rank = _graph.operands().at(inputs.at(0)).shape().rank();
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::ReduceMax({input}, outputs, param));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadPad(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  ir::operation::Pad::Param param;
-  param.rank = _graph.operands().at(inputs.at(0)).shape().rank();
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::Pad(inputs, outputs, param));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadLogistic(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::Logistic(inputs, outputs));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadExp(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::Exp(inputs, outputs));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadGather(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-  ir::operation::Gather::Param param;
-  param.axis = op->builtin_options_as_GatherOptions()->axis();
-  param.rank = _graph.operands().at(inputs.at(0)).shape().rank();
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::Gather(inputs, outputs, param));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadSpaceToBatchND(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  std::unique_ptr<ir::Operation> new_op{new ir::operation::SpaceToBatchND{inputs, outputs}};
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadBatchToSpaceND(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-  auto input = inputs.at(0);
-  auto block_shape = inputs.at(1);
-  auto crops = inputs.at(2);
-
-  if (!_graph.operands().at(crops).isConstant())
-    throw std::runtime_error("BatchToSpaceND: non-constant 'crops' is not supported.");
-
-  std::vector<std::int32_t> crops_v = _graph.operands().at(crops).template asVector<std::int32_t>();
-  assert(crops_v.size() == 4);
-  if (crops_v != std::vector<std::int32_t>{0, 0, 0, 0})
-    throw std::runtime_error("BatchToSpaceND: 'crops' other than {0, 0, 0, 0} is not supported.");
-
-  std::unique_ptr<ir::Operation> new_op{
-      new ir::operation::BatchToSpaceND{{input, block_shape}, outputs}};
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadReduceSum(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-  auto input = inputs.at(0);
-  auto axes = inputs.at(1);
-
-  // FIXME Handle ReducerOptions.
-  if (!_graph.operands().at(axes).isConstant())
-    throw std::runtime_error("ReduceSum: non-constant 'axes' is not supported.");
-
-  ir::operation::ReduceSum::Param param;
-  param.axes = _graph.operands().at(axes).template asVector<int>();
-  param.keep_dims = op->builtin_options_as_ReducerOptions()->keep_dims();
-  param.rank = _graph.operands().at(inputs.at(0)).shape().rank();
-
-  std::unique_ptr<ir::Operation> new_op{new ir::operation::ReduceSum{{input}, outputs, param}};
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadCustom(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  auto *op_code = _model->operator_codes()->Get(op->opcode_index());
-  auto custom_op_id = op_code->custom_code()->str();
-
-  auto constraint = ir::OperandConstraint::createExact(inputs.size());
-
-  assert(op->custom_options_format() == CustomOptionsFormat::CustomOptionsFormat_FLEXBUFFERS &&
-         "Unsupported custom operation options format");
-
-  size_t custom_op_data_size = op->custom_options()->size();
-  auto custom_op_data = new char[custom_op_data_size];
-  std::copy(op->custom_options()->begin(), op->custom_options()->end(), custom_op_data);
-
-  ir::operation::Custom::Userdata userdata{};
-  userdata.data = custom_op_data;
-  userdata.size = custom_op_data_size;
-
-  auto new_op = nnfw::cpp14::make_unique<ir::operation::Custom>(constraint, inputs, outputs,
-                                                                custom_op_id, userdata);
-
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadSqueeze(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  ir::operation::Squeeze::Param param{};
-  const auto *options = op->builtin_options_as_SqueezeOptions();
-  const auto *dims = options->squeeze_dims();
-  if (dims)
-  {
-    if (dims->Length() > sizeof(param.dims) / sizeof(param.dims[0]))
-      throw std::runtime_error("Squeeze: 'param.ndims' is out of range.");
-    param.ndim = dims->Length();
-    for (int i = 0; i < param.ndim; ++i)
-      param.dims[i] = dims->Get(i);
-  }
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::Squeeze(inputs, outputs, param));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadPrelu(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::PReLU(inputs, outputs));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadSplit(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-  // Notice : input order is strange for tflite split
-  auto input = inputs.at(1);
-  auto axis = inputs.at(0);
-
-  // FIXME Handle SplitOptions.
-  if (!_graph.operands().at(axis).isConstant())
-    throw std::runtime_error("Split: non-constant 'axis' is not supported.");
-
-  ir::operation::Split::Param param{};
-  param.axis = _graph.operands().at(axis).template asScalar<int>();
-  const auto *options = op->builtin_options_as_SplitOptions();
-  param.num_splits = options->num_splits();
-  param.rank = _graph.operands().at(inputs.at(0)).shape().rank();
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::Split({input}, outputs, param));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadSlice(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  ir::operation::Slice::Param param;
-  param.rank = _graph.operands().at(inputs.at(0)).shape().rank();
-
-  std::unique_ptr<ir::Operation> new_op{new ir::operation::Slice{inputs, outputs, param}};
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadStridedSlice(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  ir::operation::StridedSlice::Param param;
-
-  const auto *options = op->builtin_options_as_StridedSliceOptions();
-  param.begin_mask = options->begin_mask();
-  param.end_mask = options->end_mask();
-  param.shrink_axis_mask = options->shrink_axis_mask();
-  param.rank = _graph.operands().at(inputs.at(0)).shape().rank();
-
-  std::unique_ptr<ir::Operation> new_op{new ir::operation::StridedSlice{inputs, outputs, param}};
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadUnpack(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  ir::operation::Unpack::Param param;
-  const auto *options = op->builtin_options_as_UnpackOptions();
-  param.num = options->num();
-  param.axis = options->axis();
-  param.rank = _graph.operands().at(inputs.at(0)).shape().rank();
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::Unpack(inputs, outputs, param));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadMinimum(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::Min(inputs, outputs));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadMaximum(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::Max(inputs, outputs));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadCast(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::Cast(inputs, outputs));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadComparison(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  ir::operation::Comparison::Param param;
-
-  const auto builtin_op = _model->operator_codes()->Get(op->opcode_index())->builtin_code();
-
-  switch (builtin_op)
-  {
-    case BuiltinOperator::BuiltinOperator_EQUAL:
-      param.comparison_type = ir::operation::Comparison::ComparisonType::Equal;
-      break;
-    case BuiltinOperator::BuiltinOperator_GREATER_EQUAL:
-      param.comparison_type = ir::operation::Comparison::ComparisonType::GreaterEqual;
-      break;
-    case BuiltinOperator::BuiltinOperator_GREATER:
-      param.comparison_type = ir::operation::Comparison::ComparisonType::Greater;
-      break;
-    case BuiltinOperator::BuiltinOperator_LESS_EQUAL:
-      param.comparison_type = ir::operation::Comparison::ComparisonType::LessEqual;
-      break;
-    case BuiltinOperator::BuiltinOperator_LESS:
-      param.comparison_type = ir::operation::Comparison::ComparisonType::Less;
-      break;
-    default:
-      throw std::runtime_error(
-          std::string("Unsupported operation: ").append(EnumNameBuiltinOperator(builtin_op)));
-  }
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::Comparison(inputs, outputs, param));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadOneHot(const Operator *op)
-{
-  ir::OperandIndexSequence inputs;
-  ir::OperandIndexSequence outputs;
-
-  loadOperationIO(op, inputs, outputs);
-
-  ir::operation::OneHot::Param param{};
-  const auto *options = op->builtin_options_as_OneHotOptions();
-  const auto axis = options->axis();
-  const auto &indices = _graph.operands().at(inputs.at(ir::operation::OneHot::INDICES));
-  auto indices_dims = indices.shape().rank();
-  param.axis = (axis == -1) ? indices_dims : axis;
-
-  std::unique_ptr<ir::Operation> new_op(new ir::operation::OneHot(inputs, outputs, param));
-  _graph.addOperation(std::move(new_op));
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadOperation(const Operator *op)
-{
-  const auto builtin_op = _model->operator_codes()->Get(op->opcode_index())->builtin_code();
-
-  switch (builtin_op)
-  {
-    case BuiltinOperator::BuiltinOperator_CONV_2D:
-      loadConv2D(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_AVERAGE_POOL_2D:
-      loadAvgPool2D(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_DEPTHWISE_CONV_2D:
-      loadDepthwiseConv2D(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_TRANSPOSE_CONV:
-      loadTransposeConv(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_RESHAPE:
-      loadReshape(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_SOFTMAX:
-      loadSoftmax(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_MAX_POOL_2D:
-      loadMaxPool2D(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_CONCATENATION:
-      loadConcatenation(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_FULLY_CONNECTED:
-      loadFC(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_ADD:
-      loadAdd(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_SUB:
-      loadSub(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_MUL:
-      loadMul(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_DIV:
-      loadDiv(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_PACK:
-      loadPack(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_RELU:
-      loadRelu(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_RELU6:
-      loadRelu6(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_RESIZE_BILINEAR:
-      loadResizeBilinear(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_RSQRT:
-      loadRsqrt(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_SQRT:
-      loadSqrt(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_SQUARED_DIFFERENCE:
-      loadSquaredDifference(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_TANH:
-      loadTanh(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_TRANSPOSE:
-      loadTranspose(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_MEAN:
-      loadMean(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_REDUCE_MAX:
-      loadReduceMax(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_PAD:
-      loadPad(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_LOGISTIC:
-      loadLogistic(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_EXP:
-      loadExp(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_GATHER:
-      loadGather(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_SPACE_TO_BATCH_ND:
-      loadSpaceToBatchND(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_BATCH_TO_SPACE_ND:
-      loadBatchToSpaceND(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_SUM:
-      loadReduceSum(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_CUSTOM:
-      loadCustom(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_SQUEEZE:
-      loadSqueeze(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_PRELU:
-      loadPrelu(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_SPLIT:
-      loadSplit(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_SLICE:
-      loadSlice(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_STRIDED_SLICE:
-      loadStridedSlice(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_UNPACK:
-      loadUnpack(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_MINIMUM:
-      loadMinimum(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_MAXIMUM:
-      loadMaximum(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_CAST:
-      loadCast(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_EQUAL:
-    case BuiltinOperator::BuiltinOperator_GREATER_EQUAL:
-    case BuiltinOperator::BuiltinOperator_GREATER:
-    case BuiltinOperator::BuiltinOperator_LESS_EQUAL:
-    case BuiltinOperator::BuiltinOperator_LESS:
-      loadComparison(op);
-      return;
-    case BuiltinOperator::BuiltinOperator_ONE_HOT:
-      loadOneHot(op);
-      return;
-    default:
-      throw std::runtime_error(
-          std::string("Unsupported operation: ").append(EnumNameBuiltinOperator(builtin_op)));
-  }
-}
-
-template <typename LoaderDomain, typename SpecificLoader>
-void BaseLoader<LoaderDomain, SpecificLoader>::loadModel()
-{
-  LoaderDomain::VerifyModelBuffer(*_verifier.get());
-  _model = LoaderDomain::GetModel(_buffer.data());
-  // Version unused
-  // const auto version = _model->version();
-  // Description unused
-  // const auto *description = _model->description();
-  // Metabuffer unsued
-  // const auto *metadata_buffer = _model->metadata_buffer();
-  // Load subgraphs and mapping from op to subgraph
-  for (const auto *subgraph : *_model->subgraphs())
-  {
-    static_cast<SpecificLoader *>(this)->loadSubgraph(subgraph);
-  }
-
-  _graph.finishBuilding();
-}
-
-} // namespace base_loader
-} // namespace neurun
-
-#endif //__BASE_LOADER_BASE_LOADER_H__
diff --git a/runtime/neurun/frontend/circle/CMakeLists.txt b/runtime/neurun/frontend/circle/CMakeLists.txt
deleted file mode 100644
index fea9725c2..000000000
--- a/runtime/neurun/frontend/circle/CMakeLists.txt
+++ /dev/null
@@ -1,17 +0,0 @@
-if (NOT BUILD_CIRCLE_LOADER)
-    return()
-endif ()
-
-nnfw_find_package(FlatBuffersSource REQUIRED)
-
-set(CIRCLE_LOADER_SOURCES src/circle_loader.cc)
-
-add_library(circle_loader SHARED ${CIRCLE_LOADER_SOURCES})
-
-target_include_directories(circle_loader PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/include)
-target_include_directories(circle_loader PRIVATE ${FlatBuffersSource_DIR}/include)
-
-target_link_libraries(circle_loader PUBLIC neurun_core)
-target_link_libraries(circle_loader PRIVATE base_loader nnfw_lib_cpp14 nnfw_common nnfw_coverage)
-
-install(TARGETS circle_loader DESTINATION lib)
diff --git a/runtime/neurun/frontend/circle/include/circle_loader.h b/runtime/neurun/frontend/circle/include/circle_loader.h
deleted file mode 100644
index 898bd32b1..000000000
--- a/runtime/neurun/frontend/circle/include/circle_loader.h
+++ /dev/null
@@ -1,32 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __CIRCLE_CIRCLE_LOADER_H__
-#define __CIRCLE_CIRCLE_LOADER_H__
-
-#include "ir/Graph.h"
-
-#include <memory>
-
-namespace neurun
-{
-namespace circle_loader
-{
-std::unique_ptr<ir::Graph> loadModel(const char *filename);
-} // namespace circle_loader
-} // namespace neurun
-
-#endif // __CIRCLE_CIRCLE_LOADER_H__
diff --git a/runtime/neurun/frontend/circle/src/circle_loader.cc b/runtime/neurun/frontend/circle/src/circle_loader.cc
deleted file mode 100644
index cc48a793d..000000000
--- a/runtime/neurun/frontend/circle/src/circle_loader.cc
+++ /dev/null
@@ -1,116 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "circle_loader.h"
-#include "base_loader.h"
-#include "circle_schema_generated.h"
-
-namespace neurun
-{
-namespace circle_loader
-{
-
-namespace
-{
-
-struct LoaderDomain
-{
-  using Verifier = flatbuffers::Verifier;
-  using ActivationFunctionType = circle::ActivationFunctionType;
-  using Buffer = circle::Buffer;
-  using BuiltinOperator = circle::BuiltinOperator;
-  using CustomOptionsFormat = circle::CustomOptionsFormat;
-  using Model = circle::Model;
-  using Operator = circle::Operator;
-  using Padding = circle::Padding;
-  using Pool2DOptions = circle::Pool2DOptions;
-  using Tensor = circle::Tensor;
-  using TensorType = circle::TensorType;
-  using SubGraph = circle::SubGraph;
-
-  static const char *EnumNameBuiltinOperator(BuiltinOperator e)
-  {
-    return circle::EnumNameBuiltinOperator(e);
-  }
-  static const char *EnumNameActivationFunctionType(ActivationFunctionType e)
-  {
-    return circle::EnumNameActivationFunctionType(e);
-  }
-  static const char *EnumNameTensorType(TensorType e) { return circle::EnumNameTensorType(e); }
-  static const Model *GetModel(const void *buf) { return circle::GetModel(buf); }
-  static bool VerifyModelBuffer(Verifier &verifier) { return circle::VerifyModelBuffer(verifier); }
-};
-
-class CircleLoader final : public base_loader::BaseLoader<LoaderDomain, CircleLoader>
-{
-public:
-  using BaseLoader::BaseLoader;
-
-  void loadSubgraph(const circle::SubGraph *subgraph)
-  {
-    // Load tensors
-    _tensor_to_operand.resize(subgraph->tensors()->size());
-    for (flatbuffers::uoffset_t i = 0; i < subgraph->tensors()->size(); ++i)
-    {
-      _tensor_to_operand[i] = loadOperand(subgraph->tensors()->Get(i));
-    }
-    // Set inputs
-    for (const std::int32_t input_ind : *subgraph->inputs())
-    {
-      _graph.addInput(_tensor_to_operand[input_ind]);
-    }
-    // Set outputs
-    for (const std::int32_t output_ind : *subgraph->outputs())
-    {
-      _graph.addOutput(_tensor_to_operand[output_ind]);
-    }
-    // Create operations
-    for (const auto *op : *subgraph->operators())
-    {
-      CircleLoader::loadOperation(op);
-    }
-
-    (void)subgraph->data_format();
-  }
-
-  void loadOperation(const circle::Operator *op)
-  {
-    const auto builtin_op = _model->operator_codes()->Get(op->opcode_index())->builtin_code();
-
-    switch (builtin_op)
-    {
-      case circle::BuiltinOperator::BuiltinOperator_INSTANCE_NORM:
-        loadInstanceNorm(op);
-        return;
-      default:
-        BaseLoader::loadOperation(op);
-        return;
-    }
-  }
-};
-
-} // namespace
-
-std::unique_ptr<ir::Graph> loadModel(const char *filename)
-{
-  auto graph = nnfw::cpp14::make_unique<ir::Graph>();
-  CircleLoader loader(*graph);
-  loader.loadFromFile(filename);
-  return graph;
-}
-
-} // namespace circle_loader
-} // namespace neurun
diff --git a/runtime/neurun/frontend/circle/src/circle_schema_generated.h b/runtime/neurun/frontend/circle/src/circle_schema_generated.h
deleted file mode 100644
index 5f7aad462..000000000
--- a/runtime/neurun/frontend/circle/src/circle_schema_generated.h
+++ /dev/null
@@ -1,7546 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-// automatically generated by the FlatBuffers compiler, do not modify
-
-#ifndef FLATBUFFERS_GENERATED_CIRCLESCHEMA_CIRCLE_H_
-#define FLATBUFFERS_GENERATED_CIRCLESCHEMA_CIRCLE_H_
-
-#include "flatbuffers/flatbuffers.h"
-
-namespace circle
-{
-
-struct CustomQuantization;
-
-struct QuantizationParameters;
-
-struct Tensor;
-
-struct Conv2DOptions;
-
-struct Pool2DOptions;
-
-struct DepthwiseConv2DOptions;
-
-struct ConcatEmbeddingsOptions;
-
-struct LSHProjectionOptions;
-
-struct SVDFOptions;
-
-struct RNNOptions;
-
-struct SequenceRNNOptions;
-
-struct BidirectionalSequenceRNNOptions;
-
-struct FullyConnectedOptions;
-
-struct SoftmaxOptions;
-
-struct ConcatenationOptions;
-
-struct AddOptions;
-
-struct MulOptions;
-
-struct L2NormOptions;
-
-struct LocalResponseNormalizationOptions;
-
-struct LSTMOptions;
-
-struct UnidirectionalSequenceLSTMOptions;
-
-struct BidirectionalSequenceLSTMOptions;
-
-struct ResizeBilinearOptions;
-
-struct ResizeNearestNeighborOptions;
-
-struct CallOptions;
-
-struct PadOptions;
-
-struct PadV2Options;
-
-struct ReshapeOptions;
-
-struct SpaceToBatchNDOptions;
-
-struct BatchToSpaceNDOptions;
-
-struct SkipGramOptions;
-
-struct SpaceToDepthOptions;
-
-struct SubOptions;
-
-struct DivOptions;
-
-struct TopKV2Options;
-
-struct EmbeddingLookupSparseOptions;
-
-struct GatherOptions;
-
-struct TransposeOptions;
-
-struct ExpOptions;
-
-struct ReducerOptions;
-
-struct SqueezeOptions;
-
-struct SplitOptions;
-
-struct SplitVOptions;
-
-struct StridedSliceOptions;
-
-struct LogSoftmaxOptions;
-
-struct CastOptions;
-
-struct DequantizeOptions;
-
-struct MaximumMinimumOptions;
-
-struct TileOptions;
-
-struct ArgMaxOptions;
-
-struct ArgMinOptions;
-
-struct GreaterOptions;
-
-struct GreaterEqualOptions;
-
-struct LessOptions;
-
-struct LessEqualOptions;
-
-struct NegOptions;
-
-struct SelectOptions;
-
-struct SliceOptions;
-
-struct TransposeConvOptions;
-
-struct ExpandDimsOptions;
-
-struct SparseToDenseOptions;
-
-struct EqualOptions;
-
-struct NotEqualOptions;
-
-struct ShapeOptions;
-
-struct PowOptions;
-
-struct FakeQuantOptions;
-
-struct PackOptions;
-
-struct LogicalOrOptions;
-
-struct OneHotOptions;
-
-struct AbsOptions;
-
-struct LogicalAndOptions;
-
-struct LogicalNotOptions;
-
-struct UnpackOptions;
-
-struct FloorDivOptions;
-
-struct SquareOptions;
-
-struct ZerosLikeOptions;
-
-struct FillOptions;
-
-struct FloorModOptions;
-
-struct RangeOptions;
-
-struct LeakyReluOptions;
-
-struct SquaredDifferenceOptions;
-
-struct MirrorPadOptions;
-
-struct InstanceNormOptions;
-
-struct OperatorCode;
-
-struct Operator;
-
-struct SubGraph;
-
-struct Buffer;
-
-struct Model;
-
-enum TensorType
-{
-  TensorType_FLOAT32 = 0,
-  TensorType_FLOAT16 = 1,
-  TensorType_INT32 = 2,
-  TensorType_UINT8 = 3,
-  TensorType_INT64 = 4,
-  TensorType_STRING = 5,
-  TensorType_BOOL = 6,
-  TensorType_INT16 = 7,
-  TensorType_COMPLEX64 = 8,
-  TensorType_INT8 = 9,
-  TensorType_MIN = TensorType_FLOAT32,
-  TensorType_MAX = TensorType_INT8
-};
-
-inline const TensorType (&EnumValuesTensorType())[10]
-{
-  static const TensorType values[] = {TensorType_FLOAT32, TensorType_FLOAT16, TensorType_INT32,
-                                      TensorType_UINT8,   TensorType_INT64,   TensorType_STRING,
-                                      TensorType_BOOL,    TensorType_INT16,   TensorType_COMPLEX64,
-                                      TensorType_INT8};
-  return values;
-}
-
-inline const char *const *EnumNamesTensorType()
-{
-  static const char *const names[] = {"FLOAT32", "FLOAT16", "INT32",     "UINT8", "INT64", "STRING",
-                                      "BOOL",    "INT16",   "COMPLEX64", "INT8",  nullptr};
-  return names;
-}
-
-inline const char *EnumNameTensorType(TensorType e)
-{
-  const size_t index = static_cast<int>(e);
-  return EnumNamesTensorType()[index];
-}
-
-enum QuantizationDetails
-{
-  QuantizationDetails_NONE = 0,
-  QuantizationDetails_CustomQuantization = 1,
-  QuantizationDetails_MIN = QuantizationDetails_NONE,
-  QuantizationDetails_MAX = QuantizationDetails_CustomQuantization
-};
-
-inline const QuantizationDetails (&EnumValuesQuantizationDetails())[2]
-{
-  static const QuantizationDetails values[] = {QuantizationDetails_NONE,
-                                               QuantizationDetails_CustomQuantization};
-  return values;
-}
-
-inline const char *const *EnumNamesQuantizationDetails()
-{
-  static const char *const names[] = {"NONE", "CustomQuantization", nullptr};
-  return names;
-}
-
-inline const char *EnumNameQuantizationDetails(QuantizationDetails e)
-{
-  const size_t index = static_cast<int>(e);
-  return EnumNamesQuantizationDetails()[index];
-}
-
-template <typename T> struct QuantizationDetailsTraits
-{
-  static const QuantizationDetails enum_value = QuantizationDetails_NONE;
-};
-
-template <> struct QuantizationDetailsTraits<CustomQuantization>
-{
-  static const QuantizationDetails enum_value = QuantizationDetails_CustomQuantization;
-};
-
-bool VerifyQuantizationDetails(flatbuffers::Verifier &verifier, const void *obj,
-                               QuantizationDetails type);
-bool VerifyQuantizationDetailsVector(flatbuffers::Verifier &verifier,
-                                     const flatbuffers::Vector<flatbuffers::Offset<void>> *values,
-                                     const flatbuffers::Vector<uint8_t> *types);
-
-enum BuiltinOperator
-{
-  BuiltinOperator_ADD = 0,
-  BuiltinOperator_AVERAGE_POOL_2D = 1,
-  BuiltinOperator_CONCATENATION = 2,
-  BuiltinOperator_CONV_2D = 3,
-  BuiltinOperator_DEPTHWISE_CONV_2D = 4,
-  BuiltinOperator_DEQUANTIZE = 6,
-  BuiltinOperator_EMBEDDING_LOOKUP = 7,
-  BuiltinOperator_FLOOR = 8,
-  BuiltinOperator_FULLY_CONNECTED = 9,
-  BuiltinOperator_HASHTABLE_LOOKUP = 10,
-  BuiltinOperator_L2_NORMALIZATION = 11,
-  BuiltinOperator_L2_POOL_2D = 12,
-  BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION = 13,
-  BuiltinOperator_LOGISTIC = 14,
-  BuiltinOperator_LSH_PROJECTION = 15,
-  BuiltinOperator_LSTM = 16,
-  BuiltinOperator_MAX_POOL_2D = 17,
-  BuiltinOperator_MUL = 18,
-  BuiltinOperator_RELU = 19,
-  BuiltinOperator_RELU_N1_TO_1 = 20,
-  BuiltinOperator_RELU6 = 21,
-  BuiltinOperator_RESHAPE = 22,
-  BuiltinOperator_RESIZE_BILINEAR = 23,
-  BuiltinOperator_RNN = 24,
-  BuiltinOperator_SOFTMAX = 25,
-  BuiltinOperator_SPACE_TO_DEPTH = 26,
-  BuiltinOperator_SVDF = 27,
-  BuiltinOperator_TANH = 28,
-  BuiltinOperator_CONCAT_EMBEDDINGS = 29,
-  BuiltinOperator_SKIP_GRAM = 30,
-  BuiltinOperator_CALL = 31,
-  BuiltinOperator_CUSTOM = 32,
-  BuiltinOperator_EMBEDDING_LOOKUP_SPARSE = 33,
-  BuiltinOperator_PAD = 34,
-  BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN = 35,
-  BuiltinOperator_GATHER = 36,
-  BuiltinOperator_BATCH_TO_SPACE_ND = 37,
-  BuiltinOperator_SPACE_TO_BATCH_ND = 38,
-  BuiltinOperator_TRANSPOSE = 39,
-  BuiltinOperator_MEAN = 40,
-  BuiltinOperator_SUB = 41,
-  BuiltinOperator_DIV = 42,
-  BuiltinOperator_SQUEEZE = 43,
-  BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM = 44,
-  BuiltinOperator_STRIDED_SLICE = 45,
-  BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN = 46,
-  BuiltinOperator_EXP = 47,
-  BuiltinOperator_TOPK_V2 = 48,
-  BuiltinOperator_SPLIT = 49,
-  BuiltinOperator_LOG_SOFTMAX = 50,
-  BuiltinOperator_DELEGATE = 51,
-  BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM = 52,
-  BuiltinOperator_CAST = 53,
-  BuiltinOperator_PRELU = 54,
-  BuiltinOperator_MAXIMUM = 55,
-  BuiltinOperator_ARG_MAX = 56,
-  BuiltinOperator_MINIMUM = 57,
-  BuiltinOperator_LESS = 58,
-  BuiltinOperator_NEG = 59,
-  BuiltinOperator_PADV2 = 60,
-  BuiltinOperator_GREATER = 61,
-  BuiltinOperator_GREATER_EQUAL = 62,
-  BuiltinOperator_LESS_EQUAL = 63,
-  BuiltinOperator_SELECT = 64,
-  BuiltinOperator_SLICE = 65,
-  BuiltinOperator_SIN = 66,
-  BuiltinOperator_TRANSPOSE_CONV = 67,
-  BuiltinOperator_SPARSE_TO_DENSE = 68,
-  BuiltinOperator_TILE = 69,
-  BuiltinOperator_EXPAND_DIMS = 70,
-  BuiltinOperator_EQUAL = 71,
-  BuiltinOperator_NOT_EQUAL = 72,
-  BuiltinOperator_LOG = 73,
-  BuiltinOperator_SUM = 74,
-  BuiltinOperator_SQRT = 75,
-  BuiltinOperator_RSQRT = 76,
-  BuiltinOperator_SHAPE = 77,
-  BuiltinOperator_POW = 78,
-  BuiltinOperator_ARG_MIN = 79,
-  BuiltinOperator_FAKE_QUANT = 80,
-  BuiltinOperator_REDUCE_PROD = 81,
-  BuiltinOperator_REDUCE_MAX = 82,
-  BuiltinOperator_PACK = 83,
-  BuiltinOperator_LOGICAL_OR = 84,
-  BuiltinOperator_ONE_HOT = 85,
-  BuiltinOperator_LOGICAL_AND = 86,
-  BuiltinOperator_LOGICAL_NOT = 87,
-  BuiltinOperator_UNPACK = 88,
-  BuiltinOperator_REDUCE_MIN = 89,
-  BuiltinOperator_FLOOR_DIV = 90,
-  BuiltinOperator_REDUCE_ANY = 91,
-  BuiltinOperator_SQUARE = 92,
-  BuiltinOperator_ZEROS_LIKE = 93,
-  BuiltinOperator_FILL = 94,
-  BuiltinOperator_FLOOR_MOD = 95,
-  BuiltinOperator_RANGE = 96,
-  BuiltinOperator_RESIZE_NEAREST_NEIGHBOR = 97,
-  BuiltinOperator_LEAKY_RELU = 98,
-  BuiltinOperator_SQUARED_DIFFERENCE = 99,
-  BuiltinOperator_MIRROR_PAD = 100,
-  BuiltinOperator_ABS = 101,
-  BuiltinOperator_SPLIT_V = 102,
-  BuiltinOperator_INSTANCE_NORM = 254,
-  BuiltinOperator_MIN = BuiltinOperator_ADD,
-  BuiltinOperator_MAX = BuiltinOperator_INSTANCE_NORM
-};
-
-inline const BuiltinOperator (&EnumValuesBuiltinOperator())[103]
-{
-  static const BuiltinOperator values[] = {BuiltinOperator_ADD,
-                                           BuiltinOperator_AVERAGE_POOL_2D,
-                                           BuiltinOperator_CONCATENATION,
-                                           BuiltinOperator_CONV_2D,
-                                           BuiltinOperator_DEPTHWISE_CONV_2D,
-                                           BuiltinOperator_DEQUANTIZE,
-                                           BuiltinOperator_EMBEDDING_LOOKUP,
-                                           BuiltinOperator_FLOOR,
-                                           BuiltinOperator_FULLY_CONNECTED,
-                                           BuiltinOperator_HASHTABLE_LOOKUP,
-                                           BuiltinOperator_L2_NORMALIZATION,
-                                           BuiltinOperator_L2_POOL_2D,
-                                           BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION,
-                                           BuiltinOperator_LOGISTIC,
-                                           BuiltinOperator_LSH_PROJECTION,
-                                           BuiltinOperator_LSTM,
-                                           BuiltinOperator_MAX_POOL_2D,
-                                           BuiltinOperator_MUL,
-                                           BuiltinOperator_RELU,
-                                           BuiltinOperator_RELU_N1_TO_1,
-                                           BuiltinOperator_RELU6,
-                                           BuiltinOperator_RESHAPE,
-                                           BuiltinOperator_RESIZE_BILINEAR,
-                                           BuiltinOperator_RNN,
-                                           BuiltinOperator_SOFTMAX,
-                                           BuiltinOperator_SPACE_TO_DEPTH,
-                                           BuiltinOperator_SVDF,
-                                           BuiltinOperator_TANH,
-                                           BuiltinOperator_CONCAT_EMBEDDINGS,
-                                           BuiltinOperator_SKIP_GRAM,
-                                           BuiltinOperator_CALL,
-                                           BuiltinOperator_CUSTOM,
-                                           BuiltinOperator_EMBEDDING_LOOKUP_SPARSE,
-                                           BuiltinOperator_PAD,
-                                           BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN,
-                                           BuiltinOperator_GATHER,
-                                           BuiltinOperator_BATCH_TO_SPACE_ND,
-                                           BuiltinOperator_SPACE_TO_BATCH_ND,
-                                           BuiltinOperator_TRANSPOSE,
-                                           BuiltinOperator_MEAN,
-                                           BuiltinOperator_SUB,
-                                           BuiltinOperator_DIV,
-                                           BuiltinOperator_SQUEEZE,
-                                           BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM,
-                                           BuiltinOperator_STRIDED_SLICE,
-                                           BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN,
-                                           BuiltinOperator_EXP,
-                                           BuiltinOperator_TOPK_V2,
-                                           BuiltinOperator_SPLIT,
-                                           BuiltinOperator_LOG_SOFTMAX,
-                                           BuiltinOperator_DELEGATE,
-                                           BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM,
-                                           BuiltinOperator_CAST,
-                                           BuiltinOperator_PRELU,
-                                           BuiltinOperator_MAXIMUM,
-                                           BuiltinOperator_ARG_MAX,
-                                           BuiltinOperator_MINIMUM,
-                                           BuiltinOperator_LESS,
-                                           BuiltinOperator_NEG,
-                                           BuiltinOperator_PADV2,
-                                           BuiltinOperator_GREATER,
-                                           BuiltinOperator_GREATER_EQUAL,
-                                           BuiltinOperator_LESS_EQUAL,
-                                           BuiltinOperator_SELECT,
-                                           BuiltinOperator_SLICE,
-                                           BuiltinOperator_SIN,
-                                           BuiltinOperator_TRANSPOSE_CONV,
-                                           BuiltinOperator_SPARSE_TO_DENSE,
-                                           BuiltinOperator_TILE,
-                                           BuiltinOperator_EXPAND_DIMS,
-                                           BuiltinOperator_EQUAL,
-                                           BuiltinOperator_NOT_EQUAL,
-                                           BuiltinOperator_LOG,
-                                           BuiltinOperator_SUM,
-                                           BuiltinOperator_SQRT,
-                                           BuiltinOperator_RSQRT,
-                                           BuiltinOperator_SHAPE,
-                                           BuiltinOperator_POW,
-                                           BuiltinOperator_ARG_MIN,
-                                           BuiltinOperator_FAKE_QUANT,
-                                           BuiltinOperator_REDUCE_PROD,
-                                           BuiltinOperator_REDUCE_MAX,
-                                           BuiltinOperator_PACK,
-                                           BuiltinOperator_LOGICAL_OR,
-                                           BuiltinOperator_ONE_HOT,
-                                           BuiltinOperator_LOGICAL_AND,
-                                           BuiltinOperator_LOGICAL_NOT,
-                                           BuiltinOperator_UNPACK,
-                                           BuiltinOperator_REDUCE_MIN,
-                                           BuiltinOperator_FLOOR_DIV,
-                                           BuiltinOperator_REDUCE_ANY,
-                                           BuiltinOperator_SQUARE,
-                                           BuiltinOperator_ZEROS_LIKE,
-                                           BuiltinOperator_FILL,
-                                           BuiltinOperator_FLOOR_MOD,
-                                           BuiltinOperator_RANGE,
-                                           BuiltinOperator_RESIZE_NEAREST_NEIGHBOR,
-                                           BuiltinOperator_LEAKY_RELU,
-                                           BuiltinOperator_SQUARED_DIFFERENCE,
-                                           BuiltinOperator_MIRROR_PAD,
-                                           BuiltinOperator_ABS,
-                                           BuiltinOperator_SPLIT_V,
-                                           BuiltinOperator_INSTANCE_NORM};
-  return values;
-}
-
-inline const char *const *EnumNamesBuiltinOperator()
-{
-  static const char *const names[] = {"ADD",
-                                      "AVERAGE_POOL_2D",
-                                      "CONCATENATION",
-                                      "CONV_2D",
-                                      "DEPTHWISE_CONV_2D",
-                                      "",
-                                      "DEQUANTIZE",
-                                      "EMBEDDING_LOOKUP",
-                                      "FLOOR",
-                                      "FULLY_CONNECTED",
-                                      "HASHTABLE_LOOKUP",
-                                      "L2_NORMALIZATION",
-                                      "L2_POOL_2D",
-                                      "LOCAL_RESPONSE_NORMALIZATION",
-                                      "LOGISTIC",
-                                      "LSH_PROJECTION",
-                                      "LSTM",
-                                      "MAX_POOL_2D",
-                                      "MUL",
-                                      "RELU",
-                                      "RELU_N1_TO_1",
-                                      "RELU6",
-                                      "RESHAPE",
-                                      "RESIZE_BILINEAR",
-                                      "RNN",
-                                      "SOFTMAX",
-                                      "SPACE_TO_DEPTH",
-                                      "SVDF",
-                                      "TANH",
-                                      "CONCAT_EMBEDDINGS",
-                                      "SKIP_GRAM",
-                                      "CALL",
-                                      "CUSTOM",
-                                      "EMBEDDING_LOOKUP_SPARSE",
-                                      "PAD",
-                                      "UNIDIRECTIONAL_SEQUENCE_RNN",
-                                      "GATHER",
-                                      "BATCH_TO_SPACE_ND",
-                                      "SPACE_TO_BATCH_ND",
-                                      "TRANSPOSE",
-                                      "MEAN",
-                                      "SUB",
-                                      "DIV",
-                                      "SQUEEZE",
-                                      "UNIDIRECTIONAL_SEQUENCE_LSTM",
-                                      "STRIDED_SLICE",
-                                      "BIDIRECTIONAL_SEQUENCE_RNN",
-                                      "EXP",
-                                      "TOPK_V2",
-                                      "SPLIT",
-                                      "LOG_SOFTMAX",
-                                      "DELEGATE",
-                                      "BIDIRECTIONAL_SEQUENCE_LSTM",
-                                      "CAST",
-                                      "PRELU",
-                                      "MAXIMUM",
-                                      "ARG_MAX",
-                                      "MINIMUM",
-                                      "LESS",
-                                      "NEG",
-                                      "PADV2",
-                                      "GREATER",
-                                      "GREATER_EQUAL",
-                                      "LESS_EQUAL",
-                                      "SELECT",
-                                      "SLICE",
-                                      "SIN",
-                                      "TRANSPOSE_CONV",
-                                      "SPARSE_TO_DENSE",
-                                      "TILE",
-                                      "EXPAND_DIMS",
-                                      "EQUAL",
-                                      "NOT_EQUAL",
-                                      "LOG",
-                                      "SUM",
-                                      "SQRT",
-                                      "RSQRT",
-                                      "SHAPE",
-                                      "POW",
-                                      "ARG_MIN",
-                                      "FAKE_QUANT",
-                                      "REDUCE_PROD",
-                                      "REDUCE_MAX",
-                                      "PACK",
-                                      "LOGICAL_OR",
-                                      "ONE_HOT",
-                                      "LOGICAL_AND",
-                                      "LOGICAL_NOT",
-                                      "UNPACK",
-                                      "REDUCE_MIN",
-                                      "FLOOR_DIV",
-                                      "REDUCE_ANY",
-                                      "SQUARE",
-                                      "ZEROS_LIKE",
-                                      "FILL",
-                                      "FLOOR_MOD",
-                                      "RANGE",
-                                      "RESIZE_NEAREST_NEIGHBOR",
-                                      "LEAKY_RELU",
-                                      "SQUARED_DIFFERENCE",
-                                      "MIRROR_PAD",
-                                      "ABS",
-                                      "SPLIT_V",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "",
-                                      "INSTANCE_NORM",
-                                      nullptr};
-  return names;
-}
-
-inline const char *EnumNameBuiltinOperator(BuiltinOperator e)
-{
-  const size_t index = static_cast<int>(e);
-  return EnumNamesBuiltinOperator()[index];
-}
-
-enum BuiltinOptions
-{
-  BuiltinOptions_NONE = 0,
-  BuiltinOptions_Conv2DOptions = 1,
-  BuiltinOptions_DepthwiseConv2DOptions = 2,
-  BuiltinOptions_ConcatEmbeddingsOptions = 3,
-  BuiltinOptions_LSHProjectionOptions = 4,
-  BuiltinOptions_Pool2DOptions = 5,
-  BuiltinOptions_SVDFOptions = 6,
-  BuiltinOptions_RNNOptions = 7,
-  BuiltinOptions_FullyConnectedOptions = 8,
-  BuiltinOptions_SoftmaxOptions = 9,
-  BuiltinOptions_ConcatenationOptions = 10,
-  BuiltinOptions_AddOptions = 11,
-  BuiltinOptions_L2NormOptions = 12,
-  BuiltinOptions_LocalResponseNormalizationOptions = 13,
-  BuiltinOptions_LSTMOptions = 14,
-  BuiltinOptions_ResizeBilinearOptions = 15,
-  BuiltinOptions_CallOptions = 16,
-  BuiltinOptions_ReshapeOptions = 17,
-  BuiltinOptions_SkipGramOptions = 18,
-  BuiltinOptions_SpaceToDepthOptions = 19,
-  BuiltinOptions_EmbeddingLookupSparseOptions = 20,
-  BuiltinOptions_MulOptions = 21,
-  BuiltinOptions_PadOptions = 22,
-  BuiltinOptions_GatherOptions = 23,
-  BuiltinOptions_BatchToSpaceNDOptions = 24,
-  BuiltinOptions_SpaceToBatchNDOptions = 25,
-  BuiltinOptions_TransposeOptions = 26,
-  BuiltinOptions_ReducerOptions = 27,
-  BuiltinOptions_SubOptions = 28,
-  BuiltinOptions_DivOptions = 29,
-  BuiltinOptions_SqueezeOptions = 30,
-  BuiltinOptions_SequenceRNNOptions = 31,
-  BuiltinOptions_StridedSliceOptions = 32,
-  BuiltinOptions_ExpOptions = 33,
-  BuiltinOptions_TopKV2Options = 34,
-  BuiltinOptions_SplitOptions = 35,
-  BuiltinOptions_LogSoftmaxOptions = 36,
-  BuiltinOptions_CastOptions = 37,
-  BuiltinOptions_DequantizeOptions = 38,
-  BuiltinOptions_MaximumMinimumOptions = 39,
-  BuiltinOptions_ArgMaxOptions = 40,
-  BuiltinOptions_LessOptions = 41,
-  BuiltinOptions_NegOptions = 42,
-  BuiltinOptions_PadV2Options = 43,
-  BuiltinOptions_GreaterOptions = 44,
-  BuiltinOptions_GreaterEqualOptions = 45,
-  BuiltinOptions_LessEqualOptions = 46,
-  BuiltinOptions_SelectOptions = 47,
-  BuiltinOptions_SliceOptions = 48,
-  BuiltinOptions_TransposeConvOptions = 49,
-  BuiltinOptions_SparseToDenseOptions = 50,
-  BuiltinOptions_TileOptions = 51,
-  BuiltinOptions_ExpandDimsOptions = 52,
-  BuiltinOptions_EqualOptions = 53,
-  BuiltinOptions_NotEqualOptions = 54,
-  BuiltinOptions_ShapeOptions = 55,
-  BuiltinOptions_PowOptions = 56,
-  BuiltinOptions_ArgMinOptions = 57,
-  BuiltinOptions_FakeQuantOptions = 58,
-  BuiltinOptions_PackOptions = 59,
-  BuiltinOptions_LogicalOrOptions = 60,
-  BuiltinOptions_OneHotOptions = 61,
-  BuiltinOptions_LogicalAndOptions = 62,
-  BuiltinOptions_LogicalNotOptions = 63,
-  BuiltinOptions_UnpackOptions = 64,
-  BuiltinOptions_FloorDivOptions = 65,
-  BuiltinOptions_SquareOptions = 66,
-  BuiltinOptions_ZerosLikeOptions = 67,
-  BuiltinOptions_FillOptions = 68,
-  BuiltinOptions_BidirectionalSequenceLSTMOptions = 69,
-  BuiltinOptions_BidirectionalSequenceRNNOptions = 70,
-  BuiltinOptions_UnidirectionalSequenceLSTMOptions = 71,
-  BuiltinOptions_FloorModOptions = 72,
-  BuiltinOptions_RangeOptions = 73,
-  BuiltinOptions_ResizeNearestNeighborOptions = 74,
-  BuiltinOptions_LeakyReluOptions = 75,
-  BuiltinOptions_SquaredDifferenceOptions = 76,
-  BuiltinOptions_MirrorPadOptions = 77,
-  BuiltinOptions_AbsOptions = 78,
-  BuiltinOptions_SplitVOptions = 79,
-  BuiltinOptions_InstanceNormOptions = 80,
-  BuiltinOptions_MIN = BuiltinOptions_NONE,
-  BuiltinOptions_MAX = BuiltinOptions_InstanceNormOptions
-};
-
-inline const BuiltinOptions (&EnumValuesBuiltinOptions())[81]
-{
-  static const BuiltinOptions values[] = {BuiltinOptions_NONE,
-                                          BuiltinOptions_Conv2DOptions,
-                                          BuiltinOptions_DepthwiseConv2DOptions,
-                                          BuiltinOptions_ConcatEmbeddingsOptions,
-                                          BuiltinOptions_LSHProjectionOptions,
-                                          BuiltinOptions_Pool2DOptions,
-                                          BuiltinOptions_SVDFOptions,
-                                          BuiltinOptions_RNNOptions,
-                                          BuiltinOptions_FullyConnectedOptions,
-                                          BuiltinOptions_SoftmaxOptions,
-                                          BuiltinOptions_ConcatenationOptions,
-                                          BuiltinOptions_AddOptions,
-                                          BuiltinOptions_L2NormOptions,
-                                          BuiltinOptions_LocalResponseNormalizationOptions,
-                                          BuiltinOptions_LSTMOptions,
-                                          BuiltinOptions_ResizeBilinearOptions,
-                                          BuiltinOptions_CallOptions,
-                                          BuiltinOptions_ReshapeOptions,
-                                          BuiltinOptions_SkipGramOptions,
-                                          BuiltinOptions_SpaceToDepthOptions,
-                                          BuiltinOptions_EmbeddingLookupSparseOptions,
-                                          BuiltinOptions_MulOptions,
-                                          BuiltinOptions_PadOptions,
-                                          BuiltinOptions_GatherOptions,
-                                          BuiltinOptions_BatchToSpaceNDOptions,
-                                          BuiltinOptions_SpaceToBatchNDOptions,
-                                          BuiltinOptions_TransposeOptions,
-                                          BuiltinOptions_ReducerOptions,
-                                          BuiltinOptions_SubOptions,
-                                          BuiltinOptions_DivOptions,
-                                          BuiltinOptions_SqueezeOptions,
-                                          BuiltinOptions_SequenceRNNOptions,
-                                          BuiltinOptions_StridedSliceOptions,
-                                          BuiltinOptions_ExpOptions,
-                                          BuiltinOptions_TopKV2Options,
-                                          BuiltinOptions_SplitOptions,
-                                          BuiltinOptions_LogSoftmaxOptions,
-                                          BuiltinOptions_CastOptions,
-                                          BuiltinOptions_DequantizeOptions,
-                                          BuiltinOptions_MaximumMinimumOptions,
-                                          BuiltinOptions_ArgMaxOptions,
-                                          BuiltinOptions_LessOptions,
-                                          BuiltinOptions_NegOptions,
-                                          BuiltinOptions_PadV2Options,
-                                          BuiltinOptions_GreaterOptions,
-                                          BuiltinOptions_GreaterEqualOptions,
-                                          BuiltinOptions_LessEqualOptions,
-                                          BuiltinOptions_SelectOptions,
-                                          BuiltinOptions_SliceOptions,
-                                          BuiltinOptions_TransposeConvOptions,
-                                          BuiltinOptions_SparseToDenseOptions,
-                                          BuiltinOptions_TileOptions,
-                                          BuiltinOptions_ExpandDimsOptions,
-                                          BuiltinOptions_EqualOptions,
-                                          BuiltinOptions_NotEqualOptions,
-                                          BuiltinOptions_ShapeOptions,
-                                          BuiltinOptions_PowOptions,
-                                          BuiltinOptions_ArgMinOptions,
-                                          BuiltinOptions_FakeQuantOptions,
-                                          BuiltinOptions_PackOptions,
-                                          BuiltinOptions_LogicalOrOptions,
-                                          BuiltinOptions_OneHotOptions,
-                                          BuiltinOptions_LogicalAndOptions,
-                                          BuiltinOptions_LogicalNotOptions,
-                                          BuiltinOptions_UnpackOptions,
-                                          BuiltinOptions_FloorDivOptions,
-                                          BuiltinOptions_SquareOptions,
-                                          BuiltinOptions_ZerosLikeOptions,
-                                          BuiltinOptions_FillOptions,
-                                          BuiltinOptions_BidirectionalSequenceLSTMOptions,
-                                          BuiltinOptions_BidirectionalSequenceRNNOptions,
-                                          BuiltinOptions_UnidirectionalSequenceLSTMOptions,
-                                          BuiltinOptions_FloorModOptions,
-                                          BuiltinOptions_RangeOptions,
-                                          BuiltinOptions_ResizeNearestNeighborOptions,
-                                          BuiltinOptions_LeakyReluOptions,
-                                          BuiltinOptions_SquaredDifferenceOptions,
-                                          BuiltinOptions_MirrorPadOptions,
-                                          BuiltinOptions_AbsOptions,
-                                          BuiltinOptions_SplitVOptions,
-                                          BuiltinOptions_InstanceNormOptions};
-  return values;
-}
-
-inline const char *const *EnumNamesBuiltinOptions()
-{
-  static const char *const names[] = {"NONE",
-                                      "Conv2DOptions",
-                                      "DepthwiseConv2DOptions",
-                                      "ConcatEmbeddingsOptions",
-                                      "LSHProjectionOptions",
-                                      "Pool2DOptions",
-                                      "SVDFOptions",
-                                      "RNNOptions",
-                                      "FullyConnectedOptions",
-                                      "SoftmaxOptions",
-                                      "ConcatenationOptions",
-                                      "AddOptions",
-                                      "L2NormOptions",
-                                      "LocalResponseNormalizationOptions",
-                                      "LSTMOptions",
-                                      "ResizeBilinearOptions",
-                                      "CallOptions",
-                                      "ReshapeOptions",
-                                      "SkipGramOptions",
-                                      "SpaceToDepthOptions",
-                                      "EmbeddingLookupSparseOptions",
-                                      "MulOptions",
-                                      "PadOptions",
-                                      "GatherOptions",
-                                      "BatchToSpaceNDOptions",
-                                      "SpaceToBatchNDOptions",
-                                      "TransposeOptions",
-                                      "ReducerOptions",
-                                      "SubOptions",
-                                      "DivOptions",
-                                      "SqueezeOptions",
-                                      "SequenceRNNOptions",
-                                      "StridedSliceOptions",
-                                      "ExpOptions",
-                                      "TopKV2Options",
-                                      "SplitOptions",
-                                      "LogSoftmaxOptions",
-                                      "CastOptions",
-                                      "DequantizeOptions",
-                                      "MaximumMinimumOptions",
-                                      "ArgMaxOptions",
-                                      "LessOptions",
-                                      "NegOptions",
-                                      "PadV2Options",
-                                      "GreaterOptions",
-                                      "GreaterEqualOptions",
-                                      "LessEqualOptions",
-                                      "SelectOptions",
-                                      "SliceOptions",
-                                      "TransposeConvOptions",
-                                      "SparseToDenseOptions",
-                                      "TileOptions",
-                                      "ExpandDimsOptions",
-                                      "EqualOptions",
-                                      "NotEqualOptions",
-                                      "ShapeOptions",
-                                      "PowOptions",
-                                      "ArgMinOptions",
-                                      "FakeQuantOptions",
-                                      "PackOptions",
-                                      "LogicalOrOptions",
-                                      "OneHotOptions",
-                                      "LogicalAndOptions",
-                                      "LogicalNotOptions",
-                                      "UnpackOptions",
-                                      "FloorDivOptions",
-                                      "SquareOptions",
-                                      "ZerosLikeOptions",
-                                      "FillOptions",
-                                      "BidirectionalSequenceLSTMOptions",
-                                      "BidirectionalSequenceRNNOptions",
-                                      "UnidirectionalSequenceLSTMOptions",
-                                      "FloorModOptions",
-                                      "RangeOptions",
-                                      "ResizeNearestNeighborOptions",
-                                      "LeakyReluOptions",
-                                      "SquaredDifferenceOptions",
-                                      "MirrorPadOptions",
-                                      "AbsOptions",
-                                      "SplitVOptions",
-                                      "InstanceNormOptions",
-                                      nullptr};
-  return names;
-}
-
-inline const char *EnumNameBuiltinOptions(BuiltinOptions e)
-{
-  const size_t index = static_cast<int>(e);
-  return EnumNamesBuiltinOptions()[index];
-}
-
-template <typename T> struct BuiltinOptionsTraits
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_NONE;
-};
-
-template <> struct BuiltinOptionsTraits<Conv2DOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_Conv2DOptions;
-};
-
-template <> struct BuiltinOptionsTraits<DepthwiseConv2DOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_DepthwiseConv2DOptions;
-};
-
-template <> struct BuiltinOptionsTraits<ConcatEmbeddingsOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_ConcatEmbeddingsOptions;
-};
-
-template <> struct BuiltinOptionsTraits<LSHProjectionOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_LSHProjectionOptions;
-};
-
-template <> struct BuiltinOptionsTraits<Pool2DOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_Pool2DOptions;
-};
-
-template <> struct BuiltinOptionsTraits<SVDFOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_SVDFOptions;
-};
-
-template <> struct BuiltinOptionsTraits<RNNOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_RNNOptions;
-};
-
-template <> struct BuiltinOptionsTraits<FullyConnectedOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_FullyConnectedOptions;
-};
-
-template <> struct BuiltinOptionsTraits<SoftmaxOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_SoftmaxOptions;
-};
-
-template <> struct BuiltinOptionsTraits<ConcatenationOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_ConcatenationOptions;
-};
-
-template <> struct BuiltinOptionsTraits<AddOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_AddOptions;
-};
-
-template <> struct BuiltinOptionsTraits<L2NormOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_L2NormOptions;
-};
-
-template <> struct BuiltinOptionsTraits<LocalResponseNormalizationOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_LocalResponseNormalizationOptions;
-};
-
-template <> struct BuiltinOptionsTraits<LSTMOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_LSTMOptions;
-};
-
-template <> struct BuiltinOptionsTraits<ResizeBilinearOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_ResizeBilinearOptions;
-};
-
-template <> struct BuiltinOptionsTraits<CallOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_CallOptions;
-};
-
-template <> struct BuiltinOptionsTraits<ReshapeOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_ReshapeOptions;
-};
-
-template <> struct BuiltinOptionsTraits<SkipGramOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_SkipGramOptions;
-};
-
-template <> struct BuiltinOptionsTraits<SpaceToDepthOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_SpaceToDepthOptions;
-};
-
-template <> struct BuiltinOptionsTraits<EmbeddingLookupSparseOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_EmbeddingLookupSparseOptions;
-};
-
-template <> struct BuiltinOptionsTraits<MulOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_MulOptions;
-};
-
-template <> struct BuiltinOptionsTraits<PadOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_PadOptions;
-};
-
-template <> struct BuiltinOptionsTraits<GatherOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_GatherOptions;
-};
-
-template <> struct BuiltinOptionsTraits<BatchToSpaceNDOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_BatchToSpaceNDOptions;
-};
-
-template <> struct BuiltinOptionsTraits<SpaceToBatchNDOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_SpaceToBatchNDOptions;
-};
-
-template <> struct BuiltinOptionsTraits<TransposeOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_TransposeOptions;
-};
-
-template <> struct BuiltinOptionsTraits<ReducerOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_ReducerOptions;
-};
-
-template <> struct BuiltinOptionsTraits<SubOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_SubOptions;
-};
-
-template <> struct BuiltinOptionsTraits<DivOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_DivOptions;
-};
-
-template <> struct BuiltinOptionsTraits<SqueezeOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_SqueezeOptions;
-};
-
-template <> struct BuiltinOptionsTraits<SequenceRNNOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_SequenceRNNOptions;
-};
-
-template <> struct BuiltinOptionsTraits<StridedSliceOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_StridedSliceOptions;
-};
-
-template <> struct BuiltinOptionsTraits<ExpOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_ExpOptions;
-};
-
-template <> struct BuiltinOptionsTraits<TopKV2Options>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_TopKV2Options;
-};
-
-template <> struct BuiltinOptionsTraits<SplitOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_SplitOptions;
-};
-
-template <> struct BuiltinOptionsTraits<LogSoftmaxOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_LogSoftmaxOptions;
-};
-
-template <> struct BuiltinOptionsTraits<CastOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_CastOptions;
-};
-
-template <> struct BuiltinOptionsTraits<DequantizeOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_DequantizeOptions;
-};
-
-template <> struct BuiltinOptionsTraits<MaximumMinimumOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_MaximumMinimumOptions;
-};
-
-template <> struct BuiltinOptionsTraits<ArgMaxOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_ArgMaxOptions;
-};
-
-template <> struct BuiltinOptionsTraits<LessOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_LessOptions;
-};
-
-template <> struct BuiltinOptionsTraits<NegOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_NegOptions;
-};
-
-template <> struct BuiltinOptionsTraits<PadV2Options>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_PadV2Options;
-};
-
-template <> struct BuiltinOptionsTraits<GreaterOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_GreaterOptions;
-};
-
-template <> struct BuiltinOptionsTraits<GreaterEqualOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_GreaterEqualOptions;
-};
-
-template <> struct BuiltinOptionsTraits<LessEqualOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_LessEqualOptions;
-};
-
-template <> struct BuiltinOptionsTraits<SelectOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_SelectOptions;
-};
-
-template <> struct BuiltinOptionsTraits<SliceOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_SliceOptions;
-};
-
-template <> struct BuiltinOptionsTraits<TransposeConvOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_TransposeConvOptions;
-};
-
-template <> struct BuiltinOptionsTraits<SparseToDenseOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_SparseToDenseOptions;
-};
-
-template <> struct BuiltinOptionsTraits<TileOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_TileOptions;
-};
-
-template <> struct BuiltinOptionsTraits<ExpandDimsOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_ExpandDimsOptions;
-};
-
-template <> struct BuiltinOptionsTraits<EqualOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_EqualOptions;
-};
-
-template <> struct BuiltinOptionsTraits<NotEqualOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_NotEqualOptions;
-};
-
-template <> struct BuiltinOptionsTraits<ShapeOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_ShapeOptions;
-};
-
-template <> struct BuiltinOptionsTraits<PowOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_PowOptions;
-};
-
-template <> struct BuiltinOptionsTraits<ArgMinOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_ArgMinOptions;
-};
-
-template <> struct BuiltinOptionsTraits<FakeQuantOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_FakeQuantOptions;
-};
-
-template <> struct BuiltinOptionsTraits<PackOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_PackOptions;
-};
-
-template <> struct BuiltinOptionsTraits<LogicalOrOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_LogicalOrOptions;
-};
-
-template <> struct BuiltinOptionsTraits<OneHotOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_OneHotOptions;
-};
-
-template <> struct BuiltinOptionsTraits<LogicalAndOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_LogicalAndOptions;
-};
-
-template <> struct BuiltinOptionsTraits<LogicalNotOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_LogicalNotOptions;
-};
-
-template <> struct BuiltinOptionsTraits<UnpackOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_UnpackOptions;
-};
-
-template <> struct BuiltinOptionsTraits<FloorDivOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_FloorDivOptions;
-};
-
-template <> struct BuiltinOptionsTraits<SquareOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_SquareOptions;
-};
-
-template <> struct BuiltinOptionsTraits<ZerosLikeOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_ZerosLikeOptions;
-};
-
-template <> struct BuiltinOptionsTraits<FillOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_FillOptions;
-};
-
-template <> struct BuiltinOptionsTraits<BidirectionalSequenceLSTMOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_BidirectionalSequenceLSTMOptions;
-};
-
-template <> struct BuiltinOptionsTraits<BidirectionalSequenceRNNOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_BidirectionalSequenceRNNOptions;
-};
-
-template <> struct BuiltinOptionsTraits<UnidirectionalSequenceLSTMOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_UnidirectionalSequenceLSTMOptions;
-};
-
-template <> struct BuiltinOptionsTraits<FloorModOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_FloorModOptions;
-};
-
-template <> struct BuiltinOptionsTraits<RangeOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_RangeOptions;
-};
-
-template <> struct BuiltinOptionsTraits<ResizeNearestNeighborOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_ResizeNearestNeighborOptions;
-};
-
-template <> struct BuiltinOptionsTraits<LeakyReluOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_LeakyReluOptions;
-};
-
-template <> struct BuiltinOptionsTraits<SquaredDifferenceOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_SquaredDifferenceOptions;
-};
-
-template <> struct BuiltinOptionsTraits<MirrorPadOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_MirrorPadOptions;
-};
-
-template <> struct BuiltinOptionsTraits<AbsOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_AbsOptions;
-};
-
-template <> struct BuiltinOptionsTraits<SplitVOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_SplitVOptions;
-};
-
-template <> struct BuiltinOptionsTraits<InstanceNormOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_InstanceNormOptions;
-};
-
-bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type);
-bool VerifyBuiltinOptionsVector(flatbuffers::Verifier &verifier,
-                                const flatbuffers::Vector<flatbuffers::Offset<void>> *values,
-                                const flatbuffers::Vector<uint8_t> *types);
-
-enum Padding
-{
-  Padding_SAME = 0,
-  Padding_VALID = 1,
-  Padding_MIN = Padding_SAME,
-  Padding_MAX = Padding_VALID
-};
-
-inline const Padding (&EnumValuesPadding())[2]
-{
-  static const Padding values[] = {Padding_SAME, Padding_VALID};
-  return values;
-}
-
-inline const char *const *EnumNamesPadding()
-{
-  static const char *const names[] = {"SAME", "VALID", nullptr};
-  return names;
-}
-
-inline const char *EnumNamePadding(Padding e)
-{
-  const size_t index = static_cast<int>(e);
-  return EnumNamesPadding()[index];
-}
-
-enum ActivationFunctionType
-{
-  ActivationFunctionType_NONE = 0,
-  ActivationFunctionType_RELU = 1,
-  ActivationFunctionType_RELU_N1_TO_1 = 2,
-  ActivationFunctionType_RELU6 = 3,
-  ActivationFunctionType_TANH = 4,
-  ActivationFunctionType_SIGN_BIT = 5,
-  ActivationFunctionType_MIN = ActivationFunctionType_NONE,
-  ActivationFunctionType_MAX = ActivationFunctionType_SIGN_BIT
-};
-
-inline const ActivationFunctionType (&EnumValuesActivationFunctionType())[6]
-{
-  static const ActivationFunctionType values[] = {
-      ActivationFunctionType_NONE,         ActivationFunctionType_RELU,
-      ActivationFunctionType_RELU_N1_TO_1, ActivationFunctionType_RELU6,
-      ActivationFunctionType_TANH,         ActivationFunctionType_SIGN_BIT};
-  return values;
-}
-
-inline const char *const *EnumNamesActivationFunctionType()
-{
-  static const char *const names[] = {"NONE", "RELU",     "RELU_N1_TO_1", "RELU6",
-                                      "TANH", "SIGN_BIT", nullptr};
-  return names;
-}
-
-inline const char *EnumNameActivationFunctionType(ActivationFunctionType e)
-{
-  const size_t index = static_cast<int>(e);
-  return EnumNamesActivationFunctionType()[index];
-}
-
-enum LSHProjectionType
-{
-  LSHProjectionType_UNKNOWN = 0,
-  LSHProjectionType_SPARSE = 1,
-  LSHProjectionType_DENSE = 2,
-  LSHProjectionType_MIN = LSHProjectionType_UNKNOWN,
-  LSHProjectionType_MAX = LSHProjectionType_DENSE
-};
-
-inline const LSHProjectionType (&EnumValuesLSHProjectionType())[3]
-{
-  static const LSHProjectionType values[] = {LSHProjectionType_UNKNOWN, LSHProjectionType_SPARSE,
-                                             LSHProjectionType_DENSE};
-  return values;
-}
-
-inline const char *const *EnumNamesLSHProjectionType()
-{
-  static const char *const names[] = {"UNKNOWN", "SPARSE", "DENSE", nullptr};
-  return names;
-}
-
-inline const char *EnumNameLSHProjectionType(LSHProjectionType e)
-{
-  const size_t index = static_cast<int>(e);
-  return EnumNamesLSHProjectionType()[index];
-}
-
-enum FullyConnectedOptionsWeightsFormat
-{
-  FullyConnectedOptionsWeightsFormat_DEFAULT = 0,
-  FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8 = 1,
-  FullyConnectedOptionsWeightsFormat_MIN = FullyConnectedOptionsWeightsFormat_DEFAULT,
-  FullyConnectedOptionsWeightsFormat_MAX = FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8
-};
-
-inline const FullyConnectedOptionsWeightsFormat (&EnumValuesFullyConnectedOptionsWeightsFormat())[2]
-{
-  static const FullyConnectedOptionsWeightsFormat values[] = {
-      FullyConnectedOptionsWeightsFormat_DEFAULT,
-      FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8};
-  return values;
-}
-
-inline const char *const *EnumNamesFullyConnectedOptionsWeightsFormat()
-{
-  static const char *const names[] = {"DEFAULT", "SHUFFLED4x16INT8", nullptr};
-  return names;
-}
-
-inline const char *EnumNameFullyConnectedOptionsWeightsFormat(FullyConnectedOptionsWeightsFormat e)
-{
-  const size_t index = static_cast<int>(e);
-  return EnumNamesFullyConnectedOptionsWeightsFormat()[index];
-}
-
-enum LSTMKernelType
-{
-  LSTMKernelType_FULL = 0,
-  LSTMKernelType_BASIC = 1,
-  LSTMKernelType_MIN = LSTMKernelType_FULL,
-  LSTMKernelType_MAX = LSTMKernelType_BASIC
-};
-
-inline const LSTMKernelType (&EnumValuesLSTMKernelType())[2]
-{
-  static const LSTMKernelType values[] = {LSTMKernelType_FULL, LSTMKernelType_BASIC};
-  return values;
-}
-
-inline const char *const *EnumNamesLSTMKernelType()
-{
-  static const char *const names[] = {"FULL", "BASIC", nullptr};
-  return names;
-}
-
-inline const char *EnumNameLSTMKernelType(LSTMKernelType e)
-{
-  const size_t index = static_cast<int>(e);
-  return EnumNamesLSTMKernelType()[index];
-}
-
-enum CombinerType
-{
-  CombinerType_SUM = 0,
-  CombinerType_MEAN = 1,
-  CombinerType_SQRTN = 2,
-  CombinerType_MIN = CombinerType_SUM,
-  CombinerType_MAX = CombinerType_SQRTN
-};
-
-inline const CombinerType (&EnumValuesCombinerType())[3]
-{
-  static const CombinerType values[] = {CombinerType_SUM, CombinerType_MEAN, CombinerType_SQRTN};
-  return values;
-}
-
-inline const char *const *EnumNamesCombinerType()
-{
-  static const char *const names[] = {"SUM", "MEAN", "SQRTN", nullptr};
-  return names;
-}
-
-inline const char *EnumNameCombinerType(CombinerType e)
-{
-  const size_t index = static_cast<int>(e);
-  return EnumNamesCombinerType()[index];
-}
-
-enum MirrorPadMode
-{
-  MirrorPadMode_REFLECT = 0,
-  MirrorPadMode_SYMMETRIC = 1,
-  MirrorPadMode_MIN = MirrorPadMode_REFLECT,
-  MirrorPadMode_MAX = MirrorPadMode_SYMMETRIC
-};
-
-inline const MirrorPadMode (&EnumValuesMirrorPadMode())[2]
-{
-  static const MirrorPadMode values[] = {MirrorPadMode_REFLECT, MirrorPadMode_SYMMETRIC};
-  return values;
-}
-
-inline const char *const *EnumNamesMirrorPadMode()
-{
-  static const char *const names[] = {"REFLECT", "SYMMETRIC", nullptr};
-  return names;
-}
-
-inline const char *EnumNameMirrorPadMode(MirrorPadMode e)
-{
-  const size_t index = static_cast<int>(e);
-  return EnumNamesMirrorPadMode()[index];
-}
-
-enum CustomOptionsFormat
-{
-  CustomOptionsFormat_FLEXBUFFERS = 0,
-  CustomOptionsFormat_MIN = CustomOptionsFormat_FLEXBUFFERS,
-  CustomOptionsFormat_MAX = CustomOptionsFormat_FLEXBUFFERS
-};
-
-inline const CustomOptionsFormat (&EnumValuesCustomOptionsFormat())[1]
-{
-  static const CustomOptionsFormat values[] = {CustomOptionsFormat_FLEXBUFFERS};
-  return values;
-}
-
-inline const char *const *EnumNamesCustomOptionsFormat()
-{
-  static const char *const names[] = {"FLEXBUFFERS", nullptr};
-  return names;
-}
-
-inline const char *EnumNameCustomOptionsFormat(CustomOptionsFormat e)
-{
-  const size_t index = static_cast<int>(e);
-  return EnumNamesCustomOptionsFormat()[index];
-}
-
-enum DataFormat
-{
-  DataFormat_CHANNELS_LAST = 0,
-  DataFormat_CHANNELS_FIRST = 1,
-  DataFormat_MIN = DataFormat_CHANNELS_LAST,
-  DataFormat_MAX = DataFormat_CHANNELS_FIRST
-};
-
-inline const DataFormat (&EnumValuesDataFormat())[2]
-{
-  static const DataFormat values[] = {DataFormat_CHANNELS_LAST, DataFormat_CHANNELS_FIRST};
-  return values;
-}
-
-inline const char *const *EnumNamesDataFormat()
-{
-  static const char *const names[] = {"CHANNELS_LAST", "CHANNELS_FIRST", nullptr};
-  return names;
-}
-
-inline const char *EnumNameDataFormat(DataFormat e)
-{
-  const size_t index = static_cast<int>(e);
-  return EnumNamesDataFormat()[index];
-}
-
-struct CustomQuantization FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_CUSTOM = 4
-  };
-  const flatbuffers::Vector<uint8_t> *custom() const
-  {
-    return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_CUSTOM);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_CUSTOM) &&
-           verifier.VerifyVector(custom()) && verifier.EndTable();
-  }
-};
-
-struct CustomQuantizationBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_custom(flatbuffers::Offset<flatbuffers::Vector<uint8_t>> custom)
-  {
-    fbb_.AddOffset(CustomQuantization::VT_CUSTOM, custom);
-  }
-  explicit CustomQuantizationBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  CustomQuantizationBuilder &operator=(const CustomQuantizationBuilder &);
-  flatbuffers::Offset<CustomQuantization> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<CustomQuantization>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<CustomQuantization>
-CreateCustomQuantization(flatbuffers::FlatBufferBuilder &_fbb,
-                         flatbuffers::Offset<flatbuffers::Vector<uint8_t>> custom = 0)
-{
-  CustomQuantizationBuilder builder_(_fbb);
-  builder_.add_custom(custom);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<CustomQuantization>
-CreateCustomQuantizationDirect(flatbuffers::FlatBufferBuilder &_fbb,
-                               const std::vector<uint8_t> *custom = nullptr)
-{
-  return circle::CreateCustomQuantization(_fbb, custom ? _fbb.CreateVector<uint8_t>(*custom) : 0);
-}
-
-struct QuantizationParameters FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_MIN = 4,
-    VT_MAX = 6,
-    VT_SCALE = 8,
-    VT_ZERO_POINT = 10,
-    VT_DETAILS_TYPE = 12,
-    VT_DETAILS = 14
-  };
-  const flatbuffers::Vector<float> *min() const
-  {
-    return GetPointer<const flatbuffers::Vector<float> *>(VT_MIN);
-  }
-  const flatbuffers::Vector<float> *max() const
-  {
-    return GetPointer<const flatbuffers::Vector<float> *>(VT_MAX);
-  }
-  const flatbuffers::Vector<float> *scale() const
-  {
-    return GetPointer<const flatbuffers::Vector<float> *>(VT_SCALE);
-  }
-  const flatbuffers::Vector<int64_t> *zero_point() const
-  {
-    return GetPointer<const flatbuffers::Vector<int64_t> *>(VT_ZERO_POINT);
-  }
-  QuantizationDetails details_type() const
-  {
-    return static_cast<QuantizationDetails>(GetField<uint8_t>(VT_DETAILS_TYPE, 0));
-  }
-  const void *details() const { return GetPointer<const void *>(VT_DETAILS); }
-  template <typename T> const T *details_as() const;
-  const CustomQuantization *details_as_CustomQuantization() const
-  {
-    return details_type() == QuantizationDetails_CustomQuantization
-               ? static_cast<const CustomQuantization *>(details())
-               : nullptr;
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_MIN) &&
-           verifier.VerifyVector(min()) && VerifyOffset(verifier, VT_MAX) &&
-           verifier.VerifyVector(max()) && VerifyOffset(verifier, VT_SCALE) &&
-           verifier.VerifyVector(scale()) && VerifyOffset(verifier, VT_ZERO_POINT) &&
-           verifier.VerifyVector(zero_point()) && VerifyField<uint8_t>(verifier, VT_DETAILS_TYPE) &&
-           VerifyOffset(verifier, VT_DETAILS) &&
-           VerifyQuantizationDetails(verifier, details(), details_type()) && verifier.EndTable();
-  }
-};
-
-template <>
-inline const CustomQuantization *QuantizationParameters::details_as<CustomQuantization>() const
-{
-  return details_as_CustomQuantization();
-}
-
-struct QuantizationParametersBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_min(flatbuffers::Offset<flatbuffers::Vector<float>> min)
-  {
-    fbb_.AddOffset(QuantizationParameters::VT_MIN, min);
-  }
-  void add_max(flatbuffers::Offset<flatbuffers::Vector<float>> max)
-  {
-    fbb_.AddOffset(QuantizationParameters::VT_MAX, max);
-  }
-  void add_scale(flatbuffers::Offset<flatbuffers::Vector<float>> scale)
-  {
-    fbb_.AddOffset(QuantizationParameters::VT_SCALE, scale);
-  }
-  void add_zero_point(flatbuffers::Offset<flatbuffers::Vector<int64_t>> zero_point)
-  {
-    fbb_.AddOffset(QuantizationParameters::VT_ZERO_POINT, zero_point);
-  }
-  void add_details_type(QuantizationDetails details_type)
-  {
-    fbb_.AddElement<uint8_t>(QuantizationParameters::VT_DETAILS_TYPE,
-                             static_cast<uint8_t>(details_type), 0);
-  }
-  void add_details(flatbuffers::Offset<void> details)
-  {
-    fbb_.AddOffset(QuantizationParameters::VT_DETAILS, details);
-  }
-  explicit QuantizationParametersBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  QuantizationParametersBuilder &operator=(const QuantizationParametersBuilder &);
-  flatbuffers::Offset<QuantizationParameters> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<QuantizationParameters>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<QuantizationParameters>
-CreateQuantizationParameters(flatbuffers::FlatBufferBuilder &_fbb,
-                             flatbuffers::Offset<flatbuffers::Vector<float>> min = 0,
-                             flatbuffers::Offset<flatbuffers::Vector<float>> max = 0,
-                             flatbuffers::Offset<flatbuffers::Vector<float>> scale = 0,
-                             flatbuffers::Offset<flatbuffers::Vector<int64_t>> zero_point = 0,
-                             QuantizationDetails details_type = QuantizationDetails_NONE,
-                             flatbuffers::Offset<void> details = 0)
-{
-  QuantizationParametersBuilder builder_(_fbb);
-  builder_.add_details(details);
-  builder_.add_zero_point(zero_point);
-  builder_.add_scale(scale);
-  builder_.add_max(max);
-  builder_.add_min(min);
-  builder_.add_details_type(details_type);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<QuantizationParameters> CreateQuantizationParametersDirect(
-    flatbuffers::FlatBufferBuilder &_fbb, const std::vector<float> *min = nullptr,
-    const std::vector<float> *max = nullptr, const std::vector<float> *scale = nullptr,
-    const std::vector<int64_t> *zero_point = nullptr,
-    QuantizationDetails details_type = QuantizationDetails_NONE,
-    flatbuffers::Offset<void> details = 0)
-{
-  return circle::CreateQuantizationParameters(
-      _fbb, min ? _fbb.CreateVector<float>(*min) : 0, max ? _fbb.CreateVector<float>(*max) : 0,
-      scale ? _fbb.CreateVector<float>(*scale) : 0,
-      zero_point ? _fbb.CreateVector<int64_t>(*zero_point) : 0, details_type, details);
-}
-
-struct Tensor FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_SHAPE = 4,
-    VT_TYPE = 6,
-    VT_BUFFER = 8,
-    VT_NAME = 10,
-    VT_QUANTIZATION = 12,
-    VT_IS_VARIABLE = 14
-  };
-  const flatbuffers::Vector<int32_t> *shape() const
-  {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_SHAPE);
-  }
-  TensorType type() const { return static_cast<TensorType>(GetField<int8_t>(VT_TYPE, 0)); }
-  uint32_t buffer() const { return GetField<uint32_t>(VT_BUFFER, 0); }
-  const flatbuffers::String *name() const
-  {
-    return GetPointer<const flatbuffers::String *>(VT_NAME);
-  }
-  const QuantizationParameters *quantization() const
-  {
-    return GetPointer<const QuantizationParameters *>(VT_QUANTIZATION);
-  }
-  bool is_variable() const { return GetField<uint8_t>(VT_IS_VARIABLE, 0) != 0; }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_SHAPE) &&
-           verifier.VerifyVector(shape()) && VerifyField<int8_t>(verifier, VT_TYPE) &&
-           VerifyField<uint32_t>(verifier, VT_BUFFER) && VerifyOffset(verifier, VT_NAME) &&
-           verifier.VerifyString(name()) && VerifyOffset(verifier, VT_QUANTIZATION) &&
-           verifier.VerifyTable(quantization()) && VerifyField<uint8_t>(verifier, VT_IS_VARIABLE) &&
-           verifier.EndTable();
-  }
-};
-
-struct TensorBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_shape(flatbuffers::Offset<flatbuffers::Vector<int32_t>> shape)
-  {
-    fbb_.AddOffset(Tensor::VT_SHAPE, shape);
-  }
-  void add_type(TensorType type)
-  {
-    fbb_.AddElement<int8_t>(Tensor::VT_TYPE, static_cast<int8_t>(type), 0);
-  }
-  void add_buffer(uint32_t buffer) { fbb_.AddElement<uint32_t>(Tensor::VT_BUFFER, buffer, 0); }
-  void add_name(flatbuffers::Offset<flatbuffers::String> name)
-  {
-    fbb_.AddOffset(Tensor::VT_NAME, name);
-  }
-  void add_quantization(flatbuffers::Offset<QuantizationParameters> quantization)
-  {
-    fbb_.AddOffset(Tensor::VT_QUANTIZATION, quantization);
-  }
-  void add_is_variable(bool is_variable)
-  {
-    fbb_.AddElement<uint8_t>(Tensor::VT_IS_VARIABLE, static_cast<uint8_t>(is_variable), 0);
-  }
-  explicit TensorBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  TensorBuilder &operator=(const TensorBuilder &);
-  flatbuffers::Offset<Tensor> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<Tensor>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<Tensor>
-CreateTensor(flatbuffers::FlatBufferBuilder &_fbb,
-             flatbuffers::Offset<flatbuffers::Vector<int32_t>> shape = 0,
-             TensorType type = TensorType_FLOAT32, uint32_t buffer = 0,
-             flatbuffers::Offset<flatbuffers::String> name = 0,
-             flatbuffers::Offset<QuantizationParameters> quantization = 0, bool is_variable = false)
-{
-  TensorBuilder builder_(_fbb);
-  builder_.add_quantization(quantization);
-  builder_.add_name(name);
-  builder_.add_buffer(buffer);
-  builder_.add_shape(shape);
-  builder_.add_is_variable(is_variable);
-  builder_.add_type(type);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<Tensor> CreateTensorDirect(
-    flatbuffers::FlatBufferBuilder &_fbb, const std::vector<int32_t> *shape = nullptr,
-    TensorType type = TensorType_FLOAT32, uint32_t buffer = 0, const char *name = nullptr,
-    flatbuffers::Offset<QuantizationParameters> quantization = 0, bool is_variable = false)
-{
-  return circle::CreateTensor(_fbb, shape ? _fbb.CreateVector<int32_t>(*shape) : 0, type, buffer,
-                              name ? _fbb.CreateString(name) : 0, quantization, is_variable);
-}
-
-struct Conv2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_PADDING = 4,
-    VT_STRIDE_W = 6,
-    VT_STRIDE_H = 8,
-    VT_FUSED_ACTIVATION_FUNCTION = 10,
-    VT_DILATION_W_FACTOR = 12,
-    VT_DILATION_H_FACTOR = 14
-  };
-  Padding padding() const { return static_cast<Padding>(GetField<int8_t>(VT_PADDING, 0)); }
-  int32_t stride_w() const { return GetField<int32_t>(VT_STRIDE_W, 0); }
-  int32_t stride_h() const { return GetField<int32_t>(VT_STRIDE_H, 0); }
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  int32_t dilation_w_factor() const { return GetField<int32_t>(VT_DILATION_W_FACTOR, 1); }
-  int32_t dilation_h_factor() const { return GetField<int32_t>(VT_DILATION_H_FACTOR, 1); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_PADDING) &&
-           VerifyField<int32_t>(verifier, VT_STRIDE_W) &&
-           VerifyField<int32_t>(verifier, VT_STRIDE_H) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) &&
-           VerifyField<int32_t>(verifier, VT_DILATION_W_FACTOR) &&
-           VerifyField<int32_t>(verifier, VT_DILATION_H_FACTOR) && verifier.EndTable();
-  }
-};
-
-struct Conv2DOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_padding(Padding padding)
-  {
-    fbb_.AddElement<int8_t>(Conv2DOptions::VT_PADDING, static_cast<int8_t>(padding), 0);
-  }
-  void add_stride_w(int32_t stride_w)
-  {
-    fbb_.AddElement<int32_t>(Conv2DOptions::VT_STRIDE_W, stride_w, 0);
-  }
-  void add_stride_h(int32_t stride_h)
-  {
-    fbb_.AddElement<int32_t>(Conv2DOptions::VT_STRIDE_H, stride_h, 0);
-  }
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(Conv2DOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  void add_dilation_w_factor(int32_t dilation_w_factor)
-  {
-    fbb_.AddElement<int32_t>(Conv2DOptions::VT_DILATION_W_FACTOR, dilation_w_factor, 1);
-  }
-  void add_dilation_h_factor(int32_t dilation_h_factor)
-  {
-    fbb_.AddElement<int32_t>(Conv2DOptions::VT_DILATION_H_FACTOR, dilation_h_factor, 1);
-  }
-  explicit Conv2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  Conv2DOptionsBuilder &operator=(const Conv2DOptionsBuilder &);
-  flatbuffers::Offset<Conv2DOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<Conv2DOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<Conv2DOptions>
-CreateConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, Padding padding = Padding_SAME,
-                    int32_t stride_w = 0, int32_t stride_h = 0,
-                    ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE,
-                    int32_t dilation_w_factor = 1, int32_t dilation_h_factor = 1)
-{
-  Conv2DOptionsBuilder builder_(_fbb);
-  builder_.add_dilation_h_factor(dilation_h_factor);
-  builder_.add_dilation_w_factor(dilation_w_factor);
-  builder_.add_stride_h(stride_h);
-  builder_.add_stride_w(stride_w);
-  builder_.add_fused_activation_function(fused_activation_function);
-  builder_.add_padding(padding);
-  return builder_.Finish();
-}
-
-struct Pool2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_PADDING = 4,
-    VT_STRIDE_W = 6,
-    VT_STRIDE_H = 8,
-    VT_FILTER_WIDTH = 10,
-    VT_FILTER_HEIGHT = 12,
-    VT_FUSED_ACTIVATION_FUNCTION = 14
-  };
-  Padding padding() const { return static_cast<Padding>(GetField<int8_t>(VT_PADDING, 0)); }
-  int32_t stride_w() const { return GetField<int32_t>(VT_STRIDE_W, 0); }
-  int32_t stride_h() const { return GetField<int32_t>(VT_STRIDE_H, 0); }
-  int32_t filter_width() const { return GetField<int32_t>(VT_FILTER_WIDTH, 0); }
-  int32_t filter_height() const { return GetField<int32_t>(VT_FILTER_HEIGHT, 0); }
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_PADDING) &&
-           VerifyField<int32_t>(verifier, VT_STRIDE_W) &&
-           VerifyField<int32_t>(verifier, VT_STRIDE_H) &&
-           VerifyField<int32_t>(verifier, VT_FILTER_WIDTH) &&
-           VerifyField<int32_t>(verifier, VT_FILTER_HEIGHT) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable();
-  }
-};
-
-struct Pool2DOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_padding(Padding padding)
-  {
-    fbb_.AddElement<int8_t>(Pool2DOptions::VT_PADDING, static_cast<int8_t>(padding), 0);
-  }
-  void add_stride_w(int32_t stride_w)
-  {
-    fbb_.AddElement<int32_t>(Pool2DOptions::VT_STRIDE_W, stride_w, 0);
-  }
-  void add_stride_h(int32_t stride_h)
-  {
-    fbb_.AddElement<int32_t>(Pool2DOptions::VT_STRIDE_H, stride_h, 0);
-  }
-  void add_filter_width(int32_t filter_width)
-  {
-    fbb_.AddElement<int32_t>(Pool2DOptions::VT_FILTER_WIDTH, filter_width, 0);
-  }
-  void add_filter_height(int32_t filter_height)
-  {
-    fbb_.AddElement<int32_t>(Pool2DOptions::VT_FILTER_HEIGHT, filter_height, 0);
-  }
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(Pool2DOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  explicit Pool2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  Pool2DOptionsBuilder &operator=(const Pool2DOptionsBuilder &);
-  flatbuffers::Offset<Pool2DOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<Pool2DOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<Pool2DOptions>
-CreatePool2DOptions(flatbuffers::FlatBufferBuilder &_fbb, Padding padding = Padding_SAME,
-                    int32_t stride_w = 0, int32_t stride_h = 0, int32_t filter_width = 0,
-                    int32_t filter_height = 0,
-                    ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE)
-{
-  Pool2DOptionsBuilder builder_(_fbb);
-  builder_.add_filter_height(filter_height);
-  builder_.add_filter_width(filter_width);
-  builder_.add_stride_h(stride_h);
-  builder_.add_stride_w(stride_w);
-  builder_.add_fused_activation_function(fused_activation_function);
-  builder_.add_padding(padding);
-  return builder_.Finish();
-}
-
-struct DepthwiseConv2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_PADDING = 4,
-    VT_STRIDE_W = 6,
-    VT_STRIDE_H = 8,
-    VT_DEPTH_MULTIPLIER = 10,
-    VT_FUSED_ACTIVATION_FUNCTION = 12,
-    VT_DILATION_W_FACTOR = 14,
-    VT_DILATION_H_FACTOR = 16
-  };
-  Padding padding() const { return static_cast<Padding>(GetField<int8_t>(VT_PADDING, 0)); }
-  int32_t stride_w() const { return GetField<int32_t>(VT_STRIDE_W, 0); }
-  int32_t stride_h() const { return GetField<int32_t>(VT_STRIDE_H, 0); }
-  int32_t depth_multiplier() const { return GetField<int32_t>(VT_DEPTH_MULTIPLIER, 0); }
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  int32_t dilation_w_factor() const { return GetField<int32_t>(VT_DILATION_W_FACTOR, 1); }
-  int32_t dilation_h_factor() const { return GetField<int32_t>(VT_DILATION_H_FACTOR, 1); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_PADDING) &&
-           VerifyField<int32_t>(verifier, VT_STRIDE_W) &&
-           VerifyField<int32_t>(verifier, VT_STRIDE_H) &&
-           VerifyField<int32_t>(verifier, VT_DEPTH_MULTIPLIER) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) &&
-           VerifyField<int32_t>(verifier, VT_DILATION_W_FACTOR) &&
-           VerifyField<int32_t>(verifier, VT_DILATION_H_FACTOR) && verifier.EndTable();
-  }
-};
-
-struct DepthwiseConv2DOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_padding(Padding padding)
-  {
-    fbb_.AddElement<int8_t>(DepthwiseConv2DOptions::VT_PADDING, static_cast<int8_t>(padding), 0);
-  }
-  void add_stride_w(int32_t stride_w)
-  {
-    fbb_.AddElement<int32_t>(DepthwiseConv2DOptions::VT_STRIDE_W, stride_w, 0);
-  }
-  void add_stride_h(int32_t stride_h)
-  {
-    fbb_.AddElement<int32_t>(DepthwiseConv2DOptions::VT_STRIDE_H, stride_h, 0);
-  }
-  void add_depth_multiplier(int32_t depth_multiplier)
-  {
-    fbb_.AddElement<int32_t>(DepthwiseConv2DOptions::VT_DEPTH_MULTIPLIER, depth_multiplier, 0);
-  }
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(DepthwiseConv2DOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  void add_dilation_w_factor(int32_t dilation_w_factor)
-  {
-    fbb_.AddElement<int32_t>(DepthwiseConv2DOptions::VT_DILATION_W_FACTOR, dilation_w_factor, 1);
-  }
-  void add_dilation_h_factor(int32_t dilation_h_factor)
-  {
-    fbb_.AddElement<int32_t>(DepthwiseConv2DOptions::VT_DILATION_H_FACTOR, dilation_h_factor, 1);
-  }
-  explicit DepthwiseConv2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  DepthwiseConv2DOptionsBuilder &operator=(const DepthwiseConv2DOptionsBuilder &);
-  flatbuffers::Offset<DepthwiseConv2DOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<DepthwiseConv2DOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<DepthwiseConv2DOptions> CreateDepthwiseConv2DOptions(
-    flatbuffers::FlatBufferBuilder &_fbb, Padding padding = Padding_SAME, int32_t stride_w = 0,
-    int32_t stride_h = 0, int32_t depth_multiplier = 0,
-    ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE,
-    int32_t dilation_w_factor = 1, int32_t dilation_h_factor = 1)
-{
-  DepthwiseConv2DOptionsBuilder builder_(_fbb);
-  builder_.add_dilation_h_factor(dilation_h_factor);
-  builder_.add_dilation_w_factor(dilation_w_factor);
-  builder_.add_depth_multiplier(depth_multiplier);
-  builder_.add_stride_h(stride_h);
-  builder_.add_stride_w(stride_w);
-  builder_.add_fused_activation_function(fused_activation_function);
-  builder_.add_padding(padding);
-  return builder_.Finish();
-}
-
-struct ConcatEmbeddingsOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_NUM_CHANNELS = 4,
-    VT_NUM_COLUMNS_PER_CHANNEL = 6,
-    VT_EMBEDDING_DIM_PER_CHANNEL = 8
-  };
-  int32_t num_channels() const { return GetField<int32_t>(VT_NUM_CHANNELS, 0); }
-  const flatbuffers::Vector<int32_t> *num_columns_per_channel() const
-  {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_NUM_COLUMNS_PER_CHANNEL);
-  }
-  const flatbuffers::Vector<int32_t> *embedding_dim_per_channel() const
-  {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_EMBEDDING_DIM_PER_CHANNEL);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_NUM_CHANNELS) &&
-           VerifyOffset(verifier, VT_NUM_COLUMNS_PER_CHANNEL) &&
-           verifier.VerifyVector(num_columns_per_channel()) &&
-           VerifyOffset(verifier, VT_EMBEDDING_DIM_PER_CHANNEL) &&
-           verifier.VerifyVector(embedding_dim_per_channel()) && verifier.EndTable();
-  }
-};
-
-struct ConcatEmbeddingsOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_num_channels(int32_t num_channels)
-  {
-    fbb_.AddElement<int32_t>(ConcatEmbeddingsOptions::VT_NUM_CHANNELS, num_channels, 0);
-  }
-  void add_num_columns_per_channel(
-      flatbuffers::Offset<flatbuffers::Vector<int32_t>> num_columns_per_channel)
-  {
-    fbb_.AddOffset(ConcatEmbeddingsOptions::VT_NUM_COLUMNS_PER_CHANNEL, num_columns_per_channel);
-  }
-  void add_embedding_dim_per_channel(
-      flatbuffers::Offset<flatbuffers::Vector<int32_t>> embedding_dim_per_channel)
-  {
-    fbb_.AddOffset(ConcatEmbeddingsOptions::VT_EMBEDDING_DIM_PER_CHANNEL,
-                   embedding_dim_per_channel);
-  }
-  explicit ConcatEmbeddingsOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  ConcatEmbeddingsOptionsBuilder &operator=(const ConcatEmbeddingsOptionsBuilder &);
-  flatbuffers::Offset<ConcatEmbeddingsOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ConcatEmbeddingsOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ConcatEmbeddingsOptions> CreateConcatEmbeddingsOptions(
-    flatbuffers::FlatBufferBuilder &_fbb, int32_t num_channels = 0,
-    flatbuffers::Offset<flatbuffers::Vector<int32_t>> num_columns_per_channel = 0,
-    flatbuffers::Offset<flatbuffers::Vector<int32_t>> embedding_dim_per_channel = 0)
-{
-  ConcatEmbeddingsOptionsBuilder builder_(_fbb);
-  builder_.add_embedding_dim_per_channel(embedding_dim_per_channel);
-  builder_.add_num_columns_per_channel(num_columns_per_channel);
-  builder_.add_num_channels(num_channels);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<ConcatEmbeddingsOptions>
-CreateConcatEmbeddingsOptionsDirect(flatbuffers::FlatBufferBuilder &_fbb, int32_t num_channels = 0,
-                                    const std::vector<int32_t> *num_columns_per_channel = nullptr,
-                                    const std::vector<int32_t> *embedding_dim_per_channel = nullptr)
-{
-  return circle::CreateConcatEmbeddingsOptions(
-      _fbb, num_channels,
-      num_columns_per_channel ? _fbb.CreateVector<int32_t>(*num_columns_per_channel) : 0,
-      embedding_dim_per_channel ? _fbb.CreateVector<int32_t>(*embedding_dim_per_channel) : 0);
-}
-
-struct LSHProjectionOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_TYPE = 4
-  };
-  LSHProjectionType type() const
-  {
-    return static_cast<LSHProjectionType>(GetField<int8_t>(VT_TYPE, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_TYPE) &&
-           verifier.EndTable();
-  }
-};
-
-struct LSHProjectionOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_type(LSHProjectionType type)
-  {
-    fbb_.AddElement<int8_t>(LSHProjectionOptions::VT_TYPE, static_cast<int8_t>(type), 0);
-  }
-  explicit LSHProjectionOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  LSHProjectionOptionsBuilder &operator=(const LSHProjectionOptionsBuilder &);
-  flatbuffers::Offset<LSHProjectionOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<LSHProjectionOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<LSHProjectionOptions>
-CreateLSHProjectionOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                           LSHProjectionType type = LSHProjectionType_UNKNOWN)
-{
-  LSHProjectionOptionsBuilder builder_(_fbb);
-  builder_.add_type(type);
-  return builder_.Finish();
-}
-
-struct SVDFOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_RANK = 4,
-    VT_FUSED_ACTIVATION_FUNCTION = 6
-  };
-  int32_t rank() const { return GetField<int32_t>(VT_RANK, 0); }
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_RANK) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable();
-  }
-};
-
-struct SVDFOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_rank(int32_t rank) { fbb_.AddElement<int32_t>(SVDFOptions::VT_RANK, rank, 0); }
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(SVDFOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  explicit SVDFOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SVDFOptionsBuilder &operator=(const SVDFOptionsBuilder &);
-  flatbuffers::Offset<SVDFOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SVDFOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SVDFOptions>
-CreateSVDFOptions(flatbuffers::FlatBufferBuilder &_fbb, int32_t rank = 0,
-                  ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE)
-{
-  SVDFOptionsBuilder builder_(_fbb);
-  builder_.add_rank(rank);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-struct RNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_FUSED_ACTIVATION_FUNCTION = 4
-  };
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable();
-  }
-};
-
-struct RNNOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(RNNOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  explicit RNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  RNNOptionsBuilder &operator=(const RNNOptionsBuilder &);
-  flatbuffers::Offset<RNNOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<RNNOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<RNNOptions>
-CreateRNNOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                 ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE)
-{
-  RNNOptionsBuilder builder_(_fbb);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-struct SequenceRNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_TIME_MAJOR = 4,
-    VT_FUSED_ACTIVATION_FUNCTION = 6
-  };
-  bool time_major() const { return GetField<uint8_t>(VT_TIME_MAJOR, 0) != 0; }
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<uint8_t>(verifier, VT_TIME_MAJOR) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable();
-  }
-};
-
-struct SequenceRNNOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_time_major(bool time_major)
-  {
-    fbb_.AddElement<uint8_t>(SequenceRNNOptions::VT_TIME_MAJOR, static_cast<uint8_t>(time_major),
-                             0);
-  }
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(SequenceRNNOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  explicit SequenceRNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SequenceRNNOptionsBuilder &operator=(const SequenceRNNOptionsBuilder &);
-  flatbuffers::Offset<SequenceRNNOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SequenceRNNOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SequenceRNNOptions> CreateSequenceRNNOptions(
-    flatbuffers::FlatBufferBuilder &_fbb, bool time_major = false,
-    ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE)
-{
-  SequenceRNNOptionsBuilder builder_(_fbb);
-  builder_.add_fused_activation_function(fused_activation_function);
-  builder_.add_time_major(time_major);
-  return builder_.Finish();
-}
-
-struct BidirectionalSequenceRNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_TIME_MAJOR = 4,
-    VT_FUSED_ACTIVATION_FUNCTION = 6,
-    VT_MERGE_OUTPUTS = 8
-  };
-  bool time_major() const { return GetField<uint8_t>(VT_TIME_MAJOR, 0) != 0; }
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool merge_outputs() const { return GetField<uint8_t>(VT_MERGE_OUTPUTS, 0) != 0; }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<uint8_t>(verifier, VT_TIME_MAJOR) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) &&
-           VerifyField<uint8_t>(verifier, VT_MERGE_OUTPUTS) && verifier.EndTable();
-  }
-};
-
-struct BidirectionalSequenceRNNOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_time_major(bool time_major)
-  {
-    fbb_.AddElement<uint8_t>(BidirectionalSequenceRNNOptions::VT_TIME_MAJOR,
-                             static_cast<uint8_t>(time_major), 0);
-  }
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(BidirectionalSequenceRNNOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  void add_merge_outputs(bool merge_outputs)
-  {
-    fbb_.AddElement<uint8_t>(BidirectionalSequenceRNNOptions::VT_MERGE_OUTPUTS,
-                             static_cast<uint8_t>(merge_outputs), 0);
-  }
-  explicit BidirectionalSequenceRNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  BidirectionalSequenceRNNOptionsBuilder &operator=(const BidirectionalSequenceRNNOptionsBuilder &);
-  flatbuffers::Offset<BidirectionalSequenceRNNOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<BidirectionalSequenceRNNOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<BidirectionalSequenceRNNOptions> CreateBidirectionalSequenceRNNOptions(
-    flatbuffers::FlatBufferBuilder &_fbb, bool time_major = false,
-    ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE,
-    bool merge_outputs = false)
-{
-  BidirectionalSequenceRNNOptionsBuilder builder_(_fbb);
-  builder_.add_merge_outputs(merge_outputs);
-  builder_.add_fused_activation_function(fused_activation_function);
-  builder_.add_time_major(time_major);
-  return builder_.Finish();
-}
-
-struct FullyConnectedOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_FUSED_ACTIVATION_FUNCTION = 4,
-    VT_WEIGHTS_FORMAT = 6
-  };
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  FullyConnectedOptionsWeightsFormat weights_format() const
-  {
-    return static_cast<FullyConnectedOptionsWeightsFormat>(GetField<int8_t>(VT_WEIGHTS_FORMAT, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) &&
-           VerifyField<int8_t>(verifier, VT_WEIGHTS_FORMAT) && verifier.EndTable();
-  }
-};
-
-struct FullyConnectedOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(FullyConnectedOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  void add_weights_format(FullyConnectedOptionsWeightsFormat weights_format)
-  {
-    fbb_.AddElement<int8_t>(FullyConnectedOptions::VT_WEIGHTS_FORMAT,
-                            static_cast<int8_t>(weights_format), 0);
-  }
-  explicit FullyConnectedOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  FullyConnectedOptionsBuilder &operator=(const FullyConnectedOptionsBuilder &);
-  flatbuffers::Offset<FullyConnectedOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<FullyConnectedOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<FullyConnectedOptions> CreateFullyConnectedOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE,
-    FullyConnectedOptionsWeightsFormat weights_format = FullyConnectedOptionsWeightsFormat_DEFAULT)
-{
-  FullyConnectedOptionsBuilder builder_(_fbb);
-  builder_.add_weights_format(weights_format);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-struct SoftmaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_BETA = 4
-  };
-  float beta() const { return GetField<float>(VT_BETA, 0.0f); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<float>(verifier, VT_BETA) &&
-           verifier.EndTable();
-  }
-};
-
-struct SoftmaxOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_beta(float beta) { fbb_.AddElement<float>(SoftmaxOptions::VT_BETA, beta, 0.0f); }
-  explicit SoftmaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SoftmaxOptionsBuilder &operator=(const SoftmaxOptionsBuilder &);
-  flatbuffers::Offset<SoftmaxOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SoftmaxOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SoftmaxOptions>
-CreateSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, float beta = 0.0f)
-{
-  SoftmaxOptionsBuilder builder_(_fbb);
-  builder_.add_beta(beta);
-  return builder_.Finish();
-}
-
-struct ConcatenationOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_AXIS = 4,
-    VT_FUSED_ACTIVATION_FUNCTION = 6
-  };
-  int32_t axis() const { return GetField<int32_t>(VT_AXIS, 0); }
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_AXIS) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable();
-  }
-};
-
-struct ConcatenationOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_axis(int32_t axis) { fbb_.AddElement<int32_t>(ConcatenationOptions::VT_AXIS, axis, 0); }
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(ConcatenationOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  explicit ConcatenationOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  ConcatenationOptionsBuilder &operator=(const ConcatenationOptionsBuilder &);
-  flatbuffers::Offset<ConcatenationOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ConcatenationOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ConcatenationOptions> CreateConcatenationOptions(
-    flatbuffers::FlatBufferBuilder &_fbb, int32_t axis = 0,
-    ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE)
-{
-  ConcatenationOptionsBuilder builder_(_fbb);
-  builder_.add_axis(axis);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-struct AddOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_FUSED_ACTIVATION_FUNCTION = 4
-  };
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable();
-  }
-};
-
-struct AddOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(AddOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  explicit AddOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  AddOptionsBuilder &operator=(const AddOptionsBuilder &);
-  flatbuffers::Offset<AddOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<AddOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<AddOptions>
-CreateAddOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                 ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE)
-{
-  AddOptionsBuilder builder_(_fbb);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-struct MulOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_FUSED_ACTIVATION_FUNCTION = 4
-  };
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable();
-  }
-};
-
-struct MulOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(MulOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  explicit MulOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  MulOptionsBuilder &operator=(const MulOptionsBuilder &);
-  flatbuffers::Offset<MulOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<MulOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<MulOptions>
-CreateMulOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                 ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE)
-{
-  MulOptionsBuilder builder_(_fbb);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-struct L2NormOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_FUSED_ACTIVATION_FUNCTION = 4
-  };
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable();
-  }
-};
-
-struct L2NormOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(L2NormOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  explicit L2NormOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  L2NormOptionsBuilder &operator=(const L2NormOptionsBuilder &);
-  flatbuffers::Offset<L2NormOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<L2NormOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<L2NormOptions>
-CreateL2NormOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                    ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE)
-{
-  L2NormOptionsBuilder builder_(_fbb);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-struct LocalResponseNormalizationOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_RADIUS = 4,
-    VT_BIAS = 6,
-    VT_ALPHA = 8,
-    VT_BETA = 10
-  };
-  int32_t radius() const { return GetField<int32_t>(VT_RADIUS, 0); }
-  float bias() const { return GetField<float>(VT_BIAS, 0.0f); }
-  float alpha() const { return GetField<float>(VT_ALPHA, 0.0f); }
-  float beta() const { return GetField<float>(VT_BETA, 0.0f); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_RADIUS) &&
-           VerifyField<float>(verifier, VT_BIAS) && VerifyField<float>(verifier, VT_ALPHA) &&
-           VerifyField<float>(verifier, VT_BETA) && verifier.EndTable();
-  }
-};
-
-struct LocalResponseNormalizationOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_radius(int32_t radius)
-  {
-    fbb_.AddElement<int32_t>(LocalResponseNormalizationOptions::VT_RADIUS, radius, 0);
-  }
-  void add_bias(float bias)
-  {
-    fbb_.AddElement<float>(LocalResponseNormalizationOptions::VT_BIAS, bias, 0.0f);
-  }
-  void add_alpha(float alpha)
-  {
-    fbb_.AddElement<float>(LocalResponseNormalizationOptions::VT_ALPHA, alpha, 0.0f);
-  }
-  void add_beta(float beta)
-  {
-    fbb_.AddElement<float>(LocalResponseNormalizationOptions::VT_BETA, beta, 0.0f);
-  }
-  explicit LocalResponseNormalizationOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-      : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  LocalResponseNormalizationOptionsBuilder &
-  operator=(const LocalResponseNormalizationOptionsBuilder &);
-  flatbuffers::Offset<LocalResponseNormalizationOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<LocalResponseNormalizationOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<LocalResponseNormalizationOptions>
-CreateLocalResponseNormalizationOptions(flatbuffers::FlatBufferBuilder &_fbb, int32_t radius = 0,
-                                        float bias = 0.0f, float alpha = 0.0f, float beta = 0.0f)
-{
-  LocalResponseNormalizationOptionsBuilder builder_(_fbb);
-  builder_.add_beta(beta);
-  builder_.add_alpha(alpha);
-  builder_.add_bias(bias);
-  builder_.add_radius(radius);
-  return builder_.Finish();
-}
-
-struct LSTMOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_FUSED_ACTIVATION_FUNCTION = 4,
-    VT_CELL_CLIP = 6,
-    VT_PROJ_CLIP = 8,
-    VT_KERNEL_TYPE = 10
-  };
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  float cell_clip() const { return GetField<float>(VT_CELL_CLIP, 0.0f); }
-  float proj_clip() const { return GetField<float>(VT_PROJ_CLIP, 0.0f); }
-  LSTMKernelType kernel_type() const
-  {
-    return static_cast<LSTMKernelType>(GetField<int8_t>(VT_KERNEL_TYPE, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) &&
-           VerifyField<float>(verifier, VT_CELL_CLIP) &&
-           VerifyField<float>(verifier, VT_PROJ_CLIP) &&
-           VerifyField<int8_t>(verifier, VT_KERNEL_TYPE) && verifier.EndTable();
-  }
-};
-
-struct LSTMOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(LSTMOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  void add_cell_clip(float cell_clip)
-  {
-    fbb_.AddElement<float>(LSTMOptions::VT_CELL_CLIP, cell_clip, 0.0f);
-  }
-  void add_proj_clip(float proj_clip)
-  {
-    fbb_.AddElement<float>(LSTMOptions::VT_PROJ_CLIP, proj_clip, 0.0f);
-  }
-  void add_kernel_type(LSTMKernelType kernel_type)
-  {
-    fbb_.AddElement<int8_t>(LSTMOptions::VT_KERNEL_TYPE, static_cast<int8_t>(kernel_type), 0);
-  }
-  explicit LSTMOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  LSTMOptionsBuilder &operator=(const LSTMOptionsBuilder &);
-  flatbuffers::Offset<LSTMOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<LSTMOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<LSTMOptions>
-CreateLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                  ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE,
-                  float cell_clip = 0.0f, float proj_clip = 0.0f,
-                  LSTMKernelType kernel_type = LSTMKernelType_FULL)
-{
-  LSTMOptionsBuilder builder_(_fbb);
-  builder_.add_proj_clip(proj_clip);
-  builder_.add_cell_clip(cell_clip);
-  builder_.add_kernel_type(kernel_type);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-struct UnidirectionalSequenceLSTMOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_FUSED_ACTIVATION_FUNCTION = 4,
-    VT_CELL_CLIP = 6,
-    VT_PROJ_CLIP = 8,
-    VT_TIME_MAJOR = 10
-  };
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  float cell_clip() const { return GetField<float>(VT_CELL_CLIP, 0.0f); }
-  float proj_clip() const { return GetField<float>(VT_PROJ_CLIP, 0.0f); }
-  bool time_major() const { return GetField<uint8_t>(VT_TIME_MAJOR, 0) != 0; }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) &&
-           VerifyField<float>(verifier, VT_CELL_CLIP) &&
-           VerifyField<float>(verifier, VT_PROJ_CLIP) &&
-           VerifyField<uint8_t>(verifier, VT_TIME_MAJOR) && verifier.EndTable();
-  }
-};
-
-struct UnidirectionalSequenceLSTMOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(UnidirectionalSequenceLSTMOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  void add_cell_clip(float cell_clip)
-  {
-    fbb_.AddElement<float>(UnidirectionalSequenceLSTMOptions::VT_CELL_CLIP, cell_clip, 0.0f);
-  }
-  void add_proj_clip(float proj_clip)
-  {
-    fbb_.AddElement<float>(UnidirectionalSequenceLSTMOptions::VT_PROJ_CLIP, proj_clip, 0.0f);
-  }
-  void add_time_major(bool time_major)
-  {
-    fbb_.AddElement<uint8_t>(UnidirectionalSequenceLSTMOptions::VT_TIME_MAJOR,
-                             static_cast<uint8_t>(time_major), 0);
-  }
-  explicit UnidirectionalSequenceLSTMOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-      : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  UnidirectionalSequenceLSTMOptionsBuilder &
-  operator=(const UnidirectionalSequenceLSTMOptionsBuilder &);
-  flatbuffers::Offset<UnidirectionalSequenceLSTMOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<UnidirectionalSequenceLSTMOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<UnidirectionalSequenceLSTMOptions>
-CreateUnidirectionalSequenceLSTMOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE,
-    float cell_clip = 0.0f, float proj_clip = 0.0f, bool time_major = false)
-{
-  UnidirectionalSequenceLSTMOptionsBuilder builder_(_fbb);
-  builder_.add_proj_clip(proj_clip);
-  builder_.add_cell_clip(cell_clip);
-  builder_.add_time_major(time_major);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-struct BidirectionalSequenceLSTMOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_FUSED_ACTIVATION_FUNCTION = 4,
-    VT_CELL_CLIP = 6,
-    VT_PROJ_CLIP = 8,
-    VT_MERGE_OUTPUTS = 10
-  };
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  float cell_clip() const { return GetField<float>(VT_CELL_CLIP, 0.0f); }
-  float proj_clip() const { return GetField<float>(VT_PROJ_CLIP, 0.0f); }
-  bool merge_outputs() const { return GetField<uint8_t>(VT_MERGE_OUTPUTS, 0) != 0; }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) &&
-           VerifyField<float>(verifier, VT_CELL_CLIP) &&
-           VerifyField<float>(verifier, VT_PROJ_CLIP) &&
-           VerifyField<uint8_t>(verifier, VT_MERGE_OUTPUTS) && verifier.EndTable();
-  }
-};
-
-struct BidirectionalSequenceLSTMOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(BidirectionalSequenceLSTMOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  void add_cell_clip(float cell_clip)
-  {
-    fbb_.AddElement<float>(BidirectionalSequenceLSTMOptions::VT_CELL_CLIP, cell_clip, 0.0f);
-  }
-  void add_proj_clip(float proj_clip)
-  {
-    fbb_.AddElement<float>(BidirectionalSequenceLSTMOptions::VT_PROJ_CLIP, proj_clip, 0.0f);
-  }
-  void add_merge_outputs(bool merge_outputs)
-  {
-    fbb_.AddElement<uint8_t>(BidirectionalSequenceLSTMOptions::VT_MERGE_OUTPUTS,
-                             static_cast<uint8_t>(merge_outputs), 0);
-  }
-  explicit BidirectionalSequenceLSTMOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-      : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  BidirectionalSequenceLSTMOptionsBuilder &
-  operator=(const BidirectionalSequenceLSTMOptionsBuilder &);
-  flatbuffers::Offset<BidirectionalSequenceLSTMOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<BidirectionalSequenceLSTMOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<BidirectionalSequenceLSTMOptions> CreateBidirectionalSequenceLSTMOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE,
-    float cell_clip = 0.0f, float proj_clip = 0.0f, bool merge_outputs = false)
-{
-  BidirectionalSequenceLSTMOptionsBuilder builder_(_fbb);
-  builder_.add_proj_clip(proj_clip);
-  builder_.add_cell_clip(cell_clip);
-  builder_.add_merge_outputs(merge_outputs);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-struct ResizeBilinearOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_ALIGN_CORNERS = 8
-  };
-  bool align_corners() const { return GetField<uint8_t>(VT_ALIGN_CORNERS, 0) != 0; }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<uint8_t>(verifier, VT_ALIGN_CORNERS) &&
-           verifier.EndTable();
-  }
-};
-
-struct ResizeBilinearOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_align_corners(bool align_corners)
-  {
-    fbb_.AddElement<uint8_t>(ResizeBilinearOptions::VT_ALIGN_CORNERS,
-                             static_cast<uint8_t>(align_corners), 0);
-  }
-  explicit ResizeBilinearOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  ResizeBilinearOptionsBuilder &operator=(const ResizeBilinearOptionsBuilder &);
-  flatbuffers::Offset<ResizeBilinearOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ResizeBilinearOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ResizeBilinearOptions>
-CreateResizeBilinearOptions(flatbuffers::FlatBufferBuilder &_fbb, bool align_corners = false)
-{
-  ResizeBilinearOptionsBuilder builder_(_fbb);
-  builder_.add_align_corners(align_corners);
-  return builder_.Finish();
-}
-
-struct ResizeNearestNeighborOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_ALIGN_CORNERS = 4
-  };
-  bool align_corners() const { return GetField<uint8_t>(VT_ALIGN_CORNERS, 0) != 0; }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<uint8_t>(verifier, VT_ALIGN_CORNERS) &&
-           verifier.EndTable();
-  }
-};
-
-struct ResizeNearestNeighborOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_align_corners(bool align_corners)
-  {
-    fbb_.AddElement<uint8_t>(ResizeNearestNeighborOptions::VT_ALIGN_CORNERS,
-                             static_cast<uint8_t>(align_corners), 0);
-  }
-  explicit ResizeNearestNeighborOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  ResizeNearestNeighborOptionsBuilder &operator=(const ResizeNearestNeighborOptionsBuilder &);
-  flatbuffers::Offset<ResizeNearestNeighborOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ResizeNearestNeighborOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ResizeNearestNeighborOptions>
-CreateResizeNearestNeighborOptions(flatbuffers::FlatBufferBuilder &_fbb, bool align_corners = false)
-{
-  ResizeNearestNeighborOptionsBuilder builder_(_fbb);
-  builder_.add_align_corners(align_corners);
-  return builder_.Finish();
-}
-
-struct CallOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_SUBGRAPH = 4
-  };
-  uint32_t subgraph() const { return GetField<uint32_t>(VT_SUBGRAPH, 0); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<uint32_t>(verifier, VT_SUBGRAPH) &&
-           verifier.EndTable();
-  }
-};
-
-struct CallOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_subgraph(uint32_t subgraph)
-  {
-    fbb_.AddElement<uint32_t>(CallOptions::VT_SUBGRAPH, subgraph, 0);
-  }
-  explicit CallOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  CallOptionsBuilder &operator=(const CallOptionsBuilder &);
-  flatbuffers::Offset<CallOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<CallOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<CallOptions> CreateCallOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                                                          uint32_t subgraph = 0)
-{
-  CallOptionsBuilder builder_(_fbb);
-  builder_.add_subgraph(subgraph);
-  return builder_.Finish();
-}
-
-struct PadOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct PadOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit PadOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  PadOptionsBuilder &operator=(const PadOptionsBuilder &);
-  flatbuffers::Offset<PadOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<PadOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<PadOptions> CreatePadOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  PadOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct PadV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct PadV2OptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit PadV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  PadV2OptionsBuilder &operator=(const PadV2OptionsBuilder &);
-  flatbuffers::Offset<PadV2Options> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<PadV2Options>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<PadV2Options> CreatePadV2Options(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  PadV2OptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct ReshapeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_NEW_SHAPE = 4
-  };
-  const flatbuffers::Vector<int32_t> *new_shape() const
-  {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_NEW_SHAPE);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_NEW_SHAPE) &&
-           verifier.VerifyVector(new_shape()) && verifier.EndTable();
-  }
-};
-
-struct ReshapeOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_new_shape(flatbuffers::Offset<flatbuffers::Vector<int32_t>> new_shape)
-  {
-    fbb_.AddOffset(ReshapeOptions::VT_NEW_SHAPE, new_shape);
-  }
-  explicit ReshapeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  ReshapeOptionsBuilder &operator=(const ReshapeOptionsBuilder &);
-  flatbuffers::Offset<ReshapeOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ReshapeOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ReshapeOptions>
-CreateReshapeOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                     flatbuffers::Offset<flatbuffers::Vector<int32_t>> new_shape = 0)
-{
-  ReshapeOptionsBuilder builder_(_fbb);
-  builder_.add_new_shape(new_shape);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<ReshapeOptions>
-CreateReshapeOptionsDirect(flatbuffers::FlatBufferBuilder &_fbb,
-                           const std::vector<int32_t> *new_shape = nullptr)
-{
-  return circle::CreateReshapeOptions(_fbb, new_shape ? _fbb.CreateVector<int32_t>(*new_shape) : 0);
-}
-
-struct SpaceToBatchNDOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct SpaceToBatchNDOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit SpaceToBatchNDOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SpaceToBatchNDOptionsBuilder &operator=(const SpaceToBatchNDOptionsBuilder &);
-  flatbuffers::Offset<SpaceToBatchNDOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SpaceToBatchNDOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SpaceToBatchNDOptions>
-CreateSpaceToBatchNDOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  SpaceToBatchNDOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct BatchToSpaceNDOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct BatchToSpaceNDOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit BatchToSpaceNDOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  BatchToSpaceNDOptionsBuilder &operator=(const BatchToSpaceNDOptionsBuilder &);
-  flatbuffers::Offset<BatchToSpaceNDOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<BatchToSpaceNDOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<BatchToSpaceNDOptions>
-CreateBatchToSpaceNDOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  BatchToSpaceNDOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct SkipGramOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_NGRAM_SIZE = 4,
-    VT_MAX_SKIP_SIZE = 6,
-    VT_INCLUDE_ALL_NGRAMS = 8
-  };
-  int32_t ngram_size() const { return GetField<int32_t>(VT_NGRAM_SIZE, 0); }
-  int32_t max_skip_size() const { return GetField<int32_t>(VT_MAX_SKIP_SIZE, 0); }
-  bool include_all_ngrams() const { return GetField<uint8_t>(VT_INCLUDE_ALL_NGRAMS, 0) != 0; }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_NGRAM_SIZE) &&
-           VerifyField<int32_t>(verifier, VT_MAX_SKIP_SIZE) &&
-           VerifyField<uint8_t>(verifier, VT_INCLUDE_ALL_NGRAMS) && verifier.EndTable();
-  }
-};
-
-struct SkipGramOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_ngram_size(int32_t ngram_size)
-  {
-    fbb_.AddElement<int32_t>(SkipGramOptions::VT_NGRAM_SIZE, ngram_size, 0);
-  }
-  void add_max_skip_size(int32_t max_skip_size)
-  {
-    fbb_.AddElement<int32_t>(SkipGramOptions::VT_MAX_SKIP_SIZE, max_skip_size, 0);
-  }
-  void add_include_all_ngrams(bool include_all_ngrams)
-  {
-    fbb_.AddElement<uint8_t>(SkipGramOptions::VT_INCLUDE_ALL_NGRAMS,
-                             static_cast<uint8_t>(include_all_ngrams), 0);
-  }
-  explicit SkipGramOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SkipGramOptionsBuilder &operator=(const SkipGramOptionsBuilder &);
-  flatbuffers::Offset<SkipGramOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SkipGramOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SkipGramOptions>
-CreateSkipGramOptions(flatbuffers::FlatBufferBuilder &_fbb, int32_t ngram_size = 0,
-                      int32_t max_skip_size = 0, bool include_all_ngrams = false)
-{
-  SkipGramOptionsBuilder builder_(_fbb);
-  builder_.add_max_skip_size(max_skip_size);
-  builder_.add_ngram_size(ngram_size);
-  builder_.add_include_all_ngrams(include_all_ngrams);
-  return builder_.Finish();
-}
-
-struct SpaceToDepthOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_BLOCK_SIZE = 4
-  };
-  int32_t block_size() const { return GetField<int32_t>(VT_BLOCK_SIZE, 0); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_BLOCK_SIZE) &&
-           verifier.EndTable();
-  }
-};
-
-struct SpaceToDepthOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_block_size(int32_t block_size)
-  {
-    fbb_.AddElement<int32_t>(SpaceToDepthOptions::VT_BLOCK_SIZE, block_size, 0);
-  }
-  explicit SpaceToDepthOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SpaceToDepthOptionsBuilder &operator=(const SpaceToDepthOptionsBuilder &);
-  flatbuffers::Offset<SpaceToDepthOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SpaceToDepthOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SpaceToDepthOptions>
-CreateSpaceToDepthOptions(flatbuffers::FlatBufferBuilder &_fbb, int32_t block_size = 0)
-{
-  SpaceToDepthOptionsBuilder builder_(_fbb);
-  builder_.add_block_size(block_size);
-  return builder_.Finish();
-}
-
-struct SubOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_FUSED_ACTIVATION_FUNCTION = 4
-  };
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable();
-  }
-};
-
-struct SubOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(SubOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  explicit SubOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SubOptionsBuilder &operator=(const SubOptionsBuilder &);
-  flatbuffers::Offset<SubOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SubOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SubOptions>
-CreateSubOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                 ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE)
-{
-  SubOptionsBuilder builder_(_fbb);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-struct DivOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_FUSED_ACTIVATION_FUNCTION = 4
-  };
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable();
-  }
-};
-
-struct DivOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(DivOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  explicit DivOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  DivOptionsBuilder &operator=(const DivOptionsBuilder &);
-  flatbuffers::Offset<DivOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<DivOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<DivOptions>
-CreateDivOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                 ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE)
-{
-  DivOptionsBuilder builder_(_fbb);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-struct TopKV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct TopKV2OptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit TopKV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  TopKV2OptionsBuilder &operator=(const TopKV2OptionsBuilder &);
-  flatbuffers::Offset<TopKV2Options> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<TopKV2Options>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<TopKV2Options> CreateTopKV2Options(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  TopKV2OptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct EmbeddingLookupSparseOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_COMBINER = 4
-  };
-  CombinerType combiner() const
-  {
-    return static_cast<CombinerType>(GetField<int8_t>(VT_COMBINER, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_COMBINER) &&
-           verifier.EndTable();
-  }
-};
-
-struct EmbeddingLookupSparseOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_combiner(CombinerType combiner)
-  {
-    fbb_.AddElement<int8_t>(EmbeddingLookupSparseOptions::VT_COMBINER,
-                            static_cast<int8_t>(combiner), 0);
-  }
-  explicit EmbeddingLookupSparseOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  EmbeddingLookupSparseOptionsBuilder &operator=(const EmbeddingLookupSparseOptionsBuilder &);
-  flatbuffers::Offset<EmbeddingLookupSparseOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<EmbeddingLookupSparseOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<EmbeddingLookupSparseOptions>
-CreateEmbeddingLookupSparseOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                                   CombinerType combiner = CombinerType_SUM)
-{
-  EmbeddingLookupSparseOptionsBuilder builder_(_fbb);
-  builder_.add_combiner(combiner);
-  return builder_.Finish();
-}
-
-struct GatherOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_AXIS = 4
-  };
-  int32_t axis() const { return GetField<int32_t>(VT_AXIS, 0); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_AXIS) &&
-           verifier.EndTable();
-  }
-};
-
-struct GatherOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_axis(int32_t axis) { fbb_.AddElement<int32_t>(GatherOptions::VT_AXIS, axis, 0); }
-  explicit GatherOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  GatherOptionsBuilder &operator=(const GatherOptionsBuilder &);
-  flatbuffers::Offset<GatherOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<GatherOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<GatherOptions> CreateGatherOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                                                              int32_t axis = 0)
-{
-  GatherOptionsBuilder builder_(_fbb);
-  builder_.add_axis(axis);
-  return builder_.Finish();
-}
-
-struct TransposeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct TransposeOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit TransposeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  TransposeOptionsBuilder &operator=(const TransposeOptionsBuilder &);
-  flatbuffers::Offset<TransposeOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<TransposeOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<TransposeOptions>
-CreateTransposeOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  TransposeOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct ExpOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct ExpOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit ExpOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  ExpOptionsBuilder &operator=(const ExpOptionsBuilder &);
-  flatbuffers::Offset<ExpOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ExpOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ExpOptions> CreateExpOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  ExpOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct ReducerOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_KEEP_DIMS = 4
-  };
-  bool keep_dims() const { return GetField<uint8_t>(VT_KEEP_DIMS, 0) != 0; }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<uint8_t>(verifier, VT_KEEP_DIMS) &&
-           verifier.EndTable();
-  }
-};
-
-struct ReducerOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_keep_dims(bool keep_dims)
-  {
-    fbb_.AddElement<uint8_t>(ReducerOptions::VT_KEEP_DIMS, static_cast<uint8_t>(keep_dims), 0);
-  }
-  explicit ReducerOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  ReducerOptionsBuilder &operator=(const ReducerOptionsBuilder &);
-  flatbuffers::Offset<ReducerOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ReducerOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ReducerOptions>
-CreateReducerOptions(flatbuffers::FlatBufferBuilder &_fbb, bool keep_dims = false)
-{
-  ReducerOptionsBuilder builder_(_fbb);
-  builder_.add_keep_dims(keep_dims);
-  return builder_.Finish();
-}
-
-struct SqueezeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_SQUEEZE_DIMS = 4
-  };
-  const flatbuffers::Vector<int32_t> *squeeze_dims() const
-  {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_SQUEEZE_DIMS);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_SQUEEZE_DIMS) &&
-           verifier.VerifyVector(squeeze_dims()) && verifier.EndTable();
-  }
-};
-
-struct SqueezeOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_squeeze_dims(flatbuffers::Offset<flatbuffers::Vector<int32_t>> squeeze_dims)
-  {
-    fbb_.AddOffset(SqueezeOptions::VT_SQUEEZE_DIMS, squeeze_dims);
-  }
-  explicit SqueezeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SqueezeOptionsBuilder &operator=(const SqueezeOptionsBuilder &);
-  flatbuffers::Offset<SqueezeOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SqueezeOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SqueezeOptions>
-CreateSqueezeOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                     flatbuffers::Offset<flatbuffers::Vector<int32_t>> squeeze_dims = 0)
-{
-  SqueezeOptionsBuilder builder_(_fbb);
-  builder_.add_squeeze_dims(squeeze_dims);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<SqueezeOptions>
-CreateSqueezeOptionsDirect(flatbuffers::FlatBufferBuilder &_fbb,
-                           const std::vector<int32_t> *squeeze_dims = nullptr)
-{
-  return circle::CreateSqueezeOptions(_fbb,
-                                      squeeze_dims ? _fbb.CreateVector<int32_t>(*squeeze_dims) : 0);
-}
-
-struct SplitOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_NUM_SPLITS = 4
-  };
-  int32_t num_splits() const { return GetField<int32_t>(VT_NUM_SPLITS, 0); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_NUM_SPLITS) &&
-           verifier.EndTable();
-  }
-};
-
-struct SplitOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_num_splits(int32_t num_splits)
-  {
-    fbb_.AddElement<int32_t>(SplitOptions::VT_NUM_SPLITS, num_splits, 0);
-  }
-  explicit SplitOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SplitOptionsBuilder &operator=(const SplitOptionsBuilder &);
-  flatbuffers::Offset<SplitOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SplitOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SplitOptions> CreateSplitOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                                                            int32_t num_splits = 0)
-{
-  SplitOptionsBuilder builder_(_fbb);
-  builder_.add_num_splits(num_splits);
-  return builder_.Finish();
-}
-
-struct SplitVOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_NUM_SPLITS = 4
-  };
-  int32_t num_splits() const { return GetField<int32_t>(VT_NUM_SPLITS, 0); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_NUM_SPLITS) &&
-           verifier.EndTable();
-  }
-};
-
-struct SplitVOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_num_splits(int32_t num_splits)
-  {
-    fbb_.AddElement<int32_t>(SplitVOptions::VT_NUM_SPLITS, num_splits, 0);
-  }
-  explicit SplitVOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SplitVOptionsBuilder &operator=(const SplitVOptionsBuilder &);
-  flatbuffers::Offset<SplitVOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SplitVOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SplitVOptions> CreateSplitVOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                                                              int32_t num_splits = 0)
-{
-  SplitVOptionsBuilder builder_(_fbb);
-  builder_.add_num_splits(num_splits);
-  return builder_.Finish();
-}
-
-struct StridedSliceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_BEGIN_MASK = 4,
-    VT_END_MASK = 6,
-    VT_ELLIPSIS_MASK = 8,
-    VT_NEW_AXIS_MASK = 10,
-    VT_SHRINK_AXIS_MASK = 12
-  };
-  int32_t begin_mask() const { return GetField<int32_t>(VT_BEGIN_MASK, 0); }
-  int32_t end_mask() const { return GetField<int32_t>(VT_END_MASK, 0); }
-  int32_t ellipsis_mask() const { return GetField<int32_t>(VT_ELLIPSIS_MASK, 0); }
-  int32_t new_axis_mask() const { return GetField<int32_t>(VT_NEW_AXIS_MASK, 0); }
-  int32_t shrink_axis_mask() const { return GetField<int32_t>(VT_SHRINK_AXIS_MASK, 0); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_BEGIN_MASK) &&
-           VerifyField<int32_t>(verifier, VT_END_MASK) &&
-           VerifyField<int32_t>(verifier, VT_ELLIPSIS_MASK) &&
-           VerifyField<int32_t>(verifier, VT_NEW_AXIS_MASK) &&
-           VerifyField<int32_t>(verifier, VT_SHRINK_AXIS_MASK) && verifier.EndTable();
-  }
-};
-
-struct StridedSliceOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_begin_mask(int32_t begin_mask)
-  {
-    fbb_.AddElement<int32_t>(StridedSliceOptions::VT_BEGIN_MASK, begin_mask, 0);
-  }
-  void add_end_mask(int32_t end_mask)
-  {
-    fbb_.AddElement<int32_t>(StridedSliceOptions::VT_END_MASK, end_mask, 0);
-  }
-  void add_ellipsis_mask(int32_t ellipsis_mask)
-  {
-    fbb_.AddElement<int32_t>(StridedSliceOptions::VT_ELLIPSIS_MASK, ellipsis_mask, 0);
-  }
-  void add_new_axis_mask(int32_t new_axis_mask)
-  {
-    fbb_.AddElement<int32_t>(StridedSliceOptions::VT_NEW_AXIS_MASK, new_axis_mask, 0);
-  }
-  void add_shrink_axis_mask(int32_t shrink_axis_mask)
-  {
-    fbb_.AddElement<int32_t>(StridedSliceOptions::VT_SHRINK_AXIS_MASK, shrink_axis_mask, 0);
-  }
-  explicit StridedSliceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  StridedSliceOptionsBuilder &operator=(const StridedSliceOptionsBuilder &);
-  flatbuffers::Offset<StridedSliceOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<StridedSliceOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<StridedSliceOptions>
-CreateStridedSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, int32_t begin_mask = 0,
-                          int32_t end_mask = 0, int32_t ellipsis_mask = 0,
-                          int32_t new_axis_mask = 0, int32_t shrink_axis_mask = 0)
-{
-  StridedSliceOptionsBuilder builder_(_fbb);
-  builder_.add_shrink_axis_mask(shrink_axis_mask);
-  builder_.add_new_axis_mask(new_axis_mask);
-  builder_.add_ellipsis_mask(ellipsis_mask);
-  builder_.add_end_mask(end_mask);
-  builder_.add_begin_mask(begin_mask);
-  return builder_.Finish();
-}
-
-struct LogSoftmaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct LogSoftmaxOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit LogSoftmaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  LogSoftmaxOptionsBuilder &operator=(const LogSoftmaxOptionsBuilder &);
-  flatbuffers::Offset<LogSoftmaxOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<LogSoftmaxOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<LogSoftmaxOptions>
-CreateLogSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  LogSoftmaxOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct CastOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_IN_DATA_TYPE = 4,
-    VT_OUT_DATA_TYPE = 6
-  };
-  TensorType in_data_type() const
-  {
-    return static_cast<TensorType>(GetField<int8_t>(VT_IN_DATA_TYPE, 0));
-  }
-  TensorType out_data_type() const
-  {
-    return static_cast<TensorType>(GetField<int8_t>(VT_OUT_DATA_TYPE, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_IN_DATA_TYPE) &&
-           VerifyField<int8_t>(verifier, VT_OUT_DATA_TYPE) && verifier.EndTable();
-  }
-};
-
-struct CastOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_in_data_type(TensorType in_data_type)
-  {
-    fbb_.AddElement<int8_t>(CastOptions::VT_IN_DATA_TYPE, static_cast<int8_t>(in_data_type), 0);
-  }
-  void add_out_data_type(TensorType out_data_type)
-  {
-    fbb_.AddElement<int8_t>(CastOptions::VT_OUT_DATA_TYPE, static_cast<int8_t>(out_data_type), 0);
-  }
-  explicit CastOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  CastOptionsBuilder &operator=(const CastOptionsBuilder &);
-  flatbuffers::Offset<CastOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<CastOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<CastOptions>
-CreateCastOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                  TensorType in_data_type = TensorType_FLOAT32,
-                  TensorType out_data_type = TensorType_FLOAT32)
-{
-  CastOptionsBuilder builder_(_fbb);
-  builder_.add_out_data_type(out_data_type);
-  builder_.add_in_data_type(in_data_type);
-  return builder_.Finish();
-}
-
-struct DequantizeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct DequantizeOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit DequantizeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  DequantizeOptionsBuilder &operator=(const DequantizeOptionsBuilder &);
-  flatbuffers::Offset<DequantizeOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<DequantizeOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<DequantizeOptions>
-CreateDequantizeOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  DequantizeOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct MaximumMinimumOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct MaximumMinimumOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit MaximumMinimumOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  MaximumMinimumOptionsBuilder &operator=(const MaximumMinimumOptionsBuilder &);
-  flatbuffers::Offset<MaximumMinimumOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<MaximumMinimumOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<MaximumMinimumOptions>
-CreateMaximumMinimumOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  MaximumMinimumOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct TileOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct TileOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit TileOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  TileOptionsBuilder &operator=(const TileOptionsBuilder &);
-  flatbuffers::Offset<TileOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<TileOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<TileOptions> CreateTileOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  TileOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct ArgMaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_OUTPUT_TYPE = 4
-  };
-  TensorType output_type() const
-  {
-    return static_cast<TensorType>(GetField<int8_t>(VT_OUTPUT_TYPE, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_OUTPUT_TYPE) &&
-           verifier.EndTable();
-  }
-};
-
-struct ArgMaxOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_output_type(TensorType output_type)
-  {
-    fbb_.AddElement<int8_t>(ArgMaxOptions::VT_OUTPUT_TYPE, static_cast<int8_t>(output_type), 0);
-  }
-  explicit ArgMaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  ArgMaxOptionsBuilder &operator=(const ArgMaxOptionsBuilder &);
-  flatbuffers::Offset<ArgMaxOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ArgMaxOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ArgMaxOptions>
-CreateArgMaxOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                    TensorType output_type = TensorType_FLOAT32)
-{
-  ArgMaxOptionsBuilder builder_(_fbb);
-  builder_.add_output_type(output_type);
-  return builder_.Finish();
-}
-
-struct ArgMinOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_OUTPUT_TYPE = 4
-  };
-  TensorType output_type() const
-  {
-    return static_cast<TensorType>(GetField<int8_t>(VT_OUTPUT_TYPE, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_OUTPUT_TYPE) &&
-           verifier.EndTable();
-  }
-};
-
-struct ArgMinOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_output_type(TensorType output_type)
-  {
-    fbb_.AddElement<int8_t>(ArgMinOptions::VT_OUTPUT_TYPE, static_cast<int8_t>(output_type), 0);
-  }
-  explicit ArgMinOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  ArgMinOptionsBuilder &operator=(const ArgMinOptionsBuilder &);
-  flatbuffers::Offset<ArgMinOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ArgMinOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ArgMinOptions>
-CreateArgMinOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                    TensorType output_type = TensorType_FLOAT32)
-{
-  ArgMinOptionsBuilder builder_(_fbb);
-  builder_.add_output_type(output_type);
-  return builder_.Finish();
-}
-
-struct GreaterOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct GreaterOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit GreaterOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  GreaterOptionsBuilder &operator=(const GreaterOptionsBuilder &);
-  flatbuffers::Offset<GreaterOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<GreaterOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<GreaterOptions>
-CreateGreaterOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  GreaterOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct GreaterEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct GreaterEqualOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit GreaterEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  GreaterEqualOptionsBuilder &operator=(const GreaterEqualOptionsBuilder &);
-  flatbuffers::Offset<GreaterEqualOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<GreaterEqualOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<GreaterEqualOptions>
-CreateGreaterEqualOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  GreaterEqualOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct LessOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct LessOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit LessOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  LessOptionsBuilder &operator=(const LessOptionsBuilder &);
-  flatbuffers::Offset<LessOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<LessOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<LessOptions> CreateLessOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  LessOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct LessEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct LessEqualOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit LessEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  LessEqualOptionsBuilder &operator=(const LessEqualOptionsBuilder &);
-  flatbuffers::Offset<LessEqualOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<LessEqualOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<LessEqualOptions>
-CreateLessEqualOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  LessEqualOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct NegOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct NegOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit NegOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  NegOptionsBuilder &operator=(const NegOptionsBuilder &);
-  flatbuffers::Offset<NegOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<NegOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<NegOptions> CreateNegOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  NegOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct SelectOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct SelectOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit SelectOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SelectOptionsBuilder &operator=(const SelectOptionsBuilder &);
-  flatbuffers::Offset<SelectOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SelectOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SelectOptions> CreateSelectOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  SelectOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct SliceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct SliceOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit SliceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SliceOptionsBuilder &operator=(const SliceOptionsBuilder &);
-  flatbuffers::Offset<SliceOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SliceOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SliceOptions> CreateSliceOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  SliceOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct TransposeConvOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_PADDING = 4,
-    VT_STRIDE_W = 6,
-    VT_STRIDE_H = 8
-  };
-  Padding padding() const { return static_cast<Padding>(GetField<int8_t>(VT_PADDING, 0)); }
-  int32_t stride_w() const { return GetField<int32_t>(VT_STRIDE_W, 0); }
-  int32_t stride_h() const { return GetField<int32_t>(VT_STRIDE_H, 0); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_PADDING) &&
-           VerifyField<int32_t>(verifier, VT_STRIDE_W) &&
-           VerifyField<int32_t>(verifier, VT_STRIDE_H) && verifier.EndTable();
-  }
-};
-
-struct TransposeConvOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_padding(Padding padding)
-  {
-    fbb_.AddElement<int8_t>(TransposeConvOptions::VT_PADDING, static_cast<int8_t>(padding), 0);
-  }
-  void add_stride_w(int32_t stride_w)
-  {
-    fbb_.AddElement<int32_t>(TransposeConvOptions::VT_STRIDE_W, stride_w, 0);
-  }
-  void add_stride_h(int32_t stride_h)
-  {
-    fbb_.AddElement<int32_t>(TransposeConvOptions::VT_STRIDE_H, stride_h, 0);
-  }
-  explicit TransposeConvOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  TransposeConvOptionsBuilder &operator=(const TransposeConvOptionsBuilder &);
-  flatbuffers::Offset<TransposeConvOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<TransposeConvOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<TransposeConvOptions>
-CreateTransposeConvOptions(flatbuffers::FlatBufferBuilder &_fbb, Padding padding = Padding_SAME,
-                           int32_t stride_w = 0, int32_t stride_h = 0)
-{
-  TransposeConvOptionsBuilder builder_(_fbb);
-  builder_.add_stride_h(stride_h);
-  builder_.add_stride_w(stride_w);
-  builder_.add_padding(padding);
-  return builder_.Finish();
-}
-
-struct ExpandDimsOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct ExpandDimsOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit ExpandDimsOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  ExpandDimsOptionsBuilder &operator=(const ExpandDimsOptionsBuilder &);
-  flatbuffers::Offset<ExpandDimsOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ExpandDimsOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ExpandDimsOptions>
-CreateExpandDimsOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  ExpandDimsOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct SparseToDenseOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_VALIDATE_INDICES = 4
-  };
-  bool validate_indices() const { return GetField<uint8_t>(VT_VALIDATE_INDICES, 0) != 0; }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<uint8_t>(verifier, VT_VALIDATE_INDICES) &&
-           verifier.EndTable();
-  }
-};
-
-struct SparseToDenseOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_validate_indices(bool validate_indices)
-  {
-    fbb_.AddElement<uint8_t>(SparseToDenseOptions::VT_VALIDATE_INDICES,
-                             static_cast<uint8_t>(validate_indices), 0);
-  }
-  explicit SparseToDenseOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SparseToDenseOptionsBuilder &operator=(const SparseToDenseOptionsBuilder &);
-  flatbuffers::Offset<SparseToDenseOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SparseToDenseOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SparseToDenseOptions>
-CreateSparseToDenseOptions(flatbuffers::FlatBufferBuilder &_fbb, bool validate_indices = false)
-{
-  SparseToDenseOptionsBuilder builder_(_fbb);
-  builder_.add_validate_indices(validate_indices);
-  return builder_.Finish();
-}
-
-struct EqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct EqualOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit EqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  EqualOptionsBuilder &operator=(const EqualOptionsBuilder &);
-  flatbuffers::Offset<EqualOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<EqualOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<EqualOptions> CreateEqualOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  EqualOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct NotEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct NotEqualOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit NotEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  NotEqualOptionsBuilder &operator=(const NotEqualOptionsBuilder &);
-  flatbuffers::Offset<NotEqualOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<NotEqualOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<NotEqualOptions>
-CreateNotEqualOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  NotEqualOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct ShapeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_OUT_TYPE = 4
-  };
-  TensorType out_type() const { return static_cast<TensorType>(GetField<int8_t>(VT_OUT_TYPE, 0)); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_OUT_TYPE) &&
-           verifier.EndTable();
-  }
-};
-
-struct ShapeOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_out_type(TensorType out_type)
-  {
-    fbb_.AddElement<int8_t>(ShapeOptions::VT_OUT_TYPE, static_cast<int8_t>(out_type), 0);
-  }
-  explicit ShapeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  ShapeOptionsBuilder &operator=(const ShapeOptionsBuilder &);
-  flatbuffers::Offset<ShapeOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ShapeOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ShapeOptions>
-CreateShapeOptions(flatbuffers::FlatBufferBuilder &_fbb, TensorType out_type = TensorType_FLOAT32)
-{
-  ShapeOptionsBuilder builder_(_fbb);
-  builder_.add_out_type(out_type);
-  return builder_.Finish();
-}
-
-struct PowOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct PowOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit PowOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  PowOptionsBuilder &operator=(const PowOptionsBuilder &);
-  flatbuffers::Offset<PowOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<PowOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<PowOptions> CreatePowOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  PowOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct FakeQuantOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_MIN = 4,
-    VT_MAX = 6,
-    VT_NUM_BITS = 8,
-    VT_NARROW_RANGE = 10
-  };
-  float min() const { return GetField<float>(VT_MIN, 0.0f); }
-  float max() const { return GetField<float>(VT_MAX, 0.0f); }
-  int32_t num_bits() const { return GetField<int32_t>(VT_NUM_BITS, 0); }
-  bool narrow_range() const { return GetField<uint8_t>(VT_NARROW_RANGE, 0) != 0; }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<float>(verifier, VT_MIN) &&
-           VerifyField<float>(verifier, VT_MAX) && VerifyField<int32_t>(verifier, VT_NUM_BITS) &&
-           VerifyField<uint8_t>(verifier, VT_NARROW_RANGE) && verifier.EndTable();
-  }
-};
-
-struct FakeQuantOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_min(float min) { fbb_.AddElement<float>(FakeQuantOptions::VT_MIN, min, 0.0f); }
-  void add_max(float max) { fbb_.AddElement<float>(FakeQuantOptions::VT_MAX, max, 0.0f); }
-  void add_num_bits(int32_t num_bits)
-  {
-    fbb_.AddElement<int32_t>(FakeQuantOptions::VT_NUM_BITS, num_bits, 0);
-  }
-  void add_narrow_range(bool narrow_range)
-  {
-    fbb_.AddElement<uint8_t>(FakeQuantOptions::VT_NARROW_RANGE, static_cast<uint8_t>(narrow_range),
-                             0);
-  }
-  explicit FakeQuantOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  FakeQuantOptionsBuilder &operator=(const FakeQuantOptionsBuilder &);
-  flatbuffers::Offset<FakeQuantOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<FakeQuantOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<FakeQuantOptions>
-CreateFakeQuantOptions(flatbuffers::FlatBufferBuilder &_fbb, float min = 0.0f, float max = 0.0f,
-                       int32_t num_bits = 0, bool narrow_range = false)
-{
-  FakeQuantOptionsBuilder builder_(_fbb);
-  builder_.add_num_bits(num_bits);
-  builder_.add_max(max);
-  builder_.add_min(min);
-  builder_.add_narrow_range(narrow_range);
-  return builder_.Finish();
-}
-
-struct PackOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_VALUES_COUNT = 4,
-    VT_AXIS = 6
-  };
-  int32_t values_count() const { return GetField<int32_t>(VT_VALUES_COUNT, 0); }
-  int32_t axis() const { return GetField<int32_t>(VT_AXIS, 0); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_VALUES_COUNT) &&
-           VerifyField<int32_t>(verifier, VT_AXIS) && verifier.EndTable();
-  }
-};
-
-struct PackOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_values_count(int32_t values_count)
-  {
-    fbb_.AddElement<int32_t>(PackOptions::VT_VALUES_COUNT, values_count, 0);
-  }
-  void add_axis(int32_t axis) { fbb_.AddElement<int32_t>(PackOptions::VT_AXIS, axis, 0); }
-  explicit PackOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  PackOptionsBuilder &operator=(const PackOptionsBuilder &);
-  flatbuffers::Offset<PackOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<PackOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<PackOptions>
-CreatePackOptions(flatbuffers::FlatBufferBuilder &_fbb, int32_t values_count = 0, int32_t axis = 0)
-{
-  PackOptionsBuilder builder_(_fbb);
-  builder_.add_axis(axis);
-  builder_.add_values_count(values_count);
-  return builder_.Finish();
-}
-
-struct LogicalOrOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct LogicalOrOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit LogicalOrOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  LogicalOrOptionsBuilder &operator=(const LogicalOrOptionsBuilder &);
-  flatbuffers::Offset<LogicalOrOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<LogicalOrOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<LogicalOrOptions>
-CreateLogicalOrOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  LogicalOrOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct OneHotOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_AXIS = 4
-  };
-  int32_t axis() const { return GetField<int32_t>(VT_AXIS, 0); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_AXIS) &&
-           verifier.EndTable();
-  }
-};
-
-struct OneHotOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_axis(int32_t axis) { fbb_.AddElement<int32_t>(OneHotOptions::VT_AXIS, axis, 0); }
-  explicit OneHotOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  OneHotOptionsBuilder &operator=(const OneHotOptionsBuilder &);
-  flatbuffers::Offset<OneHotOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<OneHotOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<OneHotOptions> CreateOneHotOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                                                              int32_t axis = 0)
-{
-  OneHotOptionsBuilder builder_(_fbb);
-  builder_.add_axis(axis);
-  return builder_.Finish();
-}
-
-struct AbsOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct AbsOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit AbsOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  AbsOptionsBuilder &operator=(const AbsOptionsBuilder &);
-  flatbuffers::Offset<AbsOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<AbsOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<AbsOptions> CreateAbsOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  AbsOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct LogicalAndOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct LogicalAndOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit LogicalAndOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  LogicalAndOptionsBuilder &operator=(const LogicalAndOptionsBuilder &);
-  flatbuffers::Offset<LogicalAndOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<LogicalAndOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<LogicalAndOptions>
-CreateLogicalAndOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  LogicalAndOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct LogicalNotOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct LogicalNotOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit LogicalNotOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  LogicalNotOptionsBuilder &operator=(const LogicalNotOptionsBuilder &);
-  flatbuffers::Offset<LogicalNotOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<LogicalNotOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<LogicalNotOptions>
-CreateLogicalNotOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  LogicalNotOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct UnpackOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_NUM = 4,
-    VT_AXIS = 6
-  };
-  int32_t num() const { return GetField<int32_t>(VT_NUM, 0); }
-  int32_t axis() const { return GetField<int32_t>(VT_AXIS, 0); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_NUM) &&
-           VerifyField<int32_t>(verifier, VT_AXIS) && verifier.EndTable();
-  }
-};
-
-struct UnpackOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_num(int32_t num) { fbb_.AddElement<int32_t>(UnpackOptions::VT_NUM, num, 0); }
-  void add_axis(int32_t axis) { fbb_.AddElement<int32_t>(UnpackOptions::VT_AXIS, axis, 0); }
-  explicit UnpackOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  UnpackOptionsBuilder &operator=(const UnpackOptionsBuilder &);
-  flatbuffers::Offset<UnpackOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<UnpackOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<UnpackOptions> CreateUnpackOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                                                              int32_t num = 0, int32_t axis = 0)
-{
-  UnpackOptionsBuilder builder_(_fbb);
-  builder_.add_axis(axis);
-  builder_.add_num(num);
-  return builder_.Finish();
-}
-
-struct FloorDivOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct FloorDivOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit FloorDivOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  FloorDivOptionsBuilder &operator=(const FloorDivOptionsBuilder &);
-  flatbuffers::Offset<FloorDivOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<FloorDivOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<FloorDivOptions>
-CreateFloorDivOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  FloorDivOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct SquareOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct SquareOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit SquareOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SquareOptionsBuilder &operator=(const SquareOptionsBuilder &);
-  flatbuffers::Offset<SquareOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SquareOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SquareOptions> CreateSquareOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  SquareOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct ZerosLikeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct ZerosLikeOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit ZerosLikeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  ZerosLikeOptionsBuilder &operator=(const ZerosLikeOptionsBuilder &);
-  flatbuffers::Offset<ZerosLikeOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ZerosLikeOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ZerosLikeOptions>
-CreateZerosLikeOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  ZerosLikeOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct FillOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct FillOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit FillOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  FillOptionsBuilder &operator=(const FillOptionsBuilder &);
-  flatbuffers::Offset<FillOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<FillOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<FillOptions> CreateFillOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  FillOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct FloorModOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct FloorModOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit FloorModOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  FloorModOptionsBuilder &operator=(const FloorModOptionsBuilder &);
-  flatbuffers::Offset<FloorModOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<FloorModOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<FloorModOptions>
-CreateFloorModOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  FloorModOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct RangeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct RangeOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit RangeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  RangeOptionsBuilder &operator=(const RangeOptionsBuilder &);
-  flatbuffers::Offset<RangeOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<RangeOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<RangeOptions> CreateRangeOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  RangeOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct LeakyReluOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_ALPHA = 4
-  };
-  float alpha() const { return GetField<float>(VT_ALPHA, 0.0f); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<float>(verifier, VT_ALPHA) &&
-           verifier.EndTable();
-  }
-};
-
-struct LeakyReluOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_alpha(float alpha) { fbb_.AddElement<float>(LeakyReluOptions::VT_ALPHA, alpha, 0.0f); }
-  explicit LeakyReluOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  LeakyReluOptionsBuilder &operator=(const LeakyReluOptionsBuilder &);
-  flatbuffers::Offset<LeakyReluOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<LeakyReluOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<LeakyReluOptions>
-CreateLeakyReluOptions(flatbuffers::FlatBufferBuilder &_fbb, float alpha = 0.0f)
-{
-  LeakyReluOptionsBuilder builder_(_fbb);
-  builder_.add_alpha(alpha);
-  return builder_.Finish();
-}
-
-struct SquaredDifferenceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct SquaredDifferenceOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit SquaredDifferenceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SquaredDifferenceOptionsBuilder &operator=(const SquaredDifferenceOptionsBuilder &);
-  flatbuffers::Offset<SquaredDifferenceOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SquaredDifferenceOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SquaredDifferenceOptions>
-CreateSquaredDifferenceOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  SquaredDifferenceOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct MirrorPadOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_MODE = 4
-  };
-  MirrorPadMode mode() const { return static_cast<MirrorPadMode>(GetField<int8_t>(VT_MODE, 0)); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_MODE) &&
-           verifier.EndTable();
-  }
-};
-
-struct MirrorPadOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_mode(MirrorPadMode mode)
-  {
-    fbb_.AddElement<int8_t>(MirrorPadOptions::VT_MODE, static_cast<int8_t>(mode), 0);
-  }
-  explicit MirrorPadOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  MirrorPadOptionsBuilder &operator=(const MirrorPadOptionsBuilder &);
-  flatbuffers::Offset<MirrorPadOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<MirrorPadOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<MirrorPadOptions>
-CreateMirrorPadOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                       MirrorPadMode mode = MirrorPadMode_REFLECT)
-{
-  MirrorPadOptionsBuilder builder_(_fbb);
-  builder_.add_mode(mode);
-  return builder_.Finish();
-}
-
-struct InstanceNormOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_EPSILON = 4,
-    VT_FUSED_ACTIVATION_FUNCTION = 6
-  };
-  float epsilon() const { return GetField<float>(VT_EPSILON, 0.0f); }
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<float>(verifier, VT_EPSILON) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable();
-  }
-};
-
-struct InstanceNormOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_epsilon(float epsilon)
-  {
-    fbb_.AddElement<float>(InstanceNormOptions::VT_EPSILON, epsilon, 0.0f);
-  }
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(InstanceNormOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  explicit InstanceNormOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  InstanceNormOptionsBuilder &operator=(const InstanceNormOptionsBuilder &);
-  flatbuffers::Offset<InstanceNormOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<InstanceNormOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<InstanceNormOptions> CreateInstanceNormOptions(
-    flatbuffers::FlatBufferBuilder &_fbb, float epsilon = 0.0f,
-    ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE)
-{
-  InstanceNormOptionsBuilder builder_(_fbb);
-  builder_.add_epsilon(epsilon);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-struct OperatorCode FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_BUILTIN_CODE = 4,
-    VT_CUSTOM_CODE = 6,
-    VT_VERSION = 8
-  };
-  BuiltinOperator builtin_code() const
-  {
-    return static_cast<BuiltinOperator>(GetField<uint8_t>(VT_BUILTIN_CODE, 0));
-  }
-  const flatbuffers::String *custom_code() const
-  {
-    return GetPointer<const flatbuffers::String *>(VT_CUSTOM_CODE);
-  }
-  int32_t version() const { return GetField<int32_t>(VT_VERSION, 1); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<uint8_t>(verifier, VT_BUILTIN_CODE) &&
-           VerifyOffset(verifier, VT_CUSTOM_CODE) && verifier.VerifyString(custom_code()) &&
-           VerifyField<int32_t>(verifier, VT_VERSION) && verifier.EndTable();
-  }
-};
-
-struct OperatorCodeBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_builtin_code(BuiltinOperator builtin_code)
-  {
-    fbb_.AddElement<uint8_t>(OperatorCode::VT_BUILTIN_CODE, static_cast<uint8_t>(builtin_code), 0);
-  }
-  void add_custom_code(flatbuffers::Offset<flatbuffers::String> custom_code)
-  {
-    fbb_.AddOffset(OperatorCode::VT_CUSTOM_CODE, custom_code);
-  }
-  void add_version(int32_t version)
-  {
-    fbb_.AddElement<int32_t>(OperatorCode::VT_VERSION, version, 1);
-  }
-  explicit OperatorCodeBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  OperatorCodeBuilder &operator=(const OperatorCodeBuilder &);
-  flatbuffers::Offset<OperatorCode> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<OperatorCode>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<OperatorCode>
-CreateOperatorCode(flatbuffers::FlatBufferBuilder &_fbb,
-                   BuiltinOperator builtin_code = BuiltinOperator_ADD,
-                   flatbuffers::Offset<flatbuffers::String> custom_code = 0, int32_t version = 1)
-{
-  OperatorCodeBuilder builder_(_fbb);
-  builder_.add_version(version);
-  builder_.add_custom_code(custom_code);
-  builder_.add_builtin_code(builtin_code);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<OperatorCode>
-CreateOperatorCodeDirect(flatbuffers::FlatBufferBuilder &_fbb,
-                         BuiltinOperator builtin_code = BuiltinOperator_ADD,
-                         const char *custom_code = nullptr, int32_t version = 1)
-{
-  return circle::CreateOperatorCode(_fbb, builtin_code,
-                                    custom_code ? _fbb.CreateString(custom_code) : 0, version);
-}
-
-struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_OPCODE_INDEX = 4,
-    VT_INPUTS = 6,
-    VT_OUTPUTS = 8,
-    VT_BUILTIN_OPTIONS_TYPE = 10,
-    VT_BUILTIN_OPTIONS = 12,
-    VT_CUSTOM_OPTIONS = 14,
-    VT_CUSTOM_OPTIONS_FORMAT = 16,
-    VT_MUTATING_VARIABLE_INPUTS = 18
-  };
-  uint32_t opcode_index() const { return GetField<uint32_t>(VT_OPCODE_INDEX, 0); }
-  const flatbuffers::Vector<int32_t> *inputs() const
-  {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_INPUTS);
-  }
-  const flatbuffers::Vector<int32_t> *outputs() const
-  {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_OUTPUTS);
-  }
-  BuiltinOptions builtin_options_type() const
-  {
-    return static_cast<BuiltinOptions>(GetField<uint8_t>(VT_BUILTIN_OPTIONS_TYPE, 0));
-  }
-  const void *builtin_options() const { return GetPointer<const void *>(VT_BUILTIN_OPTIONS); }
-  template <typename T> const T *builtin_options_as() const;
-  const Conv2DOptions *builtin_options_as_Conv2DOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_Conv2DOptions
-               ? static_cast<const Conv2DOptions *>(builtin_options())
-               : nullptr;
-  }
-  const DepthwiseConv2DOptions *builtin_options_as_DepthwiseConv2DOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_DepthwiseConv2DOptions
-               ? static_cast<const DepthwiseConv2DOptions *>(builtin_options())
-               : nullptr;
-  }
-  const ConcatEmbeddingsOptions *builtin_options_as_ConcatEmbeddingsOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_ConcatEmbeddingsOptions
-               ? static_cast<const ConcatEmbeddingsOptions *>(builtin_options())
-               : nullptr;
-  }
-  const LSHProjectionOptions *builtin_options_as_LSHProjectionOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_LSHProjectionOptions
-               ? static_cast<const LSHProjectionOptions *>(builtin_options())
-               : nullptr;
-  }
-  const Pool2DOptions *builtin_options_as_Pool2DOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_Pool2DOptions
-               ? static_cast<const Pool2DOptions *>(builtin_options())
-               : nullptr;
-  }
-  const SVDFOptions *builtin_options_as_SVDFOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_SVDFOptions
-               ? static_cast<const SVDFOptions *>(builtin_options())
-               : nullptr;
-  }
-  const RNNOptions *builtin_options_as_RNNOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_RNNOptions
-               ? static_cast<const RNNOptions *>(builtin_options())
-               : nullptr;
-  }
-  const FullyConnectedOptions *builtin_options_as_FullyConnectedOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_FullyConnectedOptions
-               ? static_cast<const FullyConnectedOptions *>(builtin_options())
-               : nullptr;
-  }
-  const SoftmaxOptions *builtin_options_as_SoftmaxOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_SoftmaxOptions
-               ? static_cast<const SoftmaxOptions *>(builtin_options())
-               : nullptr;
-  }
-  const ConcatenationOptions *builtin_options_as_ConcatenationOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_ConcatenationOptions
-               ? static_cast<const ConcatenationOptions *>(builtin_options())
-               : nullptr;
-  }
-  const AddOptions *builtin_options_as_AddOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_AddOptions
-               ? static_cast<const AddOptions *>(builtin_options())
-               : nullptr;
-  }
-  const L2NormOptions *builtin_options_as_L2NormOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_L2NormOptions
-               ? static_cast<const L2NormOptions *>(builtin_options())
-               : nullptr;
-  }
-  const LocalResponseNormalizationOptions *
-  builtin_options_as_LocalResponseNormalizationOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_LocalResponseNormalizationOptions
-               ? static_cast<const LocalResponseNormalizationOptions *>(builtin_options())
-               : nullptr;
-  }
-  const LSTMOptions *builtin_options_as_LSTMOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_LSTMOptions
-               ? static_cast<const LSTMOptions *>(builtin_options())
-               : nullptr;
-  }
-  const ResizeBilinearOptions *builtin_options_as_ResizeBilinearOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_ResizeBilinearOptions
-               ? static_cast<const ResizeBilinearOptions *>(builtin_options())
-               : nullptr;
-  }
-  const CallOptions *builtin_options_as_CallOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_CallOptions
-               ? static_cast<const CallOptions *>(builtin_options())
-               : nullptr;
-  }
-  const ReshapeOptions *builtin_options_as_ReshapeOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_ReshapeOptions
-               ? static_cast<const ReshapeOptions *>(builtin_options())
-               : nullptr;
-  }
-  const SkipGramOptions *builtin_options_as_SkipGramOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_SkipGramOptions
-               ? static_cast<const SkipGramOptions *>(builtin_options())
-               : nullptr;
-  }
-  const SpaceToDepthOptions *builtin_options_as_SpaceToDepthOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_SpaceToDepthOptions
-               ? static_cast<const SpaceToDepthOptions *>(builtin_options())
-               : nullptr;
-  }
-  const EmbeddingLookupSparseOptions *builtin_options_as_EmbeddingLookupSparseOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_EmbeddingLookupSparseOptions
-               ? static_cast<const EmbeddingLookupSparseOptions *>(builtin_options())
-               : nullptr;
-  }
-  const MulOptions *builtin_options_as_MulOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_MulOptions
-               ? static_cast<const MulOptions *>(builtin_options())
-               : nullptr;
-  }
-  const PadOptions *builtin_options_as_PadOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_PadOptions
-               ? static_cast<const PadOptions *>(builtin_options())
-               : nullptr;
-  }
-  const GatherOptions *builtin_options_as_GatherOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_GatherOptions
-               ? static_cast<const GatherOptions *>(builtin_options())
-               : nullptr;
-  }
-  const BatchToSpaceNDOptions *builtin_options_as_BatchToSpaceNDOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_BatchToSpaceNDOptions
-               ? static_cast<const BatchToSpaceNDOptions *>(builtin_options())
-               : nullptr;
-  }
-  const SpaceToBatchNDOptions *builtin_options_as_SpaceToBatchNDOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_SpaceToBatchNDOptions
-               ? static_cast<const SpaceToBatchNDOptions *>(builtin_options())
-               : nullptr;
-  }
-  const TransposeOptions *builtin_options_as_TransposeOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_TransposeOptions
-               ? static_cast<const TransposeOptions *>(builtin_options())
-               : nullptr;
-  }
-  const ReducerOptions *builtin_options_as_ReducerOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_ReducerOptions
-               ? static_cast<const ReducerOptions *>(builtin_options())
-               : nullptr;
-  }
-  const SubOptions *builtin_options_as_SubOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_SubOptions
-               ? static_cast<const SubOptions *>(builtin_options())
-               : nullptr;
-  }
-  const DivOptions *builtin_options_as_DivOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_DivOptions
-               ? static_cast<const DivOptions *>(builtin_options())
-               : nullptr;
-  }
-  const SqueezeOptions *builtin_options_as_SqueezeOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_SqueezeOptions
-               ? static_cast<const SqueezeOptions *>(builtin_options())
-               : nullptr;
-  }
-  const SequenceRNNOptions *builtin_options_as_SequenceRNNOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_SequenceRNNOptions
-               ? static_cast<const SequenceRNNOptions *>(builtin_options())
-               : nullptr;
-  }
-  const StridedSliceOptions *builtin_options_as_StridedSliceOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_StridedSliceOptions
-               ? static_cast<const StridedSliceOptions *>(builtin_options())
-               : nullptr;
-  }
-  const ExpOptions *builtin_options_as_ExpOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_ExpOptions
-               ? static_cast<const ExpOptions *>(builtin_options())
-               : nullptr;
-  }
-  const TopKV2Options *builtin_options_as_TopKV2Options() const
-  {
-    return builtin_options_type() == BuiltinOptions_TopKV2Options
-               ? static_cast<const TopKV2Options *>(builtin_options())
-               : nullptr;
-  }
-  const SplitOptions *builtin_options_as_SplitOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_SplitOptions
-               ? static_cast<const SplitOptions *>(builtin_options())
-               : nullptr;
-  }
-  const LogSoftmaxOptions *builtin_options_as_LogSoftmaxOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_LogSoftmaxOptions
-               ? static_cast<const LogSoftmaxOptions *>(builtin_options())
-               : nullptr;
-  }
-  const CastOptions *builtin_options_as_CastOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_CastOptions
-               ? static_cast<const CastOptions *>(builtin_options())
-               : nullptr;
-  }
-  const DequantizeOptions *builtin_options_as_DequantizeOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_DequantizeOptions
-               ? static_cast<const DequantizeOptions *>(builtin_options())
-               : nullptr;
-  }
-  const MaximumMinimumOptions *builtin_options_as_MaximumMinimumOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_MaximumMinimumOptions
-               ? static_cast<const MaximumMinimumOptions *>(builtin_options())
-               : nullptr;
-  }
-  const ArgMaxOptions *builtin_options_as_ArgMaxOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_ArgMaxOptions
-               ? static_cast<const ArgMaxOptions *>(builtin_options())
-               : nullptr;
-  }
-  const LessOptions *builtin_options_as_LessOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_LessOptions
-               ? static_cast<const LessOptions *>(builtin_options())
-               : nullptr;
-  }
-  const NegOptions *builtin_options_as_NegOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_NegOptions
-               ? static_cast<const NegOptions *>(builtin_options())
-               : nullptr;
-  }
-  const PadV2Options *builtin_options_as_PadV2Options() const
-  {
-    return builtin_options_type() == BuiltinOptions_PadV2Options
-               ? static_cast<const PadV2Options *>(builtin_options())
-               : nullptr;
-  }
-  const GreaterOptions *builtin_options_as_GreaterOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_GreaterOptions
-               ? static_cast<const GreaterOptions *>(builtin_options())
-               : nullptr;
-  }
-  const GreaterEqualOptions *builtin_options_as_GreaterEqualOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_GreaterEqualOptions
-               ? static_cast<const GreaterEqualOptions *>(builtin_options())
-               : nullptr;
-  }
-  const LessEqualOptions *builtin_options_as_LessEqualOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_LessEqualOptions
-               ? static_cast<const LessEqualOptions *>(builtin_options())
-               : nullptr;
-  }
-  const SelectOptions *builtin_options_as_SelectOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_SelectOptions
-               ? static_cast<const SelectOptions *>(builtin_options())
-               : nullptr;
-  }
-  const SliceOptions *builtin_options_as_SliceOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_SliceOptions
-               ? static_cast<const SliceOptions *>(builtin_options())
-               : nullptr;
-  }
-  const TransposeConvOptions *builtin_options_as_TransposeConvOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_TransposeConvOptions
-               ? static_cast<const TransposeConvOptions *>(builtin_options())
-               : nullptr;
-  }
-  const SparseToDenseOptions *builtin_options_as_SparseToDenseOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_SparseToDenseOptions
-               ? static_cast<const SparseToDenseOptions *>(builtin_options())
-               : nullptr;
-  }
-  const TileOptions *builtin_options_as_TileOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_TileOptions
-               ? static_cast<const TileOptions *>(builtin_options())
-               : nullptr;
-  }
-  const ExpandDimsOptions *builtin_options_as_ExpandDimsOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_ExpandDimsOptions
-               ? static_cast<const ExpandDimsOptions *>(builtin_options())
-               : nullptr;
-  }
-  const EqualOptions *builtin_options_as_EqualOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_EqualOptions
-               ? static_cast<const EqualOptions *>(builtin_options())
-               : nullptr;
-  }
-  const NotEqualOptions *builtin_options_as_NotEqualOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_NotEqualOptions
-               ? static_cast<const NotEqualOptions *>(builtin_options())
-               : nullptr;
-  }
-  const ShapeOptions *builtin_options_as_ShapeOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_ShapeOptions
-               ? static_cast<const ShapeOptions *>(builtin_options())
-               : nullptr;
-  }
-  const PowOptions *builtin_options_as_PowOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_PowOptions
-               ? static_cast<const PowOptions *>(builtin_options())
-               : nullptr;
-  }
-  const ArgMinOptions *builtin_options_as_ArgMinOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_ArgMinOptions
-               ? static_cast<const ArgMinOptions *>(builtin_options())
-               : nullptr;
-  }
-  const FakeQuantOptions *builtin_options_as_FakeQuantOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_FakeQuantOptions
-               ? static_cast<const FakeQuantOptions *>(builtin_options())
-               : nullptr;
-  }
-  const PackOptions *builtin_options_as_PackOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_PackOptions
-               ? static_cast<const PackOptions *>(builtin_options())
-               : nullptr;
-  }
-  const LogicalOrOptions *builtin_options_as_LogicalOrOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_LogicalOrOptions
-               ? static_cast<const LogicalOrOptions *>(builtin_options())
-               : nullptr;
-  }
-  const OneHotOptions *builtin_options_as_OneHotOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_OneHotOptions
-               ? static_cast<const OneHotOptions *>(builtin_options())
-               : nullptr;
-  }
-  const LogicalAndOptions *builtin_options_as_LogicalAndOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_LogicalAndOptions
-               ? static_cast<const LogicalAndOptions *>(builtin_options())
-               : nullptr;
-  }
-  const LogicalNotOptions *builtin_options_as_LogicalNotOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_LogicalNotOptions
-               ? static_cast<const LogicalNotOptions *>(builtin_options())
-               : nullptr;
-  }
-  const UnpackOptions *builtin_options_as_UnpackOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_UnpackOptions
-               ? static_cast<const UnpackOptions *>(builtin_options())
-               : nullptr;
-  }
-  const FloorDivOptions *builtin_options_as_FloorDivOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_FloorDivOptions
-               ? static_cast<const FloorDivOptions *>(builtin_options())
-               : nullptr;
-  }
-  const SquareOptions *builtin_options_as_SquareOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_SquareOptions
-               ? static_cast<const SquareOptions *>(builtin_options())
-               : nullptr;
-  }
-  const ZerosLikeOptions *builtin_options_as_ZerosLikeOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_ZerosLikeOptions
-               ? static_cast<const ZerosLikeOptions *>(builtin_options())
-               : nullptr;
-  }
-  const FillOptions *builtin_options_as_FillOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_FillOptions
-               ? static_cast<const FillOptions *>(builtin_options())
-               : nullptr;
-  }
-  const BidirectionalSequenceLSTMOptions *
-  builtin_options_as_BidirectionalSequenceLSTMOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_BidirectionalSequenceLSTMOptions
-               ? static_cast<const BidirectionalSequenceLSTMOptions *>(builtin_options())
-               : nullptr;
-  }
-  const BidirectionalSequenceRNNOptions *builtin_options_as_BidirectionalSequenceRNNOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_BidirectionalSequenceRNNOptions
-               ? static_cast<const BidirectionalSequenceRNNOptions *>(builtin_options())
-               : nullptr;
-  }
-  const UnidirectionalSequenceLSTMOptions *
-  builtin_options_as_UnidirectionalSequenceLSTMOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_UnidirectionalSequenceLSTMOptions
-               ? static_cast<const UnidirectionalSequenceLSTMOptions *>(builtin_options())
-               : nullptr;
-  }
-  const FloorModOptions *builtin_options_as_FloorModOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_FloorModOptions
-               ? static_cast<const FloorModOptions *>(builtin_options())
-               : nullptr;
-  }
-  const RangeOptions *builtin_options_as_RangeOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_RangeOptions
-               ? static_cast<const RangeOptions *>(builtin_options())
-               : nullptr;
-  }
-  const ResizeNearestNeighborOptions *builtin_options_as_ResizeNearestNeighborOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_ResizeNearestNeighborOptions
-               ? static_cast<const ResizeNearestNeighborOptions *>(builtin_options())
-               : nullptr;
-  }
-  const LeakyReluOptions *builtin_options_as_LeakyReluOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_LeakyReluOptions
-               ? static_cast<const LeakyReluOptions *>(builtin_options())
-               : nullptr;
-  }
-  const SquaredDifferenceOptions *builtin_options_as_SquaredDifferenceOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_SquaredDifferenceOptions
-               ? static_cast<const SquaredDifferenceOptions *>(builtin_options())
-               : nullptr;
-  }
-  const MirrorPadOptions *builtin_options_as_MirrorPadOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_MirrorPadOptions
-               ? static_cast<const MirrorPadOptions *>(builtin_options())
-               : nullptr;
-  }
-  const AbsOptions *builtin_options_as_AbsOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_AbsOptions
-               ? static_cast<const AbsOptions *>(builtin_options())
-               : nullptr;
-  }
-  const SplitVOptions *builtin_options_as_SplitVOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_SplitVOptions
-               ? static_cast<const SplitVOptions *>(builtin_options())
-               : nullptr;
-  }
-  const InstanceNormOptions *builtin_options_as_InstanceNormOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_InstanceNormOptions
-               ? static_cast<const InstanceNormOptions *>(builtin_options())
-               : nullptr;
-  }
-  const flatbuffers::Vector<uint8_t> *custom_options() const
-  {
-    return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_CUSTOM_OPTIONS);
-  }
-  CustomOptionsFormat custom_options_format() const
-  {
-    return static_cast<CustomOptionsFormat>(GetField<int8_t>(VT_CUSTOM_OPTIONS_FORMAT, 0));
-  }
-  const flatbuffers::Vector<uint8_t> *mutating_variable_inputs() const
-  {
-    return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_MUTATING_VARIABLE_INPUTS);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<uint32_t>(verifier, VT_OPCODE_INDEX) &&
-           VerifyOffset(verifier, VT_INPUTS) && verifier.VerifyVector(inputs()) &&
-           VerifyOffset(verifier, VT_OUTPUTS) && verifier.VerifyVector(outputs()) &&
-           VerifyField<uint8_t>(verifier, VT_BUILTIN_OPTIONS_TYPE) &&
-           VerifyOffset(verifier, VT_BUILTIN_OPTIONS) &&
-           VerifyBuiltinOptions(verifier, builtin_options(), builtin_options_type()) &&
-           VerifyOffset(verifier, VT_CUSTOM_OPTIONS) && verifier.VerifyVector(custom_options()) &&
-           VerifyField<int8_t>(verifier, VT_CUSTOM_OPTIONS_FORMAT) &&
-           VerifyOffset(verifier, VT_MUTATING_VARIABLE_INPUTS) &&
-           verifier.VerifyVector(mutating_variable_inputs()) && verifier.EndTable();
-  }
-};
-
-template <> inline const Conv2DOptions *Operator::builtin_options_as<Conv2DOptions>() const
-{
-  return builtin_options_as_Conv2DOptions();
-}
-
-template <>
-inline const DepthwiseConv2DOptions *Operator::builtin_options_as<DepthwiseConv2DOptions>() const
-{
-  return builtin_options_as_DepthwiseConv2DOptions();
-}
-
-template <>
-inline const ConcatEmbeddingsOptions *Operator::builtin_options_as<ConcatEmbeddingsOptions>() const
-{
-  return builtin_options_as_ConcatEmbeddingsOptions();
-}
-
-template <>
-inline const LSHProjectionOptions *Operator::builtin_options_as<LSHProjectionOptions>() const
-{
-  return builtin_options_as_LSHProjectionOptions();
-}
-
-template <> inline const Pool2DOptions *Operator::builtin_options_as<Pool2DOptions>() const
-{
-  return builtin_options_as_Pool2DOptions();
-}
-
-template <> inline const SVDFOptions *Operator::builtin_options_as<SVDFOptions>() const
-{
-  return builtin_options_as_SVDFOptions();
-}
-
-template <> inline const RNNOptions *Operator::builtin_options_as<RNNOptions>() const
-{
-  return builtin_options_as_RNNOptions();
-}
-
-template <>
-inline const FullyConnectedOptions *Operator::builtin_options_as<FullyConnectedOptions>() const
-{
-  return builtin_options_as_FullyConnectedOptions();
-}
-
-template <> inline const SoftmaxOptions *Operator::builtin_options_as<SoftmaxOptions>() const
-{
-  return builtin_options_as_SoftmaxOptions();
-}
-
-template <>
-inline const ConcatenationOptions *Operator::builtin_options_as<ConcatenationOptions>() const
-{
-  return builtin_options_as_ConcatenationOptions();
-}
-
-template <> inline const AddOptions *Operator::builtin_options_as<AddOptions>() const
-{
-  return builtin_options_as_AddOptions();
-}
-
-template <> inline const L2NormOptions *Operator::builtin_options_as<L2NormOptions>() const
-{
-  return builtin_options_as_L2NormOptions();
-}
-
-template <>
-inline const LocalResponseNormalizationOptions *
-Operator::builtin_options_as<LocalResponseNormalizationOptions>() const
-{
-  return builtin_options_as_LocalResponseNormalizationOptions();
-}
-
-template <> inline const LSTMOptions *Operator::builtin_options_as<LSTMOptions>() const
-{
-  return builtin_options_as_LSTMOptions();
-}
-
-template <>
-inline const ResizeBilinearOptions *Operator::builtin_options_as<ResizeBilinearOptions>() const
-{
-  return builtin_options_as_ResizeBilinearOptions();
-}
-
-template <> inline const CallOptions *Operator::builtin_options_as<CallOptions>() const
-{
-  return builtin_options_as_CallOptions();
-}
-
-template <> inline const ReshapeOptions *Operator::builtin_options_as<ReshapeOptions>() const
-{
-  return builtin_options_as_ReshapeOptions();
-}
-
-template <> inline const SkipGramOptions *Operator::builtin_options_as<SkipGramOptions>() const
-{
-  return builtin_options_as_SkipGramOptions();
-}
-
-template <>
-inline const SpaceToDepthOptions *Operator::builtin_options_as<SpaceToDepthOptions>() const
-{
-  return builtin_options_as_SpaceToDepthOptions();
-}
-
-template <>
-inline const EmbeddingLookupSparseOptions *
-Operator::builtin_options_as<EmbeddingLookupSparseOptions>() const
-{
-  return builtin_options_as_EmbeddingLookupSparseOptions();
-}
-
-template <> inline const MulOptions *Operator::builtin_options_as<MulOptions>() const
-{
-  return builtin_options_as_MulOptions();
-}
-
-template <> inline const PadOptions *Operator::builtin_options_as<PadOptions>() const
-{
-  return builtin_options_as_PadOptions();
-}
-
-template <> inline const GatherOptions *Operator::builtin_options_as<GatherOptions>() const
-{
-  return builtin_options_as_GatherOptions();
-}
-
-template <>
-inline const BatchToSpaceNDOptions *Operator::builtin_options_as<BatchToSpaceNDOptions>() const
-{
-  return builtin_options_as_BatchToSpaceNDOptions();
-}
-
-template <>
-inline const SpaceToBatchNDOptions *Operator::builtin_options_as<SpaceToBatchNDOptions>() const
-{
-  return builtin_options_as_SpaceToBatchNDOptions();
-}
-
-template <> inline const TransposeOptions *Operator::builtin_options_as<TransposeOptions>() const
-{
-  return builtin_options_as_TransposeOptions();
-}
-
-template <> inline const ReducerOptions *Operator::builtin_options_as<ReducerOptions>() const
-{
-  return builtin_options_as_ReducerOptions();
-}
-
-template <> inline const SubOptions *Operator::builtin_options_as<SubOptions>() const
-{
-  return builtin_options_as_SubOptions();
-}
-
-template <> inline const DivOptions *Operator::builtin_options_as<DivOptions>() const
-{
-  return builtin_options_as_DivOptions();
-}
-
-template <> inline const SqueezeOptions *Operator::builtin_options_as<SqueezeOptions>() const
-{
-  return builtin_options_as_SqueezeOptions();
-}
-
-template <>
-inline const SequenceRNNOptions *Operator::builtin_options_as<SequenceRNNOptions>() const
-{
-  return builtin_options_as_SequenceRNNOptions();
-}
-
-template <>
-inline const StridedSliceOptions *Operator::builtin_options_as<StridedSliceOptions>() const
-{
-  return builtin_options_as_StridedSliceOptions();
-}
-
-template <> inline const ExpOptions *Operator::builtin_options_as<ExpOptions>() const
-{
-  return builtin_options_as_ExpOptions();
-}
-
-template <> inline const TopKV2Options *Operator::builtin_options_as<TopKV2Options>() const
-{
-  return builtin_options_as_TopKV2Options();
-}
-
-template <> inline const SplitOptions *Operator::builtin_options_as<SplitOptions>() const
-{
-  return builtin_options_as_SplitOptions();
-}
-
-template <> inline const LogSoftmaxOptions *Operator::builtin_options_as<LogSoftmaxOptions>() const
-{
-  return builtin_options_as_LogSoftmaxOptions();
-}
-
-template <> inline const CastOptions *Operator::builtin_options_as<CastOptions>() const
-{
-  return builtin_options_as_CastOptions();
-}
-
-template <> inline const DequantizeOptions *Operator::builtin_options_as<DequantizeOptions>() const
-{
-  return builtin_options_as_DequantizeOptions();
-}
-
-template <>
-inline const MaximumMinimumOptions *Operator::builtin_options_as<MaximumMinimumOptions>() const
-{
-  return builtin_options_as_MaximumMinimumOptions();
-}
-
-template <> inline const ArgMaxOptions *Operator::builtin_options_as<ArgMaxOptions>() const
-{
-  return builtin_options_as_ArgMaxOptions();
-}
-
-template <> inline const LessOptions *Operator::builtin_options_as<LessOptions>() const
-{
-  return builtin_options_as_LessOptions();
-}
-
-template <> inline const NegOptions *Operator::builtin_options_as<NegOptions>() const
-{
-  return builtin_options_as_NegOptions();
-}
-
-template <> inline const PadV2Options *Operator::builtin_options_as<PadV2Options>() const
-{
-  return builtin_options_as_PadV2Options();
-}
-
-template <> inline const GreaterOptions *Operator::builtin_options_as<GreaterOptions>() const
-{
-  return builtin_options_as_GreaterOptions();
-}
-
-template <>
-inline const GreaterEqualOptions *Operator::builtin_options_as<GreaterEqualOptions>() const
-{
-  return builtin_options_as_GreaterEqualOptions();
-}
-
-template <> inline const LessEqualOptions *Operator::builtin_options_as<LessEqualOptions>() const
-{
-  return builtin_options_as_LessEqualOptions();
-}
-
-template <> inline const SelectOptions *Operator::builtin_options_as<SelectOptions>() const
-{
-  return builtin_options_as_SelectOptions();
-}
-
-template <> inline const SliceOptions *Operator::builtin_options_as<SliceOptions>() const
-{
-  return builtin_options_as_SliceOptions();
-}
-
-template <>
-inline const TransposeConvOptions *Operator::builtin_options_as<TransposeConvOptions>() const
-{
-  return builtin_options_as_TransposeConvOptions();
-}
-
-template <>
-inline const SparseToDenseOptions *Operator::builtin_options_as<SparseToDenseOptions>() const
-{
-  return builtin_options_as_SparseToDenseOptions();
-}
-
-template <> inline const TileOptions *Operator::builtin_options_as<TileOptions>() const
-{
-  return builtin_options_as_TileOptions();
-}
-
-template <> inline const ExpandDimsOptions *Operator::builtin_options_as<ExpandDimsOptions>() const
-{
-  return builtin_options_as_ExpandDimsOptions();
-}
-
-template <> inline const EqualOptions *Operator::builtin_options_as<EqualOptions>() const
-{
-  return builtin_options_as_EqualOptions();
-}
-
-template <> inline const NotEqualOptions *Operator::builtin_options_as<NotEqualOptions>() const
-{
-  return builtin_options_as_NotEqualOptions();
-}
-
-template <> inline const ShapeOptions *Operator::builtin_options_as<ShapeOptions>() const
-{
-  return builtin_options_as_ShapeOptions();
-}
-
-template <> inline const PowOptions *Operator::builtin_options_as<PowOptions>() const
-{
-  return builtin_options_as_PowOptions();
-}
-
-template <> inline const ArgMinOptions *Operator::builtin_options_as<ArgMinOptions>() const
-{
-  return builtin_options_as_ArgMinOptions();
-}
-
-template <> inline const FakeQuantOptions *Operator::builtin_options_as<FakeQuantOptions>() const
-{
-  return builtin_options_as_FakeQuantOptions();
-}
-
-template <> inline const PackOptions *Operator::builtin_options_as<PackOptions>() const
-{
-  return builtin_options_as_PackOptions();
-}
-
-template <> inline const LogicalOrOptions *Operator::builtin_options_as<LogicalOrOptions>() const
-{
-  return builtin_options_as_LogicalOrOptions();
-}
-
-template <> inline const OneHotOptions *Operator::builtin_options_as<OneHotOptions>() const
-{
-  return builtin_options_as_OneHotOptions();
-}
-
-template <> inline const LogicalAndOptions *Operator::builtin_options_as<LogicalAndOptions>() const
-{
-  return builtin_options_as_LogicalAndOptions();
-}
-
-template <> inline const LogicalNotOptions *Operator::builtin_options_as<LogicalNotOptions>() const
-{
-  return builtin_options_as_LogicalNotOptions();
-}
-
-template <> inline const UnpackOptions *Operator::builtin_options_as<UnpackOptions>() const
-{
-  return builtin_options_as_UnpackOptions();
-}
-
-template <> inline const FloorDivOptions *Operator::builtin_options_as<FloorDivOptions>() const
-{
-  return builtin_options_as_FloorDivOptions();
-}
-
-template <> inline const SquareOptions *Operator::builtin_options_as<SquareOptions>() const
-{
-  return builtin_options_as_SquareOptions();
-}
-
-template <> inline const ZerosLikeOptions *Operator::builtin_options_as<ZerosLikeOptions>() const
-{
-  return builtin_options_as_ZerosLikeOptions();
-}
-
-template <> inline const FillOptions *Operator::builtin_options_as<FillOptions>() const
-{
-  return builtin_options_as_FillOptions();
-}
-
-template <>
-inline const BidirectionalSequenceLSTMOptions *
-Operator::builtin_options_as<BidirectionalSequenceLSTMOptions>() const
-{
-  return builtin_options_as_BidirectionalSequenceLSTMOptions();
-}
-
-template <>
-inline const BidirectionalSequenceRNNOptions *
-Operator::builtin_options_as<BidirectionalSequenceRNNOptions>() const
-{
-  return builtin_options_as_BidirectionalSequenceRNNOptions();
-}
-
-template <>
-inline const UnidirectionalSequenceLSTMOptions *
-Operator::builtin_options_as<UnidirectionalSequenceLSTMOptions>() const
-{
-  return builtin_options_as_UnidirectionalSequenceLSTMOptions();
-}
-
-template <> inline const FloorModOptions *Operator::builtin_options_as<FloorModOptions>() const
-{
-  return builtin_options_as_FloorModOptions();
-}
-
-template <> inline const RangeOptions *Operator::builtin_options_as<RangeOptions>() const
-{
-  return builtin_options_as_RangeOptions();
-}
-
-template <>
-inline const ResizeNearestNeighborOptions *
-Operator::builtin_options_as<ResizeNearestNeighborOptions>() const
-{
-  return builtin_options_as_ResizeNearestNeighborOptions();
-}
-
-template <> inline const LeakyReluOptions *Operator::builtin_options_as<LeakyReluOptions>() const
-{
-  return builtin_options_as_LeakyReluOptions();
-}
-
-template <>
-inline const SquaredDifferenceOptions *
-Operator::builtin_options_as<SquaredDifferenceOptions>() const
-{
-  return builtin_options_as_SquaredDifferenceOptions();
-}
-
-template <> inline const MirrorPadOptions *Operator::builtin_options_as<MirrorPadOptions>() const
-{
-  return builtin_options_as_MirrorPadOptions();
-}
-
-template <> inline const AbsOptions *Operator::builtin_options_as<AbsOptions>() const
-{
-  return builtin_options_as_AbsOptions();
-}
-
-template <> inline const SplitVOptions *Operator::builtin_options_as<SplitVOptions>() const
-{
-  return builtin_options_as_SplitVOptions();
-}
-
-template <>
-inline const InstanceNormOptions *Operator::builtin_options_as<InstanceNormOptions>() const
-{
-  return builtin_options_as_InstanceNormOptions();
-}
-
-struct OperatorBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_opcode_index(uint32_t opcode_index)
-  {
-    fbb_.AddElement<uint32_t>(Operator::VT_OPCODE_INDEX, opcode_index, 0);
-  }
-  void add_inputs(flatbuffers::Offset<flatbuffers::Vector<int32_t>> inputs)
-  {
-    fbb_.AddOffset(Operator::VT_INPUTS, inputs);
-  }
-  void add_outputs(flatbuffers::Offset<flatbuffers::Vector<int32_t>> outputs)
-  {
-    fbb_.AddOffset(Operator::VT_OUTPUTS, outputs);
-  }
-  void add_builtin_options_type(BuiltinOptions builtin_options_type)
-  {
-    fbb_.AddElement<uint8_t>(Operator::VT_BUILTIN_OPTIONS_TYPE,
-                             static_cast<uint8_t>(builtin_options_type), 0);
-  }
-  void add_builtin_options(flatbuffers::Offset<void> builtin_options)
-  {
-    fbb_.AddOffset(Operator::VT_BUILTIN_OPTIONS, builtin_options);
-  }
-  void add_custom_options(flatbuffers::Offset<flatbuffers::Vector<uint8_t>> custom_options)
-  {
-    fbb_.AddOffset(Operator::VT_CUSTOM_OPTIONS, custom_options);
-  }
-  void add_custom_options_format(CustomOptionsFormat custom_options_format)
-  {
-    fbb_.AddElement<int8_t>(Operator::VT_CUSTOM_OPTIONS_FORMAT,
-                            static_cast<int8_t>(custom_options_format), 0);
-  }
-  void add_mutating_variable_inputs(
-      flatbuffers::Offset<flatbuffers::Vector<uint8_t>> mutating_variable_inputs)
-  {
-    fbb_.AddOffset(Operator::VT_MUTATING_VARIABLE_INPUTS, mutating_variable_inputs);
-  }
-  explicit OperatorBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  OperatorBuilder &operator=(const OperatorBuilder &);
-  flatbuffers::Offset<Operator> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<Operator>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<Operator>
-CreateOperator(flatbuffers::FlatBufferBuilder &_fbb, uint32_t opcode_index = 0,
-               flatbuffers::Offset<flatbuffers::Vector<int32_t>> inputs = 0,
-               flatbuffers::Offset<flatbuffers::Vector<int32_t>> outputs = 0,
-               BuiltinOptions builtin_options_type = BuiltinOptions_NONE,
-               flatbuffers::Offset<void> builtin_options = 0,
-               flatbuffers::Offset<flatbuffers::Vector<uint8_t>> custom_options = 0,
-               CustomOptionsFormat custom_options_format = CustomOptionsFormat_FLEXBUFFERS,
-               flatbuffers::Offset<flatbuffers::Vector<uint8_t>> mutating_variable_inputs = 0)
-{
-  OperatorBuilder builder_(_fbb);
-  builder_.add_mutating_variable_inputs(mutating_variable_inputs);
-  builder_.add_custom_options(custom_options);
-  builder_.add_builtin_options(builtin_options);
-  builder_.add_outputs(outputs);
-  builder_.add_inputs(inputs);
-  builder_.add_opcode_index(opcode_index);
-  builder_.add_custom_options_format(custom_options_format);
-  builder_.add_builtin_options_type(builtin_options_type);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<Operator>
-CreateOperatorDirect(flatbuffers::FlatBufferBuilder &_fbb, uint32_t opcode_index = 0,
-                     const std::vector<int32_t> *inputs = nullptr,
-                     const std::vector<int32_t> *outputs = nullptr,
-                     BuiltinOptions builtin_options_type = BuiltinOptions_NONE,
-                     flatbuffers::Offset<void> builtin_options = 0,
-                     const std::vector<uint8_t> *custom_options = nullptr,
-                     CustomOptionsFormat custom_options_format = CustomOptionsFormat_FLEXBUFFERS,
-                     const std::vector<uint8_t> *mutating_variable_inputs = nullptr)
-{
-  return circle::CreateOperator(
-      _fbb, opcode_index, inputs ? _fbb.CreateVector<int32_t>(*inputs) : 0,
-      outputs ? _fbb.CreateVector<int32_t>(*outputs) : 0, builtin_options_type, builtin_options,
-      custom_options ? _fbb.CreateVector<uint8_t>(*custom_options) : 0, custom_options_format,
-      mutating_variable_inputs ? _fbb.CreateVector<uint8_t>(*mutating_variable_inputs) : 0);
-}
-
-struct SubGraph FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_TENSORS = 4,
-    VT_INPUTS = 6,
-    VT_OUTPUTS = 8,
-    VT_OPERATORS = 10,
-    VT_NAME = 12,
-    VT_DATA_FORMAT = 14
-  };
-  const flatbuffers::Vector<flatbuffers::Offset<Tensor>> *tensors() const
-  {
-    return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<Tensor>> *>(VT_TENSORS);
-  }
-  const flatbuffers::Vector<int32_t> *inputs() const
-  {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_INPUTS);
-  }
-  const flatbuffers::Vector<int32_t> *outputs() const
-  {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_OUTPUTS);
-  }
-  const flatbuffers::Vector<flatbuffers::Offset<Operator>> *operators() const
-  {
-    return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<Operator>> *>(VT_OPERATORS);
-  }
-  const flatbuffers::String *name() const
-  {
-    return GetPointer<const flatbuffers::String *>(VT_NAME);
-  }
-  DataFormat data_format() const
-  {
-    return static_cast<DataFormat>(GetField<int8_t>(VT_DATA_FORMAT, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_TENSORS) &&
-           verifier.VerifyVector(tensors()) && verifier.VerifyVectorOfTables(tensors()) &&
-           VerifyOffset(verifier, VT_INPUTS) && verifier.VerifyVector(inputs()) &&
-           VerifyOffset(verifier, VT_OUTPUTS) && verifier.VerifyVector(outputs()) &&
-           VerifyOffset(verifier, VT_OPERATORS) && verifier.VerifyVector(operators()) &&
-           verifier.VerifyVectorOfTables(operators()) && VerifyOffset(verifier, VT_NAME) &&
-           verifier.VerifyString(name()) && VerifyField<int8_t>(verifier, VT_DATA_FORMAT) &&
-           verifier.EndTable();
-  }
-};
-
-struct SubGraphBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_tensors(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<Tensor>>> tensors)
-  {
-    fbb_.AddOffset(SubGraph::VT_TENSORS, tensors);
-  }
-  void add_inputs(flatbuffers::Offset<flatbuffers::Vector<int32_t>> inputs)
-  {
-    fbb_.AddOffset(SubGraph::VT_INPUTS, inputs);
-  }
-  void add_outputs(flatbuffers::Offset<flatbuffers::Vector<int32_t>> outputs)
-  {
-    fbb_.AddOffset(SubGraph::VT_OUTPUTS, outputs);
-  }
-  void
-  add_operators(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<Operator>>> operators)
-  {
-    fbb_.AddOffset(SubGraph::VT_OPERATORS, operators);
-  }
-  void add_name(flatbuffers::Offset<flatbuffers::String> name)
-  {
-    fbb_.AddOffset(SubGraph::VT_NAME, name);
-  }
-  void add_data_format(DataFormat data_format)
-  {
-    fbb_.AddElement<int8_t>(SubGraph::VT_DATA_FORMAT, static_cast<int8_t>(data_format), 0);
-  }
-  explicit SubGraphBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SubGraphBuilder &operator=(const SubGraphBuilder &);
-  flatbuffers::Offset<SubGraph> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SubGraph>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SubGraph> CreateSubGraph(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<Tensor>>> tensors = 0,
-    flatbuffers::Offset<flatbuffers::Vector<int32_t>> inputs = 0,
-    flatbuffers::Offset<flatbuffers::Vector<int32_t>> outputs = 0,
-    flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<Operator>>> operators = 0,
-    flatbuffers::Offset<flatbuffers::String> name = 0,
-    DataFormat data_format = DataFormat_CHANNELS_LAST)
-{
-  SubGraphBuilder builder_(_fbb);
-  builder_.add_name(name);
-  builder_.add_operators(operators);
-  builder_.add_outputs(outputs);
-  builder_.add_inputs(inputs);
-  builder_.add_tensors(tensors);
-  builder_.add_data_format(data_format);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<SubGraph>
-CreateSubGraphDirect(flatbuffers::FlatBufferBuilder &_fbb,
-                     const std::vector<flatbuffers::Offset<Tensor>> *tensors = nullptr,
-                     const std::vector<int32_t> *inputs = nullptr,
-                     const std::vector<int32_t> *outputs = nullptr,
-                     const std::vector<flatbuffers::Offset<Operator>> *operators = nullptr,
-                     const char *name = nullptr, DataFormat data_format = DataFormat_CHANNELS_LAST)
-{
-  return circle::CreateSubGraph(
-      _fbb, tensors ? _fbb.CreateVector<flatbuffers::Offset<Tensor>>(*tensors) : 0,
-      inputs ? _fbb.CreateVector<int32_t>(*inputs) : 0,
-      outputs ? _fbb.CreateVector<int32_t>(*outputs) : 0,
-      operators ? _fbb.CreateVector<flatbuffers::Offset<Operator>>(*operators) : 0,
-      name ? _fbb.CreateString(name) : 0, data_format);
-}
-
-struct Buffer FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_DATA = 4
-  };
-  const flatbuffers::Vector<uint8_t> *data() const
-  {
-    return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_DATA);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_DATA) &&
-           verifier.VerifyVector(data()) && verifier.EndTable();
-  }
-};
-
-struct BufferBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_data(flatbuffers::Offset<flatbuffers::Vector<uint8_t>> data)
-  {
-    fbb_.AddOffset(Buffer::VT_DATA, data);
-  }
-  explicit BufferBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  BufferBuilder &operator=(const BufferBuilder &);
-  flatbuffers::Offset<Buffer> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<Buffer>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<Buffer>
-CreateBuffer(flatbuffers::FlatBufferBuilder &_fbb,
-             flatbuffers::Offset<flatbuffers::Vector<uint8_t>> data = 0)
-{
-  BufferBuilder builder_(_fbb);
-  builder_.add_data(data);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<Buffer> CreateBufferDirect(flatbuffers::FlatBufferBuilder &_fbb,
-                                                      const std::vector<uint8_t> *data = nullptr)
-{
-  return circle::CreateBuffer(_fbb, data ? _fbb.CreateVector<uint8_t>(*data) : 0);
-}
-
-struct Model FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_VERSION = 4,
-    VT_OPERATOR_CODES = 6,
-    VT_SUBGRAPHS = 8,
-    VT_DESCRIPTION = 10,
-    VT_BUFFERS = 12,
-    VT_METADATA_BUFFER = 14
-  };
-  uint32_t version() const { return GetField<uint32_t>(VT_VERSION, 0); }
-  const flatbuffers::Vector<flatbuffers::Offset<OperatorCode>> *operator_codes() const
-  {
-    return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<OperatorCode>> *>(
-        VT_OPERATOR_CODES);
-  }
-  const flatbuffers::Vector<flatbuffers::Offset<SubGraph>> *subgraphs() const
-  {
-    return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<SubGraph>> *>(VT_SUBGRAPHS);
-  }
-  const flatbuffers::String *description() const
-  {
-    return GetPointer<const flatbuffers::String *>(VT_DESCRIPTION);
-  }
-  const flatbuffers::Vector<flatbuffers::Offset<Buffer>> *buffers() const
-  {
-    return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<Buffer>> *>(VT_BUFFERS);
-  }
-  const flatbuffers::Vector<int32_t> *metadata_buffer() const
-  {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_METADATA_BUFFER);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<uint32_t>(verifier, VT_VERSION) &&
-           VerifyOffset(verifier, VT_OPERATOR_CODES) && verifier.VerifyVector(operator_codes()) &&
-           verifier.VerifyVectorOfTables(operator_codes()) &&
-           VerifyOffset(verifier, VT_SUBGRAPHS) && verifier.VerifyVector(subgraphs()) &&
-           verifier.VerifyVectorOfTables(subgraphs()) && VerifyOffset(verifier, VT_DESCRIPTION) &&
-           verifier.VerifyString(description()) && VerifyOffset(verifier, VT_BUFFERS) &&
-           verifier.VerifyVector(buffers()) && verifier.VerifyVectorOfTables(buffers()) &&
-           VerifyOffset(verifier, VT_METADATA_BUFFER) && verifier.VerifyVector(metadata_buffer()) &&
-           verifier.EndTable();
-  }
-};
-
-struct ModelBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_version(uint32_t version) { fbb_.AddElement<uint32_t>(Model::VT_VERSION, version, 0); }
-  void add_operator_codes(
-      flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<OperatorCode>>> operator_codes)
-  {
-    fbb_.AddOffset(Model::VT_OPERATOR_CODES, operator_codes);
-  }
-  void
-  add_subgraphs(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<SubGraph>>> subgraphs)
-  {
-    fbb_.AddOffset(Model::VT_SUBGRAPHS, subgraphs);
-  }
-  void add_description(flatbuffers::Offset<flatbuffers::String> description)
-  {
-    fbb_.AddOffset(Model::VT_DESCRIPTION, description);
-  }
-  void add_buffers(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<Buffer>>> buffers)
-  {
-    fbb_.AddOffset(Model::VT_BUFFERS, buffers);
-  }
-  void add_metadata_buffer(flatbuffers::Offset<flatbuffers::Vector<int32_t>> metadata_buffer)
-  {
-    fbb_.AddOffset(Model::VT_METADATA_BUFFER, metadata_buffer);
-  }
-  explicit ModelBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  ModelBuilder &operator=(const ModelBuilder &);
-  flatbuffers::Offset<Model> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<Model>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<Model> CreateModel(
-    flatbuffers::FlatBufferBuilder &_fbb, uint32_t version = 0,
-    flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<OperatorCode>>> operator_codes = 0,
-    flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<SubGraph>>> subgraphs = 0,
-    flatbuffers::Offset<flatbuffers::String> description = 0,
-    flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<Buffer>>> buffers = 0,
-    flatbuffers::Offset<flatbuffers::Vector<int32_t>> metadata_buffer = 0)
-{
-  ModelBuilder builder_(_fbb);
-  builder_.add_metadata_buffer(metadata_buffer);
-  builder_.add_buffers(buffers);
-  builder_.add_description(description);
-  builder_.add_subgraphs(subgraphs);
-  builder_.add_operator_codes(operator_codes);
-  builder_.add_version(version);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<Model>
-CreateModelDirect(flatbuffers::FlatBufferBuilder &_fbb, uint32_t version = 0,
-                  const std::vector<flatbuffers::Offset<OperatorCode>> *operator_codes = nullptr,
-                  const std::vector<flatbuffers::Offset<SubGraph>> *subgraphs = nullptr,
-                  const char *description = nullptr,
-                  const std::vector<flatbuffers::Offset<Buffer>> *buffers = nullptr,
-                  const std::vector<int32_t> *metadata_buffer = nullptr)
-{
-  return circle::CreateModel(
-      _fbb, version,
-      operator_codes ? _fbb.CreateVector<flatbuffers::Offset<OperatorCode>>(*operator_codes) : 0,
-      subgraphs ? _fbb.CreateVector<flatbuffers::Offset<SubGraph>>(*subgraphs) : 0,
-      description ? _fbb.CreateString(description) : 0,
-      buffers ? _fbb.CreateVector<flatbuffers::Offset<Buffer>>(*buffers) : 0,
-      metadata_buffer ? _fbb.CreateVector<int32_t>(*metadata_buffer) : 0);
-}
-
-inline bool VerifyQuantizationDetails(flatbuffers::Verifier &verifier, const void *obj,
-                                      QuantizationDetails type)
-{
-  switch (type)
-  {
-    case QuantizationDetails_NONE:
-    {
-      return true;
-    }
-    case QuantizationDetails_CustomQuantization:
-    {
-      auto ptr = reinterpret_cast<const CustomQuantization *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    default:
-      return false;
-  }
-}
-
-inline bool
-VerifyQuantizationDetailsVector(flatbuffers::Verifier &verifier,
-                                const flatbuffers::Vector<flatbuffers::Offset<void>> *values,
-                                const flatbuffers::Vector<uint8_t> *types)
-{
-  if (!values || !types)
-    return !values && !types;
-  if (values->size() != types->size())
-    return false;
-  for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i)
-  {
-    if (!VerifyQuantizationDetails(verifier, values->Get(i),
-                                   types->GetEnum<QuantizationDetails>(i)))
-    {
-      return false;
-    }
-  }
-  return true;
-}
-
-inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj,
-                                 BuiltinOptions type)
-{
-  switch (type)
-  {
-    case BuiltinOptions_NONE:
-    {
-      return true;
-    }
-    case BuiltinOptions_Conv2DOptions:
-    {
-      auto ptr = reinterpret_cast<const Conv2DOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_DepthwiseConv2DOptions:
-    {
-      auto ptr = reinterpret_cast<const DepthwiseConv2DOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ConcatEmbeddingsOptions:
-    {
-      auto ptr = reinterpret_cast<const ConcatEmbeddingsOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_LSHProjectionOptions:
-    {
-      auto ptr = reinterpret_cast<const LSHProjectionOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_Pool2DOptions:
-    {
-      auto ptr = reinterpret_cast<const Pool2DOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SVDFOptions:
-    {
-      auto ptr = reinterpret_cast<const SVDFOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_RNNOptions:
-    {
-      auto ptr = reinterpret_cast<const RNNOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_FullyConnectedOptions:
-    {
-      auto ptr = reinterpret_cast<const FullyConnectedOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SoftmaxOptions:
-    {
-      auto ptr = reinterpret_cast<const SoftmaxOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ConcatenationOptions:
-    {
-      auto ptr = reinterpret_cast<const ConcatenationOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_AddOptions:
-    {
-      auto ptr = reinterpret_cast<const AddOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_L2NormOptions:
-    {
-      auto ptr = reinterpret_cast<const L2NormOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_LocalResponseNormalizationOptions:
-    {
-      auto ptr = reinterpret_cast<const LocalResponseNormalizationOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_LSTMOptions:
-    {
-      auto ptr = reinterpret_cast<const LSTMOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ResizeBilinearOptions:
-    {
-      auto ptr = reinterpret_cast<const ResizeBilinearOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_CallOptions:
-    {
-      auto ptr = reinterpret_cast<const CallOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ReshapeOptions:
-    {
-      auto ptr = reinterpret_cast<const ReshapeOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SkipGramOptions:
-    {
-      auto ptr = reinterpret_cast<const SkipGramOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SpaceToDepthOptions:
-    {
-      auto ptr = reinterpret_cast<const SpaceToDepthOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_EmbeddingLookupSparseOptions:
-    {
-      auto ptr = reinterpret_cast<const EmbeddingLookupSparseOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_MulOptions:
-    {
-      auto ptr = reinterpret_cast<const MulOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_PadOptions:
-    {
-      auto ptr = reinterpret_cast<const PadOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_GatherOptions:
-    {
-      auto ptr = reinterpret_cast<const GatherOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_BatchToSpaceNDOptions:
-    {
-      auto ptr = reinterpret_cast<const BatchToSpaceNDOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SpaceToBatchNDOptions:
-    {
-      auto ptr = reinterpret_cast<const SpaceToBatchNDOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_TransposeOptions:
-    {
-      auto ptr = reinterpret_cast<const TransposeOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ReducerOptions:
-    {
-      auto ptr = reinterpret_cast<const ReducerOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SubOptions:
-    {
-      auto ptr = reinterpret_cast<const SubOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_DivOptions:
-    {
-      auto ptr = reinterpret_cast<const DivOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SqueezeOptions:
-    {
-      auto ptr = reinterpret_cast<const SqueezeOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SequenceRNNOptions:
-    {
-      auto ptr = reinterpret_cast<const SequenceRNNOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_StridedSliceOptions:
-    {
-      auto ptr = reinterpret_cast<const StridedSliceOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ExpOptions:
-    {
-      auto ptr = reinterpret_cast<const ExpOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_TopKV2Options:
-    {
-      auto ptr = reinterpret_cast<const TopKV2Options *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SplitOptions:
-    {
-      auto ptr = reinterpret_cast<const SplitOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_LogSoftmaxOptions:
-    {
-      auto ptr = reinterpret_cast<const LogSoftmaxOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_CastOptions:
-    {
-      auto ptr = reinterpret_cast<const CastOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_DequantizeOptions:
-    {
-      auto ptr = reinterpret_cast<const DequantizeOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_MaximumMinimumOptions:
-    {
-      auto ptr = reinterpret_cast<const MaximumMinimumOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ArgMaxOptions:
-    {
-      auto ptr = reinterpret_cast<const ArgMaxOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_LessOptions:
-    {
-      auto ptr = reinterpret_cast<const LessOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_NegOptions:
-    {
-      auto ptr = reinterpret_cast<const NegOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_PadV2Options:
-    {
-      auto ptr = reinterpret_cast<const PadV2Options *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_GreaterOptions:
-    {
-      auto ptr = reinterpret_cast<const GreaterOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_GreaterEqualOptions:
-    {
-      auto ptr = reinterpret_cast<const GreaterEqualOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_LessEqualOptions:
-    {
-      auto ptr = reinterpret_cast<const LessEqualOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SelectOptions:
-    {
-      auto ptr = reinterpret_cast<const SelectOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SliceOptions:
-    {
-      auto ptr = reinterpret_cast<const SliceOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_TransposeConvOptions:
-    {
-      auto ptr = reinterpret_cast<const TransposeConvOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SparseToDenseOptions:
-    {
-      auto ptr = reinterpret_cast<const SparseToDenseOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_TileOptions:
-    {
-      auto ptr = reinterpret_cast<const TileOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ExpandDimsOptions:
-    {
-      auto ptr = reinterpret_cast<const ExpandDimsOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_EqualOptions:
-    {
-      auto ptr = reinterpret_cast<const EqualOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_NotEqualOptions:
-    {
-      auto ptr = reinterpret_cast<const NotEqualOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ShapeOptions:
-    {
-      auto ptr = reinterpret_cast<const ShapeOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_PowOptions:
-    {
-      auto ptr = reinterpret_cast<const PowOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ArgMinOptions:
-    {
-      auto ptr = reinterpret_cast<const ArgMinOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_FakeQuantOptions:
-    {
-      auto ptr = reinterpret_cast<const FakeQuantOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_PackOptions:
-    {
-      auto ptr = reinterpret_cast<const PackOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_LogicalOrOptions:
-    {
-      auto ptr = reinterpret_cast<const LogicalOrOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_OneHotOptions:
-    {
-      auto ptr = reinterpret_cast<const OneHotOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_LogicalAndOptions:
-    {
-      auto ptr = reinterpret_cast<const LogicalAndOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_LogicalNotOptions:
-    {
-      auto ptr = reinterpret_cast<const LogicalNotOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_UnpackOptions:
-    {
-      auto ptr = reinterpret_cast<const UnpackOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_FloorDivOptions:
-    {
-      auto ptr = reinterpret_cast<const FloorDivOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SquareOptions:
-    {
-      auto ptr = reinterpret_cast<const SquareOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ZerosLikeOptions:
-    {
-      auto ptr = reinterpret_cast<const ZerosLikeOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_FillOptions:
-    {
-      auto ptr = reinterpret_cast<const FillOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_BidirectionalSequenceLSTMOptions:
-    {
-      auto ptr = reinterpret_cast<const BidirectionalSequenceLSTMOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_BidirectionalSequenceRNNOptions:
-    {
-      auto ptr = reinterpret_cast<const BidirectionalSequenceRNNOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_UnidirectionalSequenceLSTMOptions:
-    {
-      auto ptr = reinterpret_cast<const UnidirectionalSequenceLSTMOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_FloorModOptions:
-    {
-      auto ptr = reinterpret_cast<const FloorModOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_RangeOptions:
-    {
-      auto ptr = reinterpret_cast<const RangeOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ResizeNearestNeighborOptions:
-    {
-      auto ptr = reinterpret_cast<const ResizeNearestNeighborOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_LeakyReluOptions:
-    {
-      auto ptr = reinterpret_cast<const LeakyReluOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SquaredDifferenceOptions:
-    {
-      auto ptr = reinterpret_cast<const SquaredDifferenceOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_MirrorPadOptions:
-    {
-      auto ptr = reinterpret_cast<const MirrorPadOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_AbsOptions:
-    {
-      auto ptr = reinterpret_cast<const AbsOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SplitVOptions:
-    {
-      auto ptr = reinterpret_cast<const SplitVOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_InstanceNormOptions:
-    {
-      auto ptr = reinterpret_cast<const InstanceNormOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    default:
-      return false;
-  }
-}
-
-inline bool VerifyBuiltinOptionsVector(flatbuffers::Verifier &verifier,
-                                       const flatbuffers::Vector<flatbuffers::Offset<void>> *values,
-                                       const flatbuffers::Vector<uint8_t> *types)
-{
-  if (!values || !types)
-    return !values && !types;
-  if (values->size() != types->size())
-    return false;
-  for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i)
-  {
-    if (!VerifyBuiltinOptions(verifier, values->Get(i), types->GetEnum<BuiltinOptions>(i)))
-    {
-      return false;
-    }
-  }
-  return true;
-}
-
-inline const circle::Model *GetModel(const void *buf)
-{
-  return flatbuffers::GetRoot<circle::Model>(buf);
-}
-
-inline const circle::Model *GetSizePrefixedModel(const void *buf)
-{
-  return flatbuffers::GetSizePrefixedRoot<circle::Model>(buf);
-}
-
-inline const char *ModelIdentifier() { return "CIR0"; }
-
-inline bool ModelBufferHasIdentifier(const void *buf)
-{
-  return flatbuffers::BufferHasIdentifier(buf, ModelIdentifier());
-}
-
-inline bool VerifyModelBuffer(flatbuffers::Verifier &verifier)
-{
-  return verifier.VerifyBuffer<circle::Model>(ModelIdentifier());
-}
-
-inline bool VerifySizePrefixedModelBuffer(flatbuffers::Verifier &verifier)
-{
-  return verifier.VerifySizePrefixedBuffer<circle::Model>(ModelIdentifier());
-}
-
-inline const char *ModelExtension() { return "circle"; }
-
-inline void FinishModelBuffer(flatbuffers::FlatBufferBuilder &fbb,
-                              flatbuffers::Offset<circle::Model> root)
-{
-  fbb.Finish(root, ModelIdentifier());
-}
-
-inline void FinishSizePrefixedModelBuffer(flatbuffers::FlatBufferBuilder &fbb,
-                                          flatbuffers::Offset<circle::Model> root)
-{
-  fbb.FinishSizePrefixed(root, ModelIdentifier());
-}
-
-} // namespace circle
-
-#endif // FLATBUFFERS_GENERATED_CIRCLESCHEMA_CIRCLE_H_
diff --git a/runtime/neurun/frontend/nnapi/ANeuralNetworksModel.test.cc b/runtime/neurun/frontend/nnapi/ANeuralNetworksModel.test.cc
deleted file mode 100644
index 15a279a7e..000000000
--- a/runtime/neurun/frontend/nnapi/ANeuralNetworksModel.test.cc
+++ /dev/null
@@ -1,25 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <gtest/gtest.h>
-
-#include "wrapper/ANeuralNetworksModel.h"
-
-TEST(MODEL, model_build)
-{
-  ANeuralNetworksModel model;
-  ASSERT_EQ(model.isFinished(), false);
-}
diff --git a/runtime/neurun/frontend/nnapi/CMakeLists.txt b/runtime/neurun/frontend/nnapi/CMakeLists.txt
deleted file mode 100644
index 3c3411e05..000000000
--- a/runtime/neurun/frontend/nnapi/CMakeLists.txt
+++ /dev/null
@@ -1,23 +0,0 @@
-file(GLOB_RECURSE SOURCES_FRONTEND "*.cc")
-file(GLOB_RECURSE TESTS_FRONTEND "*.test.cc")
-list(REMOVE_ITEM SOURCES_FRONTEND ${TESTS_FRONTEND})
-
-set(LIB_NEURUN neurun)
-
-add_library(${LIB_NEURUN} SHARED ${SOURCES_FRONTEND})
-target_link_libraries(${LIB_NEURUN} PUBLIC nnfw-nnapi-header)
-target_link_libraries(${LIB_NEURUN} PUBLIC neurun_core) # TODO Link PRIVATE neurun_core
-target_link_libraries(${LIB_NEURUN} PRIVATE nnfw_common)
-target_link_libraries(${LIB_NEURUN} PRIVATE nnfw_coverage)
-
-set_target_properties(${LIB_NEURUN} PROPERTIES OUTPUT_NAME neuralnetworks)
-
-install(TARGETS ${LIB_NEURUN} DESTINATION lib)
-
-add_executable(test_neurun_frontend_nnapi ${TESTS_FRONTEND})
-
-target_link_libraries(test_neurun_frontend_nnapi PRIVATE ${LIB_NEURUN} dl)
-target_link_libraries(test_neurun_frontend_nnapi PRIVATE gtest)
-target_link_libraries(test_neurun_frontend_nnapi PRIVATE gtest_main)
-
-install(TARGETS test_neurun_frontend_nnapi DESTINATION unittest)
diff --git a/runtime/neurun/frontend/nnapi/compilation.cc b/runtime/neurun/frontend/nnapi/compilation.cc
deleted file mode 100644
index 164158f19..000000000
--- a/runtime/neurun/frontend/nnapi/compilation.cc
+++ /dev/null
@@ -1,110 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <NeuralNetworks.h>
-
-#include <new>
-
-#include "wrapper/ANeuralNetworksModel.h"
-#include "wrapper/ANeuralNetworksCompilation.h"
-#include "util/logging.h"
-
-//
-// NNAPI Implementation
-//
-int ANeuralNetworksCompilation_create(ANeuralNetworksModel *model,
-                                      ANeuralNetworksCompilation **compilation)
-{
-  if ((model == nullptr) || (compilation == nullptr))
-  {
-    VERBOSE(NNAPI::Compilation) << "create: Incorrect null pointer parameter(s)" << std::endl;
-    return ANEURALNETWORKS_UNEXPECTED_NULL;
-  }
-
-  if (!model->isFinished())
-  {
-    VERBOSE(NNAPI::Compilation) << "create: Model define is not finished" << std::endl;
-    return ANEURALNETWORKS_BAD_STATE;
-  }
-
-  std::shared_ptr<neurun::ir::Graph> internal;
-
-  model->release(internal);
-
-  *compilation = new (std::nothrow) ANeuralNetworksCompilation(internal);
-  if (*compilation == nullptr)
-  {
-    VERBOSE(NNAPI::Compilation) << "create: ail to create compilation object" << std::endl;
-    return ANEURALNETWORKS_OUT_OF_MEMORY;
-  }
-
-  return ANEURALNETWORKS_NO_ERROR;
-}
-
-int ANeuralNetworksCompilation_finish(ANeuralNetworksCompilation *compilation)
-{
-  if (compilation == nullptr)
-  {
-    VERBOSE(NNAPI::Compilation) << "finish: Incorrect null pointer parameter" << std::endl;
-    return ANEURALNETWORKS_UNEXPECTED_NULL;
-  }
-
-  if (compilation->state() != ::neurun::compiler::State::CREATED)
-  {
-    VERBOSE(NNAPI::Compilation) << "finish: Already finished" << std::endl;
-    return ANEURALNETWORKS_BAD_STATE;
-  }
-
-  if (!compilation->finish())
-  {
-    VERBOSE(NNAPI::Compilation) << "finish: Fail to compile" << std::endl;
-    return ANEURALNETWORKS_BAD_STATE;
-  }
-
-  return ANEURALNETWORKS_NO_ERROR;
-}
-
-void ANeuralNetworksCompilation_free(ANeuralNetworksCompilation *compilation)
-{
-  delete compilation;
-}
-
-int ANeuralNetworksCompilation_setPreference(ANeuralNetworksCompilation *compilation,
-                                             int32_t preference)
-{
-  if (compilation == nullptr)
-  {
-    VERBOSE(NNAPI::Compilation) << "setPreference: Incorrect null pointer parameter" << std::endl;
-    return ANEURALNETWORKS_UNEXPECTED_NULL;
-  }
-
-  if (compilation->state() != ::neurun::compiler::State::CREATED)
-  {
-    VERBOSE(NNAPI::Compilation) << "setPreference: Already finished" << std::endl;
-    return ANEURALNETWORKS_BAD_STATE;
-  }
-
-  const PreferenceCode FIRST_PREFERENCE_CODE = ANEURALNETWORKS_PREFER_LOW_POWER;
-  const PreferenceCode LAST_PREFERENCE_CODE = ANEURALNETWORKS_PREFER_SUSTAINED_SPEED;
-  if ((preference < FIRST_PREFERENCE_CODE) || (preference > LAST_PREFERENCE_CODE))
-  {
-    VERBOSE(NNAPI::Compilation) << "setPreference: Incorrect preference code" << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-
-  // NYI: nothing to set
-  return ANEURALNETWORKS_NO_ERROR;
-}
diff --git a/runtime/neurun/frontend/nnapi/event.cc b/runtime/neurun/frontend/nnapi/event.cc
deleted file mode 100644
index 593b74e90..000000000
--- a/runtime/neurun/frontend/nnapi/event.cc
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <NeuralNetworks.h>
-
-#include "wrapper/ANeuralNetworksEvent.h"
-
-int ANeuralNetworksEvent_wait(ANeuralNetworksEvent *event)
-{
-  if (event == nullptr)
-  {
-    return ANEURALNETWORKS_UNEXPECTED_NULL;
-  }
-
-  if (!event->waitFinish())
-  {
-    return ANEURALNETWORKS_BAD_STATE;
-  }
-
-  return ANEURALNETWORKS_NO_ERROR;
-}
-
-void ANeuralNetworksEvent_free(ANeuralNetworksEvent *event) { delete event; }
diff --git a/runtime/neurun/frontend/nnapi/execution.cc b/runtime/neurun/frontend/nnapi/execution.cc
deleted file mode 100644
index 08f2df4c2..000000000
--- a/runtime/neurun/frontend/nnapi/execution.cc
+++ /dev/null
@@ -1,480 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <NeuralNetworks.h>
-
-#include <new>
-
-#include "wrapper/ANeuralNetworksCompilation.h"
-#include "wrapper/ANeuralNetworksExecution.h"
-#include "wrapper/ANeuralNetworksMemory.h"
-#include "wrapper/ANeuralNetworksEvent.h"
-#include "wrapper/NNAPIConvert.h"
-#include "util/logging.h"
-
-//
-// NNAPI Implementation
-//
-int ANeuralNetworksExecution_create(ANeuralNetworksCompilation *compilation,
-                                    ANeuralNetworksExecution **execution)
-{
-  if ((compilation == nullptr) || (execution == nullptr))
-  {
-    VERBOSE(NNAPI::Execution) << "create: Incorrect null pointer parameter(s)" << std::endl;
-    return ANEURALNETWORKS_UNEXPECTED_NULL;
-  }
-
-  std::shared_ptr<neurun::exec::IExecutor> executor;
-
-  compilation->publish(executor);
-
-  if (executor == nullptr)
-  {
-    VERBOSE(NNAPI::Execution) << "create: Never compiled yet" << std::endl;
-    return ANEURALNETWORKS_BAD_STATE;
-  }
-
-  *execution = new (std::nothrow) ANeuralNetworksExecution{executor};
-  if (*execution == nullptr)
-  {
-    VERBOSE(NNAPI::Execution) << "create: Fail to create execution object" << std::endl;
-    return ANEURALNETWORKS_OUT_OF_MEMORY;
-  }
-
-  return ANEURALNETWORKS_NO_ERROR;
-}
-
-// NOTE Handle optional input
-//  Unspecified shape on model build
-//    Optional and omitted input on execution: skip input setting (workaround for LSTM)
-//    Optional but not omitted input on execution: cannot handle
-//    Normal input on execution: cannot handle
-//  Fully specified shape on model build
-//    Optional input on execution: cannot handle
-//    Normal input: handle normally
-int ANeuralNetworksExecution_setInput(ANeuralNetworksExecution *execution, int32_t index,
-                                      const ANeuralNetworksOperandType *type, const void *buffer,
-                                      size_t length)
-{
-  // Don't check type
-  // Comment about ANeuralNetworksOperandType in NeuralNetworks.h:
-  //  If the input or output is optional and omitted then it need not have a fully specified tensor
-  //  operand type
-  if ((execution == nullptr) || ((buffer == nullptr) && (length != 0)))
-  {
-    VERBOSE(NNAPI::Execution) << "setInput: Incorrect null pointer parameter(s)" << std::endl;
-    return ANEURALNETWORKS_UNEXPECTED_NULL;
-  }
-
-  if ((buffer != nullptr) && (length == 0))
-  {
-    VERBOSE(NNAPI::Execution) << "setInput: Zero length input" << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-
-  const auto operand_index = execution->getInputOperandIndex(index);
-  if (!operand_index.valid())
-  {
-    VERBOSE(NNAPI::Execution) << "setInput: Invalid input index" << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-
-  // Omitted optional input
-  // LSTM operation's some inputs can be optional input
-  if ((buffer == nullptr) && (length == 0))
-  {
-    if (execution->haveUnspecifiedDims(operand_index))
-    {
-      return ANEURALNETWORKS_NO_ERROR;
-    }
-    else
-    {
-      VERBOSE(NNAPI::Execution) << "setInput: Cannot handle fully-specified shape on model build "
-                                   "but omitted input on execution"
-                                << std::endl;
-      return ANEURALNETWORKS_BAD_DATA;
-    }
-  }
-
-  if (type != nullptr)
-  {
-    if (!execution->compareDataType(type, operand_index))
-    {
-      VERBOSE(NNAPI::Execution) << "setInput: Data type mismatch" << std::endl;
-      return ANEURALNETWORKS_BAD_DATA;
-    }
-
-    if (!execution->compareShape(type, operand_index))
-    {
-      VERBOSE(NNAPI::Execution) << "setInput: Shape mismatch" << std::endl;
-      return ANEURALNETWORKS_BAD_DATA;
-    }
-
-    if (NNAPIConvert::calculateSizeFromType(type) != length)
-    {
-      VERBOSE(NNAPI::Execution) << "setInput: Invalid length" << std::endl;
-      return ANEURALNETWORKS_BAD_DATA;
-    }
-  }
-  else
-  {
-    if (execution->haveUnspecifiedDims(operand_index))
-    {
-      VERBOSE(NNAPI::Execution) << "setInput: Unspecified dimension value" << std::endl;
-      return ANEURALNETWORKS_BAD_DATA;
-    }
-
-    if (execution->getOperandSize(operand_index) != length)
-    {
-      VERBOSE(NNAPI::Execution) << "setInput: Invalid length" << std::endl;
-      return ANEURALNETWORKS_BAD_DATA;
-    }
-  }
-
-  if (!execution->setInput(index, type, buffer, length))
-  {
-    VERBOSE(NNAPI::Execution) << "setInput: Fail to set input" << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-
-  return ANEURALNETWORKS_NO_ERROR;
-}
-
-int ANeuralNetworksExecution_setOutput(ANeuralNetworksExecution *execution, int32_t index,
-                                       const ANeuralNetworksOperandType *type, void *buffer,
-                                       size_t length)
-{
-  // Don't check type
-  // Comment about ANeuralNetworksOperandType in NeuralNetworks.h:
-  //  If the input or output is optional and omitted then it need not have a fully specified tensor
-  //  operand type
-  if ((execution == nullptr) || ((buffer == nullptr) && (length != 0)))
-  {
-    VERBOSE(NNAPI::Execution) << "setOutput: Incorrect null pointer parameter(s)" << std::endl;
-    return ANEURALNETWORKS_UNEXPECTED_NULL;
-  }
-
-  if ((buffer != nullptr) && (length == 0))
-  {
-    VERBOSE(NNAPI::Execution) << "setOutput: Zero length output" << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-
-  // Handle optional output
-  if (buffer == nullptr)
-  {
-    return ANEURALNETWORKS_NO_ERROR;
-  }
-
-  const auto operand_index = execution->getOutputOperandIndex(index);
-  if (!operand_index.valid())
-  {
-    VERBOSE(NNAPI::Execution) << "setOutput: Invalid output index" << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-
-  if (type != nullptr)
-  {
-    if (!execution->compareDataType(type, operand_index))
-    {
-      VERBOSE(NNAPI::Execution) << "setOutput: Data type mismatch" << std::endl;
-      return ANEURALNETWORKS_BAD_DATA;
-    }
-
-    if (!execution->compareShape(type, operand_index))
-    {
-      VERBOSE(NNAPI::Execution) << "setOutput: Shape mismatch" << std::endl;
-      return ANEURALNETWORKS_BAD_DATA;
-    }
-
-    if (NNAPIConvert::calculateSizeFromType(type) != length)
-    {
-      VERBOSE(NNAPI::Execution) << "setOutput: Invalid length" << std::endl;
-      return ANEURALNETWORKS_BAD_DATA;
-    }
-  }
-  else
-  {
-    if (execution->haveUnspecifiedDims(operand_index))
-    {
-      VERBOSE(NNAPI::Execution) << "setOutput: Unspecified dimension value" << std::endl;
-      return ANEURALNETWORKS_BAD_DATA;
-    }
-
-    if (execution->getOperandSize(operand_index) != length)
-    {
-      VERBOSE(NNAPI::Execution) << "setInput: Invalid length" << std::endl;
-      return ANEURALNETWORKS_BAD_DATA;
-    }
-  }
-
-  if (!execution->setOutput(index, type, buffer, length))
-  {
-    VERBOSE(NNAPI::Execution) << "setOutput: Fail to set output" << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-
-  return ANEURALNETWORKS_NO_ERROR;
-}
-
-int ANeuralNetworksExecution_startCompute(ANeuralNetworksExecution *execution,
-                                          ANeuralNetworksEvent **event)
-{
-  if ((execution == nullptr) || (event == nullptr))
-  {
-    VERBOSE(NNAPI::Execution) << "startCompute: Incorrect null pointer parameter(s)" << std::endl;
-    return ANEURALNETWORKS_UNEXPECTED_NULL;
-  }
-
-  // TODO: Handle event
-  auto instance = execution->instance();
-  *event = new (std::nothrow) ANeuralNetworksEvent{instance};
-  if (*event == nullptr)
-  {
-    VERBOSE(NNAPI::Execution) << "startCompute: Fail to create event" << std::endl;
-    return ANEURALNETWORKS_OUT_OF_MEMORY;
-  }
-
-  if (!execution->startExecute())
-  {
-    VERBOSE(NNAPI::Execution) << "startCompute: Fail to start execution" << std::endl;
-    return ANEURALNETWORKS_BAD_STATE;
-  }
-
-  return ANEURALNETWORKS_NO_ERROR;
-}
-
-int ANeuralNetworksExecution_compute(ANeuralNetworksExecution *execution)
-{
-  if (execution == nullptr)
-  {
-    VERBOSE(NNAPI::Execution) << "Compute: Incorrect null pointer parameter" << std::endl;
-    return ANEURALNETWORKS_UNEXPECTED_NULL;
-  }
-
-  if (!execution->execute())
-  {
-    VERBOSE(NNAPI::Execution) << "Compute: Fail to execution" << std::endl;
-    return ANEURALNETWORKS_BAD_STATE;
-  }
-
-  return ANEURALNETWORKS_NO_ERROR;
-}
-
-void ANeuralNetworksExecution_free(ANeuralNetworksExecution *execution) { delete execution; }
-
-int ANeuralNetworksExecution_setInputFromMemory(ANeuralNetworksExecution *execution, int32_t index,
-                                                const ANeuralNetworksOperandType *type,
-                                                const ANeuralNetworksMemory *memory, size_t offset,
-                                                size_t length)
-{
-  if ((execution == nullptr) || (memory == nullptr))
-  {
-    VERBOSE(NNAPI::Execution) << "setInputFromMemory: Incorrect null pointer parameter(s)"
-                              << std::endl;
-    return ANEURALNETWORKS_UNEXPECTED_NULL;
-  }
-
-  if (length == 0)
-  {
-    VERBOSE(NNAPI::Execution) << "setInputFromMemory: Zero length input" << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-
-  const auto operand_index = execution->getInputOperandIndex(index);
-  if (!operand_index.valid())
-  {
-    VERBOSE(NNAPI::Execution) << "setInputFromMemory: Invalid input index" << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-
-  if (type != nullptr)
-  {
-    if (!execution->compareDataType(type, operand_index))
-    {
-      VERBOSE(NNAPI::Execution) << "setInputFromMemory: Data type mismatch" << std::endl;
-      return ANEURALNETWORKS_BAD_DATA;
-    }
-
-    if (!execution->compareShape(type, operand_index))
-    {
-      VERBOSE(NNAPI::Execution) << "setInputFromMemory: Shape mismatch" << std::endl;
-      return ANEURALNETWORKS_BAD_DATA;
-    }
-
-    if (NNAPIConvert::calculateSizeFromType(type) != length)
-    {
-      VERBOSE(NNAPI::Execution) << "setInputFromMemory: Invalid length" << std::endl;
-      return ANEURALNETWORKS_BAD_DATA;
-    }
-  }
-  else
-  {
-    if (execution->haveUnspecifiedDims(operand_index))
-    {
-      VERBOSE(NNAPI::Execution) << "setInputFromMemory: Unspecified dimension value" << std::endl;
-      return ANEURALNETWORKS_BAD_DATA;
-    }
-
-    if (execution->getOperandSize(operand_index) != length)
-    {
-      VERBOSE(NNAPI::Execution) << "setInputFromMemory: Invalid length" << std::endl;
-      return ANEURALNETWORKS_BAD_DATA;
-    }
-  }
-
-  if (!memory->vaildAccess(offset, length))
-  {
-    VERBOSE(NNAPI::Execution) << "setInputFromMemory: Invalid memory access" << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-
-  if (!execution->setInput(index, type, reinterpret_cast<const void *>(memory->base() + offset),
-                           length))
-  {
-    VERBOSE(NNAPI::Execution) << "setInputFromMemory: Fail to set input" << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-
-  return ANEURALNETWORKS_NO_ERROR;
-}
-
-int ANeuralNetworksExecution_setOutputFromMemory(ANeuralNetworksExecution *execution, int32_t index,
-                                                 const ANeuralNetworksOperandType *type,
-                                                 const ANeuralNetworksMemory *memory, size_t offset,
-                                                 size_t length)
-{
-  if ((execution == nullptr) || (memory == nullptr))
-  {
-    VERBOSE(NNAPI::Execution) << "setOutputFromMemory: Incorrect null pointer parameter(s)"
-                              << std::endl;
-    return ANEURALNETWORKS_UNEXPECTED_NULL;
-  }
-
-  if (length == 0)
-  {
-    VERBOSE(NNAPI::Execution) << "setOutputFromMemory: Zero length input" << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-
-  const auto operand_index = execution->getOutputOperandIndex(index);
-  if (!operand_index.valid())
-  {
-    VERBOSE(NNAPI::Execution) << "setOutputFromMemory: Invalid output index" << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-
-  if (type != nullptr)
-  {
-    if (!execution->compareDataType(type, operand_index))
-    {
-      VERBOSE(NNAPI::Execution) << "setOutputFromMemory: Data type mismatch" << std::endl;
-      return ANEURALNETWORKS_BAD_DATA;
-    }
-
-    if (!execution->compareShape(type, operand_index))
-    {
-      VERBOSE(NNAPI::Execution) << "setOutputFromMemory: Shape mismatch" << std::endl;
-      return ANEURALNETWORKS_BAD_DATA;
-    }
-
-    if (NNAPIConvert::calculateSizeFromType(type) != length)
-    {
-      VERBOSE(NNAPI::Execution) << "setOutputFromMemory: Invalid length" << std::endl;
-      return ANEURALNETWORKS_BAD_DATA;
-    }
-  }
-  else
-  {
-    if (execution->haveUnspecifiedDims(operand_index))
-    {
-      VERBOSE(NNAPI::Execution) << "setOutputFromMemory: Unspecified dimension value" << std::endl;
-      return ANEURALNETWORKS_BAD_DATA;
-    }
-
-    if (execution->getOperandSize(operand_index) != length)
-    {
-      VERBOSE(NNAPI::Execution) << "setOutputFromMemory: Invalid length" << std::endl;
-      return ANEURALNETWORKS_BAD_DATA;
-    }
-  }
-
-  if (!memory->vaildAccess(offset, length))
-  {
-    VERBOSE(NNAPI::Execution) << "setOutputFromMemory: Invalid memory access" << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-
-  if (!execution->setOutput(index, type, reinterpret_cast<void *>(memory->base() + offset), length))
-  {
-    VERBOSE(NNAPI::Execution) << "setOutputFromMemory: Fail to set input" << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-
-  return ANEURALNETWORKS_NO_ERROR;
-}
-
-int ANeuralNetworksExecution_getOutputOperandRank(ANeuralNetworksExecution *execution,
-                                                  int32_t index, uint32_t *rank)
-{
-  if ((execution == nullptr) || (rank == nullptr))
-  {
-    VERBOSE(NNAPI::Execution) << "getOutputOperandRank: Incorrect null pointer parameter(s)"
-                              << std::endl;
-    return ANEURALNETWORKS_UNEXPECTED_NULL;
-  }
-
-  const auto operand_index = execution->getOutputOperandIndex(index);
-  if (!operand_index.valid())
-  {
-    VERBOSE(NNAPI::Execution) << "getOutputOperandRank: Invalid output index" << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-
-  if (!execution->getOutputOperandRank(index, rank))
-  {
-    VERBOSE(NNAPI::Execution) << "getOutputOperandRank: Fail to get rank" << std::endl;
-    return ANEURALNETWORKS_BAD_STATE;
-  }
-
-  return ANEURALNETWORKS_NO_ERROR;
-}
-
-int ANeuralNetworksExecution_getOutputOperandDimensions(ANeuralNetworksExecution *execution,
-                                                        int32_t index, uint32_t *dimensions)
-{
-  if ((execution == nullptr) || (dimensions == nullptr))
-  {
-    VERBOSE(NNAPI::Execution) << "getOutputOperandDimensions: Incorrect null pointer parameter(s)"
-                              << std::endl;
-    return ANEURALNETWORKS_UNEXPECTED_NULL;
-  }
-
-  const auto operand_index = execution->getOutputOperandIndex(index);
-  if (!operand_index.valid())
-  {
-    VERBOSE(NNAPI::Execution) << "getOutputOperandDimensions: Invalid output index" << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-
-  if (!execution->getOutputOperandDimensions(index, dimensions))
-  {
-    VERBOSE(NNAPI::Execution) << "getOutputOperandDimensions: Fail to get rank" << std::endl;
-    return ANEURALNETWORKS_BAD_STATE;
-  }
-
-  return ANEURALNETWORKS_NO_ERROR;
-}
diff --git a/runtime/neurun/frontend/nnapi/memory.cc b/runtime/neurun/frontend/nnapi/memory.cc
deleted file mode 100644
index fbe1a48e8..000000000
--- a/runtime/neurun/frontend/nnapi/memory.cc
+++ /dev/null
@@ -1,42 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <NeuralNetworks.h>
-#include <sys/mman.h>
-#include <new>
-#include <memory>
-
-#include "cpp14/memory.h"
-#include "wrapper/ANeuralNetworksMemory.h"
-
-int ANeuralNetworksMemory_createFromFd(size_t size, int protect, int fd, size_t offset,
-                                       ANeuralNetworksMemory **memory)
-{
-  if (memory == nullptr)
-  {
-    return ANEURALNETWORKS_UNEXPECTED_NULL;
-  }
-
-  *memory = new (std::nothrow) ANeuralNetworksMemory{size, protect, fd, offset};
-  if (*memory == nullptr)
-  {
-    return ANEURALNETWORKS_OUT_OF_MEMORY;
-  }
-
-  return ANEURALNETWORKS_NO_ERROR;
-}
-
-void ANeuralNetworksMemory_free(ANeuralNetworksMemory *memory) { delete memory; }
diff --git a/runtime/neurun/frontend/nnapi/model.cc b/runtime/neurun/frontend/nnapi/model.cc
deleted file mode 100644
index 72a66e630..000000000
--- a/runtime/neurun/frontend/nnapi/model.cc
+++ /dev/null
@@ -1,411 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <NeuralNetworks.h>
-#include <NeuralNetworksEx.h>
-
-#include <new>
-
-#include "wrapper/ANeuralNetworksModel.h"
-#include "wrapper/ANeuralNetworksMemory.h"
-#include "util/logging.h"
-
-int ANeuralNetworksModel_create(ANeuralNetworksModel **model)
-{
-  if (model == nullptr)
-  {
-    VERBOSE(NNAPI::Model) << "create: Incorrect null pointer parameter" << std::endl;
-    return ANEURALNETWORKS_UNEXPECTED_NULL;
-  }
-
-  *model = new (std::nothrow) ANeuralNetworksModel{};
-  if (*model == nullptr)
-  {
-    VERBOSE(NNAPI::Model) << "create: Fail to create model object" << std::endl;
-    return ANEURALNETWORKS_OUT_OF_MEMORY;
-  }
-
-  return ANEURALNETWORKS_NO_ERROR;
-}
-
-void ANeuralNetworksModel_free(ANeuralNetworksModel *model) { delete model; }
-
-int ANeuralNetworksModel_addOperand(ANeuralNetworksModel *model,
-                                    const ANeuralNetworksOperandType *type)
-{
-  if ((model == nullptr) || (type == nullptr))
-  {
-    VERBOSE(NNAPI::Model) << "addOperand: Incorrect null pointer parameter(s)" << std::endl;
-    return ANEURALNETWORKS_UNEXPECTED_NULL;
-  }
-
-  if (model->isFinished())
-  {
-    VERBOSE(NNAPI::Model) << "addOperand: Already finished" << std::endl;
-    return ANEURALNETWORKS_BAD_STATE;
-  }
-
-  // scale and zeroPoint should be zero for scalars and non-fixed point tensors
-  // Quantized:
-  //  scale: a 32 bit floating point value greater than zero
-  //  zeroPoint: a 32 bit integer, in range [0, 255]
-  if (type->type == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM)
-  {
-    if (!(type->scale > 0.0f))
-    {
-      VERBOSE(NNAPI::Model) << "addOperand: Incorrect scale value for quantization" << std::endl;
-      return ANEURALNETWORKS_BAD_DATA;
-    }
-
-    if ((type->zeroPoint < 0) || (type->zeroPoint > 255))
-    {
-      VERBOSE(NNAPI::Model) << "addOperand: Incorrect zeroPoint value for quantization"
-                            << std::endl;
-      return ANEURALNETWORKS_BAD_DATA;
-    }
-  }
-  // NOTE Validation of scale and zeroPoint would be skipped for a while.
-  //      We do not know whether scalar type can have scale and zeroPoint.
-  //      To pass ValidationTest and GeneratedTest, this validation code
-  //      would not be implemented until we can define this issue clearly.
-  //
-  // scale and zeroPoint should be zero for scalars and non-fixed point tensors
-  // else if ((type->scale != 0.0f) || (type->zeroPoint != 0))
-  // {
-  //   return ANEURALNETWORKS_BAD_DATA;
-  // }
-
-  // dimensionCount should be zero for scalars
-  if ((type->dimensionCount != 0) &&
-      ((type->type == ANEURALNETWORKS_FLOAT32) || (type->type == ANEURALNETWORKS_INT32) ||
-       (type->type == ANEURALNETWORKS_UINT32)))
-  {
-    VERBOSE(NNAPI::Model) << "addOperand: Incorrect data type" << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-
-  if (!model->addOperand(type))
-  {
-    VERBOSE(NNAPI::Model) << "addOperand: Fail to add operand" << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-
-  return ANEURALNETWORKS_NO_ERROR;
-}
-
-int ANeuralNetworksModel_setOperandValue(ANeuralNetworksModel *model, int32_t index,
-                                         const void *buffer, size_t length)
-{
-  const bool optional_operand = ((buffer == nullptr) && (length == 0));
-
-  if ((model == nullptr) || ((buffer == nullptr) && (length != 0)))
-  {
-    VERBOSE(NNAPI::Model) << "setOperandValue: Incorrect null pointer parameter(s)" << std::endl;
-    return ANEURALNETWORKS_UNEXPECTED_NULL;
-  }
-
-  if (model->isFinished())
-  {
-    VERBOSE(NNAPI::Model) << "setOperandValue: Already finished" << std::endl;
-    return ANEURALNETWORKS_BAD_STATE;
-  }
-
-  // Negative index value is not allowed
-  if (index < 0)
-  {
-    VERBOSE(NNAPI::Model) << "setOperandValue: Invalid index value (negative)" << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-  // NOTE OperandIndex uses uint32_t as its underlying type as various NNAPI
-  //      functions such as ANeuralNetworksModel_addOperation use uint32_t to represent operand
-  //      index
-  //      ANeuralNetworksModel_setOperandValue, however, uses int32_t to represent operand index.
-  //
-  //      Below, static_cast<uint32_t>(...) is introduced to eliminate compiler warning.
-  uint32_t ind = static_cast<uint32_t>(index);
-
-  if (!model->isExistOperand(ind))
-  {
-    VERBOSE(NNAPI::Model) << "setOperandValue: Invalid index value (not exist)" << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-
-  if (!optional_operand && (model->operandSize(ind) != length))
-  {
-    VERBOSE(NNAPI::Model) << "setOperandValue: Invalid data length" << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-
-  if (model->isUsageSet(ind))
-  {
-    VERBOSE(NNAPI::Model) << "setOperandValue: Already set operand" << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-
-  // NNAPI spec in NeuralNetworks.h
-  // For values of length greater than ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES,
-  // the application is responsible for not changing the content of this region
-  // until all executions using this model have completed
-  bool copy_value = false;
-  if (length <= ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES)
-  {
-    copy_value = true;
-  }
-
-  if (!model->setOperandValue(ind, buffer, length, optional_operand, copy_value))
-  {
-    VERBOSE(NNAPI::Model) << "setOperandValue: Fail to set operand value" << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-
-  return ANEURALNETWORKS_NO_ERROR;
-}
-
-int ANeuralNetworksModel_setOperandValueFromMemory(ANeuralNetworksModel *model, int32_t index,
-                                                   const ANeuralNetworksMemory *memory,
-                                                   size_t offset, size_t length)
-{
-  if ((model == nullptr) || (memory == nullptr))
-  {
-    VERBOSE(NNAPI::Model) << "setOperandValueFromMemory: Incorrect null pointer parameter(s)"
-                          << std::endl;
-    return ANEURALNETWORKS_UNEXPECTED_NULL;
-  }
-
-  if (model->isFinished())
-  {
-    VERBOSE(NNAPI::Model) << "setOperandValueFromMemory: Already finished" << std::endl;
-    return ANEURALNETWORKS_BAD_STATE;
-  }
-
-  // Negative index value is not allowed
-  if (index < 0)
-  {
-    VERBOSE(NNAPI::Model) << "setOperandValueFromMemory: Invalid index value (negative)"
-                          << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-  // NOTE OperandIndex uses uint32_t as its underlying type as various NNAPI
-  //      functions such as ANeuralNetworksModel_addOperation use uint32_t to represent operand
-  //      index
-  //      ANeuralNetworksModel_setOperandValue, however, uses int32_t to represent operand index.
-  //
-  //      Below, static_cast<uint32_t>(...) is introduced to eliminate compiler warning.
-  uint32_t ind = static_cast<uint32_t>(index);
-
-  if (!model->isExistOperand(ind))
-  {
-    VERBOSE(NNAPI::Model) << "setOperandValueFromMemory: Invalid index value (not exist)"
-                          << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-
-  if ((model->operandSize(ind) != length) || (memory->size() < (offset + length)))
-  {
-    VERBOSE(NNAPI::Model) << "setOperandValueFromMemory: Invalid data length" << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-
-  if (model->isUsageSet(ind))
-  {
-    VERBOSE(NNAPI::Model) << "setOperandValueFromMemory: Already set operand" << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-
-  if (!model->setOperandValue(ind, memory->base() + offset, length))
-  {
-    VERBOSE(NNAPI::Model) << "setOperandValueFromMemory: Fail to set operand value" << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-
-  return ANEURALNETWORKS_NO_ERROR;
-}
-
-int ANeuralNetworksModel_addOperation(ANeuralNetworksModel *model,
-                                      ANeuralNetworksOperationType type, uint32_t inputCount,
-                                      const uint32_t *inputs, uint32_t outputCount,
-                                      const uint32_t *outputs)
-{
-  if ((model == nullptr) || (inputs == nullptr) || (outputs == nullptr))
-  {
-    VERBOSE(NNAPI::Model) << "addOperation: Incorrect null pointer parameter(s)" << std::endl;
-    return ANEURALNETWORKS_UNEXPECTED_NULL;
-  }
-
-  if (model->isFinished())
-  {
-    VERBOSE(NNAPI::Model) << "addOperation: Already finished" << std::endl;
-    return ANEURALNETWORKS_BAD_STATE;
-  }
-
-  const ANeuralNetworksOperationType FIRST_OPERATION = ANEURALNETWORKS_ADD;
-  const ANeuralNetworksOperationType LAST_OPERATION = ANEURALNETWORKS_RESIZE_NEAREST_NEIGHBOR;
-  if ((type < FIRST_OPERATION) || (type > LAST_OPERATION))
-  {
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-
-  for (uint32_t i = 0; i < outputCount; i++)
-  {
-    if (model->isUsageSet(outputs[i]))
-    {
-      VERBOSE(NNAPI::Model) << "addOperation: Already set output operand" << std::endl;
-      return ANEURALNETWORKS_BAD_DATA;
-    }
-  }
-
-  if (!model->addOperation(type, inputCount, inputs, outputCount, outputs))
-  {
-    VERBOSE(NNAPI::Model) << "addOperation: Fail to add operation" << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-
-  return ANEURALNETWORKS_NO_ERROR;
-}
-
-int ANeuralNetworksModel_addOperationEx(ANeuralNetworksModel *model,
-                                        ANeuralNetworksOperationTypeEx type, uint32_t inputCount,
-                                        const uint32_t *inputs, uint32_t outputCount,
-                                        const uint32_t *outputs)
-{
-  if ((model == nullptr) || (inputs == nullptr) || (outputs == nullptr))
-  {
-    VERBOSE(NNAPI::Model) << "addOperation: Incorrect null pointer parameter(s)" << std::endl;
-    return ANEURALNETWORKS_UNEXPECTED_NULL;
-  }
-
-  if (model->isFinished())
-  {
-    VERBOSE(NNAPI::Model) << "addOperation: Already finished" << std::endl;
-    return ANEURALNETWORKS_BAD_STATE;
-  }
-
-  const ANeuralNetworksOperationTypeEx FIRST_OPERATION = ANEURALNETWORKS_CAST_EX;
-  const ANeuralNetworksOperationTypeEx LAST_OPERATION = ANEURALNETWORKS_LESS_EX;
-  if ((type < FIRST_OPERATION) || (type > LAST_OPERATION))
-  {
-    VERBOSE(NNAPI::Model) << "addOperation: Invalid operation type" << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-
-  for (uint32_t i = 0; i < outputCount; i++)
-  {
-    if (model->isUsageSet(outputs[i]))
-    {
-      VERBOSE(NNAPI::Model) << "addOperation: Already set output operand" << std::endl;
-      return ANEURALNETWORKS_BAD_DATA;
-    }
-  }
-
-  if (!model->addOperationEx(type, inputCount, inputs, outputCount, outputs))
-  {
-    VERBOSE(NNAPI::Model) << "addOperation: Fail to add operation" << std::endl;
-    return ANEURALNETWORKS_BAD_DATA;
-  }
-
-  return ANEURALNETWORKS_NO_ERROR;
-}
-
-int ANeuralNetworksModel_identifyInputsAndOutputs(ANeuralNetworksModel *model, uint32_t inputCount,
-                                                  const uint32_t *inputs, uint32_t outputCount,
-                                                  const uint32_t *outputs)
-{
-  if ((model == nullptr) || (inputs == nullptr) || (outputs == nullptr))
-  {
-    VERBOSE(NNAPI::Model) << "identifyInputsAndOutputs: Incorrect null pointer parameter(s)"
-                          << std::endl;
-    return ANEURALNETWORKS_UNEXPECTED_NULL;
-  }
-
-  if (model->isFinished())
-  {
-    VERBOSE(NNAPI::Model) << "identifyInputsAndOutputs: Already finished" << std::endl;
-    return ANEURALNETWORKS_BAD_STATE;
-  }
-
-  for (uint32_t n = 0; n < inputCount; ++n)
-  {
-    uint32_t ind = inputs[n];
-    if (model->isUsageSet(ind))
-    {
-      VERBOSE(NNAPI::Model) << "identifyInputsAndOutputs: Already set input operand" << std::endl;
-      return ANEURALNETWORKS_BAD_DATA;
-    }
-
-    if (!model->addModelInput(ind))
-    {
-      VERBOSE(NNAPI::Model) << "identifyInputsAndOutputs: Fail to add input" << std::endl;
-      return ANEURALNETWORKS_BAD_DATA;
-    }
-  }
-
-  for (uint32_t n = 0; n < outputCount; ++n)
-  {
-    uint32_t ind = outputs[n];
-
-    if (!model->isOperationOutput(ind))
-    {
-      VERBOSE(NNAPI::Model) << "identifyInputsAndOutputs: Need to set output operand" << std::endl;
-      return ANEURALNETWORKS_BAD_DATA;
-    }
-
-    if (!model->addModelOutput(ind))
-    {
-      VERBOSE(NNAPI::Model) << "identifyInputsAndOutputs: Fail to add output" << std::endl;
-      return ANEURALNETWORKS_BAD_DATA;
-    }
-  }
-
-  return ANEURALNETWORKS_NO_ERROR;
-}
-
-int ANeuralNetworksModel_finish(ANeuralNetworksModel *model)
-{
-  if (model == nullptr)
-  {
-    VERBOSE(NNAPI::Model) << "finish: Incorrect null pointer parameter" << std::endl;
-    return ANEURALNETWORKS_UNEXPECTED_NULL;
-  }
-
-  if (model->isFinished())
-  {
-    VERBOSE(NNAPI::Model) << "finish: Already finished" << std::endl;
-    return ANEURALNETWORKS_BAD_STATE;
-  }
-
-  if (!model->finish())
-  {
-    VERBOSE(NNAPI::Model) << "finish: Fail to generate internal graph" << std::endl;
-    return ANEURALNETWORKS_BAD_STATE;
-  }
-
-  return ANEURALNETWORKS_NO_ERROR;
-}
-
-int ANeuralNetworksModel_relaxComputationFloat32toFloat16(ANeuralNetworksModel *model, bool)
-{
-  if (model == nullptr)
-  {
-    VERBOSE(NNAPI::Model) << "relaxComputationFloat32toFloat16: Incorrect null pointer parameter"
-                          << std::endl;
-    return ANEURALNETWORKS_UNEXPECTED_NULL;
-  }
-
-  // NYI: nothing to set
-  VERBOSE(NNAPI::Model) << "relaxComputationFloat32toFloat16: Do nothing yet" << std::endl;
-
-  return ANEURALNETWORKS_NO_ERROR;
-}
diff --git a/runtime/neurun/frontend/nnapi/wrapper/ANeuralNetworksCompilation.cc b/runtime/neurun/frontend/nnapi/wrapper/ANeuralNetworksCompilation.cc
deleted file mode 100644
index 1aa1583aa..000000000
--- a/runtime/neurun/frontend/nnapi/wrapper/ANeuralNetworksCompilation.cc
+++ /dev/null
@@ -1,42 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ANeuralNetworksCompilation.h"
-
-#include "util/logging.h"
-
-ANeuralNetworksCompilation::ANeuralNetworksCompilation(
-    const std::shared_ptr<neurun::ir::Graph> &model) noexcept
-    : _compiler{new neurun::compiler::Compiler{model}}
-{
-  // DO NOTHING
-}
-
-bool ANeuralNetworksCompilation::finish() noexcept
-{
-  try
-  {
-    _compiler->compile();
-  }
-  catch (const std::exception &e)
-  {
-    VERBOSE(EXCEPTION) << e.what() << std::endl;
-
-    return false;
-  }
-
-  return true;
-}
diff --git a/runtime/neurun/frontend/nnapi/wrapper/ANeuralNetworksCompilation.h b/runtime/neurun/frontend/nnapi/wrapper/ANeuralNetworksCompilation.h
deleted file mode 100644
index 56b402d16..000000000
--- a/runtime/neurun/frontend/nnapi/wrapper/ANeuralNetworksCompilation.h
+++ /dev/null
@@ -1,42 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __COMPILATION_H__
-#define __COMPILATION_H__
-
-#include "compiler/Compiler.h"
-#include "ir/Graph.h"
-#include "exec/IExecutor.h"
-
-struct ANeuralNetworksCompilation
-{
-public:
-  ANeuralNetworksCompilation(const std::shared_ptr<neurun::ir::Graph> &graph) noexcept;
-
-public:
-  bool finish() noexcept;
-
-  neurun::compiler::State state(void) noexcept { return _compiler->state(); }
-  void publish(std::shared_ptr<neurun::exec::IExecutor> &executor) noexcept
-  {
-    _compiler->release(executor);
-  }
-
-private:
-  std::shared_ptr<neurun::compiler::Compiler> _compiler;
-};
-
-#endif
diff --git a/runtime/neurun/frontend/nnapi/wrapper/ANeuralNetworksEvent.cc b/runtime/neurun/frontend/nnapi/wrapper/ANeuralNetworksEvent.cc
deleted file mode 100644
index b09f9abe6..000000000
--- a/runtime/neurun/frontend/nnapi/wrapper/ANeuralNetworksEvent.cc
+++ /dev/null
@@ -1,43 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ANeuralNetworksEvent.h"
-
-#include "exec/Execution.h"
-#include "util/logging.h"
-
-ANeuralNetworksEvent::ANeuralNetworksEvent(
-    const std::shared_ptr<neurun::exec::Execution> &execution)
-    : _execution{execution}
-{
-  // DO NOTHING
-}
-
-bool ANeuralNetworksEvent::waitFinish(void) noexcept
-{
-  try
-  {
-    _execution->waitFinish();
-  }
-  catch (const std::exception &e)
-  {
-    VERBOSE(EXCEPTION) << e.what() << std::endl;
-
-    return false;
-  }
-
-  return true;
-}
diff --git a/runtime/neurun/frontend/nnapi/wrapper/ANeuralNetworksEvent.h b/runtime/neurun/frontend/nnapi/wrapper/ANeuralNetworksEvent.h
deleted file mode 100644
index e499bab77..000000000
--- a/runtime/neurun/frontend/nnapi/wrapper/ANeuralNetworksEvent.h
+++ /dev/null
@@ -1,44 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __EVENT_H__
-#define __EVENT_H__
-
-#include <NeuralNetworks.h>
-
-#include <memory>
-
-namespace neurun
-{
-namespace exec
-{
-class Execution;
-} // namespace exec
-} // namespace neurun
-
-struct ANeuralNetworksEvent
-{
-public:
-  ANeuralNetworksEvent(const std::shared_ptr<neurun::exec::Execution> &execution);
-
-public:
-  bool waitFinish(void) noexcept;
-
-private:
-  const std::shared_ptr<neurun::exec::Execution> _execution;
-};
-
-#endif
diff --git a/runtime/neurun/frontend/nnapi/wrapper/ANeuralNetworksExecution.cc b/runtime/neurun/frontend/nnapi/wrapper/ANeuralNetworksExecution.cc
deleted file mode 100644
index b8e43a691..000000000
--- a/runtime/neurun/frontend/nnapi/wrapper/ANeuralNetworksExecution.cc
+++ /dev/null
@@ -1,289 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ANeuralNetworksExecution.h"
-#include "NNAPIConvert.h"
-#include "util/logging.h"
-
-const neurun::ir::OperandIndex
-ANeuralNetworksExecution::getInputOperandIndex(int32_t index) noexcept
-{
-  if (index < 0)
-  {
-    // Negative index: return invalid index
-    return neurun::ir::OperandIndex{};
-  }
-
-  uint32_t cast_index = static_cast<uint32_t>(index);
-  if (cast_index >= _execution->graph().getInputs().size())
-  {
-    // Return invalid index
-    return neurun::ir::OperandIndex{};
-  }
-
-  neurun::ir::IOIndex input_index{cast_index};
-  const auto operand_index = _execution->graph().getInputs().at(input_index);
-  return operand_index;
-}
-
-const neurun::ir::OperandIndex
-ANeuralNetworksExecution::getOutputOperandIndex(int32_t index) noexcept
-{
-  if (index < 0)
-  {
-    // Negative index: return invalid index
-    return neurun::ir::OperandIndex{};
-  }
-
-  uint32_t cast_index = static_cast<uint32_t>(index);
-  if (cast_index >= _execution->graph().getOutputs().size())
-  {
-    // Return invalid index
-    return neurun::ir::OperandIndex{};
-  }
-
-  neurun::ir::IOIndex output_index{cast_index};
-  const auto operand_index = _execution->graph().getOutputs().at(output_index);
-  return operand_index;
-}
-
-bool ANeuralNetworksExecution::compareDataType(const ANeuralNetworksOperandType *type,
-                                               const neurun::ir::OperandIndex index) noexcept
-{
-  try
-  {
-    const auto operand_type = _execution->graph().operands().at(index).typeInfo();
-    const auto typeInfo = NNAPIConvert::getTypeInfo(type);
-
-    if (operand_type != typeInfo)
-    {
-      // Data type mismatch
-      return false;
-    }
-  }
-  catch (const std::exception &e)
-  {
-    VERBOSE(EXCEPTION) << e.what() << std::endl;
-
-    return false;
-  }
-
-  return true;
-}
-
-bool ANeuralNetworksExecution::compareShape(const ANeuralNetworksOperandType *type,
-                                            const neurun::ir::OperandIndex index) noexcept
-{
-  // Passed shape should be specified
-  if (haveUnspecifiedDims(index))
-  {
-    return false;
-  }
-
-  const auto &operand_shape = _execution->graph().operands().at(index).shape();
-  const auto &shape_from_type = NNAPIConvert::getShape(type);
-
-  return operand_shape == shape_from_type;
-}
-
-bool ANeuralNetworksExecution::haveUnspecifiedDims(const neurun::ir::OperandIndex index) noexcept
-{
-  const auto operand_shape = _execution->graph().operands().at(index).shape();
-
-  return operand_shape.num_elements() == 0;
-}
-
-size_t ANeuralNetworksExecution::getOperandSize(const neurun::ir::OperandIndex index) noexcept
-{
-  try
-  {
-    return _execution->graph().operands().at(index).operandSize();
-  }
-  catch (const std::exception &e)
-  {
-    VERBOSE(EXCEPTION) << e.what() << std::endl;
-
-    return 0;
-  }
-}
-
-bool ANeuralNetworksExecution::setInput(uint32_t index, const ANeuralNetworksOperandType *type,
-                                        const void *buffer, size_t length) noexcept
-{
-  try
-  {
-    neurun::ir::IOIndex input_index{index};
-    const auto operand_index = getInputOperandIndex(index);
-
-    const auto type_info = _execution->graph().operands().at(operand_index).typeInfo();
-    const auto shape = (type != nullptr) ? NNAPIConvert::getShape(type)
-                                         : _execution->graph().operands().at(operand_index).shape();
-
-    // NOTE The nnapi does not provide setting io_layout and not support changing layout. In other
-    // words, we can assume that io_layout from nnapi always is the same as layout of the used
-    // model.
-    // TODO Set layout of model
-    _execution->setInput(input_index, type_info, shape, buffer, length, neurun::ir::Layout::NHWC);
-  }
-  catch (const std::exception &e)
-  {
-    VERBOSE(EXCEPTION) << e.what() << std::endl;
-
-    return false;
-  }
-
-  return true;
-}
-
-bool ANeuralNetworksExecution::setOutput(uint32_t index, const ANeuralNetworksOperandType *type,
-                                         void *buffer, size_t length) noexcept
-{
-  try
-  {
-    neurun::ir::IOIndex output_index{index};
-    const auto operand_index = getOutputOperandIndex(index);
-
-    const auto type_info = _execution->graph().operands().at(operand_index).typeInfo();
-    const auto shape = (type != nullptr) ? NNAPIConvert::getShape(type)
-                                         : _execution->graph().operands().at(operand_index).shape();
-
-    // NOTE The nnapi does not provide setting io_layout and not support changing layout. In other
-    // words, we can assume that io_layout from nnapi always is the same as layout of the used
-    // model.
-    // TODO Set layout of model
-    _execution->setOutput(output_index, type_info, shape, buffer, length, neurun::ir::Layout::NHWC);
-  }
-  catch (const std::exception &e)
-  {
-    VERBOSE(EXCEPTION) << e.what() << std::endl;
-
-    return false;
-  }
-
-  return true;
-}
-
-bool ANeuralNetworksExecution::startExecute(void) noexcept
-{
-  try
-  {
-    _execution->startExecute();
-  }
-  catch (const std::exception &e)
-  {
-    VERBOSE(EXCEPTION) << e.what() << std::endl;
-
-    return false;
-  }
-
-  return true;
-}
-
-bool ANeuralNetworksExecution::execute(void) noexcept
-{
-  try
-  {
-    _execution->execute();
-  }
-  catch (const std::exception &e)
-  {
-    VERBOSE(EXCEPTION) << e.what() << std::endl;
-
-    return false;
-  }
-
-  return true;
-}
-
-const std::shared_ptr<neurun::exec::Execution> ANeuralNetworksExecution::instance(void) noexcept
-{
-  return _execution;
-}
-
-bool ANeuralNetworksExecution::getOutputOperandRank(uint32_t index, uint32_t *rank) noexcept
-{
-  try
-  {
-    neurun::ir::IOIndex output_index{index};
-    const auto operand_index = getOutputOperandIndex(index);
-    bool unspecified = haveUnspecifiedDims(operand_index);
-
-    // TODO Get unspecified output operand's rank
-    if (unspecified)
-    {
-      throw std::runtime_error{"Unsupport feature"};
-    }
-
-    // Check execution is finished
-    // Output rank and shape may be decided after execution if output is unspecified operand
-    if (!_execution->isFinished())
-    {
-      return false;
-    }
-
-    *rank = _execution->graph().operands().at(operand_index).shape().rank();
-  }
-  catch (const std::exception &e)
-  {
-    VERBOSE(EXCEPTION) << e.what() << std::endl;
-
-    return false;
-  }
-
-  return true;
-}
-
-bool ANeuralNetworksExecution::getOutputOperandDimensions(uint32_t index, uint32_t *dimensions)
-{
-  try
-  {
-    neurun::ir::IOIndex output_index{index};
-    const auto operand_index = getOutputOperandIndex(index);
-    bool unspecified = haveUnspecifiedDims(operand_index);
-    if (unspecified)
-    {
-      throw std::runtime_error{"NYI: Models with unspecified output dimensions"};
-    }
-
-    // Check execution is finished
-    // Output rank and shape may be decided after execution if output is unspecified operand
-    if (!_execution->isFinished())
-    {
-      return false;
-    }
-
-    auto shape = _execution->graph().operands().at(operand_index).shape();
-    for (int i = 0; i < shape.rank(); i++)
-    {
-      auto dim = shape.dim(i);
-
-      if (dim <= 0)
-      {
-        throw std::runtime_error{"Invalid dimension value"};
-      }
-
-      dimensions[i] = static_cast<uint32_t>(dim);
-    }
-  }
-  catch (const std::exception &e)
-  {
-    VERBOSE(EXCEPTION) << e.what() << std::endl;
-
-    return false;
-  }
-
-  return true;
-}
diff --git a/runtime/neurun/frontend/nnapi/wrapper/ANeuralNetworksExecution.h b/runtime/neurun/frontend/nnapi/wrapper/ANeuralNetworksExecution.h
deleted file mode 100644
index ecffedc0a..000000000
--- a/runtime/neurun/frontend/nnapi/wrapper/ANeuralNetworksExecution.h
+++ /dev/null
@@ -1,74 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __EXECUTION_H__
-#define __EXECUTION_H__
-
-#include <NeuralNetworks.h>
-
-#include <memory>
-
-#include "exec/Execution.h"
-
-struct ANeuralNetworksExecution
-{
-public:
-  ANeuralNetworksExecution(const std::shared_ptr<neurun::exec::IExecutor> &executor)
-      : _execution{std::make_shared<neurun::exec::Execution>(executor)}
-  {
-    // DO NOTHING
-  }
-
-public:
-  bool setInput(uint32_t index, const ANeuralNetworksOperandType *type, const void *buffer,
-                size_t length) noexcept;
-  bool setOutput(uint32_t index, const ANeuralNetworksOperandType *type, void *buffer,
-                 size_t length) noexcept;
-  bool startExecute(void) noexcept;
-  bool execute(void) noexcept;
-
-  const neurun::ir::OperandIndex getInputOperandIndex(int32_t index) noexcept;
-  const neurun::ir::OperandIndex getOutputOperandIndex(int32_t index) noexcept;
-  bool compareDataType(const ANeuralNetworksOperandType *type,
-                       const neurun::ir::OperandIndex index) noexcept;
-  bool compareShape(const ANeuralNetworksOperandType *type,
-                    const neurun::ir::OperandIndex index) noexcept;
-  bool haveUnspecifiedDims(const neurun::ir::OperandIndex index) noexcept;
-  size_t getOperandSize(const neurun::ir::OperandIndex index) noexcept;
-  const std::shared_ptr<neurun::exec::Execution> instance(void) noexcept;
-
-  /**
-   * @brief       Get output operand's rank
-   * @param[in]   index Output index
-   * @param[out]  rank  Output operand's rank
-   * @return      @c true if success to get rank, otherwise @c false
-   */
-  bool getOutputOperandRank(uint32_t index, uint32_t *rank) noexcept;
-  /**
-   * @brief       Get dimensions of the output operand
-   * @param[in]   index Output index
-   * @param[out]  dimensions  Output operand's dimensions
-   * @return      @c true if success to get rank, otherwise @c false
-   * @note        This must be called after execution is finished to get resolved output shape
-   *              unspecified in model
-   */
-  bool getOutputOperandDimensions(uint32_t index, uint32_t *dimensions);
-
-private:
-  std::shared_ptr<neurun::exec::Execution> _execution;
-};
-
-#endif
diff --git a/runtime/neurun/frontend/nnapi/wrapper/ANeuralNetworksMemory.cc b/runtime/neurun/frontend/nnapi/wrapper/ANeuralNetworksMemory.cc
deleted file mode 100644
index 9cc100585..000000000
--- a/runtime/neurun/frontend/nnapi/wrapper/ANeuralNetworksMemory.cc
+++ /dev/null
@@ -1,46 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <NeuralNetworks.h>
-#include <sys/mman.h>
-
-#include "ANeuralNetworksMemory.h"
-
-//
-// ANeuralNetworksMemory
-//
-ANeuralNetworksMemory::ANeuralNetworksMemory(size_t size, int protect, int fd, size_t offset)
-{
-  _base = reinterpret_cast<uint8_t *>(mmap(nullptr, size, protect, MAP_PRIVATE, fd, offset));
-  _size = size;
-}
-
-ANeuralNetworksMemory::~ANeuralNetworksMemory() { munmap(reinterpret_cast<void *>(_base), _size); }
-
-bool ANeuralNetworksMemory::vaildAccess(size_t offset, size_t length) const
-{
-  if ((offset >= _size) || (length > _size))
-  {
-    return false;
-  }
-
-  if ((offset + length) >= _size)
-  {
-    return false;
-  }
-
-  return true;
-}
diff --git a/runtime/neurun/frontend/nnapi/wrapper/ANeuralNetworksMemory.h b/runtime/neurun/frontend/nnapi/wrapper/ANeuralNetworksMemory.h
deleted file mode 100644
index 48a1bc5fc..000000000
--- a/runtime/neurun/frontend/nnapi/wrapper/ANeuralNetworksMemory.h
+++ /dev/null
@@ -1,39 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __MEMORY_H__
-#define __MEMORY_H__
-
-#include <cstdint>
-
-struct ANeuralNetworksMemory
-{
-public:
-  ANeuralNetworksMemory(size_t size, int protect, int fd, size_t offset);
-  ~ANeuralNetworksMemory();
-
-public:
-  size_t size(void) const { return _size; }
-  uint8_t *base(void) { return _base; }
-  uint8_t *base(void) const { return _base; }
-  bool vaildAccess(size_t offset, size_t length) const;
-
-private:
-  size_t _size;
-  uint8_t *_base;
-};
-
-#endif // __MEMORY_H__
diff --git a/runtime/neurun/frontend/nnapi/wrapper/ANeuralNetworksModel.cc b/runtime/neurun/frontend/nnapi/wrapper/ANeuralNetworksModel.cc
deleted file mode 100644
index 5542a2e83..000000000
--- a/runtime/neurun/frontend/nnapi/wrapper/ANeuralNetworksModel.cc
+++ /dev/null
@@ -1,268 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "ANeuralNetworksModel.h"
-#include "OperationFactory.h"
-#include "NNAPIConvert.h"
-
-#include "ir/Operations.Include.h"
-#include "util/logging.h"
-
-#include "cpp14/memory.h"
-
-//
-// ANeuralNetworksModel
-//
-ANeuralNetworksModel::ANeuralNetworksModel() noexcept : _optional_operands{}, _operand_usages{}
-{
-  _graph = std::make_shared<neurun::ir::Graph>();
-}
-
-bool ANeuralNetworksModel::addOperand(const ANeuralNetworksOperandType *type) noexcept
-{
-  try
-  {
-    const auto shape = NNAPIConvert::getShape(type);
-    const auto typeInfo = NNAPIConvert::getTypeInfo(type);
-    _graph->addOperand(shape, typeInfo);
-    _operand_usages.emplace_back(OperandUsage::NOT_DEFINED);
-  }
-  catch (const std::exception &e)
-  {
-    VERBOSE(EXCEPTION) << e.what() << std::endl;
-
-    return false;
-  }
-
-  return true;
-}
-
-bool ANeuralNetworksModel::setOperandValue(uint32_t index, const void *buffer, size_t length,
-                                           bool optional, bool copy) noexcept
-{
-  const neurun::ir::OperandIndex ind{index};
-
-  try
-  {
-    _operand_usages[index] = OperandUsage::CONSTANT;
-
-    // Remain operands.at(ind).data()->base() as nullptr for optional operand
-    // This will be filled when model finished
-    if (optional)
-    {
-      setOptionalOperand(ind);
-    }
-
-    using neurun::ir::CachedData;
-    using neurun::ir::ExternalData;
-    if (copy)
-    {
-      _graph->operands().at(ind).data(
-          nnfw::cpp14::make_unique<CachedData>(reinterpret_cast<const uint8_t *>(buffer), length));
-    }
-    else
-    {
-      _graph->operands().at(ind).data(nnfw::cpp14::make_unique<ExternalData>(
-          reinterpret_cast<const uint8_t *>(buffer), length));
-    }
-  }
-  catch (const std::exception &e)
-  {
-    VERBOSE(EXCEPTION) << e.what() << std::endl;
-
-    return false;
-  }
-
-  return true;
-}
-
-bool ANeuralNetworksModel::addOperation(ANeuralNetworksOperationType type, uint32_t inputCount,
-                                        const uint32_t *inputs, uint32_t outputCount,
-                                        const uint32_t *outputs) noexcept
-{
-  try
-  {
-    for (uint32_t i = 0; i < outputCount; i++)
-    {
-      _operand_usages[outputs[i]] = OperandUsage::OPERATION_OUTPUT;
-    }
-
-    auto &factory = OperationFactory::get();
-    OperationFactory::Param param{inputCount, inputs, outputCount, outputs};
-
-    auto node = factory.create(type, param, _graph->operands());
-    _graph->addOperation(std::unique_ptr<neurun::ir::Operation>{node});
-
-    // TODO Move these codes to delegate.cpp
-    if (type == ANEURALNETWORKS_FULLY_CONNECTED)
-    {
-      const auto &input_operand =
-          _graph->operands().at(node->getInputs().at(neurun::ir::operation::FullyConnected::INPUT));
-      auto &weights_operand = _graph->operands().at(
-          node->getInputs().at(neurun::ir::operation::FullyConnected::WEIGHT));
-      if (input_operand.typeInfo().type() == neurun::ir::DataType::FLOAT32 &&
-          weights_operand.typeInfo().type() == neurun::ir::DataType::QUANT8_ASYMM)
-      {
-        weights_operand.type(neurun::ir::DataType::QUANT8_SYMM);
-      }
-    }
-  }
-  catch (const std::exception &e)
-  {
-    VERBOSE(EXCEPTION) << e.what() << std::endl;
-
-    return false;
-  }
-
-  return true;
-}
-
-bool ANeuralNetworksModel::addOperationEx(ANeuralNetworksOperationTypeEx type, uint32_t inputCount,
-                                          const uint32_t *inputs, uint32_t outputCount,
-                                          const uint32_t *outputs) noexcept
-{
-  try
-  {
-    for (uint32_t i = 0; i < outputCount; i++)
-    {
-      _operand_usages[outputs[i]] = OperandUsage::OPERATION_OUTPUT;
-    }
-
-    auto &factory = OperationFactory::get();
-    OperationFactory::Param param{inputCount, inputs, outputCount, outputs};
-
-    auto node = factory.create(type, param, _graph->operands());
-    _graph->addOperation(std::unique_ptr<neurun::ir::Operation>{node});
-  }
-  catch (const std::exception &e)
-  {
-    return false;
-  }
-  return true;
-}
-
-bool ANeuralNetworksModel::addModelInput(uint32_t index) noexcept
-{
-  try
-  {
-    _operand_usages[index] = OperandUsage::MODEL_INPUT;
-
-    const neurun::ir::OperandIndex ind{index};
-    _graph->addInput(ind);
-  }
-  catch (const std::exception &e)
-  {
-    VERBOSE(EXCEPTION) << e.what() << std::endl;
-
-    return false;
-  }
-
-  return true;
-}
-bool ANeuralNetworksModel::addModelOutput(uint32_t index) noexcept
-{
-  try
-  {
-    const neurun::ir::OperandIndex ind{index};
-
-    // Duplicated output is not allowed
-    if (_graph->getOutputs().contains(ind))
-    {
-      return false;
-    }
-
-    _graph->addOutput(ind);
-  }
-  catch (const std::exception &e)
-  {
-    VERBOSE(EXCEPTION) << e.what() << std::endl;
-
-    return false;
-  }
-
-  return true;
-}
-
-bool ANeuralNetworksModel::finish() noexcept
-{
-  try
-  {
-    fillOptionalOperand();
-
-    _graph->finishBuilding();
-
-    _operand_usages.clear();
-  }
-  catch (const std::exception &e)
-  {
-    VERBOSE(EXCEPTION) << e.what() << '\n';
-
-    return false;
-  }
-
-  return true;
-}
-
-bool ANeuralNetworksModel::isFinished() noexcept { return !_graph->isBuildingPhase(); }
-
-bool ANeuralNetworksModel::isExistOperand(uint32_t index) noexcept
-{
-  return _graph->operands().exist(neurun::ir::OperandIndex{index});
-}
-
-size_t ANeuralNetworksModel::operandSize(uint32_t index) noexcept
-{
-  try
-  {
-    return _graph->operands().at(neurun::ir::OperandIndex{index}).operandSize();
-  }
-  catch (const std::exception &e)
-  {
-    VERBOSE(EXCEPTION) << e.what() << '\n';
-
-    return 0;
-  }
-}
-
-bool ANeuralNetworksModel::isUsageSet(uint32_t index) noexcept
-{
-  return (_operand_usages[index] != OperandUsage::NOT_DEFINED);
-}
-
-bool ANeuralNetworksModel::isOperationOutput(uint32_t index) noexcept
-{
-  return (_operand_usages[index] == OperandUsage::OPERATION_OUTPUT);
-}
-
-void ANeuralNetworksModel::setOptionalOperand(const neurun::ir::OperandIndex idx)
-{
-  _optional_operands.insert(idx);
-}
-
-void ANeuralNetworksModel::fillOptionalOperand(void)
-{
-  _graph->operations().iterate(
-      [&](const neurun::ir::OperationIndex &, neurun::ir::Operation &node) {
-        for (auto input : node.getInputs())
-        {
-          // TODO fill default value for optional operands
-          if (_optional_operands.find(input) != _optional_operands.end())
-          {
-            throw std::runtime_error{"Optional operand is not supported yet"};
-          }
-        }
-      });
-}
diff --git a/runtime/neurun/frontend/nnapi/wrapper/ANeuralNetworksModel.h b/runtime/neurun/frontend/nnapi/wrapper/ANeuralNetworksModel.h
deleted file mode 100644
index d364ee39e..000000000
--- a/runtime/neurun/frontend/nnapi/wrapper/ANeuralNetworksModel.h
+++ /dev/null
@@ -1,71 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __MODEL_H__
-#define __MODEL_H__
-
-#include <unordered_set>
-#include <NeuralNetworks.h>
-#include <NeuralNetworksEx.h>
-
-#include "ir/Graph.h"
-
-struct ANeuralNetworksModel
-{
-public:
-  enum class OperandUsage
-  {
-    NOT_DEFINED = 0,
-    MODEL_INPUT,
-    CONSTANT,
-    OPERATION_OUTPUT,
-  };
-
-public:
-  ANeuralNetworksModel() noexcept;
-
-public:
-  bool addOperand(const ANeuralNetworksOperandType *type) noexcept;
-  bool setOperandValue(uint32_t index, const void *buffer, size_t length, bool optional = false,
-                       bool copy = false) noexcept;
-  bool addOperation(ANeuralNetworksOperationType type, uint32_t inputCount, const uint32_t *inputs,
-                    uint32_t outputCount, const uint32_t *outputs) noexcept;
-  bool addOperationEx(ANeuralNetworksOperationTypeEx type, uint32_t inputCount,
-                      const uint32_t *inputs, uint32_t outputCount,
-                      const uint32_t *outputs) noexcept;
-  bool addModelInput(uint32_t index) noexcept;
-  bool addModelOutput(uint32_t index) noexcept;
-  bool finish() noexcept;
-
-  neurun::ir::Graph &deref(void) { return *_graph; }
-  bool isFinished() noexcept;
-  bool isExistOperand(uint32_t index) noexcept;
-  size_t operandSize(uint32_t index) noexcept;
-  bool isUsageSet(uint32_t index) noexcept;
-  bool isOperationOutput(uint32_t index) noexcept;
-  void release(std::shared_ptr<neurun::ir::Graph> &graph) { graph = _graph; }
-
-private:
-  void setOptionalOperand(const neurun::ir::OperandIndex idx);
-  void fillOptionalOperand(void);
-
-private:
-  std::shared_ptr<neurun::ir::Graph> _graph;
-  std::unordered_set<neurun::ir::OperandIndex> _optional_operands;
-  std::vector<OperandUsage> _operand_usages;
-};
-
-#endif // __MODEL_H__
diff --git a/runtime/neurun/frontend/nnapi/wrapper/NNAPIConvert.cc b/runtime/neurun/frontend/nnapi/wrapper/NNAPIConvert.cc
deleted file mode 100644
index 79589be75..000000000
--- a/runtime/neurun/frontend/nnapi/wrapper/NNAPIConvert.cc
+++ /dev/null
@@ -1,100 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "NNAPIConvert.h"
-
-#include <numeric>
-
-using namespace neurun::ir;
-
-DataType NNAPIConvert::getDataType(OperandCode type)
-{
-  switch (type)
-  {
-    case ANEURALNETWORKS_FLOAT32:
-    case ANEURALNETWORKS_TENSOR_FLOAT32:
-      return DataType::FLOAT32;
-    case ANEURALNETWORKS_INT32:
-    case ANEURALNETWORKS_TENSOR_INT32:
-      return DataType::INT32;
-    case ANEURALNETWORKS_UINT32:
-      return DataType::UINT32;
-    case ANEURALNETWORKS_TENSOR_QUANT8_ASYMM:
-      return DataType::QUANT8_ASYMM;
-    case ANEURALNETWORKS_TENSOR_QUANT8_SYMM:
-      return DataType::QUANT8_SYMM;
-    case ANEURALNETWORKS_BOOL:
-    case ANEURALNETWORKS_TENSOR_BOOL8:
-      return DataType::BOOL8;
-    default:
-      throw std::runtime_error("Unsupported type");
-  }
-}
-
-TypeInfo NNAPIConvert::getTypeInfo(const ANeuralNetworksOperandType *type)
-{
-  return TypeInfo(getDataType((OperandCode)(type->type)), type->scale, type->zeroPoint);
-}
-
-Shape NNAPIConvert::getShape(const ANeuralNetworksOperandType *type)
-{
-  Shape shape(type->dimensionCount);
-
-  for (uint32_t axis = 0; axis < type->dimensionCount; ++axis)
-  {
-    shape.dim(axis) = type->dimensions[axis];
-  }
-
-  return shape;
-}
-
-size_t NNAPIConvert::calculateSizeFromType(const ANeuralNetworksOperandType *type)
-{
-  auto shape = getShape(type);
-  auto data_type = getDataType((OperandCode)(type->type));
-
-  return shape.num_elements() * sizeOfDataType(data_type);
-}
-
-Activation NNAPIConvert::getFusedActivation(FuseCode act)
-{
-  switch (act)
-  {
-    case ANEURALNETWORKS_FUSED_NONE:
-      return Activation::NONE;
-    case ANEURALNETWORKS_FUSED_RELU:
-      return Activation::RELU;
-    case ANEURALNETWORKS_FUSED_RELU1:
-      return Activation::RELU1;
-    case ANEURALNETWORKS_FUSED_RELU6:
-      return Activation::RELU6;
-    default:
-      throw std::runtime_error("Unsupported activation type");
-  }
-}
-
-PaddingType NNAPIConvert::getPaddingType(PaddingCode type)
-{
-  switch (type)
-  {
-    case ANEURALNETWORKS_PADDING_SAME:
-      return PaddingType::SAME;
-    case ANEURALNETWORKS_PADDING_VALID:
-      return PaddingType::VALID;
-    default:
-      throw std::runtime_error("Unsupported type");
-  }
-}
diff --git a/runtime/neurun/frontend/nnapi/wrapper/NNAPIConvert.h b/runtime/neurun/frontend/nnapi/wrapper/NNAPIConvert.h
deleted file mode 100644
index 91f84b983..000000000
--- a/runtime/neurun/frontend/nnapi/wrapper/NNAPIConvert.h
+++ /dev/null
@@ -1,78 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/**
- * @file  NNAPIConvert.h
- * @brief This file contains convereter(s)\n
- *        from NNAPI frontend's struct to neurun's internal struct
- */
-#ifndef __NEURUN_NNAPI_CONVERT_H__
-#define __NEURUN_NNAPI_CONVERT_H__
-
-#include <NeuralNetworks.h>
-
-#include <ir/TypeInfo.h>
-#include <ir/Shape.h>
-#include <ir/InternalType.h>
-
-class NNAPIConvert
-{
-
-public:
-  /**
-   * @brief     Convert data type from NNAPI to internal data type
-   * @param[in] type  NNAPI's data type
-   * @return    neurun's internal data type
-   */
-  static neurun::ir::DataType getDataType(OperandCode type);
-
-  /**
-   * @brief     Convert operand type info from NNAPI to interanl operand type info
-   * @param[in] type  NNAPI's operand type
-   * @return    neurun's internal operand type info
-   */
-  static neurun::ir::TypeInfo getTypeInfo(const ANeuralNetworksOperandType *type);
-
-  /**
-   * @brief     Convert operand shape info from NNAPI to internal operand shape
-   * @param[in] type  NNAPI's operand type
-   * @return    neurun's internal operand shape
-   */
-  static neurun::ir::Shape getShape(const ANeuralNetworksOperandType *type);
-
-  /**
-   * @brief     Calcaulate operand size from NNAPI type
-   * @param[in] type  NNAPI's operand type
-   * @return    Operand size
-   */
-  static size_t calculateSizeFromType(const ANeuralNetworksOperandType *type);
-
-  /**
-   * @brief     Convert NNAPI FuseCode to internal activation type
-   * @param[in] act NNAPI's FuseCode type
-   * @return    neurun's internal activation type
-   */
-  static neurun::ir::Activation getFusedActivation(FuseCode act);
-
-  /**
-   * @brief     Convert NNAPI PaddingCode to internal padding type
-   * @param[in] type NNAPI's PaddingCode type
-   * @return    neurun's internal padding type
-   */
-  static neurun::ir::PaddingType getPaddingType(PaddingCode type);
-};
-
-#endif // __NEURUN_NNAPI_CONVERT_H__
diff --git a/runtime/neurun/frontend/nnapi/wrapper/OperationFactory.cc b/runtime/neurun/frontend/nnapi/wrapper/OperationFactory.cc
deleted file mode 100644
index 84f876e86..000000000
--- a/runtime/neurun/frontend/nnapi/wrapper/OperationFactory.cc
+++ /dev/null
@@ -1,1680 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "OperationFactory.h"
-#include "NNAPIConvert.h"
-
-#include <ir/Operations.Include.h>
-#include <string.h>
-
-namespace
-{
-using namespace neurun::ir;
-
-void replaceDataType(Operands &operands, const OperandIndex &index, const DataType type)
-{
-  assert(operands.exist(index));
-  operands.at(index).type(type);
-}
-
-ExplicitPadding makeExplicitPadding(Operands &operands, const OperandIndex &left_index,
-                                    const OperandIndex &right_index, const OperandIndex &top_index,
-                                    const OperandIndex &bottom_index)
-{
-  auto left = operands.at(left_index).asScalar<int32_t>();
-  auto right = operands.at(right_index).asScalar<int32_t>();
-  auto top = operands.at(top_index).asScalar<int32_t>();
-  auto bottom = operands.at(bottom_index).asScalar<int32_t>();
-
-  if (left < 0 || right < 0 || top < 0 || bottom < 0)
-  {
-    throw std::runtime_error{"Cannot handle negative explicit padding value"};
-  }
-
-  ExplicitPadding param;
-  param.left = static_cast<uint32_t>(left);
-  param.right = static_cast<uint32_t>(right);
-  param.top = static_cast<uint32_t>(top);
-  param.bottom = static_cast<uint32_t>(bottom);
-
-  return param;
-}
-
-Stride makeStride(Operands &operands, const OperandIndex &horizontal_index,
-                  const OperandIndex &vertical_index)
-{
-  auto horizontal = operands.at(horizontal_index).asScalar<int32_t>();
-  auto vertical = operands.at(vertical_index).asScalar<int32_t>();
-
-  if (vertical < 0 || horizontal < 0)
-  {
-    throw std::runtime_error{"Cannot handle negative stride value"};
-  }
-
-  Stride stride;
-  stride.horizontal = static_cast<uint32_t>(horizontal);
-  stride.vertical = static_cast<uint32_t>(vertical);
-
-  return stride;
-}
-
-uint32_t getUint32Scalar(Operands &operands, const OperandIndex index)
-{
-  auto int32_value = operands.at(index).asScalar<int32_t>();
-  if (int32_value < 0)
-  {
-    throw std::runtime_error{"Cannot handle negative value"};
-  }
-
-  return static_cast<uint32_t>(int32_value);
-}
-
-} // namespace
-
-OperationFactory &OperationFactory::get()
-{
-  static OperationFactory factory;
-  return factory;
-}
-
-OperationFactory::OperationFactory()
-{
-  _map[ANEURALNETWORKS_BATCH_TO_SPACE_ND] = [](const OperationFactory::Param &init_param,
-                                               Operands &) {
-    assert(init_param.input_count == 2 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> Input Tensor Index
-    //  1 -> Block size Index
-    OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]};
-
-    return new operation::BatchToSpaceND{inputs, outputs};
-  };
-
-  _map[ANEURALNETWORKS_DEPTHWISE_CONV_2D] = [](const OperationFactory::Param &init_param,
-                                               Operands &operands) {
-    assert((init_param.input_count == 8 || init_param.input_count == 11) &&
-           init_param.output_count == 1);
-
-    // In common
-    // 0 -> IFM Tensor Index
-    // 1 -> Kernel Tensor Index
-    // 2 -> Bias Tensor Index
-    OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1], init_param.inputs[2]};
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    operation::DepthwiseConv2D::Param param;
-    if (init_param.input_count == 8)
-    {
-      // Imlicit Padding case
-      // Each input should be interpreted as follows:
-      //
-      // 3 -> Padding Code (ANEURALNETWORKS_PADDING_SAME or ANEURALNETWORKS_PADDING_VALID) Index
-      // 4 -> Stride (width) Index
-      // 5 -> Stride (height) INdex
-      // 6 -> Depthwise multiplier
-      // 7 -> Activation Index
-
-      const auto padding_index = OperandIndex{init_param.inputs[3]};
-      const auto hstride_index = OperandIndex{init_param.inputs[4]};
-      const auto vstride_index = OperandIndex{init_param.inputs[5]};
-      const auto multiplier_index = OperandIndex{init_param.inputs[6]};
-      const auto activation_index = OperandIndex{init_param.inputs[7]};
-
-      param.padding.type =
-          NNAPIConvert::getPaddingType(operands.at(padding_index).asScalar<PaddingCode>());
-      param.stride = makeStride(operands, hstride_index, vstride_index);
-      param.multiplier = getUint32Scalar(operands, multiplier_index);
-      param.activation =
-          NNAPIConvert::getFusedActivation(operands.at(activation_index).asScalar<FuseCode>());
-    }
-    else
-    {
-      // Explicit Padding case
-      // Each input should be interpreted as follows:
-      //
-      // 3 -> Padding On the Left
-      // 4 -> Padding On the Right
-      // 5 -> Padding On the Top
-      // 6 -> Padding On the Bottom
-      // 7 -> Stride (width) Index
-      // 8 -> Stride (height) Index
-      // 9 -> Depthwise multiplier
-      // 10-> Activation Index
-
-      const auto padding_left_index = OperandIndex{init_param.inputs[3]};
-      const auto padding_right_index = OperandIndex{init_param.inputs[4]};
-      const auto padding_top_index = OperandIndex{init_param.inputs[5]};
-      const auto padding_bottom_index = OperandIndex{init_param.inputs[6]};
-      const auto hstride_index = OperandIndex{init_param.inputs[7]};
-      const auto vstride_index = OperandIndex{init_param.inputs[8]};
-      const auto multiplier_index = OperandIndex{init_param.inputs[9]};
-      const auto activation_index = OperandIndex{init_param.inputs[10]};
-
-      param.padding.type = PaddingType::EXPLICIT;
-      param.padding.param = makeExplicitPadding(operands, padding_left_index, padding_right_index,
-                                                padding_top_index, padding_bottom_index);
-      param.stride = makeStride(operands, hstride_index, vstride_index);
-      param.multiplier = getUint32Scalar(operands, multiplier_index);
-      param.activation =
-          NNAPIConvert::getFusedActivation(operands.at(activation_index).asScalar<FuseCode>());
-    }
-
-    return new operation::DepthwiseConv2D{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_MAX_POOL_2D] = [](const OperationFactory::Param &init_param,
-                                         Operands &operands) {
-    assert(init_param.input_count == 7 || init_param.input_count == 10);
-    assert(init_param.output_count == 1);
-
-    // In common
-    //  0 -> IFM Tensor Index
-    OperandIndexSequence inputs{init_param.inputs[0]};
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    operation::MaxPool2D::Param param;
-    if (init_param.input_count == 7) // support implicit padding
-    {
-      // Each input should be interpreted as follows:
-      //
-      //  1 -> Padding Code (ANEURALNETWORKS_PADDING_SAME or ANEURALNETWORKS_PADDING_VALID) Index
-      //  2 -> Horizontal (over width) Stride Index
-      //  3 -> Vertial (over height) Stride Index
-      //  4 -> Filter Width Index
-      //  5 -> Filter Height Index
-      //  6 -> FuseCode (activation) Index
-
-      const auto padding_index = OperandIndex{init_param.inputs[1]};
-      const auto hstride_index = OperandIndex{init_param.inputs[2]};
-      const auto vstride_index = OperandIndex{init_param.inputs[3]};
-      const auto kw_index = OperandIndex{init_param.inputs[4]};
-      const auto kh_index = OperandIndex{init_param.inputs[5]};
-      const auto activation_index = OperandIndex{init_param.inputs[6]};
-
-      param.padding.type =
-          NNAPIConvert::getPaddingType(operands.at(padding_index).asScalar<PaddingCode>());
-      param.stride = makeStride(operands, hstride_index, vstride_index);
-      param.kw = getUint32Scalar(operands, kw_index);
-      param.kh = operands.at(kh_index).asScalar<uint32_t>();
-      param.activation =
-          NNAPIConvert::getFusedActivation(operands.at(activation_index).asScalar<FuseCode>());
-    }
-    else if (init_param.input_count == 10) // support explicit padding
-    {
-      // Each input should be interpreted as follows:
-      //
-      //  1 -> Padding_left index
-      //  2 -> Padding_right index
-      //  3 -> Padding_top index
-      //  4 -> Padding_bottom index
-      //  5 -> Horizontal (over width) Stride Index
-      //  6 -> Vertial (over height) Stride Index
-      //  7 -> Filter Width Index
-      //  8 -> Filter Height Index
-      //  9 -> FuseCode (activation) Index
-
-      const auto padding_left_index = OperandIndex{init_param.inputs[1]};
-      const auto padding_right_index = OperandIndex{init_param.inputs[2]};
-      const auto padding_top_index = OperandIndex{init_param.inputs[3]};
-      const auto padding_bottom_index = OperandIndex{init_param.inputs[4]};
-      const auto hstride_index = OperandIndex{init_param.inputs[5]};
-      const auto vstride_index = OperandIndex{init_param.inputs[6]};
-      const auto kw_index = OperandIndex{init_param.inputs[7]};
-      const auto kh_index = OperandIndex{init_param.inputs[8]};
-      const auto activation_index = OperandIndex{init_param.inputs[9]};
-
-      param.padding.type = PaddingType::EXPLICIT;
-      param.padding.param = makeExplicitPadding(operands, padding_left_index, padding_right_index,
-                                                padding_top_index, padding_bottom_index);
-      param.stride = makeStride(operands, hstride_index, vstride_index);
-      param.kw = getUint32Scalar(operands, kw_index);
-      param.kh = getUint32Scalar(operands, kh_index);
-      param.activation =
-          NNAPIConvert::getFusedActivation(operands.at(activation_index).asScalar<FuseCode>());
-    }
-    return new operation::MaxPool2D{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_AVERAGE_POOL_2D] = [](const OperationFactory::Param &init_param,
-                                             Operands &operands) {
-    // TODO We may reuse code here for MAX_POOL_2D. Seems like these two are identical
-    assert(init_param.input_count == 7 || init_param.input_count == 10);
-    assert(init_param.output_count == 1);
-
-    // In common
-    //  0 -> IFM Tensor Index
-    OperandIndexSequence inputs{init_param.inputs[0]};
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    operation::AvgPool2D::Param param;
-    if (init_param.input_count == 7) // support implicit padding
-    {
-      // Each input should be interpreted as follows:
-      //
-      //  1 -> Padding Code (ANEURALNETWORKS_PADDING_SAME or ANEURALNETWORKS_PADDING_VALID) Index
-      //  2 -> Horizontal (over width) Stride Index
-      //  3 -> Vertial (over height) Stride Index
-      //  4 -> Filter Width Index
-      //  5 -> Filter Height Index
-      //  6 -> FuseCode (activation) Index
-
-      const auto padding_index = OperandIndex{init_param.inputs[1]};
-      const auto hstride_index = OperandIndex{init_param.inputs[2]};
-      const auto vstride_index = OperandIndex{init_param.inputs[3]};
-      const auto kw_index = OperandIndex{init_param.inputs[4]};
-      const auto kh_index = OperandIndex{init_param.inputs[5]};
-      const auto activation_index = OperandIndex{init_param.inputs[6]};
-
-      param.padding.type =
-          NNAPIConvert::getPaddingType(operands.at(padding_index).asScalar<PaddingCode>());
-      param.stride = makeStride(operands, hstride_index, vstride_index);
-      param.kw = getUint32Scalar(operands, kw_index);
-      param.kh = getUint32Scalar(operands, kh_index);
-      param.activation =
-          NNAPIConvert::getFusedActivation(operands.at(activation_index).asScalar<FuseCode>());
-    }
-    else if (init_param.input_count == 10) // support explicit padding
-    {
-      // Each input should be interpreted as follows:
-      //
-      //  1 -> Padding_left index
-      //  2 -> Padding_right index
-      //  3 -> Padding_top index
-      //  4 -> Padding_bottom index
-      //  5 -> Horizontal (over width) Stride Index
-      //  6 -> Vertial (over height) Stride Index
-      //  7 -> Filter Width Index
-      //  8 -> Filter Height Index
-      //  9 -> FuseCode (activation) Index
-
-      const auto padding_left_index = OperandIndex{init_param.inputs[1]};
-      const auto padding_right_index = OperandIndex{init_param.inputs[2]};
-      const auto padding_top_index = OperandIndex{init_param.inputs[3]};
-      const auto padding_bottom_index = OperandIndex{init_param.inputs[4]};
-      const auto hstride_index = OperandIndex{init_param.inputs[5]};
-      const auto vstride_index = OperandIndex{init_param.inputs[6]};
-      const auto kw_index = OperandIndex{init_param.inputs[7]};
-      const auto kh_index = OperandIndex{init_param.inputs[8]};
-      const auto activation_index = OperandIndex{init_param.inputs[9]};
-
-      param.padding.type = PaddingType::EXPLICIT;
-      param.padding.param = makeExplicitPadding(operands, padding_left_index, padding_right_index,
-                                                padding_top_index, padding_bottom_index);
-      param.stride = makeStride(operands, hstride_index, vstride_index);
-      param.kw = getUint32Scalar(operands, kw_index);
-      param.kh = getUint32Scalar(operands, kh_index);
-      param.activation =
-          NNAPIConvert::getFusedActivation(operands.at(activation_index).asScalar<FuseCode>());
-    }
-
-    return new operation::AvgPool2D{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_CONCATENATION] = [](const OperationFactory::Param &init_param,
-                                           Operands &operands) {
-    assert(init_param.input_count >= 2); // At least one one input tensor and axis
-    assert(init_param.output_count == 1);
-
-    // When there are N + 1 inputs, each input should be interpreted as follows:
-    //
-    //  [0, N) -> Input tensors
-    //  N -> Axis
-    //
-
-    OperandIndexSequence inputs;
-    for (uint32_t n = 0; n < init_param.input_count - 1; ++n)
-    {
-      inputs.append(OperandIndex{init_param.inputs[n]});
-    }
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    operation::Concat::Param param;
-    const OperandIndex axis_index{init_param.inputs[init_param.input_count - 1]};
-    param.axis = operands.at(axis_index).asScalar<int32_t>();
-    param.rank = operands.at(outputs.at(0)).shape().rank();
-
-    return new operation::Concat{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_RESHAPE] = [](const OperationFactory::Param &init_param, Operands &) {
-    assert(init_param.input_count == 2 && init_param.output_count == 1);
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> A tensor, specifying the tensor to be reshaped.
-    //  1 -> A 1-D tensor of type ANEURALNETWORKS_TENSOR_INT32, defining the shape of the output
-    //  tensor
-
-    // TODO Second input should be shape tensor (init_param.inputs[1])
-    //      Currently unused since assume that it is same with output tensor size
-    OperandIndexSequence inputs{init_param.inputs[0] /* , init_param.inputs[1] */};
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    return new operation::Reshape{inputs, outputs};
-  };
-
-  _map[ANEURALNETWORKS_FULLY_CONNECTED] = [](const OperationFactory::Param &init_param,
-                                             Operands &operands) {
-    assert(init_param.input_count == 4 && init_param.output_count == 1);
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> A tensor, specifying the input.
-    //  1 -> A 2-D tensor, specifying the weights
-    //  2 -> A 1-D tensor, specifying the bias
-    //  3 -> An INT32 value, and has to be one of the FuseCode values
-
-    OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1], init_param.inputs[2]};
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    operation::FullyConnected::Param param;
-    const auto activation_index = OperandIndex{init_param.inputs[3]};
-    param.activation =
-        NNAPIConvert::getFusedActivation(operands.at(activation_index).asScalar<FuseCode>());
-
-    return new operation::FullyConnected{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_SOFTMAX] = [](const OperationFactory::Param &init_param,
-                                     Operands &operands) {
-    assert(init_param.input_count == 2 && init_param.output_count == 1);
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> A 2-D or 4-D tensor, specifying the tensor to be reshaped.
-    //  1 ->  FLOAT32 value, specifying the positive scaling factor for the exponent, beta.
-
-    OperandIndexSequence inputs{init_param.inputs[0]};
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    const auto beta_index = OperandIndex{init_param.inputs[1]};
-
-    operation::Softmax::Param param;
-    param.beta = operands.at(beta_index).asScalar<float>();
-
-    return new operation::Softmax{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_CAST] = [](const OperationFactory::Param &init_param, Operands &operands) {
-    assert(init_param.input_count == 1 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //  0 -> input Tensor Index
-    OperandIndexSequence inputs{init_param.inputs[0]};
-
-    // NNAPI uses QUANT8_ASYMM to represent UINT8 type for ANEURALNETWORKS_CAST's input/output
-    if (operands.at(inputs.at(0)).typeInfo().type() == DataType::QUANT8_ASYMM)
-    {
-      replaceDataType(operands, inputs.at(0), DataType::UINT8);
-    }
-    if (operands.at(outputs.at(0)).typeInfo().type() == DataType::QUANT8_ASYMM)
-    {
-      replaceDataType(operands, outputs.at(0), DataType::UINT8);
-    }
-
-    return new operation::Cast{inputs, outputs};
-  };
-
-  // ANEURALNETWORKS_CAST_EX is deprecated
-  // TODO Remove ANEURALNETWORKS_CAST_EX
-  _map[ANEURALNETWORKS_CAST_EX] = _map[ANEURALNETWORKS_CAST];
-
-  _map[ANEURALNETWORKS_CONV_2D] = [](const OperationFactory::Param &init_param,
-                                     Operands &operands) {
-    using operation::Conv2D;
-
-    // inputCount is either 7 or 10 acccording to NN API specification.
-    //  - Padding is implicit when inputCount is 7
-    //  - Padding is explicit when inputCount is 10
-    assert(init_param.input_count == 7 || init_param.input_count == 10);
-    assert(init_param.output_count == 1);
-
-    //  0 -> IFM Tensor Index
-    //  1 -> Kernel Tensor Index
-    //  2 -> Bias Tensor Index
-
-    OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1], init_param.inputs[2]};
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    Conv2D::Param param;
-
-    if (init_param.input_count == 7) // support implicit padding
-    {
-      // Each input should be interpreted as follows:
-      //
-      //  3 -> Padding Code (ANEURALNETWORKS_PADDING_SAME or ANEURALNETWORKS_PADDING_VALID) Index
-      //  4 -> Stride (width) Index
-      //  5 -> Stride (height) INdex
-      //  6 -> Activation Index
-
-      const auto padding_index = OperandIndex{init_param.inputs[3]};
-      const auto hstride_index = OperandIndex{init_param.inputs[4]};
-      const auto vstride_index = OperandIndex{init_param.inputs[5]};
-      const auto activation_index = OperandIndex{init_param.inputs[6]};
-
-      param.padding.type =
-          NNAPIConvert::getPaddingType(operands.at(padding_index).asScalar<PaddingCode>());
-      param.stride = makeStride(operands, hstride_index, vstride_index);
-      param.activation =
-          NNAPIConvert::getFusedActivation(operands.at(activation_index).asScalar<FuseCode>());
-    }
-    else if (init_param.input_count == 10) // support explicit padding
-    {
-      // Each input should be interpreted as follows:
-      //
-      //  3 -> Padding_left index
-      //  4 -> Padding_right index
-      //  5 -> Padding_top index
-      //  6 -> Padding_bottom index
-      //  7 -> Stride (width) Index
-      //  8 -> Stride (height) INdex
-      //  9 -> Activation Index
-
-      const auto padding_left_index = OperandIndex{init_param.inputs[3]};
-      const auto padding_right_index = OperandIndex{init_param.inputs[4]};
-      const auto padding_top_index = OperandIndex{init_param.inputs[5]};
-      const auto padding_bottom_index = OperandIndex{init_param.inputs[6]};
-      const auto hstride_index = OperandIndex{init_param.inputs[7]};
-      const auto vstride_index = OperandIndex{init_param.inputs[8]};
-      const auto activation_index = OperandIndex{init_param.inputs[9]};
-
-      param.padding.type = PaddingType::EXPLICIT;
-      param.padding.param = makeExplicitPadding(operands, padding_left_index, padding_right_index,
-                                                padding_top_index, padding_bottom_index);
-      param.stride = makeStride(operands, hstride_index, vstride_index);
-      param.activation =
-          NNAPIConvert::getFusedActivation(operands.at(activation_index).asScalar<FuseCode>());
-    }
-
-    return new Conv2D{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_ADD] = [](const OperationFactory::Param &init_param, Operands &operands) {
-    assert(init_param.input_count == 3);
-    assert(init_param.output_count == 1);
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> Lefthand side operand
-    //  1 -> Righthand side operand
-
-    OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]};
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    operation::Add::Param param;
-
-    const auto activation_index = OperandIndex{init_param.inputs[2]};
-    param.activation =
-        NNAPIConvert::getFusedActivation(operands.at(activation_index).asScalar<FuseCode>());
-
-    return new operation::Add{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_REDUCE_SUM_EX] = [](const OperationFactory::Param &init_param,
-                                           Operands &operands) {
-    assert(init_param.input_count == 3);
-    assert(init_param.output_count == 1);
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> Input Tensor Index
-    //  1 -> Reduced Axes Tensor Index
-    //  2 -> keep_dims Index
-
-    OperandIndexSequence inputs{init_param.inputs[0]};
-    OperandIndexSequence outputs{init_param.outputs[0]};
-    std::vector<std::int32_t> axes =
-        operands.at(OperandIndex{init_param.inputs[1]}).asVector<std::int32_t>();
-
-    operation::ReduceSum::Param param;
-    param.axes.assign(axes.cbegin(), axes.cend());
-    param.keep_dims = operands.at(OperandIndex{init_param.inputs[2]}).asScalar<int32_t>() != 0;
-    param.rank = operands.at(inputs.at(0)).shape().rank();
-
-    return new operation::ReduceSum{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_SUB] = [](const OperationFactory::Param &init_param, Operands &operands) {
-    assert(init_param.input_count == 3);
-    assert(init_param.output_count == 1);
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> Lefthand side operand
-    //  1 -> Righthand side operand
-
-    OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]};
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    operation::Sub::Param param;
-
-    const auto activation_index = OperandIndex{init_param.inputs[2]};
-    param.activation =
-        NNAPIConvert::getFusedActivation(operands.at(activation_index).asScalar<FuseCode>());
-
-    return new operation::Sub{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_SLICE] = [](const OperationFactory::Param &init_param, Operands &operands) {
-    assert(init_param.input_count == 3 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> Input Tensor Index
-    //  1 -> Begins Tensor Index
-    //  2 -> Sizes Tensor Index
-    OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1], init_param.inputs[2]};
-
-    operation::Slice::Param param;
-    param.rank = operands.at(inputs.at(0)).shape().rank();
-
-    return new operation::Slice{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_STRIDED_SLICE] = [](const OperationFactory::Param &init_param,
-                                           Operands &operands) {
-    assert(init_param.input_count == 7 && init_param.output_count == 1);
-
-    OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1], init_param.inputs[2],
-                                init_param.inputs[3]};
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  1 -> A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the starts of
-    //       the dimensions of the input tensor to be sliced. The length must be
-    //       of rank(input0).
-    //  2 -> A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the ends of
-    //       the dimensions of the input tensor to be sliced. The length must be
-    //       of rank(input0).
-    //  3 -> A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the strides of
-    //       the dimensions of the input tensor to be sliced. The length must be
-    //       of rank(input0).
-    //  4 -> An {@link ANEURALNETWORKS_INT32} scalar, begin_mask. If the ith bit
-    //       of begin_mask is set, begin[i] is ignored and the fullest possible
-    //       range in that dimension is used instead.
-    //  5 -> An {@link ANEURALNETWORKS_INT32} scalar, end_mask. If the ith bit of
-    //       end_mask is set, end[i] is ignored and the fullest possible range in
-    //       that dimension is used instead.
-    //  6 -> An {@link ANEURALNETWORKS_INT32} scalar, shrink_axis_mask. An int32
-    //       mask. If the ith bit of shrink_axis_mask is set, it implies that the
-    //       ith specification shrinks the dimensionality by 1. A slice of size 1
-    //       starting from begin[i] in the dimension must be preserved.
-
-    operation::StridedSlice::Param param;
-
-    param.begin_mask = operands.at(OperandIndex{init_param.inputs[4]}).asScalar<std::int32_t>();
-    param.end_mask = operands.at(OperandIndex{init_param.inputs[5]}).asScalar<std::int32_t>();
-    param.shrink_axis_mask =
-        operands.at(OperandIndex{init_param.inputs[6]}).asScalar<std::int32_t>();
-    param.rank = operands.at(inputs.at(0)).shape().rank();
-
-    return new operation::StridedSlice{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_TRANSPOSE] = [](const OperationFactory::Param &init_param,
-                                       Operands &operands) {
-    // TODO make this work with init_param.input_count == 1 (when permutation vector is optional)
-
-    // Inputs
-    // 0: An n-D tensor, specifying the tensor to be transposed.
-    // 1: An optional 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32},
-    //    the permutation of the dimensions of the input tensor.
-    //    The returned tensor's dimension i corresponds to the input dimension
-    //    perm[i]. If perm is not given, it is set to (n-1...0), where n is the
-    //    rank of the input tensor. Hence by default, this operation performs a
-    //    regular matrix transpose on 2-D input Tensors.
-    assert(init_param.input_count == 2);
-    assert(init_param.output_count == 1);
-
-    OperandIndexSequence inputs{init_param.inputs[0]};
-    OperandIndexSequence outputs{init_param.outputs[0]};
-    std::vector<std::int32_t> perm =
-        operands.at(OperandIndex{init_param.inputs[1]}).asVector<std::int32_t>();
-
-    operation::Transpose::Param param;
-    param.perm.assign(perm.cbegin(), perm.cend());
-    param.rank = operands.at(inputs.at(0)).shape().rank();
-
-    return new operation::Transpose{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_MUL] = [](const OperationFactory::Param &init_param, Operands &operands) {
-    assert(init_param.input_count == 3 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> LHS Tensor Index
-    //  1 -> RHS Tensor Index
-    //  2 -> Activation Index
-
-    OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]};
-
-    operation::Mul::Param param;
-
-    const auto activation_index = OperandIndex{init_param.inputs[2]};
-    param.activation =
-        NNAPIConvert::getFusedActivation(operands.at(activation_index).asScalar<FuseCode>());
-
-    return new operation::Mul{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_SQUEEZE] = [](const OperationFactory::Param &init_param,
-                                     Operands &operands) {
-    assert(init_param.input_count == 1 || init_param.input_count == 2);
-    assert(init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    // 0 -> An n-D tensor, the tensor to be squeezed.
-    // 1 -> An optional 1-D tensor of ANEURALNETWORKS_TENSOR_INT32. The dimensions to squeeze.
-    //      If specified only squeezes the dimensions listed. Otherwise, squeezes all dimensions.
-    //      The dimension index starts at 0. An error must be reported if squeezing a dimension that
-    //      is not 1.
-
-    // Add mandatory input index
-    OperandIndexSequence inputs{init_param.inputs[0]};
-
-    // Add dims index if specified
-    operation::Squeeze::Param param{};
-    if (init_param.input_count == 2)
-    {
-      auto squeeze_dims_idx = OperandIndex{init_param.inputs[1]};
-      assert(operands.at(squeeze_dims_idx).shape().rank() == 1);
-      assert(operands.at(squeeze_dims_idx).shape().dim(0) >= 0);
-      assert(static_cast<uint32_t>(operands.at(squeeze_dims_idx).shape().dim(0)) <=
-             sizeof(param.dims));
-      param.ndim = operands.at(squeeze_dims_idx).shape().dim(0);
-      if (param.ndim > 0)
-        memcpy(param.dims, operands.at(squeeze_dims_idx).data().base(),
-               param.ndim * sizeof(param.dims[0]));
-    }
-
-    return new operation::Squeeze{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_TANH] = [](const OperationFactory::Param &init_param, Operands &) {
-    assert(init_param.input_count == 1 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> Input Tensor Index
-    OperandIndexSequence inputs{init_param.inputs[0]};
-
-    return new operation::Tanh{inputs, outputs};
-  };
-
-  _map[ANEURALNETWORKS_LOGISTIC] = [](const OperationFactory::Param &init_param, Operands &) {
-    assert(init_param.input_count == 1 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> Input Tensor Index
-    OperandIndexSequence inputs{init_param.inputs[0]};
-
-    return new operation::Logistic{inputs, outputs};
-  };
-
-  _map[ANEURALNETWORKS_DIV] = [](const OperationFactory::Param &init_param, Operands &operands) {
-    assert(init_param.input_count == 3 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> LHS Tensor Index
-    //  1 -> RHS Tensor Index
-    //  2 -> Activation Index
-    OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]};
-
-    operation::Div::Param param;
-
-    const auto activation_index = OperandIndex{init_param.inputs[2]};
-    param.activation =
-        NNAPIConvert::getFusedActivation(operands.at(activation_index).asScalar<FuseCode>());
-
-    return new operation::Div{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_EXP] = [](const OperationFactory::Param &init_param, Operands &) {
-    assert(init_param.input_count == 1 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> Input Tensor Index
-    OperandIndexSequence inputs{init_param.inputs[0]};
-
-    return new operation::Exp{inputs, outputs};
-  };
-
-  // ANEURALNETWORKS_EXP_EX is deprecated
-  // TODO Remove ANEURALNETWORKS_EXP_EX
-  _map[ANEURALNETWORKS_EXP_EX] = _map[ANEURALNETWORKS_EXP];
-
-  _map[ANEURALNETWORKS_GREATER_EQUAL_EX] = [](const OperationFactory::Param &init_param,
-                                              Operands &operands) {
-    assert(init_param.input_count == 2 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> input0 Tensor Index
-    //  1 -> input1 Tensor Index
-    OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]};
-
-    operation::Comparison::Param param;
-    param.comparison_type = operation::Comparison::ComparisonType::GreaterEqual;
-
-    // Output operand type must be boolean
-    replaceDataType(operands, outputs.at(0), DataType::BOOL8);
-
-    return new operation::Comparison{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_LESS_EX] = [](const OperationFactory::Param &init_param,
-                                     Operands &operands) {
-    assert(init_param.input_count == 2 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> input0 Tensor Index
-    //  1 -> input1 Tensor Index
-    OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]};
-
-    operation::Comparison::Param param;
-    param.comparison_type = operation::Comparison::ComparisonType::Less;
-
-    // Output operand type must be boolean
-    replaceDataType(operands, outputs.at(0), DataType::BOOL8);
-
-    return new operation::Comparison{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_REDUCE_MAX] = [](const OperationFactory::Param &init_param,
-                                        Operands &operands) {
-    assert(init_param.input_count == 3 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> Input Tensor Index
-    //  1 -> Axis Tensor Index
-    //  2 -> keep_dims Index
-    OperandIndexSequence inputs{init_param.inputs[0]};
-    std::vector<std::int32_t> axes =
-        operands.at(OperandIndex{init_param.inputs[1]}).asVector<std::int32_t>();
-
-    operation::ReduceMax::Param param;
-    param.axes.assign(axes.cbegin(), axes.cend());
-    param.keep_dims = operands.at(OperandIndex{init_param.inputs[2]}).asScalar<int8_t>() != 0;
-    param.rank = operands.at(inputs.at(0)).shape().rank();
-
-    return new operation::ReduceMax{inputs, outputs, param};
-  };
-
-  // ANEURALNETWORKS_REDUCE_MAX_EX is deprecated
-  // TODO Remove ANEURALNETWORKS_REDUCE_MAX_EX
-  _map[ANEURALNETWORKS_REDUCE_MAX_EX] = _map[ANEURALNETWORKS_REDUCE_MAX];
-
-  _map[ANEURALNETWORKS_NOT_EQUAL_EX] = [](const OperationFactory::Param &init_param,
-                                          Operands &operands) {
-    assert(init_param.input_count == 2 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> input1 Tensor Index
-    //  1 -> input2 Tensor Index
-    OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]};
-
-    operation::Comparison::Param param;
-    param.comparison_type = operation::Comparison::ComparisonType::NotEqual;
-
-    // Output operand type must be boolean
-    replaceDataType(operands, outputs.at(0), DataType::BOOL8);
-
-    return new operation::Comparison{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_LOGICAL_AND_EX] = [](const OperationFactory::Param &init_param,
-                                            Operands &operands) {
-    assert(init_param.input_count == 2 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> input0 Tensor Index
-    //  1 -> input1 Tensor Index
-    OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]};
-
-    // This operation's operands must be boolean type.
-    replaceDataType(operands, inputs.at(0), DataType::BOOL8);
-    replaceDataType(operands, inputs.at(1), DataType::BOOL8);
-    replaceDataType(operands, outputs.at(0), DataType::BOOL8);
-
-    return new operation::LogicalAnd{inputs, outputs};
-  };
-
-  _map[ANEURALNETWORKS_RSQRT] = [](const OperationFactory::Param &init_param, Operands &) {
-    assert(init_param.input_count == 1 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> Input Tensor Index
-    OperandIndexSequence inputs{init_param.inputs[0]};
-
-    return new operation::RSQRT{inputs, outputs};
-  };
-
-  // ANEURALNETWORKS_RSQRT_EX is deprecated
-  // TODO Remove ANEURALNETWORKS_RSQRT_EX
-  _map[ANEURALNETWORKS_RSQRT_EX] = _map[ANEURALNETWORKS_RSQRT];
-
-  _map[ANEURALNETWORKS_RELU] = [](const OperationFactory::Param &init_param, Operands &) {
-    assert(init_param.input_count == 1 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> Input Tensor Index
-    OperandIndexSequence inputs{init_param.inputs[0]};
-
-    return new operation::ReLU{inputs, outputs};
-  };
-
-  _map[ANEURALNETWORKS_RESIZE_BILINEAR] = [](const OperationFactory::Param &init_param,
-                                             Operands &operands) {
-    assert(init_param.input_count == 3 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> IFM Index
-    //  1 -> Height Index
-    //  2 -> Width Index
-    OperandIndexSequence inputs{init_param.inputs[0]};
-
-    operation::ResizeBilinear::Param param;
-    param.height_out = operands.at(OperandIndex{init_param.inputs[1]}).asScalar<int32_t>();
-    param.width_out = operands.at(OperandIndex{init_param.inputs[2]}).asScalar<int32_t>();
-
-    return new operation::ResizeBilinear{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_RELU1] = [](const OperationFactory::Param &init_param, Operands &) {
-    assert(init_param.input_count == 1 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> input Tensor Index
-    OperandIndexSequence inputs{init_param.inputs[0]};
-
-    return new operation::ReLU1{inputs, outputs};
-  };
-
-  _map[ANEURALNETWORKS_RELU6] = [](const OperationFactory::Param &init_param, Operands &) {
-    assert(init_param.input_count == 1 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> input Tensor Index
-    OperandIndexSequence inputs{init_param.inputs[0]};
-
-    return new operation::ReLU6{inputs, outputs};
-  };
-
-  _map[ANEURALNETWORKS_RNN] = [](const OperationFactory::Param &init_param, Operands &operands) {
-    assert(init_param.input_count == 6 && init_param.output_count == 2);
-
-    // Each input should be interpreted as follows:
-    //
-    // 0 -> Input Tensor Index
-    // 1 -> Weights Tensor Index
-    // 2 -> Recurrent Weights Tensor Index
-    // 3 -> Bias Tensor Index
-    // 4 -> Hidden state (in) Index
-    // 5 -> Activation Index
-
-    OperandIndexSequence inputs;
-    for (uint32_t n = 0; n < init_param.input_count - 1; ++n)
-    {
-      inputs.append(OperandIndex{init_param.inputs[n]});
-    }
-    OperandIndexSequence outputs;
-    for (uint32_t n = 0; n < init_param.output_count; ++n)
-    {
-      outputs.append(OperandIndex{init_param.outputs[n]});
-    }
-
-    operation::RNN::Param param;
-    const auto activation_index = OperandIndex{init_param.inputs[5]};
-    param.activation =
-        NNAPIConvert::getFusedActivation(operands.at(activation_index).asScalar<FuseCode>());
-
-    return new operation::RNN{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_FLOOR] = [](const OperationFactory::Param &init_param, Operands &) {
-    assert(init_param.input_count == 1 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //  0 -> input Tensor Index
-    OperandIndexSequence inputs{init_param.inputs[0]};
-
-    return new operation::Floor{inputs, outputs};
-  };
-
-  _map[ANEURALNETWORKS_SPACE_TO_BATCH_ND] = [](const OperationFactory::Param &init_param,
-                                               Operands &) {
-    assert(init_param.input_count == 3 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> Input Tensor Index
-    //  1 -> Block size Index
-    //  2 -> Paddings Index
-    OperandIndexSequence inputs;
-    for (uint32_t n = 0; n < init_param.input_count; ++n)
-    {
-      inputs.append(OperandIndex{init_param.inputs[n]});
-    }
-
-    return new operation::SpaceToBatchND{inputs, outputs};
-  };
-
-  _map[ANEURALNETWORKS_SPACE_TO_DEPTH] = [](const OperationFactory::Param &init_param,
-                                            Operands &operands) {
-    assert(init_param.input_count == 2 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> Input Tensor Index
-    //  1 -> Block size Index
-    OperandIndexSequence inputs{init_param.inputs[0]};
-
-    operation::SpaceToDepth::Param param;
-    param.block_size = operands.at(OperandIndex{init_param.inputs[1]}).asScalar<std::int32_t>();
-
-    return new operation::SpaceToDepth{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_L2_POOL_2D] = [](const OperationFactory::Param &init_param,
-                                        Operands &operands) {
-    assert(init_param.input_count == 10 || init_param.input_count == 7);
-    assert(init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> IFM Tensor Index
-    OperandIndexSequence inputs{init_param.inputs[0]};
-
-    operation::L2Pool2D::Param param;
-
-    if (init_param.input_count == 7) // Imlicit Padding case
-    {
-      //  1 -> Padding Code (ANEURALNETWORKS_PADDING_SAME or ANEURALNETWORKS_PADDING_VALID) Index
-      //  2 -> Horizontal (over width) Stride Index
-      //  3 -> Vertial (over height) Stride Index
-      //  4 -> Filter Width Index
-      //  5 -> Filter Height Index
-      //  6 -> FuseCode (activation) Index
-      const auto padding_index = OperandIndex{init_param.inputs[1]};
-      const auto hstride_index = OperandIndex{init_param.inputs[2]};
-      const auto vstride_index = OperandIndex{init_param.inputs[3]};
-      const auto kw_index = OperandIndex{init_param.inputs[4]};
-      const auto kh_index = OperandIndex{init_param.inputs[5]};
-      const auto activation_index = OperandIndex{init_param.inputs[6]};
-
-      param.padding.type =
-          NNAPIConvert::getPaddingType(operands.at(padding_index).asScalar<PaddingCode>());
-      param.stride = makeStride(operands, hstride_index, vstride_index);
-      param.kw = getUint32Scalar(operands, kw_index);
-      param.kh = getUint32Scalar(operands, kh_index);
-      param.activation =
-          NNAPIConvert::getFusedActivation(operands.at(activation_index).asScalar<FuseCode>());
-    }
-    else // Explicit Padding case
-    {
-      //  1 -> Padding_left index
-      //  2 -> Padding_right index
-      //  3 -> Padding_top index
-      //  4 -> Padding_bottom index
-      //  5 -> Horizontal (over width) Stride Index
-      //  6 -> Vertial (over height) Stride Index
-      //  7 -> Filter Width Index
-      //  8 -> Filter Height Index
-      //  9 -> FuseCode (activation) Index
-      const auto padding_left_index = OperandIndex{init_param.inputs[1]};
-      const auto padding_right_index = OperandIndex{init_param.inputs[2]};
-      const auto padding_top_index = OperandIndex{init_param.inputs[3]};
-      const auto padding_bottom_index = OperandIndex{init_param.inputs[4]};
-      const auto hstride_index = OperandIndex{init_param.inputs[5]};
-      const auto vstride_index = OperandIndex{init_param.inputs[6]};
-      const auto kw_index = OperandIndex{init_param.inputs[7]};
-      const auto kh_index = OperandIndex{init_param.inputs[8]};
-      const auto activation_index = OperandIndex{init_param.inputs[9]};
-
-      param.padding.type = PaddingType::EXPLICIT;
-      param.padding.param = makeExplicitPadding(operands, padding_left_index, padding_right_index,
-                                                padding_top_index, padding_bottom_index);
-      param.stride = makeStride(operands, hstride_index, vstride_index);
-      param.kw = getUint32Scalar(operands, kw_index);
-      param.kh = getUint32Scalar(operands, kh_index);
-      param.activation =
-          NNAPIConvert::getFusedActivation(operands.at(activation_index).asScalar<FuseCode>());
-    }
-
-    return new operation::L2Pool2D{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_EMBEDDING_LOOKUP] = [](const OperationFactory::Param &init_param,
-                                              Operands &) {
-    assert(init_param.input_count == 2 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> Lookups Index
-    //  1 -> Values Index
-    OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]};
-
-    return new operation::EmbeddingLookup{inputs, outputs};
-  };
-
-  _map[ANEURALNETWORKS_L2_NORMALIZATION] = [](const OperationFactory::Param &init_param,
-                                              Operands &operands) {
-    assert(init_param.input_count == 1 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //  0 -> input Tensor Index
-    OperandIndexSequence inputs{init_param.inputs[0]};
-
-    operation::L2Normalization::Param param;
-    param.rank = operands.at(inputs.at(0)).shape().rank();
-
-    return new operation::L2Normalization{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_HASHTABLE_LOOKUP] = [](const OperationFactory::Param &init_param,
-                                              Operands &) {
-    assert(init_param.input_count == 3 && init_param.output_count == 2);
-
-    // Each output should be interpreted as follows:
-    //
-    //  0 -> Output Index
-    //  1 -> Hits Index
-    OperandIndexSequence outputs{init_param.outputs[0], init_param.outputs[1]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> Lookups Index
-    //  1 -> Keys Index
-    //  2 -> Values Index
-    OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1], init_param.inputs[2]};
-
-    return new operation::HashtableLookup{inputs, outputs};
-  };
-
-  _map[ANEURALNETWORKS_PRELU_EX] = [](const OperationFactory::Param &init_param, Operands &) {
-    assert(init_param.input_count == 2 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> input Tensor Index
-    //  1 -> alpha Tensor Index
-    OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]};
-
-    return new operation::PReLU{inputs, outputs};
-  };
-
-  _map[ANEURALNETWORKS_TRANSPOSE_CONV_EX] = [](const OperationFactory::Param &init_param,
-                                               Operands &operands) {
-    assert(init_param.input_count == 6 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> Output Shape Index
-    //  1 -> Weights Index
-    //  2 -> Input Tensor Index
-    //  3 -> Padding Type
-    //  4 -> Stride width
-    //  5 -> Stride height
-
-    OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1], init_param.inputs[2]};
-
-    operation::TransposeConv::Param param;
-
-    const auto padding_index = OperandIndex{init_param.inputs[3]};
-    const auto hstride_index = OperandIndex{init_param.inputs[4]};
-    const auto vstride_index = OperandIndex{init_param.inputs[5]};
-
-    param.padding.type =
-        NNAPIConvert::getPaddingType(operands.at(padding_index).asScalar<PaddingCode>());
-    param.stride = makeStride(operands, hstride_index, vstride_index);
-
-    return new operation::TransposeConv{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_SQRT] = [](const OperationFactory::Param &init_param, Operands &) {
-    assert(init_param.input_count == 1 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //  0 -> input Tensor Index
-
-    OperandIndexSequence inputs{init_param.inputs[0]};
-    return new operation::SQRT{inputs, outputs};
-  };
-
-  // ANEURALNETWORKS_SQRT_EX is deprecated
-  // TODO Remove ANEURALNETWORKS_SQRT_EX
-  _map[ANEURALNETWORKS_SQRT_EX] = _map[ANEURALNETWORKS_SQRT];
-
-  _map[ANEURALNETWORKS_LOGICAL_OR_EX] = [](const OperationFactory::Param &init_param,
-                                           Operands &operands) {
-    assert(init_param.input_count == 2 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> input0 Tensor Index
-    //  1 -> input1 Tensor Index
-    OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]};
-
-    // This operation's operands must be boolean type.
-    replaceDataType(operands, inputs.at(0), DataType::BOOL8);
-    replaceDataType(operands, inputs.at(1), DataType::BOOL8);
-    replaceDataType(operands, outputs.at(0), DataType::BOOL8);
-
-    return new operation::LogicalOr{inputs, outputs};
-  };
-
-  _map[ANEURALNETWORKS_LOGICAL_NOT_EX] = [](const OperationFactory::Param &init_param,
-                                            Operands &operands) {
-    assert(init_param.input_count == 1 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> input Tensor Index
-    OperandIndexSequence inputs{init_param.inputs[0]};
-
-    // This operation's operands must be boolean type.
-    replaceDataType(operands, inputs.at(0), DataType::BOOL8);
-    replaceDataType(operands, outputs.at(0), DataType::BOOL8);
-
-    return new operation::LogicalNot{inputs, outputs};
-  };
-
-  _map[ANEURALNETWORKS_LSTM] = [](const OperationFactory::Param &init_param, Operands &operands) {
-    assert(init_param.input_count == 23 && init_param.output_count == 4);
-
-    // Each input should be interpreted as follows:
-    //
-    // 0 -> Input Tensor Index
-    // 1 -> Input to Input Tensor Index
-    // 2 -> Input to Forget Tensor Index
-    // 3 -> Input to Cell Tensor Index
-    // 4 -> Input to Output Tensor Index
-    // 5 -> Recurrent to Input Weights Tensor Index
-    // 6 -> Recurrent to Forget Weights Tensor Index
-    // 7 -> Recurrent to Cell Weights Tensor Index
-    // 8 -> Recurrent to Output Weights Tensor Index
-    // 9 -> Cell to Input Weights Tensor Index
-    // 10 -> Cell to Forget Weights Tensor Index
-    // 11 -> Cell to Output Weights Tensor Index
-    // 12 -> Input Gate Bias Tensor Index
-    // 13 -> Forget Gate Bias Tensor Index
-    // 14 -> Cell Bias Tensor Index
-    // 15 -> Output Gate Bias Tensor Index
-    // 16 -> Projection Weights Tensor Index
-    // 17 -> Projection Bias Tensor Index
-    // 18 -> Output State In Tensor Index
-    // 19 -> Cell State In Tensor Index
-    OperandIndexSequence inputs;
-    for (uint32_t n = 0; n < init_param.input_count - 3; ++n)
-    {
-      inputs.append(OperandIndex{init_param.inputs[n]});
-    }
-
-    // Each output should be interpreted as follows:
-    //
-    // 0 -> Scratch Buffer Tensor Index
-    // 1 -> Output State Out Tensor Index
-    // 2 -> Cell State Out Tensor Index
-    // 3 -> Output Tensor Index
-    OperandIndexSequence outputs;
-    for (uint32_t n = 0; n < init_param.output_count; ++n)
-    {
-      outputs.append(OperandIndex{init_param.outputs[n]});
-    }
-
-    operation::LSTM::Param param;
-    const auto activation_index = OperandIndex{init_param.inputs[20]};
-    switch (operands.at(activation_index).asScalar<int32_t>())
-    {
-      case 0:
-        param.activation = Activation::NONE;
-        break;
-      case 1:
-        param.activation = Activation::RELU;
-        break;
-      case 2:
-        param.activation = Activation::RELU1;
-        break;
-      case 3:
-        param.activation = Activation::RELU6;
-        break;
-      case 4:
-        param.activation = Activation::TANH;
-        break;
-      case 6:
-        param.activation = Activation::SIGMOID;
-        break;
-      default:
-        throw std::runtime_error("Unsupported activation type");
-        break;
-    }
-    param.cell_threshold = operands.at(OperandIndex{init_param.inputs[21]}).asScalar<float>();
-    param.projection_threshold = operands.at(OperandIndex{init_param.inputs[22]}).asScalar<float>();
-
-    return new operation::LSTM{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_EQUAL_EX] = [](const OperationFactory::Param &init_param,
-                                      Operands &operands) {
-    assert(init_param.input_count == 2 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> input0 Tensor Index
-    //  1 -> input1 Tensor Index
-    OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]};
-
-    operation::Comparison::Param param;
-    param.comparison_type = operation::Comparison::ComparisonType::Equal;
-
-    // Output operand type must be boolean
-    replaceDataType(operands, outputs.at(0), DataType::BOOL8);
-
-    return new operation::Comparison{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_SQUARED_DIFFERENCE_EX] = [](const OperationFactory::Param &init_param,
-                                                   Operands &) {
-    assert(init_param.input_count == 2 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> LHS Tensor Index
-    //  1 -> RHS Tensor Index
-    OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]};
-
-    return new operation::SquaredDifference{inputs, outputs};
-  };
-
-  _map[ANEURALNETWORKS_TOPK_V2] = [](const OperationFactory::Param &init_param,
-                                     Operands &operands) {
-    assert(init_param.input_count == 2 && init_param.output_count == 2);
-
-    // Each output should be interpreted as follows:
-    //
-    //  0 -> Index for Output Values
-    //  1 -> Index for Output Indices
-    OperandIndexSequence outputs{init_param.outputs[0], init_param.outputs[1]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> Index for Input Data
-    //  1 -> Index for K
-    OperandIndexSequence inputs{init_param.inputs[0]};
-
-    operation::TopKV2::Param param;
-    param.k = operands.at(OperandIndex{init_param.inputs[1]}).asScalar<std::int32_t>();
-
-    return new operation::TopKV2{inputs, outputs, param};
-  };
-
-  // ANEURALNETWORKS_CAST_EX is deprecated
-  // TODO Remove ANEURALNETWORKS_CAST_EX
-  _map[ANEURALNETWORKS_TOPK_V2_EX] = _map[ANEURALNETWORKS_TOPK_V2];
-
-  _map[ANEURALNETWORKS_GATHER] = [](const OperationFactory::Param &init_param, Operands &operands) {
-    assert(init_param.input_count == 3 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> input Tensor Index
-    //  1 -> axis Index
-    //  2 -> indices Tensor Index
-    OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[2]};
-
-    operation::Gather::Param param;
-    param.axis = operands.at(OperandIndex{init_param.inputs[1]}).asScalar<int32_t>();
-    param.rank = operands.at(inputs.at(0)).shape().rank();
-
-    return new operation::Gather{inputs, outputs, param};
-  };
-
-  // ANEURALNETWORKS_GATHER_EX is deprecated
-  // TODO Remove ANEURALNETWORKS_GATHER_EX
-  _map[ANEURALNETWORKS_GATHER_EX] = _map[ANEURALNETWORKS_GATHER];
-
-  _map[ANEURALNETWORKS_NEG] = [](const OperationFactory::Param &init_param, Operands &) {
-    assert(init_param.input_count == 1 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> Input Tensor Index
-    OperandIndexSequence inputs{init_param.inputs[0]};
-
-    return new operation::Neg{inputs, outputs};
-  };
-
-  // ANEURALNETWORKS_NEG_EX is deprecated
-  // TODO Remove ANEURALNETWORKS_NEG_EX
-  _map[ANEURALNETWORKS_NEG_EX] = _map[ANEURALNETWORKS_NEG];
-
-  _map[ANEURALNETWORKS_ABS] = [](const OperationFactory::Param &init_param, Operands &) {
-    assert(init_param.input_count == 1 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> Input Tensor Index
-    OperandIndexSequence inputs{init_param.inputs[0]};
-
-    return new operation::Abs{inputs, outputs};
-  };
-
-  // ANEURALNETWORKS_ABS_EX is deprecated
-  // TODO Remove ANEURALNETWORKS_ABS_EX
-  _map[ANEURALNETWORKS_ABS_EX] = _map[ANEURALNETWORKS_ABS];
-
-  _map[ANEURALNETWORKS_ARGMAX_EX] = [](const OperationFactory::Param &init_param,
-                                       Operands &operands) {
-    assert(init_param.input_count == 2 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> Input Tensor Index
-    //  1 -> Axis Tensor Index
-    OperandIndexSequence inputs{init_param.inputs[0]};
-
-    operation::ArgMax::Param param;
-    param.axis = operands.at(OperandIndex{init_param.inputs[1]}).asScalar<std::int32_t>();
-    param.rank = operands.at(inputs.at(0)).shape().rank();
-
-    return new operation::ArgMax{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_DEQUANTIZE] = [](const OperationFactory::Param &init_param, Operands &) {
-    assert(init_param.input_count == 1 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> Input Tensor Index
-    OperandIndexSequence inputs{init_param.inputs[0]};
-
-    return new operation::Dequantize{inputs, outputs};
-  };
-
-  _map[ANEURALNETWORKS_MEAN] = [](const OperationFactory::Param &init_param, Operands &operands) {
-    assert(init_param.input_count == 3 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> ifm Tensor Index
-    //  1 -> axis Tensor Index
-    //  2 -> keep_dims Index
-    OperandIndexSequence inputs{init_param.inputs[0]};
-    std::vector<std::int32_t> axes =
-        operands.at(OperandIndex{init_param.inputs[1]}).asVector<std::int32_t>();
-
-    operation::Mean::Param param;
-    param.axes.assign(axes.cbegin(), axes.cend());
-    param.keep_dims = operands.at(OperandIndex{init_param.inputs[2]}).asScalar<int32_t>() != 0;
-    param.rank = operands.at(inputs.at(0)).shape().rank();
-
-    return new operation::Mean{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION] = [](const OperationFactory::Param &init_param,
-                                                          Operands &operands) {
-    assert(init_param.input_count == 5 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    OperandIndexSequence inputs{init_param.inputs[0]};
-
-    operation::LocalResponseNormalization::Param param;
-    param.radius = operands.at(OperandIndex{init_param.inputs[1]}).asScalar<std::int32_t>();
-    param.bias = operands.at(OperandIndex{init_param.inputs[2]}).asScalar<float>();
-    param.alpha = operands.at(OperandIndex{init_param.inputs[3]}).asScalar<float>();
-    param.beta = operands.at(OperandIndex{init_param.inputs[4]}).asScalar<float>();
-
-    return new operation::LocalResponseNormalization{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_DEPTH_TO_SPACE] = [](const OperationFactory::Param &init_param,
-                                            Operands &operands) {
-    assert(init_param.input_count == 2 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> Input Tensor Index
-    //  1 -> Block size Index
-    OperandIndexSequence inputs{init_param.inputs[0]};
-
-    operation::DepthToSpace::Param param;
-    param.block_size = operands.at(OperandIndex{init_param.inputs[1]}).asScalar<std::int32_t>();
-
-    return new operation::DepthToSpace{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_PACK_EX] = [](const OperationFactory::Param &init_param,
-                                     Operands &operands) {
-    assert(init_param.input_count >= 3 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-    OperandIndexSequence inputs;
-    for (uint32_t n = 0; n < init_param.input_count - 2; ++n)
-    {
-      inputs.append(OperandIndex{init_param.inputs[n]});
-    }
-
-    operation::Pack::Param param;
-    const auto num_index = OperandIndex{init_param.inputs[init_param.input_count - 2]};
-    const auto axis_index = OperandIndex{init_param.inputs[init_param.input_count - 1]};
-    param.num = operands.at(num_index).asScalar<int32_t>();
-    param.axis = operands.at(axis_index).asScalar<int32_t>();
-    param.rank = operands.at(outputs.at(0)).shape().rank();
-
-    return new operation::Pack{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_REDUCE_MIN_EX] = [](const OperationFactory::Param &init_param,
-                                           Operands &operands) {
-    assert(init_param.input_count == 3 && init_param.output_count == 1);
-
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    // Each input should be interpreted as follows:
-    //
-    //  0 -> Input Tensor Index
-    //  1 -> Axis Tensor Index
-    //  2 -> keep_dims Index
-    OperandIndexSequence inputs{init_param.inputs[0]};
-    std::vector<std::int32_t> axes =
-        operands.at(OperandIndex{init_param.inputs[1]}).asVector<std::int32_t>();
-
-    operation::ReduceMin::Param param;
-    param.axes.assign(axes.cbegin(), axes.cend());
-    param.keep_dims = operands.at(OperandIndex{init_param.inputs[2]}).asScalar<int32_t>() != 0;
-    param.rank = operands.at(inputs.at(0)).shape().rank();
-
-    return new operation::ReduceMin{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_SPLIT_EX] = [](const OperationFactory::Param &init_param,
-                                      Operands &operands) {
-    assert(init_param.input_count == 3);
-    assert(init_param.output_count >= 1); // At least one output tensor and axis
-
-    OperandIndexSequence inputs{init_param.inputs[0]};
-    OperandIndexSequence outputs;
-    for (uint32_t n = 0; n < init_param.output_count; ++n)
-    {
-      outputs.append(OperandIndex{init_param.outputs[n]});
-    }
-
-    operation::Split::Param param;
-    param.axis = operands.at(OperandIndex{init_param.inputs[1]}).asScalar<std::int32_t>();
-    param.num_splits = operands.at(OperandIndex{init_param.inputs[2]}).asScalar<std::int32_t>();
-    param.rank = operands.at(inputs.at(0)).shape().rank();
-
-    return new operation::Split{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_UNPACK_EX] = [](const OperationFactory::Param &init_param,
-                                       Operands &operands) {
-    assert(init_param.input_count == 3 && init_param.output_count >= 1);
-
-    OperandIndexSequence inputs{init_param.inputs[0]};
-    OperandIndexSequence outputs;
-    for (uint32_t n = 0; n < init_param.output_count; ++n)
-    {
-      outputs.append(OperandIndex{init_param.outputs[n]});
-    }
-
-    operation::Unpack::Param param;
-    const auto num_index = OperandIndex{init_param.inputs[1]};
-    const auto axis_index = OperandIndex{init_param.inputs[2]};
-    param.num = operands.at(num_index).asScalar<int32_t>();
-    param.axis = operands.at(axis_index).asScalar<int32_t>();
-    param.rank = operands.at(inputs.at(0)).shape().rank();
-
-    return new operation::Unpack{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_PAD] = [](const OperationFactory::Param &init_param, Operands &operands) {
-    assert(init_param.input_count == 2 && init_param.output_count >= 1);
-
-    OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]};
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    operation::Pad::Param param;
-    param.rank = operands.at(inputs.at(0)).shape().rank();
-
-    return new operation::Pad{inputs, outputs, param};
-  };
-
-  _map[ANEURALNETWORKS_MINIMUM] = [](const OperationFactory::Param &init_param, Operands &) {
-    assert(init_param.input_count == 2 && init_param.output_count == 1);
-
-    OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]};
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    return new operation::Min{inputs, outputs};
-  };
-
-  _map[ANEURALNETWORKS_MAXIMUM] = [](const OperationFactory::Param &init_param, Operands &) {
-    assert(init_param.input_count == 2 && init_param.output_count == 1);
-
-    OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]};
-    OperandIndexSequence outputs{init_param.outputs[0]};
-
-    return new operation::Max{inputs, outputs};
-  };
-}
-
-Operation *OperationFactory::create(ANeuralNetworksOperationType type,
-                                    const OperationFactory::Param &param, Operands &operands)
-{
-  auto it = _map.find(type);
-  if (it == _map.end())
-  {
-    throw std::runtime_error("Unsupported operation type: " + std::to_string(type));
-  }
-  return it->second(param, operands);
-}
diff --git a/runtime/neurun/frontend/nnapi/wrapper/OperationFactory.h b/runtime/neurun/frontend/nnapi/wrapper/OperationFactory.h
deleted file mode 100644
index 003e4eb7a..000000000
--- a/runtime/neurun/frontend/nnapi/wrapper/OperationFactory.h
+++ /dev/null
@@ -1,60 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __OPERATION_FACTORY_H__
-#define __OPERATION_FACTORY_H__
-
-#include <unordered_map>
-
-#include "ir/Operands.h"
-#include "ir/Operation.h"
-#include "NeuralNetworks.h"
-#include "NeuralNetworksEx.h"
-
-/**
- * @brief A class to create a neurun operation object from NN API input parameters
- */
-class OperationFactory
-{
-public:
-  struct Param
-  {
-    uint32_t input_count;
-    const uint32_t *inputs;
-    uint32_t output_count;
-    const uint32_t *outputs;
-  };
-
-public:
-  using Generator = std::function<neurun::ir::Operation *(const OperationFactory::Param &,
-                                                          neurun::ir::Operands &)>;
-
-public:
-  static OperationFactory &get();
-
-private:
-  OperationFactory();
-
-public:
-  neurun::ir::Operation *create(ANeuralNetworksOperationType, const OperationFactory::Param &param,
-                                neurun::ir::Operands &operands);
-  // TODO add "register" method for separating registration, possibly supporting custom-ops
-
-private:
-  std::unordered_map<ANeuralNetworksOperationType, Generator> _map;
-};
-
-#endif // __OPERATION_FACTORY_H__
diff --git a/runtime/neurun/frontend/tflite/CMakeLists.txt b/runtime/neurun/frontend/tflite/CMakeLists.txt
deleted file mode 100644
index 5157869f3..000000000
--- a/runtime/neurun/frontend/tflite/CMakeLists.txt
+++ /dev/null
@@ -1,17 +0,0 @@
-if(NOT BUILD_TFLITE_LOADER)
-  return()
-endif(NOT BUILD_TFLITE_LOADER)
-
-nnfw_find_package(FlatBuffersSource REQUIRED)
-
-set(TFLITE_LOADER_SOURCES src/tflite_loader.cc)
-
-add_library(tflite_loader SHARED ${TFLITE_LOADER_SOURCES})
-
-target_include_directories(tflite_loader PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/include)
-target_include_directories(tflite_loader PRIVATE ${FlatBuffersSource_DIR}/include)
-
-target_link_libraries(tflite_loader PUBLIC neurun_core)
-target_link_libraries(tflite_loader PRIVATE base_loader nnfw_lib_cpp14 nnfw_common nnfw_coverage)
-
-install(TARGETS tflite_loader DESTINATION lib)
diff --git a/runtime/neurun/frontend/tflite/include/tflite_loader.h b/runtime/neurun/frontend/tflite/include/tflite_loader.h
deleted file mode 100644
index 033230b4b..000000000
--- a/runtime/neurun/frontend/tflite/include/tflite_loader.h
+++ /dev/null
@@ -1,34 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __TFLITE_TFLITE_LOADER_H__
-#define __TFLITE_TFLITE_LOADER_H__
-
-#include "ir/Graph.h"
-
-#include <memory>
-
-namespace neurun
-{
-namespace tflite_loader
-{
-
-std::unique_ptr<ir::Graph> loadModel(const char *filename);
-
-} // namespace tflite_loader
-} // namespace neurun
-
-#endif // __TFLITE_TFLITE_LOADER_H__
diff --git a/runtime/neurun/frontend/tflite/src/tflite_loader.cc b/runtime/neurun/frontend/tflite/src/tflite_loader.cc
deleted file mode 100644
index 10a4fc095..000000000
--- a/runtime/neurun/frontend/tflite/src/tflite_loader.cc
+++ /dev/null
@@ -1,105 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "tflite_loader.h"
-#include "base_loader.h"
-#include "tflite_schema_generated.h"
-
-namespace neurun
-{
-namespace tflite_loader
-{
-
-namespace
-{
-
-struct LoaderDomain
-{
-  using Verifier = flatbuffers::Verifier;
-  using ActivationFunctionType = neurun_tflite::ActivationFunctionType;
-  using Buffer = neurun_tflite::Buffer;
-  using BuiltinOperator = neurun_tflite::BuiltinOperator;
-  using CustomOptionsFormat = neurun_tflite::CustomOptionsFormat;
-  using Model = neurun_tflite::Model;
-  using Operator = neurun_tflite::Operator;
-  using Padding = neurun_tflite::Padding;
-  using Pool2DOptions = neurun_tflite::Pool2DOptions;
-  using Tensor = neurun_tflite::Tensor;
-  using TensorType = neurun_tflite::TensorType;
-  using SubGraph = neurun_tflite::SubGraph;
-
-  static const char *EnumNameBuiltinOperator(BuiltinOperator e)
-  {
-    return neurun_tflite::EnumNameBuiltinOperator(e);
-  }
-  static const char *EnumNameActivationFunctionType(ActivationFunctionType e)
-  {
-    return neurun_tflite::EnumNameActivationFunctionType(e);
-  }
-  static const char *EnumNameTensorType(TensorType e)
-  {
-    return neurun_tflite::EnumNameTensorType(e);
-  }
-  static const Model *GetModel(const void *buf) { return neurun_tflite::GetModel(buf); }
-  static bool VerifyModelBuffer(Verifier &verifier)
-  {
-    return neurun_tflite::VerifyModelBuffer(verifier);
-  }
-};
-
-class TFLiteLoader final : public base_loader::BaseLoader<LoaderDomain, TFLiteLoader>
-{
-public:
-  using BaseLoader::BaseLoader;
-
-  void loadSubgraph(const neurun_tflite::SubGraph *subgraph)
-  {
-    // Load tensors
-    _tensor_to_operand.resize(subgraph->tensors()->size());
-    for (flatbuffers::uoffset_t i = 0; i < subgraph->tensors()->size(); ++i)
-    {
-      _tensor_to_operand[i] = loadOperand(subgraph->tensors()->Get(i));
-    }
-    // Set inputs
-    for (const std::int32_t input_ind : *subgraph->inputs())
-    {
-      _graph.addInput(_tensor_to_operand[input_ind]);
-    }
-    // Set outputs
-    for (const std::int32_t output_ind : *subgraph->outputs())
-    {
-      _graph.addOutput(_tensor_to_operand[output_ind]);
-    }
-    // Create operations
-    for (const auto *op : *subgraph->operators())
-    {
-      loadOperation(op);
-    }
-  }
-};
-
-} // namespace
-
-std::unique_ptr<ir::Graph> loadModel(const char *filename)
-{
-  auto graph = nnfw::cpp14::make_unique<ir::Graph>();
-  TFLiteLoader loader(*graph);
-  loader.loadFromFile(filename);
-  return graph;
-}
-
-} // namespace tflite_loader
-} // namespace neurun
diff --git a/runtime/neurun/frontend/tflite/src/tflite_schema_generated.h b/runtime/neurun/frontend/tflite/src/tflite_schema_generated.h
deleted file mode 100644
index 21669e2ff..000000000
--- a/runtime/neurun/frontend/tflite/src/tflite_schema_generated.h
+++ /dev/null
@@ -1,7275 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-// automatically generated by the FlatBuffers compiler, do not modify
-
-#ifndef FLATBUFFERS_GENERATED_SCHEMA_NEURUN_TFLITE_H_
-#define FLATBUFFERS_GENERATED_SCHEMA_NEURUN_TFLITE_H_
-
-#include "flatbuffers/flatbuffers.h"
-
-namespace neurun_tflite
-{
-
-struct CustomQuantization;
-
-struct QuantizationParameters;
-
-struct Tensor;
-
-struct Conv2DOptions;
-
-struct Pool2DOptions;
-
-struct DepthwiseConv2DOptions;
-
-struct ConcatEmbeddingsOptions;
-
-struct LSHProjectionOptions;
-
-struct SVDFOptions;
-
-struct RNNOptions;
-
-struct SequenceRNNOptions;
-
-struct BidirectionalSequenceRNNOptions;
-
-struct FullyConnectedOptions;
-
-struct SoftmaxOptions;
-
-struct ConcatenationOptions;
-
-struct AddOptions;
-
-struct MulOptions;
-
-struct L2NormOptions;
-
-struct LocalResponseNormalizationOptions;
-
-struct LSTMOptions;
-
-struct UnidirectionalSequenceLSTMOptions;
-
-struct BidirectionalSequenceLSTMOptions;
-
-struct ResizeBilinearOptions;
-
-struct ResizeNearestNeighborOptions;
-
-struct CallOptions;
-
-struct PadOptions;
-
-struct PadV2Options;
-
-struct ReshapeOptions;
-
-struct SpaceToBatchNDOptions;
-
-struct BatchToSpaceNDOptions;
-
-struct SkipGramOptions;
-
-struct SpaceToDepthOptions;
-
-struct SubOptions;
-
-struct DivOptions;
-
-struct TopKV2Options;
-
-struct EmbeddingLookupSparseOptions;
-
-struct GatherOptions;
-
-struct TransposeOptions;
-
-struct ExpOptions;
-
-struct ReducerOptions;
-
-struct SqueezeOptions;
-
-struct SplitOptions;
-
-struct SplitVOptions;
-
-struct StridedSliceOptions;
-
-struct LogSoftmaxOptions;
-
-struct CastOptions;
-
-struct DequantizeOptions;
-
-struct MaximumMinimumOptions;
-
-struct TileOptions;
-
-struct ArgMaxOptions;
-
-struct ArgMinOptions;
-
-struct GreaterOptions;
-
-struct GreaterEqualOptions;
-
-struct LessOptions;
-
-struct LessEqualOptions;
-
-struct NegOptions;
-
-struct SelectOptions;
-
-struct SliceOptions;
-
-struct TransposeConvOptions;
-
-struct ExpandDimsOptions;
-
-struct SparseToDenseOptions;
-
-struct EqualOptions;
-
-struct NotEqualOptions;
-
-struct ShapeOptions;
-
-struct PowOptions;
-
-struct FakeQuantOptions;
-
-struct PackOptions;
-
-struct LogicalOrOptions;
-
-struct OneHotOptions;
-
-struct AbsOptions;
-
-struct LogicalAndOptions;
-
-struct LogicalNotOptions;
-
-struct UnpackOptions;
-
-struct FloorDivOptions;
-
-struct SquareOptions;
-
-struct ZerosLikeOptions;
-
-struct FillOptions;
-
-struct FloorModOptions;
-
-struct RangeOptions;
-
-struct LeakyReluOptions;
-
-struct SquaredDifferenceOptions;
-
-struct MirrorPadOptions;
-
-struct OperatorCode;
-
-struct Operator;
-
-struct SubGraph;
-
-struct Buffer;
-
-struct Model;
-
-enum TensorType
-{
-  TensorType_FLOAT32 = 0,
-  TensorType_FLOAT16 = 1,
-  TensorType_INT32 = 2,
-  TensorType_UINT8 = 3,
-  TensorType_INT64 = 4,
-  TensorType_STRING = 5,
-  TensorType_BOOL = 6,
-  TensorType_INT16 = 7,
-  TensorType_COMPLEX64 = 8,
-  TensorType_INT8 = 9,
-  TensorType_MIN = TensorType_FLOAT32,
-  TensorType_MAX = TensorType_INT8
-};
-
-inline const TensorType (&EnumValuesTensorType())[10]
-{
-  static const TensorType values[] = {TensorType_FLOAT32, TensorType_FLOAT16, TensorType_INT32,
-                                      TensorType_UINT8,   TensorType_INT64,   TensorType_STRING,
-                                      TensorType_BOOL,    TensorType_INT16,   TensorType_COMPLEX64,
-                                      TensorType_INT8};
-  return values;
-}
-
-inline const char *const *EnumNamesTensorType()
-{
-  static const char *const names[] = {"FLOAT32", "FLOAT16", "INT32",     "UINT8", "INT64", "STRING",
-                                      "BOOL",    "INT16",   "COMPLEX64", "INT8",  nullptr};
-  return names;
-}
-
-inline const char *EnumNameTensorType(TensorType e)
-{
-  const size_t index = static_cast<int>(e);
-  return EnumNamesTensorType()[index];
-}
-
-enum QuantizationDetails
-{
-  QuantizationDetails_NONE = 0,
-  QuantizationDetails_CustomQuantization = 1,
-  QuantizationDetails_MIN = QuantizationDetails_NONE,
-  QuantizationDetails_MAX = QuantizationDetails_CustomQuantization
-};
-
-inline const QuantizationDetails (&EnumValuesQuantizationDetails())[2]
-{
-  static const QuantizationDetails values[] = {QuantizationDetails_NONE,
-                                               QuantizationDetails_CustomQuantization};
-  return values;
-}
-
-inline const char *const *EnumNamesQuantizationDetails()
-{
-  static const char *const names[] = {"NONE", "CustomQuantization", nullptr};
-  return names;
-}
-
-inline const char *EnumNameQuantizationDetails(QuantizationDetails e)
-{
-  const size_t index = static_cast<int>(e);
-  return EnumNamesQuantizationDetails()[index];
-}
-
-template <typename T> struct QuantizationDetailsTraits
-{
-  static const QuantizationDetails enum_value = QuantizationDetails_NONE;
-};
-
-template <> struct QuantizationDetailsTraits<CustomQuantization>
-{
-  static const QuantizationDetails enum_value = QuantizationDetails_CustomQuantization;
-};
-
-bool VerifyQuantizationDetails(flatbuffers::Verifier &verifier, const void *obj,
-                               QuantizationDetails type);
-bool VerifyQuantizationDetailsVector(flatbuffers::Verifier &verifier,
-                                     const flatbuffers::Vector<flatbuffers::Offset<void>> *values,
-                                     const flatbuffers::Vector<uint8_t> *types);
-
-enum BuiltinOperator
-{
-  BuiltinOperator_ADD = 0,
-  BuiltinOperator_AVERAGE_POOL_2D = 1,
-  BuiltinOperator_CONCATENATION = 2,
-  BuiltinOperator_CONV_2D = 3,
-  BuiltinOperator_DEPTHWISE_CONV_2D = 4,
-  BuiltinOperator_DEQUANTIZE = 6,
-  BuiltinOperator_EMBEDDING_LOOKUP = 7,
-  BuiltinOperator_FLOOR = 8,
-  BuiltinOperator_FULLY_CONNECTED = 9,
-  BuiltinOperator_HASHTABLE_LOOKUP = 10,
-  BuiltinOperator_L2_NORMALIZATION = 11,
-  BuiltinOperator_L2_POOL_2D = 12,
-  BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION = 13,
-  BuiltinOperator_LOGISTIC = 14,
-  BuiltinOperator_LSH_PROJECTION = 15,
-  BuiltinOperator_LSTM = 16,
-  BuiltinOperator_MAX_POOL_2D = 17,
-  BuiltinOperator_MUL = 18,
-  BuiltinOperator_RELU = 19,
-  BuiltinOperator_RELU_N1_TO_1 = 20,
-  BuiltinOperator_RELU6 = 21,
-  BuiltinOperator_RESHAPE = 22,
-  BuiltinOperator_RESIZE_BILINEAR = 23,
-  BuiltinOperator_RNN = 24,
-  BuiltinOperator_SOFTMAX = 25,
-  BuiltinOperator_SPACE_TO_DEPTH = 26,
-  BuiltinOperator_SVDF = 27,
-  BuiltinOperator_TANH = 28,
-  BuiltinOperator_CONCAT_EMBEDDINGS = 29,
-  BuiltinOperator_SKIP_GRAM = 30,
-  BuiltinOperator_CALL = 31,
-  BuiltinOperator_CUSTOM = 32,
-  BuiltinOperator_EMBEDDING_LOOKUP_SPARSE = 33,
-  BuiltinOperator_PAD = 34,
-  BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN = 35,
-  BuiltinOperator_GATHER = 36,
-  BuiltinOperator_BATCH_TO_SPACE_ND = 37,
-  BuiltinOperator_SPACE_TO_BATCH_ND = 38,
-  BuiltinOperator_TRANSPOSE = 39,
-  BuiltinOperator_MEAN = 40,
-  BuiltinOperator_SUB = 41,
-  BuiltinOperator_DIV = 42,
-  BuiltinOperator_SQUEEZE = 43,
-  BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM = 44,
-  BuiltinOperator_STRIDED_SLICE = 45,
-  BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN = 46,
-  BuiltinOperator_EXP = 47,
-  BuiltinOperator_TOPK_V2 = 48,
-  BuiltinOperator_SPLIT = 49,
-  BuiltinOperator_LOG_SOFTMAX = 50,
-  BuiltinOperator_DELEGATE = 51,
-  BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM = 52,
-  BuiltinOperator_CAST = 53,
-  BuiltinOperator_PRELU = 54,
-  BuiltinOperator_MAXIMUM = 55,
-  BuiltinOperator_ARG_MAX = 56,
-  BuiltinOperator_MINIMUM = 57,
-  BuiltinOperator_LESS = 58,
-  BuiltinOperator_NEG = 59,
-  BuiltinOperator_PADV2 = 60,
-  BuiltinOperator_GREATER = 61,
-  BuiltinOperator_GREATER_EQUAL = 62,
-  BuiltinOperator_LESS_EQUAL = 63,
-  BuiltinOperator_SELECT = 64,
-  BuiltinOperator_SLICE = 65,
-  BuiltinOperator_SIN = 66,
-  BuiltinOperator_TRANSPOSE_CONV = 67,
-  BuiltinOperator_SPARSE_TO_DENSE = 68,
-  BuiltinOperator_TILE = 69,
-  BuiltinOperator_EXPAND_DIMS = 70,
-  BuiltinOperator_EQUAL = 71,
-  BuiltinOperator_NOT_EQUAL = 72,
-  BuiltinOperator_LOG = 73,
-  BuiltinOperator_SUM = 74,
-  BuiltinOperator_SQRT = 75,
-  BuiltinOperator_RSQRT = 76,
-  BuiltinOperator_SHAPE = 77,
-  BuiltinOperator_POW = 78,
-  BuiltinOperator_ARG_MIN = 79,
-  BuiltinOperator_FAKE_QUANT = 80,
-  BuiltinOperator_REDUCE_PROD = 81,
-  BuiltinOperator_REDUCE_MAX = 82,
-  BuiltinOperator_PACK = 83,
-  BuiltinOperator_LOGICAL_OR = 84,
-  BuiltinOperator_ONE_HOT = 85,
-  BuiltinOperator_LOGICAL_AND = 86,
-  BuiltinOperator_LOGICAL_NOT = 87,
-  BuiltinOperator_UNPACK = 88,
-  BuiltinOperator_REDUCE_MIN = 89,
-  BuiltinOperator_FLOOR_DIV = 90,
-  BuiltinOperator_REDUCE_ANY = 91,
-  BuiltinOperator_SQUARE = 92,
-  BuiltinOperator_ZEROS_LIKE = 93,
-  BuiltinOperator_FILL = 94,
-  BuiltinOperator_FLOOR_MOD = 95,
-  BuiltinOperator_RANGE = 96,
-  BuiltinOperator_RESIZE_NEAREST_NEIGHBOR = 97,
-  BuiltinOperator_LEAKY_RELU = 98,
-  BuiltinOperator_SQUARED_DIFFERENCE = 99,
-  BuiltinOperator_MIRROR_PAD = 100,
-  BuiltinOperator_ABS = 101,
-  BuiltinOperator_SPLIT_V = 102,
-  BuiltinOperator_MIN = BuiltinOperator_ADD,
-  BuiltinOperator_MAX = BuiltinOperator_SPLIT_V
-};
-
-inline const BuiltinOperator (&EnumValuesBuiltinOperator())[102]
-{
-  static const BuiltinOperator values[] = {BuiltinOperator_ADD,
-                                           BuiltinOperator_AVERAGE_POOL_2D,
-                                           BuiltinOperator_CONCATENATION,
-                                           BuiltinOperator_CONV_2D,
-                                           BuiltinOperator_DEPTHWISE_CONV_2D,
-                                           BuiltinOperator_DEQUANTIZE,
-                                           BuiltinOperator_EMBEDDING_LOOKUP,
-                                           BuiltinOperator_FLOOR,
-                                           BuiltinOperator_FULLY_CONNECTED,
-                                           BuiltinOperator_HASHTABLE_LOOKUP,
-                                           BuiltinOperator_L2_NORMALIZATION,
-                                           BuiltinOperator_L2_POOL_2D,
-                                           BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION,
-                                           BuiltinOperator_LOGISTIC,
-                                           BuiltinOperator_LSH_PROJECTION,
-                                           BuiltinOperator_LSTM,
-                                           BuiltinOperator_MAX_POOL_2D,
-                                           BuiltinOperator_MUL,
-                                           BuiltinOperator_RELU,
-                                           BuiltinOperator_RELU_N1_TO_1,
-                                           BuiltinOperator_RELU6,
-                                           BuiltinOperator_RESHAPE,
-                                           BuiltinOperator_RESIZE_BILINEAR,
-                                           BuiltinOperator_RNN,
-                                           BuiltinOperator_SOFTMAX,
-                                           BuiltinOperator_SPACE_TO_DEPTH,
-                                           BuiltinOperator_SVDF,
-                                           BuiltinOperator_TANH,
-                                           BuiltinOperator_CONCAT_EMBEDDINGS,
-                                           BuiltinOperator_SKIP_GRAM,
-                                           BuiltinOperator_CALL,
-                                           BuiltinOperator_CUSTOM,
-                                           BuiltinOperator_EMBEDDING_LOOKUP_SPARSE,
-                                           BuiltinOperator_PAD,
-                                           BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN,
-                                           BuiltinOperator_GATHER,
-                                           BuiltinOperator_BATCH_TO_SPACE_ND,
-                                           BuiltinOperator_SPACE_TO_BATCH_ND,
-                                           BuiltinOperator_TRANSPOSE,
-                                           BuiltinOperator_MEAN,
-                                           BuiltinOperator_SUB,
-                                           BuiltinOperator_DIV,
-                                           BuiltinOperator_SQUEEZE,
-                                           BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM,
-                                           BuiltinOperator_STRIDED_SLICE,
-                                           BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN,
-                                           BuiltinOperator_EXP,
-                                           BuiltinOperator_TOPK_V2,
-                                           BuiltinOperator_SPLIT,
-                                           BuiltinOperator_LOG_SOFTMAX,
-                                           BuiltinOperator_DELEGATE,
-                                           BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM,
-                                           BuiltinOperator_CAST,
-                                           BuiltinOperator_PRELU,
-                                           BuiltinOperator_MAXIMUM,
-                                           BuiltinOperator_ARG_MAX,
-                                           BuiltinOperator_MINIMUM,
-                                           BuiltinOperator_LESS,
-                                           BuiltinOperator_NEG,
-                                           BuiltinOperator_PADV2,
-                                           BuiltinOperator_GREATER,
-                                           BuiltinOperator_GREATER_EQUAL,
-                                           BuiltinOperator_LESS_EQUAL,
-                                           BuiltinOperator_SELECT,
-                                           BuiltinOperator_SLICE,
-                                           BuiltinOperator_SIN,
-                                           BuiltinOperator_TRANSPOSE_CONV,
-                                           BuiltinOperator_SPARSE_TO_DENSE,
-                                           BuiltinOperator_TILE,
-                                           BuiltinOperator_EXPAND_DIMS,
-                                           BuiltinOperator_EQUAL,
-                                           BuiltinOperator_NOT_EQUAL,
-                                           BuiltinOperator_LOG,
-                                           BuiltinOperator_SUM,
-                                           BuiltinOperator_SQRT,
-                                           BuiltinOperator_RSQRT,
-                                           BuiltinOperator_SHAPE,
-                                           BuiltinOperator_POW,
-                                           BuiltinOperator_ARG_MIN,
-                                           BuiltinOperator_FAKE_QUANT,
-                                           BuiltinOperator_REDUCE_PROD,
-                                           BuiltinOperator_REDUCE_MAX,
-                                           BuiltinOperator_PACK,
-                                           BuiltinOperator_LOGICAL_OR,
-                                           BuiltinOperator_ONE_HOT,
-                                           BuiltinOperator_LOGICAL_AND,
-                                           BuiltinOperator_LOGICAL_NOT,
-                                           BuiltinOperator_UNPACK,
-                                           BuiltinOperator_REDUCE_MIN,
-                                           BuiltinOperator_FLOOR_DIV,
-                                           BuiltinOperator_REDUCE_ANY,
-                                           BuiltinOperator_SQUARE,
-                                           BuiltinOperator_ZEROS_LIKE,
-                                           BuiltinOperator_FILL,
-                                           BuiltinOperator_FLOOR_MOD,
-                                           BuiltinOperator_RANGE,
-                                           BuiltinOperator_RESIZE_NEAREST_NEIGHBOR,
-                                           BuiltinOperator_LEAKY_RELU,
-                                           BuiltinOperator_SQUARED_DIFFERENCE,
-                                           BuiltinOperator_MIRROR_PAD,
-                                           BuiltinOperator_ABS,
-                                           BuiltinOperator_SPLIT_V};
-  return values;
-}
-
-inline const char *const *EnumNamesBuiltinOperator()
-{
-  static const char *const names[] = {"ADD",
-                                      "AVERAGE_POOL_2D",
-                                      "CONCATENATION",
-                                      "CONV_2D",
-                                      "DEPTHWISE_CONV_2D",
-                                      "",
-                                      "DEQUANTIZE",
-                                      "EMBEDDING_LOOKUP",
-                                      "FLOOR",
-                                      "FULLY_CONNECTED",
-                                      "HASHTABLE_LOOKUP",
-                                      "L2_NORMALIZATION",
-                                      "L2_POOL_2D",
-                                      "LOCAL_RESPONSE_NORMALIZATION",
-                                      "LOGISTIC",
-                                      "LSH_PROJECTION",
-                                      "LSTM",
-                                      "MAX_POOL_2D",
-                                      "MUL",
-                                      "RELU",
-                                      "RELU_N1_TO_1",
-                                      "RELU6",
-                                      "RESHAPE",
-                                      "RESIZE_BILINEAR",
-                                      "RNN",
-                                      "SOFTMAX",
-                                      "SPACE_TO_DEPTH",
-                                      "SVDF",
-                                      "TANH",
-                                      "CONCAT_EMBEDDINGS",
-                                      "SKIP_GRAM",
-                                      "CALL",
-                                      "CUSTOM",
-                                      "EMBEDDING_LOOKUP_SPARSE",
-                                      "PAD",
-                                      "UNIDIRECTIONAL_SEQUENCE_RNN",
-                                      "GATHER",
-                                      "BATCH_TO_SPACE_ND",
-                                      "SPACE_TO_BATCH_ND",
-                                      "TRANSPOSE",
-                                      "MEAN",
-                                      "SUB",
-                                      "DIV",
-                                      "SQUEEZE",
-                                      "UNIDIRECTIONAL_SEQUENCE_LSTM",
-                                      "STRIDED_SLICE",
-                                      "BIDIRECTIONAL_SEQUENCE_RNN",
-                                      "EXP",
-                                      "TOPK_V2",
-                                      "SPLIT",
-                                      "LOG_SOFTMAX",
-                                      "DELEGATE",
-                                      "BIDIRECTIONAL_SEQUENCE_LSTM",
-                                      "CAST",
-                                      "PRELU",
-                                      "MAXIMUM",
-                                      "ARG_MAX",
-                                      "MINIMUM",
-                                      "LESS",
-                                      "NEG",
-                                      "PADV2",
-                                      "GREATER",
-                                      "GREATER_EQUAL",
-                                      "LESS_EQUAL",
-                                      "SELECT",
-                                      "SLICE",
-                                      "SIN",
-                                      "TRANSPOSE_CONV",
-                                      "SPARSE_TO_DENSE",
-                                      "TILE",
-                                      "EXPAND_DIMS",
-                                      "EQUAL",
-                                      "NOT_EQUAL",
-                                      "LOG",
-                                      "SUM",
-                                      "SQRT",
-                                      "RSQRT",
-                                      "SHAPE",
-                                      "POW",
-                                      "ARG_MIN",
-                                      "FAKE_QUANT",
-                                      "REDUCE_PROD",
-                                      "REDUCE_MAX",
-                                      "PACK",
-                                      "LOGICAL_OR",
-                                      "ONE_HOT",
-                                      "LOGICAL_AND",
-                                      "LOGICAL_NOT",
-                                      "UNPACK",
-                                      "REDUCE_MIN",
-                                      "FLOOR_DIV",
-                                      "REDUCE_ANY",
-                                      "SQUARE",
-                                      "ZEROS_LIKE",
-                                      "FILL",
-                                      "FLOOR_MOD",
-                                      "RANGE",
-                                      "RESIZE_NEAREST_NEIGHBOR",
-                                      "LEAKY_RELU",
-                                      "SQUARED_DIFFERENCE",
-                                      "MIRROR_PAD",
-                                      "ABS",
-                                      "SPLIT_V",
-                                      nullptr};
-  return names;
-}
-
-inline const char *EnumNameBuiltinOperator(BuiltinOperator e)
-{
-  const size_t index = static_cast<int>(e);
-  return EnumNamesBuiltinOperator()[index];
-}
-
-enum BuiltinOptions
-{
-  BuiltinOptions_NONE = 0,
-  BuiltinOptions_Conv2DOptions = 1,
-  BuiltinOptions_DepthwiseConv2DOptions = 2,
-  BuiltinOptions_ConcatEmbeddingsOptions = 3,
-  BuiltinOptions_LSHProjectionOptions = 4,
-  BuiltinOptions_Pool2DOptions = 5,
-  BuiltinOptions_SVDFOptions = 6,
-  BuiltinOptions_RNNOptions = 7,
-  BuiltinOptions_FullyConnectedOptions = 8,
-  BuiltinOptions_SoftmaxOptions = 9,
-  BuiltinOptions_ConcatenationOptions = 10,
-  BuiltinOptions_AddOptions = 11,
-  BuiltinOptions_L2NormOptions = 12,
-  BuiltinOptions_LocalResponseNormalizationOptions = 13,
-  BuiltinOptions_LSTMOptions = 14,
-  BuiltinOptions_ResizeBilinearOptions = 15,
-  BuiltinOptions_CallOptions = 16,
-  BuiltinOptions_ReshapeOptions = 17,
-  BuiltinOptions_SkipGramOptions = 18,
-  BuiltinOptions_SpaceToDepthOptions = 19,
-  BuiltinOptions_EmbeddingLookupSparseOptions = 20,
-  BuiltinOptions_MulOptions = 21,
-  BuiltinOptions_PadOptions = 22,
-  BuiltinOptions_GatherOptions = 23,
-  BuiltinOptions_BatchToSpaceNDOptions = 24,
-  BuiltinOptions_SpaceToBatchNDOptions = 25,
-  BuiltinOptions_TransposeOptions = 26,
-  BuiltinOptions_ReducerOptions = 27,
-  BuiltinOptions_SubOptions = 28,
-  BuiltinOptions_DivOptions = 29,
-  BuiltinOptions_SqueezeOptions = 30,
-  BuiltinOptions_SequenceRNNOptions = 31,
-  BuiltinOptions_StridedSliceOptions = 32,
-  BuiltinOptions_ExpOptions = 33,
-  BuiltinOptions_TopKV2Options = 34,
-  BuiltinOptions_SplitOptions = 35,
-  BuiltinOptions_LogSoftmaxOptions = 36,
-  BuiltinOptions_CastOptions = 37,
-  BuiltinOptions_DequantizeOptions = 38,
-  BuiltinOptions_MaximumMinimumOptions = 39,
-  BuiltinOptions_ArgMaxOptions = 40,
-  BuiltinOptions_LessOptions = 41,
-  BuiltinOptions_NegOptions = 42,
-  BuiltinOptions_PadV2Options = 43,
-  BuiltinOptions_GreaterOptions = 44,
-  BuiltinOptions_GreaterEqualOptions = 45,
-  BuiltinOptions_LessEqualOptions = 46,
-  BuiltinOptions_SelectOptions = 47,
-  BuiltinOptions_SliceOptions = 48,
-  BuiltinOptions_TransposeConvOptions = 49,
-  BuiltinOptions_SparseToDenseOptions = 50,
-  BuiltinOptions_TileOptions = 51,
-  BuiltinOptions_ExpandDimsOptions = 52,
-  BuiltinOptions_EqualOptions = 53,
-  BuiltinOptions_NotEqualOptions = 54,
-  BuiltinOptions_ShapeOptions = 55,
-  BuiltinOptions_PowOptions = 56,
-  BuiltinOptions_ArgMinOptions = 57,
-  BuiltinOptions_FakeQuantOptions = 58,
-  BuiltinOptions_PackOptions = 59,
-  BuiltinOptions_LogicalOrOptions = 60,
-  BuiltinOptions_OneHotOptions = 61,
-  BuiltinOptions_LogicalAndOptions = 62,
-  BuiltinOptions_LogicalNotOptions = 63,
-  BuiltinOptions_UnpackOptions = 64,
-  BuiltinOptions_FloorDivOptions = 65,
-  BuiltinOptions_SquareOptions = 66,
-  BuiltinOptions_ZerosLikeOptions = 67,
-  BuiltinOptions_FillOptions = 68,
-  BuiltinOptions_BidirectionalSequenceLSTMOptions = 69,
-  BuiltinOptions_BidirectionalSequenceRNNOptions = 70,
-  BuiltinOptions_UnidirectionalSequenceLSTMOptions = 71,
-  BuiltinOptions_FloorModOptions = 72,
-  BuiltinOptions_RangeOptions = 73,
-  BuiltinOptions_ResizeNearestNeighborOptions = 74,
-  BuiltinOptions_LeakyReluOptions = 75,
-  BuiltinOptions_SquaredDifferenceOptions = 76,
-  BuiltinOptions_MirrorPadOptions = 77,
-  BuiltinOptions_AbsOptions = 78,
-  BuiltinOptions_SplitVOptions = 79,
-  BuiltinOptions_MIN = BuiltinOptions_NONE,
-  BuiltinOptions_MAX = BuiltinOptions_SplitVOptions
-};
-
-inline const BuiltinOptions (&EnumValuesBuiltinOptions())[80]
-{
-  static const BuiltinOptions values[] = {BuiltinOptions_NONE,
-                                          BuiltinOptions_Conv2DOptions,
-                                          BuiltinOptions_DepthwiseConv2DOptions,
-                                          BuiltinOptions_ConcatEmbeddingsOptions,
-                                          BuiltinOptions_LSHProjectionOptions,
-                                          BuiltinOptions_Pool2DOptions,
-                                          BuiltinOptions_SVDFOptions,
-                                          BuiltinOptions_RNNOptions,
-                                          BuiltinOptions_FullyConnectedOptions,
-                                          BuiltinOptions_SoftmaxOptions,
-                                          BuiltinOptions_ConcatenationOptions,
-                                          BuiltinOptions_AddOptions,
-                                          BuiltinOptions_L2NormOptions,
-                                          BuiltinOptions_LocalResponseNormalizationOptions,
-                                          BuiltinOptions_LSTMOptions,
-                                          BuiltinOptions_ResizeBilinearOptions,
-                                          BuiltinOptions_CallOptions,
-                                          BuiltinOptions_ReshapeOptions,
-                                          BuiltinOptions_SkipGramOptions,
-                                          BuiltinOptions_SpaceToDepthOptions,
-                                          BuiltinOptions_EmbeddingLookupSparseOptions,
-                                          BuiltinOptions_MulOptions,
-                                          BuiltinOptions_PadOptions,
-                                          BuiltinOptions_GatherOptions,
-                                          BuiltinOptions_BatchToSpaceNDOptions,
-                                          BuiltinOptions_SpaceToBatchNDOptions,
-                                          BuiltinOptions_TransposeOptions,
-                                          BuiltinOptions_ReducerOptions,
-                                          BuiltinOptions_SubOptions,
-                                          BuiltinOptions_DivOptions,
-                                          BuiltinOptions_SqueezeOptions,
-                                          BuiltinOptions_SequenceRNNOptions,
-                                          BuiltinOptions_StridedSliceOptions,
-                                          BuiltinOptions_ExpOptions,
-                                          BuiltinOptions_TopKV2Options,
-                                          BuiltinOptions_SplitOptions,
-                                          BuiltinOptions_LogSoftmaxOptions,
-                                          BuiltinOptions_CastOptions,
-                                          BuiltinOptions_DequantizeOptions,
-                                          BuiltinOptions_MaximumMinimumOptions,
-                                          BuiltinOptions_ArgMaxOptions,
-                                          BuiltinOptions_LessOptions,
-                                          BuiltinOptions_NegOptions,
-                                          BuiltinOptions_PadV2Options,
-                                          BuiltinOptions_GreaterOptions,
-                                          BuiltinOptions_GreaterEqualOptions,
-                                          BuiltinOptions_LessEqualOptions,
-                                          BuiltinOptions_SelectOptions,
-                                          BuiltinOptions_SliceOptions,
-                                          BuiltinOptions_TransposeConvOptions,
-                                          BuiltinOptions_SparseToDenseOptions,
-                                          BuiltinOptions_TileOptions,
-                                          BuiltinOptions_ExpandDimsOptions,
-                                          BuiltinOptions_EqualOptions,
-                                          BuiltinOptions_NotEqualOptions,
-                                          BuiltinOptions_ShapeOptions,
-                                          BuiltinOptions_PowOptions,
-                                          BuiltinOptions_ArgMinOptions,
-                                          BuiltinOptions_FakeQuantOptions,
-                                          BuiltinOptions_PackOptions,
-                                          BuiltinOptions_LogicalOrOptions,
-                                          BuiltinOptions_OneHotOptions,
-                                          BuiltinOptions_LogicalAndOptions,
-                                          BuiltinOptions_LogicalNotOptions,
-                                          BuiltinOptions_UnpackOptions,
-                                          BuiltinOptions_FloorDivOptions,
-                                          BuiltinOptions_SquareOptions,
-                                          BuiltinOptions_ZerosLikeOptions,
-                                          BuiltinOptions_FillOptions,
-                                          BuiltinOptions_BidirectionalSequenceLSTMOptions,
-                                          BuiltinOptions_BidirectionalSequenceRNNOptions,
-                                          BuiltinOptions_UnidirectionalSequenceLSTMOptions,
-                                          BuiltinOptions_FloorModOptions,
-                                          BuiltinOptions_RangeOptions,
-                                          BuiltinOptions_ResizeNearestNeighborOptions,
-                                          BuiltinOptions_LeakyReluOptions,
-                                          BuiltinOptions_SquaredDifferenceOptions,
-                                          BuiltinOptions_MirrorPadOptions,
-                                          BuiltinOptions_AbsOptions,
-                                          BuiltinOptions_SplitVOptions};
-  return values;
-}
-
-inline const char *const *EnumNamesBuiltinOptions()
-{
-  static const char *const names[] = {"NONE",
-                                      "Conv2DOptions",
-                                      "DepthwiseConv2DOptions",
-                                      "ConcatEmbeddingsOptions",
-                                      "LSHProjectionOptions",
-                                      "Pool2DOptions",
-                                      "SVDFOptions",
-                                      "RNNOptions",
-                                      "FullyConnectedOptions",
-                                      "SoftmaxOptions",
-                                      "ConcatenationOptions",
-                                      "AddOptions",
-                                      "L2NormOptions",
-                                      "LocalResponseNormalizationOptions",
-                                      "LSTMOptions",
-                                      "ResizeBilinearOptions",
-                                      "CallOptions",
-                                      "ReshapeOptions",
-                                      "SkipGramOptions",
-                                      "SpaceToDepthOptions",
-                                      "EmbeddingLookupSparseOptions",
-                                      "MulOptions",
-                                      "PadOptions",
-                                      "GatherOptions",
-                                      "BatchToSpaceNDOptions",
-                                      "SpaceToBatchNDOptions",
-                                      "TransposeOptions",
-                                      "ReducerOptions",
-                                      "SubOptions",
-                                      "DivOptions",
-                                      "SqueezeOptions",
-                                      "SequenceRNNOptions",
-                                      "StridedSliceOptions",
-                                      "ExpOptions",
-                                      "TopKV2Options",
-                                      "SplitOptions",
-                                      "LogSoftmaxOptions",
-                                      "CastOptions",
-                                      "DequantizeOptions",
-                                      "MaximumMinimumOptions",
-                                      "ArgMaxOptions",
-                                      "LessOptions",
-                                      "NegOptions",
-                                      "PadV2Options",
-                                      "GreaterOptions",
-                                      "GreaterEqualOptions",
-                                      "LessEqualOptions",
-                                      "SelectOptions",
-                                      "SliceOptions",
-                                      "TransposeConvOptions",
-                                      "SparseToDenseOptions",
-                                      "TileOptions",
-                                      "ExpandDimsOptions",
-                                      "EqualOptions",
-                                      "NotEqualOptions",
-                                      "ShapeOptions",
-                                      "PowOptions",
-                                      "ArgMinOptions",
-                                      "FakeQuantOptions",
-                                      "PackOptions",
-                                      "LogicalOrOptions",
-                                      "OneHotOptions",
-                                      "LogicalAndOptions",
-                                      "LogicalNotOptions",
-                                      "UnpackOptions",
-                                      "FloorDivOptions",
-                                      "SquareOptions",
-                                      "ZerosLikeOptions",
-                                      "FillOptions",
-                                      "BidirectionalSequenceLSTMOptions",
-                                      "BidirectionalSequenceRNNOptions",
-                                      "UnidirectionalSequenceLSTMOptions",
-                                      "FloorModOptions",
-                                      "RangeOptions",
-                                      "ResizeNearestNeighborOptions",
-                                      "LeakyReluOptions",
-                                      "SquaredDifferenceOptions",
-                                      "MirrorPadOptions",
-                                      "AbsOptions",
-                                      "SplitVOptions",
-                                      nullptr};
-  return names;
-}
-
-inline const char *EnumNameBuiltinOptions(BuiltinOptions e)
-{
-  const size_t index = static_cast<int>(e);
-  return EnumNamesBuiltinOptions()[index];
-}
-
-template <typename T> struct BuiltinOptionsTraits
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_NONE;
-};
-
-template <> struct BuiltinOptionsTraits<Conv2DOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_Conv2DOptions;
-};
-
-template <> struct BuiltinOptionsTraits<DepthwiseConv2DOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_DepthwiseConv2DOptions;
-};
-
-template <> struct BuiltinOptionsTraits<ConcatEmbeddingsOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_ConcatEmbeddingsOptions;
-};
-
-template <> struct BuiltinOptionsTraits<LSHProjectionOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_LSHProjectionOptions;
-};
-
-template <> struct BuiltinOptionsTraits<Pool2DOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_Pool2DOptions;
-};
-
-template <> struct BuiltinOptionsTraits<SVDFOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_SVDFOptions;
-};
-
-template <> struct BuiltinOptionsTraits<RNNOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_RNNOptions;
-};
-
-template <> struct BuiltinOptionsTraits<FullyConnectedOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_FullyConnectedOptions;
-};
-
-template <> struct BuiltinOptionsTraits<SoftmaxOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_SoftmaxOptions;
-};
-
-template <> struct BuiltinOptionsTraits<ConcatenationOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_ConcatenationOptions;
-};
-
-template <> struct BuiltinOptionsTraits<AddOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_AddOptions;
-};
-
-template <> struct BuiltinOptionsTraits<L2NormOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_L2NormOptions;
-};
-
-template <> struct BuiltinOptionsTraits<LocalResponseNormalizationOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_LocalResponseNormalizationOptions;
-};
-
-template <> struct BuiltinOptionsTraits<LSTMOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_LSTMOptions;
-};
-
-template <> struct BuiltinOptionsTraits<ResizeBilinearOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_ResizeBilinearOptions;
-};
-
-template <> struct BuiltinOptionsTraits<CallOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_CallOptions;
-};
-
-template <> struct BuiltinOptionsTraits<ReshapeOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_ReshapeOptions;
-};
-
-template <> struct BuiltinOptionsTraits<SkipGramOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_SkipGramOptions;
-};
-
-template <> struct BuiltinOptionsTraits<SpaceToDepthOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_SpaceToDepthOptions;
-};
-
-template <> struct BuiltinOptionsTraits<EmbeddingLookupSparseOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_EmbeddingLookupSparseOptions;
-};
-
-template <> struct BuiltinOptionsTraits<MulOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_MulOptions;
-};
-
-template <> struct BuiltinOptionsTraits<PadOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_PadOptions;
-};
-
-template <> struct BuiltinOptionsTraits<GatherOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_GatherOptions;
-};
-
-template <> struct BuiltinOptionsTraits<BatchToSpaceNDOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_BatchToSpaceNDOptions;
-};
-
-template <> struct BuiltinOptionsTraits<SpaceToBatchNDOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_SpaceToBatchNDOptions;
-};
-
-template <> struct BuiltinOptionsTraits<TransposeOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_TransposeOptions;
-};
-
-template <> struct BuiltinOptionsTraits<ReducerOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_ReducerOptions;
-};
-
-template <> struct BuiltinOptionsTraits<SubOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_SubOptions;
-};
-
-template <> struct BuiltinOptionsTraits<DivOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_DivOptions;
-};
-
-template <> struct BuiltinOptionsTraits<SqueezeOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_SqueezeOptions;
-};
-
-template <> struct BuiltinOptionsTraits<SequenceRNNOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_SequenceRNNOptions;
-};
-
-template <> struct BuiltinOptionsTraits<StridedSliceOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_StridedSliceOptions;
-};
-
-template <> struct BuiltinOptionsTraits<ExpOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_ExpOptions;
-};
-
-template <> struct BuiltinOptionsTraits<TopKV2Options>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_TopKV2Options;
-};
-
-template <> struct BuiltinOptionsTraits<SplitOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_SplitOptions;
-};
-
-template <> struct BuiltinOptionsTraits<LogSoftmaxOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_LogSoftmaxOptions;
-};
-
-template <> struct BuiltinOptionsTraits<CastOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_CastOptions;
-};
-
-template <> struct BuiltinOptionsTraits<DequantizeOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_DequantizeOptions;
-};
-
-template <> struct BuiltinOptionsTraits<MaximumMinimumOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_MaximumMinimumOptions;
-};
-
-template <> struct BuiltinOptionsTraits<ArgMaxOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_ArgMaxOptions;
-};
-
-template <> struct BuiltinOptionsTraits<LessOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_LessOptions;
-};
-
-template <> struct BuiltinOptionsTraits<NegOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_NegOptions;
-};
-
-template <> struct BuiltinOptionsTraits<PadV2Options>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_PadV2Options;
-};
-
-template <> struct BuiltinOptionsTraits<GreaterOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_GreaterOptions;
-};
-
-template <> struct BuiltinOptionsTraits<GreaterEqualOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_GreaterEqualOptions;
-};
-
-template <> struct BuiltinOptionsTraits<LessEqualOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_LessEqualOptions;
-};
-
-template <> struct BuiltinOptionsTraits<SelectOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_SelectOptions;
-};
-
-template <> struct BuiltinOptionsTraits<SliceOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_SliceOptions;
-};
-
-template <> struct BuiltinOptionsTraits<TransposeConvOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_TransposeConvOptions;
-};
-
-template <> struct BuiltinOptionsTraits<SparseToDenseOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_SparseToDenseOptions;
-};
-
-template <> struct BuiltinOptionsTraits<TileOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_TileOptions;
-};
-
-template <> struct BuiltinOptionsTraits<ExpandDimsOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_ExpandDimsOptions;
-};
-
-template <> struct BuiltinOptionsTraits<EqualOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_EqualOptions;
-};
-
-template <> struct BuiltinOptionsTraits<NotEqualOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_NotEqualOptions;
-};
-
-template <> struct BuiltinOptionsTraits<ShapeOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_ShapeOptions;
-};
-
-template <> struct BuiltinOptionsTraits<PowOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_PowOptions;
-};
-
-template <> struct BuiltinOptionsTraits<ArgMinOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_ArgMinOptions;
-};
-
-template <> struct BuiltinOptionsTraits<FakeQuantOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_FakeQuantOptions;
-};
-
-template <> struct BuiltinOptionsTraits<PackOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_PackOptions;
-};
-
-template <> struct BuiltinOptionsTraits<LogicalOrOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_LogicalOrOptions;
-};
-
-template <> struct BuiltinOptionsTraits<OneHotOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_OneHotOptions;
-};
-
-template <> struct BuiltinOptionsTraits<LogicalAndOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_LogicalAndOptions;
-};
-
-template <> struct BuiltinOptionsTraits<LogicalNotOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_LogicalNotOptions;
-};
-
-template <> struct BuiltinOptionsTraits<UnpackOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_UnpackOptions;
-};
-
-template <> struct BuiltinOptionsTraits<FloorDivOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_FloorDivOptions;
-};
-
-template <> struct BuiltinOptionsTraits<SquareOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_SquareOptions;
-};
-
-template <> struct BuiltinOptionsTraits<ZerosLikeOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_ZerosLikeOptions;
-};
-
-template <> struct BuiltinOptionsTraits<FillOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_FillOptions;
-};
-
-template <> struct BuiltinOptionsTraits<BidirectionalSequenceLSTMOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_BidirectionalSequenceLSTMOptions;
-};
-
-template <> struct BuiltinOptionsTraits<BidirectionalSequenceRNNOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_BidirectionalSequenceRNNOptions;
-};
-
-template <> struct BuiltinOptionsTraits<UnidirectionalSequenceLSTMOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_UnidirectionalSequenceLSTMOptions;
-};
-
-template <> struct BuiltinOptionsTraits<FloorModOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_FloorModOptions;
-};
-
-template <> struct BuiltinOptionsTraits<RangeOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_RangeOptions;
-};
-
-template <> struct BuiltinOptionsTraits<ResizeNearestNeighborOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_ResizeNearestNeighborOptions;
-};
-
-template <> struct BuiltinOptionsTraits<LeakyReluOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_LeakyReluOptions;
-};
-
-template <> struct BuiltinOptionsTraits<SquaredDifferenceOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_SquaredDifferenceOptions;
-};
-
-template <> struct BuiltinOptionsTraits<MirrorPadOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_MirrorPadOptions;
-};
-
-template <> struct BuiltinOptionsTraits<AbsOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_AbsOptions;
-};
-
-template <> struct BuiltinOptionsTraits<SplitVOptions>
-{
-  static const BuiltinOptions enum_value = BuiltinOptions_SplitVOptions;
-};
-
-bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type);
-bool VerifyBuiltinOptionsVector(flatbuffers::Verifier &verifier,
-                                const flatbuffers::Vector<flatbuffers::Offset<void>> *values,
-                                const flatbuffers::Vector<uint8_t> *types);
-
-enum Padding
-{
-  Padding_SAME = 0,
-  Padding_VALID = 1,
-  Padding_MIN = Padding_SAME,
-  Padding_MAX = Padding_VALID
-};
-
-inline const Padding (&EnumValuesPadding())[2]
-{
-  static const Padding values[] = {Padding_SAME, Padding_VALID};
-  return values;
-}
-
-inline const char *const *EnumNamesPadding()
-{
-  static const char *const names[] = {"SAME", "VALID", nullptr};
-  return names;
-}
-
-inline const char *EnumNamePadding(Padding e)
-{
-  const size_t index = static_cast<int>(e);
-  return EnumNamesPadding()[index];
-}
-
-enum ActivationFunctionType
-{
-  ActivationFunctionType_NONE = 0,
-  ActivationFunctionType_RELU = 1,
-  ActivationFunctionType_RELU_N1_TO_1 = 2,
-  ActivationFunctionType_RELU6 = 3,
-  ActivationFunctionType_TANH = 4,
-  ActivationFunctionType_SIGN_BIT = 5,
-  ActivationFunctionType_MIN = ActivationFunctionType_NONE,
-  ActivationFunctionType_MAX = ActivationFunctionType_SIGN_BIT
-};
-
-inline const ActivationFunctionType (&EnumValuesActivationFunctionType())[6]
-{
-  static const ActivationFunctionType values[] = {
-      ActivationFunctionType_NONE,         ActivationFunctionType_RELU,
-      ActivationFunctionType_RELU_N1_TO_1, ActivationFunctionType_RELU6,
-      ActivationFunctionType_TANH,         ActivationFunctionType_SIGN_BIT};
-  return values;
-}
-
-inline const char *const *EnumNamesActivationFunctionType()
-{
-  static const char *const names[] = {"NONE", "RELU",     "RELU_N1_TO_1", "RELU6",
-                                      "TANH", "SIGN_BIT", nullptr};
-  return names;
-}
-
-inline const char *EnumNameActivationFunctionType(ActivationFunctionType e)
-{
-  const size_t index = static_cast<int>(e);
-  return EnumNamesActivationFunctionType()[index];
-}
-
-enum LSHProjectionType
-{
-  LSHProjectionType_UNKNOWN = 0,
-  LSHProjectionType_SPARSE = 1,
-  LSHProjectionType_DENSE = 2,
-  LSHProjectionType_MIN = LSHProjectionType_UNKNOWN,
-  LSHProjectionType_MAX = LSHProjectionType_DENSE
-};
-
-inline const LSHProjectionType (&EnumValuesLSHProjectionType())[3]
-{
-  static const LSHProjectionType values[] = {LSHProjectionType_UNKNOWN, LSHProjectionType_SPARSE,
-                                             LSHProjectionType_DENSE};
-  return values;
-}
-
-inline const char *const *EnumNamesLSHProjectionType()
-{
-  static const char *const names[] = {"UNKNOWN", "SPARSE", "DENSE", nullptr};
-  return names;
-}
-
-inline const char *EnumNameLSHProjectionType(LSHProjectionType e)
-{
-  const size_t index = static_cast<int>(e);
-  return EnumNamesLSHProjectionType()[index];
-}
-
-enum FullyConnectedOptionsWeightsFormat
-{
-  FullyConnectedOptionsWeightsFormat_DEFAULT = 0,
-  FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8 = 1,
-  FullyConnectedOptionsWeightsFormat_MIN = FullyConnectedOptionsWeightsFormat_DEFAULT,
-  FullyConnectedOptionsWeightsFormat_MAX = FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8
-};
-
-inline const FullyConnectedOptionsWeightsFormat (&EnumValuesFullyConnectedOptionsWeightsFormat())[2]
-{
-  static const FullyConnectedOptionsWeightsFormat values[] = {
-      FullyConnectedOptionsWeightsFormat_DEFAULT,
-      FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8};
-  return values;
-}
-
-inline const char *const *EnumNamesFullyConnectedOptionsWeightsFormat()
-{
-  static const char *const names[] = {"DEFAULT", "SHUFFLED4x16INT8", nullptr};
-  return names;
-}
-
-inline const char *EnumNameFullyConnectedOptionsWeightsFormat(FullyConnectedOptionsWeightsFormat e)
-{
-  const size_t index = static_cast<int>(e);
-  return EnumNamesFullyConnectedOptionsWeightsFormat()[index];
-}
-
-enum LSTMKernelType
-{
-  LSTMKernelType_FULL = 0,
-  LSTMKernelType_BASIC = 1,
-  LSTMKernelType_MIN = LSTMKernelType_FULL,
-  LSTMKernelType_MAX = LSTMKernelType_BASIC
-};
-
-inline const LSTMKernelType (&EnumValuesLSTMKernelType())[2]
-{
-  static const LSTMKernelType values[] = {LSTMKernelType_FULL, LSTMKernelType_BASIC};
-  return values;
-}
-
-inline const char *const *EnumNamesLSTMKernelType()
-{
-  static const char *const names[] = {"FULL", "BASIC", nullptr};
-  return names;
-}
-
-inline const char *EnumNameLSTMKernelType(LSTMKernelType e)
-{
-  const size_t index = static_cast<int>(e);
-  return EnumNamesLSTMKernelType()[index];
-}
-
-enum CombinerType
-{
-  CombinerType_SUM = 0,
-  CombinerType_MEAN = 1,
-  CombinerType_SQRTN = 2,
-  CombinerType_MIN = CombinerType_SUM,
-  CombinerType_MAX = CombinerType_SQRTN
-};
-
-inline const CombinerType (&EnumValuesCombinerType())[3]
-{
-  static const CombinerType values[] = {CombinerType_SUM, CombinerType_MEAN, CombinerType_SQRTN};
-  return values;
-}
-
-inline const char *const *EnumNamesCombinerType()
-{
-  static const char *const names[] = {"SUM", "MEAN", "SQRTN", nullptr};
-  return names;
-}
-
-inline const char *EnumNameCombinerType(CombinerType e)
-{
-  const size_t index = static_cast<int>(e);
-  return EnumNamesCombinerType()[index];
-}
-
-enum MirrorPadMode
-{
-  MirrorPadMode_REFLECT = 0,
-  MirrorPadMode_SYMMETRIC = 1,
-  MirrorPadMode_MIN = MirrorPadMode_REFLECT,
-  MirrorPadMode_MAX = MirrorPadMode_SYMMETRIC
-};
-
-inline const MirrorPadMode (&EnumValuesMirrorPadMode())[2]
-{
-  static const MirrorPadMode values[] = {MirrorPadMode_REFLECT, MirrorPadMode_SYMMETRIC};
-  return values;
-}
-
-inline const char *const *EnumNamesMirrorPadMode()
-{
-  static const char *const names[] = {"REFLECT", "SYMMETRIC", nullptr};
-  return names;
-}
-
-inline const char *EnumNameMirrorPadMode(MirrorPadMode e)
-{
-  const size_t index = static_cast<int>(e);
-  return EnumNamesMirrorPadMode()[index];
-}
-
-enum CustomOptionsFormat
-{
-  CustomOptionsFormat_FLEXBUFFERS = 0,
-  CustomOptionsFormat_MIN = CustomOptionsFormat_FLEXBUFFERS,
-  CustomOptionsFormat_MAX = CustomOptionsFormat_FLEXBUFFERS
-};
-
-inline const CustomOptionsFormat (&EnumValuesCustomOptionsFormat())[1]
-{
-  static const CustomOptionsFormat values[] = {CustomOptionsFormat_FLEXBUFFERS};
-  return values;
-}
-
-inline const char *const *EnumNamesCustomOptionsFormat()
-{
-  static const char *const names[] = {"FLEXBUFFERS", nullptr};
-  return names;
-}
-
-inline const char *EnumNameCustomOptionsFormat(CustomOptionsFormat e)
-{
-  const size_t index = static_cast<int>(e);
-  return EnumNamesCustomOptionsFormat()[index];
-}
-
-struct CustomQuantization FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_CUSTOM = 4
-  };
-  const flatbuffers::Vector<uint8_t> *custom() const
-  {
-    return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_CUSTOM);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_CUSTOM) &&
-           verifier.VerifyVector(custom()) && verifier.EndTable();
-  }
-};
-
-struct CustomQuantizationBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_custom(flatbuffers::Offset<flatbuffers::Vector<uint8_t>> custom)
-  {
-    fbb_.AddOffset(CustomQuantization::VT_CUSTOM, custom);
-  }
-  explicit CustomQuantizationBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  CustomQuantizationBuilder &operator=(const CustomQuantizationBuilder &);
-  flatbuffers::Offset<CustomQuantization> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<CustomQuantization>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<CustomQuantization>
-CreateCustomQuantization(flatbuffers::FlatBufferBuilder &_fbb,
-                         flatbuffers::Offset<flatbuffers::Vector<uint8_t>> custom = 0)
-{
-  CustomQuantizationBuilder builder_(_fbb);
-  builder_.add_custom(custom);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<CustomQuantization>
-CreateCustomQuantizationDirect(flatbuffers::FlatBufferBuilder &_fbb,
-                               const std::vector<uint8_t> *custom = nullptr)
-{
-  return neurun_tflite::CreateCustomQuantization(_fbb,
-                                                 custom ? _fbb.CreateVector<uint8_t>(*custom) : 0);
-}
-
-struct QuantizationParameters FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_MIN = 4,
-    VT_MAX = 6,
-    VT_SCALE = 8,
-    VT_ZERO_POINT = 10,
-    VT_DETAILS_TYPE = 12,
-    VT_DETAILS = 14
-  };
-  const flatbuffers::Vector<float> *min() const
-  {
-    return GetPointer<const flatbuffers::Vector<float> *>(VT_MIN);
-  }
-  const flatbuffers::Vector<float> *max() const
-  {
-    return GetPointer<const flatbuffers::Vector<float> *>(VT_MAX);
-  }
-  const flatbuffers::Vector<float> *scale() const
-  {
-    return GetPointer<const flatbuffers::Vector<float> *>(VT_SCALE);
-  }
-  const flatbuffers::Vector<int64_t> *zero_point() const
-  {
-    return GetPointer<const flatbuffers::Vector<int64_t> *>(VT_ZERO_POINT);
-  }
-  QuantizationDetails details_type() const
-  {
-    return static_cast<QuantizationDetails>(GetField<uint8_t>(VT_DETAILS_TYPE, 0));
-  }
-  const void *details() const { return GetPointer<const void *>(VT_DETAILS); }
-  template <typename T> const T *details_as() const;
-  const CustomQuantization *details_as_CustomQuantization() const
-  {
-    return details_type() == QuantizationDetails_CustomQuantization
-               ? static_cast<const CustomQuantization *>(details())
-               : nullptr;
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_MIN) &&
-           verifier.VerifyVector(min()) && VerifyOffset(verifier, VT_MAX) &&
-           verifier.VerifyVector(max()) && VerifyOffset(verifier, VT_SCALE) &&
-           verifier.VerifyVector(scale()) && VerifyOffset(verifier, VT_ZERO_POINT) &&
-           verifier.VerifyVector(zero_point()) && VerifyField<uint8_t>(verifier, VT_DETAILS_TYPE) &&
-           VerifyOffset(verifier, VT_DETAILS) &&
-           VerifyQuantizationDetails(verifier, details(), details_type()) && verifier.EndTable();
-  }
-};
-
-template <>
-inline const CustomQuantization *QuantizationParameters::details_as<CustomQuantization>() const
-{
-  return details_as_CustomQuantization();
-}
-
-struct QuantizationParametersBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_min(flatbuffers::Offset<flatbuffers::Vector<float>> min)
-  {
-    fbb_.AddOffset(QuantizationParameters::VT_MIN, min);
-  }
-  void add_max(flatbuffers::Offset<flatbuffers::Vector<float>> max)
-  {
-    fbb_.AddOffset(QuantizationParameters::VT_MAX, max);
-  }
-  void add_scale(flatbuffers::Offset<flatbuffers::Vector<float>> scale)
-  {
-    fbb_.AddOffset(QuantizationParameters::VT_SCALE, scale);
-  }
-  void add_zero_point(flatbuffers::Offset<flatbuffers::Vector<int64_t>> zero_point)
-  {
-    fbb_.AddOffset(QuantizationParameters::VT_ZERO_POINT, zero_point);
-  }
-  void add_details_type(QuantizationDetails details_type)
-  {
-    fbb_.AddElement<uint8_t>(QuantizationParameters::VT_DETAILS_TYPE,
-                             static_cast<uint8_t>(details_type), 0);
-  }
-  void add_details(flatbuffers::Offset<void> details)
-  {
-    fbb_.AddOffset(QuantizationParameters::VT_DETAILS, details);
-  }
-  explicit QuantizationParametersBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  QuantizationParametersBuilder &operator=(const QuantizationParametersBuilder &);
-  flatbuffers::Offset<QuantizationParameters> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<QuantizationParameters>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<QuantizationParameters>
-CreateQuantizationParameters(flatbuffers::FlatBufferBuilder &_fbb,
-                             flatbuffers::Offset<flatbuffers::Vector<float>> min = 0,
-                             flatbuffers::Offset<flatbuffers::Vector<float>> max = 0,
-                             flatbuffers::Offset<flatbuffers::Vector<float>> scale = 0,
-                             flatbuffers::Offset<flatbuffers::Vector<int64_t>> zero_point = 0,
-                             QuantizationDetails details_type = QuantizationDetails_NONE,
-                             flatbuffers::Offset<void> details = 0)
-{
-  QuantizationParametersBuilder builder_(_fbb);
-  builder_.add_details(details);
-  builder_.add_zero_point(zero_point);
-  builder_.add_scale(scale);
-  builder_.add_max(max);
-  builder_.add_min(min);
-  builder_.add_details_type(details_type);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<QuantizationParameters> CreateQuantizationParametersDirect(
-    flatbuffers::FlatBufferBuilder &_fbb, const std::vector<float> *min = nullptr,
-    const std::vector<float> *max = nullptr, const std::vector<float> *scale = nullptr,
-    const std::vector<int64_t> *zero_point = nullptr,
-    QuantizationDetails details_type = QuantizationDetails_NONE,
-    flatbuffers::Offset<void> details = 0)
-{
-  return neurun_tflite::CreateQuantizationParameters(
-      _fbb, min ? _fbb.CreateVector<float>(*min) : 0, max ? _fbb.CreateVector<float>(*max) : 0,
-      scale ? _fbb.CreateVector<float>(*scale) : 0,
-      zero_point ? _fbb.CreateVector<int64_t>(*zero_point) : 0, details_type, details);
-}
-
-struct Tensor FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_SHAPE = 4,
-    VT_TYPE = 6,
-    VT_BUFFER = 8,
-    VT_NAME = 10,
-    VT_QUANTIZATION = 12,
-    VT_IS_VARIABLE = 14
-  };
-  const flatbuffers::Vector<int32_t> *shape() const
-  {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_SHAPE);
-  }
-  TensorType type() const { return static_cast<TensorType>(GetField<int8_t>(VT_TYPE, 0)); }
-  uint32_t buffer() const { return GetField<uint32_t>(VT_BUFFER, 0); }
-  const flatbuffers::String *name() const
-  {
-    return GetPointer<const flatbuffers::String *>(VT_NAME);
-  }
-  const QuantizationParameters *quantization() const
-  {
-    return GetPointer<const QuantizationParameters *>(VT_QUANTIZATION);
-  }
-  bool is_variable() const { return GetField<uint8_t>(VT_IS_VARIABLE, 0) != 0; }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_SHAPE) &&
-           verifier.VerifyVector(shape()) && VerifyField<int8_t>(verifier, VT_TYPE) &&
-           VerifyField<uint32_t>(verifier, VT_BUFFER) && VerifyOffset(verifier, VT_NAME) &&
-           verifier.VerifyString(name()) && VerifyOffset(verifier, VT_QUANTIZATION) &&
-           verifier.VerifyTable(quantization()) && VerifyField<uint8_t>(verifier, VT_IS_VARIABLE) &&
-           verifier.EndTable();
-  }
-};
-
-struct TensorBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_shape(flatbuffers::Offset<flatbuffers::Vector<int32_t>> shape)
-  {
-    fbb_.AddOffset(Tensor::VT_SHAPE, shape);
-  }
-  void add_type(TensorType type)
-  {
-    fbb_.AddElement<int8_t>(Tensor::VT_TYPE, static_cast<int8_t>(type), 0);
-  }
-  void add_buffer(uint32_t buffer) { fbb_.AddElement<uint32_t>(Tensor::VT_BUFFER, buffer, 0); }
-  void add_name(flatbuffers::Offset<flatbuffers::String> name)
-  {
-    fbb_.AddOffset(Tensor::VT_NAME, name);
-  }
-  void add_quantization(flatbuffers::Offset<QuantizationParameters> quantization)
-  {
-    fbb_.AddOffset(Tensor::VT_QUANTIZATION, quantization);
-  }
-  void add_is_variable(bool is_variable)
-  {
-    fbb_.AddElement<uint8_t>(Tensor::VT_IS_VARIABLE, static_cast<uint8_t>(is_variable), 0);
-  }
-  explicit TensorBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  TensorBuilder &operator=(const TensorBuilder &);
-  flatbuffers::Offset<Tensor> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<Tensor>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<Tensor>
-CreateTensor(flatbuffers::FlatBufferBuilder &_fbb,
-             flatbuffers::Offset<flatbuffers::Vector<int32_t>> shape = 0,
-             TensorType type = TensorType_FLOAT32, uint32_t buffer = 0,
-             flatbuffers::Offset<flatbuffers::String> name = 0,
-             flatbuffers::Offset<QuantizationParameters> quantization = 0, bool is_variable = false)
-{
-  TensorBuilder builder_(_fbb);
-  builder_.add_quantization(quantization);
-  builder_.add_name(name);
-  builder_.add_buffer(buffer);
-  builder_.add_shape(shape);
-  builder_.add_is_variable(is_variable);
-  builder_.add_type(type);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<Tensor> CreateTensorDirect(
-    flatbuffers::FlatBufferBuilder &_fbb, const std::vector<int32_t> *shape = nullptr,
-    TensorType type = TensorType_FLOAT32, uint32_t buffer = 0, const char *name = nullptr,
-    flatbuffers::Offset<QuantizationParameters> quantization = 0, bool is_variable = false)
-{
-  return neurun_tflite::CreateTensor(_fbb, shape ? _fbb.CreateVector<int32_t>(*shape) : 0, type,
-                                     buffer, name ? _fbb.CreateString(name) : 0, quantization,
-                                     is_variable);
-}
-
-struct Conv2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_PADDING = 4,
-    VT_STRIDE_W = 6,
-    VT_STRIDE_H = 8,
-    VT_FUSED_ACTIVATION_FUNCTION = 10,
-    VT_DILATION_W_FACTOR = 12,
-    VT_DILATION_H_FACTOR = 14
-  };
-  Padding padding() const { return static_cast<Padding>(GetField<int8_t>(VT_PADDING, 0)); }
-  int32_t stride_w() const { return GetField<int32_t>(VT_STRIDE_W, 0); }
-  int32_t stride_h() const { return GetField<int32_t>(VT_STRIDE_H, 0); }
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  int32_t dilation_w_factor() const { return GetField<int32_t>(VT_DILATION_W_FACTOR, 1); }
-  int32_t dilation_h_factor() const { return GetField<int32_t>(VT_DILATION_H_FACTOR, 1); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_PADDING) &&
-           VerifyField<int32_t>(verifier, VT_STRIDE_W) &&
-           VerifyField<int32_t>(verifier, VT_STRIDE_H) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) &&
-           VerifyField<int32_t>(verifier, VT_DILATION_W_FACTOR) &&
-           VerifyField<int32_t>(verifier, VT_DILATION_H_FACTOR) && verifier.EndTable();
-  }
-};
-
-struct Conv2DOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_padding(Padding padding)
-  {
-    fbb_.AddElement<int8_t>(Conv2DOptions::VT_PADDING, static_cast<int8_t>(padding), 0);
-  }
-  void add_stride_w(int32_t stride_w)
-  {
-    fbb_.AddElement<int32_t>(Conv2DOptions::VT_STRIDE_W, stride_w, 0);
-  }
-  void add_stride_h(int32_t stride_h)
-  {
-    fbb_.AddElement<int32_t>(Conv2DOptions::VT_STRIDE_H, stride_h, 0);
-  }
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(Conv2DOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  void add_dilation_w_factor(int32_t dilation_w_factor)
-  {
-    fbb_.AddElement<int32_t>(Conv2DOptions::VT_DILATION_W_FACTOR, dilation_w_factor, 1);
-  }
-  void add_dilation_h_factor(int32_t dilation_h_factor)
-  {
-    fbb_.AddElement<int32_t>(Conv2DOptions::VT_DILATION_H_FACTOR, dilation_h_factor, 1);
-  }
-  explicit Conv2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  Conv2DOptionsBuilder &operator=(const Conv2DOptionsBuilder &);
-  flatbuffers::Offset<Conv2DOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<Conv2DOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<Conv2DOptions>
-CreateConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, Padding padding = Padding_SAME,
-                    int32_t stride_w = 0, int32_t stride_h = 0,
-                    ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE,
-                    int32_t dilation_w_factor = 1, int32_t dilation_h_factor = 1)
-{
-  Conv2DOptionsBuilder builder_(_fbb);
-  builder_.add_dilation_h_factor(dilation_h_factor);
-  builder_.add_dilation_w_factor(dilation_w_factor);
-  builder_.add_stride_h(stride_h);
-  builder_.add_stride_w(stride_w);
-  builder_.add_fused_activation_function(fused_activation_function);
-  builder_.add_padding(padding);
-  return builder_.Finish();
-}
-
-struct Pool2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_PADDING = 4,
-    VT_STRIDE_W = 6,
-    VT_STRIDE_H = 8,
-    VT_FILTER_WIDTH = 10,
-    VT_FILTER_HEIGHT = 12,
-    VT_FUSED_ACTIVATION_FUNCTION = 14
-  };
-  Padding padding() const { return static_cast<Padding>(GetField<int8_t>(VT_PADDING, 0)); }
-  int32_t stride_w() const { return GetField<int32_t>(VT_STRIDE_W, 0); }
-  int32_t stride_h() const { return GetField<int32_t>(VT_STRIDE_H, 0); }
-  int32_t filter_width() const { return GetField<int32_t>(VT_FILTER_WIDTH, 0); }
-  int32_t filter_height() const { return GetField<int32_t>(VT_FILTER_HEIGHT, 0); }
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_PADDING) &&
-           VerifyField<int32_t>(verifier, VT_STRIDE_W) &&
-           VerifyField<int32_t>(verifier, VT_STRIDE_H) &&
-           VerifyField<int32_t>(verifier, VT_FILTER_WIDTH) &&
-           VerifyField<int32_t>(verifier, VT_FILTER_HEIGHT) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable();
-  }
-};
-
-struct Pool2DOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_padding(Padding padding)
-  {
-    fbb_.AddElement<int8_t>(Pool2DOptions::VT_PADDING, static_cast<int8_t>(padding), 0);
-  }
-  void add_stride_w(int32_t stride_w)
-  {
-    fbb_.AddElement<int32_t>(Pool2DOptions::VT_STRIDE_W, stride_w, 0);
-  }
-  void add_stride_h(int32_t stride_h)
-  {
-    fbb_.AddElement<int32_t>(Pool2DOptions::VT_STRIDE_H, stride_h, 0);
-  }
-  void add_filter_width(int32_t filter_width)
-  {
-    fbb_.AddElement<int32_t>(Pool2DOptions::VT_FILTER_WIDTH, filter_width, 0);
-  }
-  void add_filter_height(int32_t filter_height)
-  {
-    fbb_.AddElement<int32_t>(Pool2DOptions::VT_FILTER_HEIGHT, filter_height, 0);
-  }
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(Pool2DOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  explicit Pool2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  Pool2DOptionsBuilder &operator=(const Pool2DOptionsBuilder &);
-  flatbuffers::Offset<Pool2DOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<Pool2DOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<Pool2DOptions>
-CreatePool2DOptions(flatbuffers::FlatBufferBuilder &_fbb, Padding padding = Padding_SAME,
-                    int32_t stride_w = 0, int32_t stride_h = 0, int32_t filter_width = 0,
-                    int32_t filter_height = 0,
-                    ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE)
-{
-  Pool2DOptionsBuilder builder_(_fbb);
-  builder_.add_filter_height(filter_height);
-  builder_.add_filter_width(filter_width);
-  builder_.add_stride_h(stride_h);
-  builder_.add_stride_w(stride_w);
-  builder_.add_fused_activation_function(fused_activation_function);
-  builder_.add_padding(padding);
-  return builder_.Finish();
-}
-
-struct DepthwiseConv2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_PADDING = 4,
-    VT_STRIDE_W = 6,
-    VT_STRIDE_H = 8,
-    VT_DEPTH_MULTIPLIER = 10,
-    VT_FUSED_ACTIVATION_FUNCTION = 12,
-    VT_DILATION_W_FACTOR = 14,
-    VT_DILATION_H_FACTOR = 16
-  };
-  Padding padding() const { return static_cast<Padding>(GetField<int8_t>(VT_PADDING, 0)); }
-  int32_t stride_w() const { return GetField<int32_t>(VT_STRIDE_W, 0); }
-  int32_t stride_h() const { return GetField<int32_t>(VT_STRIDE_H, 0); }
-  int32_t depth_multiplier() const { return GetField<int32_t>(VT_DEPTH_MULTIPLIER, 0); }
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  int32_t dilation_w_factor() const { return GetField<int32_t>(VT_DILATION_W_FACTOR, 1); }
-  int32_t dilation_h_factor() const { return GetField<int32_t>(VT_DILATION_H_FACTOR, 1); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_PADDING) &&
-           VerifyField<int32_t>(verifier, VT_STRIDE_W) &&
-           VerifyField<int32_t>(verifier, VT_STRIDE_H) &&
-           VerifyField<int32_t>(verifier, VT_DEPTH_MULTIPLIER) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) &&
-           VerifyField<int32_t>(verifier, VT_DILATION_W_FACTOR) &&
-           VerifyField<int32_t>(verifier, VT_DILATION_H_FACTOR) && verifier.EndTable();
-  }
-};
-
-struct DepthwiseConv2DOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_padding(Padding padding)
-  {
-    fbb_.AddElement<int8_t>(DepthwiseConv2DOptions::VT_PADDING, static_cast<int8_t>(padding), 0);
-  }
-  void add_stride_w(int32_t stride_w)
-  {
-    fbb_.AddElement<int32_t>(DepthwiseConv2DOptions::VT_STRIDE_W, stride_w, 0);
-  }
-  void add_stride_h(int32_t stride_h)
-  {
-    fbb_.AddElement<int32_t>(DepthwiseConv2DOptions::VT_STRIDE_H, stride_h, 0);
-  }
-  void add_depth_multiplier(int32_t depth_multiplier)
-  {
-    fbb_.AddElement<int32_t>(DepthwiseConv2DOptions::VT_DEPTH_MULTIPLIER, depth_multiplier, 0);
-  }
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(DepthwiseConv2DOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  void add_dilation_w_factor(int32_t dilation_w_factor)
-  {
-    fbb_.AddElement<int32_t>(DepthwiseConv2DOptions::VT_DILATION_W_FACTOR, dilation_w_factor, 1);
-  }
-  void add_dilation_h_factor(int32_t dilation_h_factor)
-  {
-    fbb_.AddElement<int32_t>(DepthwiseConv2DOptions::VT_DILATION_H_FACTOR, dilation_h_factor, 1);
-  }
-  explicit DepthwiseConv2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  DepthwiseConv2DOptionsBuilder &operator=(const DepthwiseConv2DOptionsBuilder &);
-  flatbuffers::Offset<DepthwiseConv2DOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<DepthwiseConv2DOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<DepthwiseConv2DOptions> CreateDepthwiseConv2DOptions(
-    flatbuffers::FlatBufferBuilder &_fbb, Padding padding = Padding_SAME, int32_t stride_w = 0,
-    int32_t stride_h = 0, int32_t depth_multiplier = 0,
-    ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE,
-    int32_t dilation_w_factor = 1, int32_t dilation_h_factor = 1)
-{
-  DepthwiseConv2DOptionsBuilder builder_(_fbb);
-  builder_.add_dilation_h_factor(dilation_h_factor);
-  builder_.add_dilation_w_factor(dilation_w_factor);
-  builder_.add_depth_multiplier(depth_multiplier);
-  builder_.add_stride_h(stride_h);
-  builder_.add_stride_w(stride_w);
-  builder_.add_fused_activation_function(fused_activation_function);
-  builder_.add_padding(padding);
-  return builder_.Finish();
-}
-
-struct ConcatEmbeddingsOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_NUM_CHANNELS = 4,
-    VT_NUM_COLUMNS_PER_CHANNEL = 6,
-    VT_EMBEDDING_DIM_PER_CHANNEL = 8
-  };
-  int32_t num_channels() const { return GetField<int32_t>(VT_NUM_CHANNELS, 0); }
-  const flatbuffers::Vector<int32_t> *num_columns_per_channel() const
-  {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_NUM_COLUMNS_PER_CHANNEL);
-  }
-  const flatbuffers::Vector<int32_t> *embedding_dim_per_channel() const
-  {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_EMBEDDING_DIM_PER_CHANNEL);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_NUM_CHANNELS) &&
-           VerifyOffset(verifier, VT_NUM_COLUMNS_PER_CHANNEL) &&
-           verifier.VerifyVector(num_columns_per_channel()) &&
-           VerifyOffset(verifier, VT_EMBEDDING_DIM_PER_CHANNEL) &&
-           verifier.VerifyVector(embedding_dim_per_channel()) && verifier.EndTable();
-  }
-};
-
-struct ConcatEmbeddingsOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_num_channels(int32_t num_channels)
-  {
-    fbb_.AddElement<int32_t>(ConcatEmbeddingsOptions::VT_NUM_CHANNELS, num_channels, 0);
-  }
-  void add_num_columns_per_channel(
-      flatbuffers::Offset<flatbuffers::Vector<int32_t>> num_columns_per_channel)
-  {
-    fbb_.AddOffset(ConcatEmbeddingsOptions::VT_NUM_COLUMNS_PER_CHANNEL, num_columns_per_channel);
-  }
-  void add_embedding_dim_per_channel(
-      flatbuffers::Offset<flatbuffers::Vector<int32_t>> embedding_dim_per_channel)
-  {
-    fbb_.AddOffset(ConcatEmbeddingsOptions::VT_EMBEDDING_DIM_PER_CHANNEL,
-                   embedding_dim_per_channel);
-  }
-  explicit ConcatEmbeddingsOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  ConcatEmbeddingsOptionsBuilder &operator=(const ConcatEmbeddingsOptionsBuilder &);
-  flatbuffers::Offset<ConcatEmbeddingsOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ConcatEmbeddingsOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ConcatEmbeddingsOptions> CreateConcatEmbeddingsOptions(
-    flatbuffers::FlatBufferBuilder &_fbb, int32_t num_channels = 0,
-    flatbuffers::Offset<flatbuffers::Vector<int32_t>> num_columns_per_channel = 0,
-    flatbuffers::Offset<flatbuffers::Vector<int32_t>> embedding_dim_per_channel = 0)
-{
-  ConcatEmbeddingsOptionsBuilder builder_(_fbb);
-  builder_.add_embedding_dim_per_channel(embedding_dim_per_channel);
-  builder_.add_num_columns_per_channel(num_columns_per_channel);
-  builder_.add_num_channels(num_channels);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<ConcatEmbeddingsOptions>
-CreateConcatEmbeddingsOptionsDirect(flatbuffers::FlatBufferBuilder &_fbb, int32_t num_channels = 0,
-                                    const std::vector<int32_t> *num_columns_per_channel = nullptr,
-                                    const std::vector<int32_t> *embedding_dim_per_channel = nullptr)
-{
-  return neurun_tflite::CreateConcatEmbeddingsOptions(
-      _fbb, num_channels,
-      num_columns_per_channel ? _fbb.CreateVector<int32_t>(*num_columns_per_channel) : 0,
-      embedding_dim_per_channel ? _fbb.CreateVector<int32_t>(*embedding_dim_per_channel) : 0);
-}
-
-struct LSHProjectionOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_TYPE = 4
-  };
-  LSHProjectionType type() const
-  {
-    return static_cast<LSHProjectionType>(GetField<int8_t>(VT_TYPE, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_TYPE) &&
-           verifier.EndTable();
-  }
-};
-
-struct LSHProjectionOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_type(LSHProjectionType type)
-  {
-    fbb_.AddElement<int8_t>(LSHProjectionOptions::VT_TYPE, static_cast<int8_t>(type), 0);
-  }
-  explicit LSHProjectionOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  LSHProjectionOptionsBuilder &operator=(const LSHProjectionOptionsBuilder &);
-  flatbuffers::Offset<LSHProjectionOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<LSHProjectionOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<LSHProjectionOptions>
-CreateLSHProjectionOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                           LSHProjectionType type = LSHProjectionType_UNKNOWN)
-{
-  LSHProjectionOptionsBuilder builder_(_fbb);
-  builder_.add_type(type);
-  return builder_.Finish();
-}
-
-struct SVDFOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_RANK = 4,
-    VT_FUSED_ACTIVATION_FUNCTION = 6
-  };
-  int32_t rank() const { return GetField<int32_t>(VT_RANK, 0); }
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_RANK) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable();
-  }
-};
-
-struct SVDFOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_rank(int32_t rank) { fbb_.AddElement<int32_t>(SVDFOptions::VT_RANK, rank, 0); }
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(SVDFOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  explicit SVDFOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SVDFOptionsBuilder &operator=(const SVDFOptionsBuilder &);
-  flatbuffers::Offset<SVDFOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SVDFOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SVDFOptions>
-CreateSVDFOptions(flatbuffers::FlatBufferBuilder &_fbb, int32_t rank = 0,
-                  ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE)
-{
-  SVDFOptionsBuilder builder_(_fbb);
-  builder_.add_rank(rank);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-struct RNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_FUSED_ACTIVATION_FUNCTION = 4
-  };
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable();
-  }
-};
-
-struct RNNOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(RNNOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  explicit RNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  RNNOptionsBuilder &operator=(const RNNOptionsBuilder &);
-  flatbuffers::Offset<RNNOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<RNNOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<RNNOptions>
-CreateRNNOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                 ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE)
-{
-  RNNOptionsBuilder builder_(_fbb);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-struct SequenceRNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_TIME_MAJOR = 4,
-    VT_FUSED_ACTIVATION_FUNCTION = 6
-  };
-  bool time_major() const { return GetField<uint8_t>(VT_TIME_MAJOR, 0) != 0; }
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<uint8_t>(verifier, VT_TIME_MAJOR) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable();
-  }
-};
-
-struct SequenceRNNOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_time_major(bool time_major)
-  {
-    fbb_.AddElement<uint8_t>(SequenceRNNOptions::VT_TIME_MAJOR, static_cast<uint8_t>(time_major),
-                             0);
-  }
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(SequenceRNNOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  explicit SequenceRNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SequenceRNNOptionsBuilder &operator=(const SequenceRNNOptionsBuilder &);
-  flatbuffers::Offset<SequenceRNNOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SequenceRNNOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SequenceRNNOptions> CreateSequenceRNNOptions(
-    flatbuffers::FlatBufferBuilder &_fbb, bool time_major = false,
-    ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE)
-{
-  SequenceRNNOptionsBuilder builder_(_fbb);
-  builder_.add_fused_activation_function(fused_activation_function);
-  builder_.add_time_major(time_major);
-  return builder_.Finish();
-}
-
-struct BidirectionalSequenceRNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_TIME_MAJOR = 4,
-    VT_FUSED_ACTIVATION_FUNCTION = 6,
-    VT_MERGE_OUTPUTS = 8
-  };
-  bool time_major() const { return GetField<uint8_t>(VT_TIME_MAJOR, 0) != 0; }
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool merge_outputs() const { return GetField<uint8_t>(VT_MERGE_OUTPUTS, 0) != 0; }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<uint8_t>(verifier, VT_TIME_MAJOR) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) &&
-           VerifyField<uint8_t>(verifier, VT_MERGE_OUTPUTS) && verifier.EndTable();
-  }
-};
-
-struct BidirectionalSequenceRNNOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_time_major(bool time_major)
-  {
-    fbb_.AddElement<uint8_t>(BidirectionalSequenceRNNOptions::VT_TIME_MAJOR,
-                             static_cast<uint8_t>(time_major), 0);
-  }
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(BidirectionalSequenceRNNOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  void add_merge_outputs(bool merge_outputs)
-  {
-    fbb_.AddElement<uint8_t>(BidirectionalSequenceRNNOptions::VT_MERGE_OUTPUTS,
-                             static_cast<uint8_t>(merge_outputs), 0);
-  }
-  explicit BidirectionalSequenceRNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  BidirectionalSequenceRNNOptionsBuilder &operator=(const BidirectionalSequenceRNNOptionsBuilder &);
-  flatbuffers::Offset<BidirectionalSequenceRNNOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<BidirectionalSequenceRNNOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<BidirectionalSequenceRNNOptions> CreateBidirectionalSequenceRNNOptions(
-    flatbuffers::FlatBufferBuilder &_fbb, bool time_major = false,
-    ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE,
-    bool merge_outputs = false)
-{
-  BidirectionalSequenceRNNOptionsBuilder builder_(_fbb);
-  builder_.add_merge_outputs(merge_outputs);
-  builder_.add_fused_activation_function(fused_activation_function);
-  builder_.add_time_major(time_major);
-  return builder_.Finish();
-}
-
-struct FullyConnectedOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_FUSED_ACTIVATION_FUNCTION = 4,
-    VT_WEIGHTS_FORMAT = 6
-  };
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  FullyConnectedOptionsWeightsFormat weights_format() const
-  {
-    return static_cast<FullyConnectedOptionsWeightsFormat>(GetField<int8_t>(VT_WEIGHTS_FORMAT, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) &&
-           VerifyField<int8_t>(verifier, VT_WEIGHTS_FORMAT) && verifier.EndTable();
-  }
-};
-
-struct FullyConnectedOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(FullyConnectedOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  void add_weights_format(FullyConnectedOptionsWeightsFormat weights_format)
-  {
-    fbb_.AddElement<int8_t>(FullyConnectedOptions::VT_WEIGHTS_FORMAT,
-                            static_cast<int8_t>(weights_format), 0);
-  }
-  explicit FullyConnectedOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  FullyConnectedOptionsBuilder &operator=(const FullyConnectedOptionsBuilder &);
-  flatbuffers::Offset<FullyConnectedOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<FullyConnectedOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<FullyConnectedOptions> CreateFullyConnectedOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE,
-    FullyConnectedOptionsWeightsFormat weights_format = FullyConnectedOptionsWeightsFormat_DEFAULT)
-{
-  FullyConnectedOptionsBuilder builder_(_fbb);
-  builder_.add_weights_format(weights_format);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-struct SoftmaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_BETA = 4
-  };
-  float beta() const { return GetField<float>(VT_BETA, 0.0f); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<float>(verifier, VT_BETA) &&
-           verifier.EndTable();
-  }
-};
-
-struct SoftmaxOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_beta(float beta) { fbb_.AddElement<float>(SoftmaxOptions::VT_BETA, beta, 0.0f); }
-  explicit SoftmaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SoftmaxOptionsBuilder &operator=(const SoftmaxOptionsBuilder &);
-  flatbuffers::Offset<SoftmaxOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SoftmaxOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SoftmaxOptions>
-CreateSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, float beta = 0.0f)
-{
-  SoftmaxOptionsBuilder builder_(_fbb);
-  builder_.add_beta(beta);
-  return builder_.Finish();
-}
-
-struct ConcatenationOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_AXIS = 4,
-    VT_FUSED_ACTIVATION_FUNCTION = 6
-  };
-  int32_t axis() const { return GetField<int32_t>(VT_AXIS, 0); }
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_AXIS) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable();
-  }
-};
-
-struct ConcatenationOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_axis(int32_t axis) { fbb_.AddElement<int32_t>(ConcatenationOptions::VT_AXIS, axis, 0); }
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(ConcatenationOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  explicit ConcatenationOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  ConcatenationOptionsBuilder &operator=(const ConcatenationOptionsBuilder &);
-  flatbuffers::Offset<ConcatenationOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ConcatenationOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ConcatenationOptions> CreateConcatenationOptions(
-    flatbuffers::FlatBufferBuilder &_fbb, int32_t axis = 0,
-    ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE)
-{
-  ConcatenationOptionsBuilder builder_(_fbb);
-  builder_.add_axis(axis);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-struct AddOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_FUSED_ACTIVATION_FUNCTION = 4
-  };
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable();
-  }
-};
-
-struct AddOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(AddOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  explicit AddOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  AddOptionsBuilder &operator=(const AddOptionsBuilder &);
-  flatbuffers::Offset<AddOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<AddOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<AddOptions>
-CreateAddOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                 ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE)
-{
-  AddOptionsBuilder builder_(_fbb);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-struct MulOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_FUSED_ACTIVATION_FUNCTION = 4
-  };
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable();
-  }
-};
-
-struct MulOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(MulOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  explicit MulOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  MulOptionsBuilder &operator=(const MulOptionsBuilder &);
-  flatbuffers::Offset<MulOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<MulOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<MulOptions>
-CreateMulOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                 ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE)
-{
-  MulOptionsBuilder builder_(_fbb);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-struct L2NormOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_FUSED_ACTIVATION_FUNCTION = 4
-  };
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable();
-  }
-};
-
-struct L2NormOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(L2NormOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  explicit L2NormOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  L2NormOptionsBuilder &operator=(const L2NormOptionsBuilder &);
-  flatbuffers::Offset<L2NormOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<L2NormOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<L2NormOptions>
-CreateL2NormOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                    ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE)
-{
-  L2NormOptionsBuilder builder_(_fbb);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-struct LocalResponseNormalizationOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_RADIUS = 4,
-    VT_BIAS = 6,
-    VT_ALPHA = 8,
-    VT_BETA = 10
-  };
-  int32_t radius() const { return GetField<int32_t>(VT_RADIUS, 0); }
-  float bias() const { return GetField<float>(VT_BIAS, 0.0f); }
-  float alpha() const { return GetField<float>(VT_ALPHA, 0.0f); }
-  float beta() const { return GetField<float>(VT_BETA, 0.0f); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_RADIUS) &&
-           VerifyField<float>(verifier, VT_BIAS) && VerifyField<float>(verifier, VT_ALPHA) &&
-           VerifyField<float>(verifier, VT_BETA) && verifier.EndTable();
-  }
-};
-
-struct LocalResponseNormalizationOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_radius(int32_t radius)
-  {
-    fbb_.AddElement<int32_t>(LocalResponseNormalizationOptions::VT_RADIUS, radius, 0);
-  }
-  void add_bias(float bias)
-  {
-    fbb_.AddElement<float>(LocalResponseNormalizationOptions::VT_BIAS, bias, 0.0f);
-  }
-  void add_alpha(float alpha)
-  {
-    fbb_.AddElement<float>(LocalResponseNormalizationOptions::VT_ALPHA, alpha, 0.0f);
-  }
-  void add_beta(float beta)
-  {
-    fbb_.AddElement<float>(LocalResponseNormalizationOptions::VT_BETA, beta, 0.0f);
-  }
-  explicit LocalResponseNormalizationOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-      : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  LocalResponseNormalizationOptionsBuilder &
-  operator=(const LocalResponseNormalizationOptionsBuilder &);
-  flatbuffers::Offset<LocalResponseNormalizationOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<LocalResponseNormalizationOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<LocalResponseNormalizationOptions>
-CreateLocalResponseNormalizationOptions(flatbuffers::FlatBufferBuilder &_fbb, int32_t radius = 0,
-                                        float bias = 0.0f, float alpha = 0.0f, float beta = 0.0f)
-{
-  LocalResponseNormalizationOptionsBuilder builder_(_fbb);
-  builder_.add_beta(beta);
-  builder_.add_alpha(alpha);
-  builder_.add_bias(bias);
-  builder_.add_radius(radius);
-  return builder_.Finish();
-}
-
-struct LSTMOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_FUSED_ACTIVATION_FUNCTION = 4,
-    VT_CELL_CLIP = 6,
-    VT_PROJ_CLIP = 8,
-    VT_KERNEL_TYPE = 10
-  };
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  float cell_clip() const { return GetField<float>(VT_CELL_CLIP, 0.0f); }
-  float proj_clip() const { return GetField<float>(VT_PROJ_CLIP, 0.0f); }
-  LSTMKernelType kernel_type() const
-  {
-    return static_cast<LSTMKernelType>(GetField<int8_t>(VT_KERNEL_TYPE, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) &&
-           VerifyField<float>(verifier, VT_CELL_CLIP) &&
-           VerifyField<float>(verifier, VT_PROJ_CLIP) &&
-           VerifyField<int8_t>(verifier, VT_KERNEL_TYPE) && verifier.EndTable();
-  }
-};
-
-struct LSTMOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(LSTMOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  void add_cell_clip(float cell_clip)
-  {
-    fbb_.AddElement<float>(LSTMOptions::VT_CELL_CLIP, cell_clip, 0.0f);
-  }
-  void add_proj_clip(float proj_clip)
-  {
-    fbb_.AddElement<float>(LSTMOptions::VT_PROJ_CLIP, proj_clip, 0.0f);
-  }
-  void add_kernel_type(LSTMKernelType kernel_type)
-  {
-    fbb_.AddElement<int8_t>(LSTMOptions::VT_KERNEL_TYPE, static_cast<int8_t>(kernel_type), 0);
-  }
-  explicit LSTMOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  LSTMOptionsBuilder &operator=(const LSTMOptionsBuilder &);
-  flatbuffers::Offset<LSTMOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<LSTMOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<LSTMOptions>
-CreateLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                  ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE,
-                  float cell_clip = 0.0f, float proj_clip = 0.0f,
-                  LSTMKernelType kernel_type = LSTMKernelType_FULL)
-{
-  LSTMOptionsBuilder builder_(_fbb);
-  builder_.add_proj_clip(proj_clip);
-  builder_.add_cell_clip(cell_clip);
-  builder_.add_kernel_type(kernel_type);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-struct UnidirectionalSequenceLSTMOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_FUSED_ACTIVATION_FUNCTION = 4,
-    VT_CELL_CLIP = 6,
-    VT_PROJ_CLIP = 8,
-    VT_TIME_MAJOR = 10
-  };
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  float cell_clip() const { return GetField<float>(VT_CELL_CLIP, 0.0f); }
-  float proj_clip() const { return GetField<float>(VT_PROJ_CLIP, 0.0f); }
-  bool time_major() const { return GetField<uint8_t>(VT_TIME_MAJOR, 0) != 0; }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) &&
-           VerifyField<float>(verifier, VT_CELL_CLIP) &&
-           VerifyField<float>(verifier, VT_PROJ_CLIP) &&
-           VerifyField<uint8_t>(verifier, VT_TIME_MAJOR) && verifier.EndTable();
-  }
-};
-
-struct UnidirectionalSequenceLSTMOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(UnidirectionalSequenceLSTMOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  void add_cell_clip(float cell_clip)
-  {
-    fbb_.AddElement<float>(UnidirectionalSequenceLSTMOptions::VT_CELL_CLIP, cell_clip, 0.0f);
-  }
-  void add_proj_clip(float proj_clip)
-  {
-    fbb_.AddElement<float>(UnidirectionalSequenceLSTMOptions::VT_PROJ_CLIP, proj_clip, 0.0f);
-  }
-  void add_time_major(bool time_major)
-  {
-    fbb_.AddElement<uint8_t>(UnidirectionalSequenceLSTMOptions::VT_TIME_MAJOR,
-                             static_cast<uint8_t>(time_major), 0);
-  }
-  explicit UnidirectionalSequenceLSTMOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-      : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  UnidirectionalSequenceLSTMOptionsBuilder &
-  operator=(const UnidirectionalSequenceLSTMOptionsBuilder &);
-  flatbuffers::Offset<UnidirectionalSequenceLSTMOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<UnidirectionalSequenceLSTMOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<UnidirectionalSequenceLSTMOptions>
-CreateUnidirectionalSequenceLSTMOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE,
-    float cell_clip = 0.0f, float proj_clip = 0.0f, bool time_major = false)
-{
-  UnidirectionalSequenceLSTMOptionsBuilder builder_(_fbb);
-  builder_.add_proj_clip(proj_clip);
-  builder_.add_cell_clip(cell_clip);
-  builder_.add_time_major(time_major);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-struct BidirectionalSequenceLSTMOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_FUSED_ACTIVATION_FUNCTION = 4,
-    VT_CELL_CLIP = 6,
-    VT_PROJ_CLIP = 8,
-    VT_MERGE_OUTPUTS = 10
-  };
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  float cell_clip() const { return GetField<float>(VT_CELL_CLIP, 0.0f); }
-  float proj_clip() const { return GetField<float>(VT_PROJ_CLIP, 0.0f); }
-  bool merge_outputs() const { return GetField<uint8_t>(VT_MERGE_OUTPUTS, 0) != 0; }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) &&
-           VerifyField<float>(verifier, VT_CELL_CLIP) &&
-           VerifyField<float>(verifier, VT_PROJ_CLIP) &&
-           VerifyField<uint8_t>(verifier, VT_MERGE_OUTPUTS) && verifier.EndTable();
-  }
-};
-
-struct BidirectionalSequenceLSTMOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(BidirectionalSequenceLSTMOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  void add_cell_clip(float cell_clip)
-  {
-    fbb_.AddElement<float>(BidirectionalSequenceLSTMOptions::VT_CELL_CLIP, cell_clip, 0.0f);
-  }
-  void add_proj_clip(float proj_clip)
-  {
-    fbb_.AddElement<float>(BidirectionalSequenceLSTMOptions::VT_PROJ_CLIP, proj_clip, 0.0f);
-  }
-  void add_merge_outputs(bool merge_outputs)
-  {
-    fbb_.AddElement<uint8_t>(BidirectionalSequenceLSTMOptions::VT_MERGE_OUTPUTS,
-                             static_cast<uint8_t>(merge_outputs), 0);
-  }
-  explicit BidirectionalSequenceLSTMOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-      : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  BidirectionalSequenceLSTMOptionsBuilder &
-  operator=(const BidirectionalSequenceLSTMOptionsBuilder &);
-  flatbuffers::Offset<BidirectionalSequenceLSTMOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<BidirectionalSequenceLSTMOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<BidirectionalSequenceLSTMOptions> CreateBidirectionalSequenceLSTMOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE,
-    float cell_clip = 0.0f, float proj_clip = 0.0f, bool merge_outputs = false)
-{
-  BidirectionalSequenceLSTMOptionsBuilder builder_(_fbb);
-  builder_.add_proj_clip(proj_clip);
-  builder_.add_cell_clip(cell_clip);
-  builder_.add_merge_outputs(merge_outputs);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-struct ResizeBilinearOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_ALIGN_CORNERS = 8
-  };
-  bool align_corners() const { return GetField<uint8_t>(VT_ALIGN_CORNERS, 0) != 0; }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<uint8_t>(verifier, VT_ALIGN_CORNERS) &&
-           verifier.EndTable();
-  }
-};
-
-struct ResizeBilinearOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_align_corners(bool align_corners)
-  {
-    fbb_.AddElement<uint8_t>(ResizeBilinearOptions::VT_ALIGN_CORNERS,
-                             static_cast<uint8_t>(align_corners), 0);
-  }
-  explicit ResizeBilinearOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  ResizeBilinearOptionsBuilder &operator=(const ResizeBilinearOptionsBuilder &);
-  flatbuffers::Offset<ResizeBilinearOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ResizeBilinearOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ResizeBilinearOptions>
-CreateResizeBilinearOptions(flatbuffers::FlatBufferBuilder &_fbb, bool align_corners = false)
-{
-  ResizeBilinearOptionsBuilder builder_(_fbb);
-  builder_.add_align_corners(align_corners);
-  return builder_.Finish();
-}
-
-struct ResizeNearestNeighborOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_ALIGN_CORNERS = 4
-  };
-  bool align_corners() const { return GetField<uint8_t>(VT_ALIGN_CORNERS, 0) != 0; }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<uint8_t>(verifier, VT_ALIGN_CORNERS) &&
-           verifier.EndTable();
-  }
-};
-
-struct ResizeNearestNeighborOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_align_corners(bool align_corners)
-  {
-    fbb_.AddElement<uint8_t>(ResizeNearestNeighborOptions::VT_ALIGN_CORNERS,
-                             static_cast<uint8_t>(align_corners), 0);
-  }
-  explicit ResizeNearestNeighborOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  ResizeNearestNeighborOptionsBuilder &operator=(const ResizeNearestNeighborOptionsBuilder &);
-  flatbuffers::Offset<ResizeNearestNeighborOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ResizeNearestNeighborOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ResizeNearestNeighborOptions>
-CreateResizeNearestNeighborOptions(flatbuffers::FlatBufferBuilder &_fbb, bool align_corners = false)
-{
-  ResizeNearestNeighborOptionsBuilder builder_(_fbb);
-  builder_.add_align_corners(align_corners);
-  return builder_.Finish();
-}
-
-struct CallOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_SUBGRAPH = 4
-  };
-  uint32_t subgraph() const { return GetField<uint32_t>(VT_SUBGRAPH, 0); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<uint32_t>(verifier, VT_SUBGRAPH) &&
-           verifier.EndTable();
-  }
-};
-
-struct CallOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_subgraph(uint32_t subgraph)
-  {
-    fbb_.AddElement<uint32_t>(CallOptions::VT_SUBGRAPH, subgraph, 0);
-  }
-  explicit CallOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  CallOptionsBuilder &operator=(const CallOptionsBuilder &);
-  flatbuffers::Offset<CallOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<CallOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<CallOptions> CreateCallOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                                                          uint32_t subgraph = 0)
-{
-  CallOptionsBuilder builder_(_fbb);
-  builder_.add_subgraph(subgraph);
-  return builder_.Finish();
-}
-
-struct PadOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct PadOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit PadOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  PadOptionsBuilder &operator=(const PadOptionsBuilder &);
-  flatbuffers::Offset<PadOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<PadOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<PadOptions> CreatePadOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  PadOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct PadV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct PadV2OptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit PadV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  PadV2OptionsBuilder &operator=(const PadV2OptionsBuilder &);
-  flatbuffers::Offset<PadV2Options> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<PadV2Options>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<PadV2Options> CreatePadV2Options(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  PadV2OptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct ReshapeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_NEW_SHAPE = 4
-  };
-  const flatbuffers::Vector<int32_t> *new_shape() const
-  {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_NEW_SHAPE);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_NEW_SHAPE) &&
-           verifier.VerifyVector(new_shape()) && verifier.EndTable();
-  }
-};
-
-struct ReshapeOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_new_shape(flatbuffers::Offset<flatbuffers::Vector<int32_t>> new_shape)
-  {
-    fbb_.AddOffset(ReshapeOptions::VT_NEW_SHAPE, new_shape);
-  }
-  explicit ReshapeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  ReshapeOptionsBuilder &operator=(const ReshapeOptionsBuilder &);
-  flatbuffers::Offset<ReshapeOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ReshapeOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ReshapeOptions>
-CreateReshapeOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                     flatbuffers::Offset<flatbuffers::Vector<int32_t>> new_shape = 0)
-{
-  ReshapeOptionsBuilder builder_(_fbb);
-  builder_.add_new_shape(new_shape);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<ReshapeOptions>
-CreateReshapeOptionsDirect(flatbuffers::FlatBufferBuilder &_fbb,
-                           const std::vector<int32_t> *new_shape = nullptr)
-{
-  return neurun_tflite::CreateReshapeOptions(
-      _fbb, new_shape ? _fbb.CreateVector<int32_t>(*new_shape) : 0);
-}
-
-struct SpaceToBatchNDOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct SpaceToBatchNDOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit SpaceToBatchNDOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SpaceToBatchNDOptionsBuilder &operator=(const SpaceToBatchNDOptionsBuilder &);
-  flatbuffers::Offset<SpaceToBatchNDOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SpaceToBatchNDOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SpaceToBatchNDOptions>
-CreateSpaceToBatchNDOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  SpaceToBatchNDOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct BatchToSpaceNDOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct BatchToSpaceNDOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit BatchToSpaceNDOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  BatchToSpaceNDOptionsBuilder &operator=(const BatchToSpaceNDOptionsBuilder &);
-  flatbuffers::Offset<BatchToSpaceNDOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<BatchToSpaceNDOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<BatchToSpaceNDOptions>
-CreateBatchToSpaceNDOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  BatchToSpaceNDOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct SkipGramOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_NGRAM_SIZE = 4,
-    VT_MAX_SKIP_SIZE = 6,
-    VT_INCLUDE_ALL_NGRAMS = 8
-  };
-  int32_t ngram_size() const { return GetField<int32_t>(VT_NGRAM_SIZE, 0); }
-  int32_t max_skip_size() const { return GetField<int32_t>(VT_MAX_SKIP_SIZE, 0); }
-  bool include_all_ngrams() const { return GetField<uint8_t>(VT_INCLUDE_ALL_NGRAMS, 0) != 0; }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_NGRAM_SIZE) &&
-           VerifyField<int32_t>(verifier, VT_MAX_SKIP_SIZE) &&
-           VerifyField<uint8_t>(verifier, VT_INCLUDE_ALL_NGRAMS) && verifier.EndTable();
-  }
-};
-
-struct SkipGramOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_ngram_size(int32_t ngram_size)
-  {
-    fbb_.AddElement<int32_t>(SkipGramOptions::VT_NGRAM_SIZE, ngram_size, 0);
-  }
-  void add_max_skip_size(int32_t max_skip_size)
-  {
-    fbb_.AddElement<int32_t>(SkipGramOptions::VT_MAX_SKIP_SIZE, max_skip_size, 0);
-  }
-  void add_include_all_ngrams(bool include_all_ngrams)
-  {
-    fbb_.AddElement<uint8_t>(SkipGramOptions::VT_INCLUDE_ALL_NGRAMS,
-                             static_cast<uint8_t>(include_all_ngrams), 0);
-  }
-  explicit SkipGramOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SkipGramOptionsBuilder &operator=(const SkipGramOptionsBuilder &);
-  flatbuffers::Offset<SkipGramOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SkipGramOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SkipGramOptions>
-CreateSkipGramOptions(flatbuffers::FlatBufferBuilder &_fbb, int32_t ngram_size = 0,
-                      int32_t max_skip_size = 0, bool include_all_ngrams = false)
-{
-  SkipGramOptionsBuilder builder_(_fbb);
-  builder_.add_max_skip_size(max_skip_size);
-  builder_.add_ngram_size(ngram_size);
-  builder_.add_include_all_ngrams(include_all_ngrams);
-  return builder_.Finish();
-}
-
-struct SpaceToDepthOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_BLOCK_SIZE = 4
-  };
-  int32_t block_size() const { return GetField<int32_t>(VT_BLOCK_SIZE, 0); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_BLOCK_SIZE) &&
-           verifier.EndTable();
-  }
-};
-
-struct SpaceToDepthOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_block_size(int32_t block_size)
-  {
-    fbb_.AddElement<int32_t>(SpaceToDepthOptions::VT_BLOCK_SIZE, block_size, 0);
-  }
-  explicit SpaceToDepthOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SpaceToDepthOptionsBuilder &operator=(const SpaceToDepthOptionsBuilder &);
-  flatbuffers::Offset<SpaceToDepthOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SpaceToDepthOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SpaceToDepthOptions>
-CreateSpaceToDepthOptions(flatbuffers::FlatBufferBuilder &_fbb, int32_t block_size = 0)
-{
-  SpaceToDepthOptionsBuilder builder_(_fbb);
-  builder_.add_block_size(block_size);
-  return builder_.Finish();
-}
-
-struct SubOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_FUSED_ACTIVATION_FUNCTION = 4
-  };
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable();
-  }
-};
-
-struct SubOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(SubOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  explicit SubOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SubOptionsBuilder &operator=(const SubOptionsBuilder &);
-  flatbuffers::Offset<SubOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SubOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SubOptions>
-CreateSubOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                 ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE)
-{
-  SubOptionsBuilder builder_(_fbb);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-struct DivOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_FUSED_ACTIVATION_FUNCTION = 4
-  };
-  ActivationFunctionType fused_activation_function() const
-  {
-    return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable();
-  }
-};
-
-struct DivOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_fused_activation_function(ActivationFunctionType fused_activation_function)
-  {
-    fbb_.AddElement<int8_t>(DivOptions::VT_FUSED_ACTIVATION_FUNCTION,
-                            static_cast<int8_t>(fused_activation_function), 0);
-  }
-  explicit DivOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  DivOptionsBuilder &operator=(const DivOptionsBuilder &);
-  flatbuffers::Offset<DivOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<DivOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<DivOptions>
-CreateDivOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                 ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE)
-{
-  DivOptionsBuilder builder_(_fbb);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-struct TopKV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct TopKV2OptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit TopKV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  TopKV2OptionsBuilder &operator=(const TopKV2OptionsBuilder &);
-  flatbuffers::Offset<TopKV2Options> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<TopKV2Options>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<TopKV2Options> CreateTopKV2Options(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  TopKV2OptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct EmbeddingLookupSparseOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_COMBINER = 4
-  };
-  CombinerType combiner() const
-  {
-    return static_cast<CombinerType>(GetField<int8_t>(VT_COMBINER, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_COMBINER) &&
-           verifier.EndTable();
-  }
-};
-
-struct EmbeddingLookupSparseOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_combiner(CombinerType combiner)
-  {
-    fbb_.AddElement<int8_t>(EmbeddingLookupSparseOptions::VT_COMBINER,
-                            static_cast<int8_t>(combiner), 0);
-  }
-  explicit EmbeddingLookupSparseOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  EmbeddingLookupSparseOptionsBuilder &operator=(const EmbeddingLookupSparseOptionsBuilder &);
-  flatbuffers::Offset<EmbeddingLookupSparseOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<EmbeddingLookupSparseOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<EmbeddingLookupSparseOptions>
-CreateEmbeddingLookupSparseOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                                   CombinerType combiner = CombinerType_SUM)
-{
-  EmbeddingLookupSparseOptionsBuilder builder_(_fbb);
-  builder_.add_combiner(combiner);
-  return builder_.Finish();
-}
-
-struct GatherOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_AXIS = 4
-  };
-  int32_t axis() const { return GetField<int32_t>(VT_AXIS, 0); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_AXIS) &&
-           verifier.EndTable();
-  }
-};
-
-struct GatherOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_axis(int32_t axis) { fbb_.AddElement<int32_t>(GatherOptions::VT_AXIS, axis, 0); }
-  explicit GatherOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  GatherOptionsBuilder &operator=(const GatherOptionsBuilder &);
-  flatbuffers::Offset<GatherOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<GatherOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<GatherOptions> CreateGatherOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                                                              int32_t axis = 0)
-{
-  GatherOptionsBuilder builder_(_fbb);
-  builder_.add_axis(axis);
-  return builder_.Finish();
-}
-
-struct TransposeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct TransposeOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit TransposeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  TransposeOptionsBuilder &operator=(const TransposeOptionsBuilder &);
-  flatbuffers::Offset<TransposeOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<TransposeOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<TransposeOptions>
-CreateTransposeOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  TransposeOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct ExpOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct ExpOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit ExpOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  ExpOptionsBuilder &operator=(const ExpOptionsBuilder &);
-  flatbuffers::Offset<ExpOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ExpOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ExpOptions> CreateExpOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  ExpOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct ReducerOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_KEEP_DIMS = 4
-  };
-  bool keep_dims() const { return GetField<uint8_t>(VT_KEEP_DIMS, 0) != 0; }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<uint8_t>(verifier, VT_KEEP_DIMS) &&
-           verifier.EndTable();
-  }
-};
-
-struct ReducerOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_keep_dims(bool keep_dims)
-  {
-    fbb_.AddElement<uint8_t>(ReducerOptions::VT_KEEP_DIMS, static_cast<uint8_t>(keep_dims), 0);
-  }
-  explicit ReducerOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  ReducerOptionsBuilder &operator=(const ReducerOptionsBuilder &);
-  flatbuffers::Offset<ReducerOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ReducerOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ReducerOptions>
-CreateReducerOptions(flatbuffers::FlatBufferBuilder &_fbb, bool keep_dims = false)
-{
-  ReducerOptionsBuilder builder_(_fbb);
-  builder_.add_keep_dims(keep_dims);
-  return builder_.Finish();
-}
-
-struct SqueezeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_SQUEEZE_DIMS = 4
-  };
-  const flatbuffers::Vector<int32_t> *squeeze_dims() const
-  {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_SQUEEZE_DIMS);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_SQUEEZE_DIMS) &&
-           verifier.VerifyVector(squeeze_dims()) && verifier.EndTable();
-  }
-};
-
-struct SqueezeOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_squeeze_dims(flatbuffers::Offset<flatbuffers::Vector<int32_t>> squeeze_dims)
-  {
-    fbb_.AddOffset(SqueezeOptions::VT_SQUEEZE_DIMS, squeeze_dims);
-  }
-  explicit SqueezeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SqueezeOptionsBuilder &operator=(const SqueezeOptionsBuilder &);
-  flatbuffers::Offset<SqueezeOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SqueezeOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SqueezeOptions>
-CreateSqueezeOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                     flatbuffers::Offset<flatbuffers::Vector<int32_t>> squeeze_dims = 0)
-{
-  SqueezeOptionsBuilder builder_(_fbb);
-  builder_.add_squeeze_dims(squeeze_dims);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<SqueezeOptions>
-CreateSqueezeOptionsDirect(flatbuffers::FlatBufferBuilder &_fbb,
-                           const std::vector<int32_t> *squeeze_dims = nullptr)
-{
-  return neurun_tflite::CreateSqueezeOptions(
-      _fbb, squeeze_dims ? _fbb.CreateVector<int32_t>(*squeeze_dims) : 0);
-}
-
-struct SplitOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_NUM_SPLITS = 4
-  };
-  int32_t num_splits() const { return GetField<int32_t>(VT_NUM_SPLITS, 0); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_NUM_SPLITS) &&
-           verifier.EndTable();
-  }
-};
-
-struct SplitOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_num_splits(int32_t num_splits)
-  {
-    fbb_.AddElement<int32_t>(SplitOptions::VT_NUM_SPLITS, num_splits, 0);
-  }
-  explicit SplitOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SplitOptionsBuilder &operator=(const SplitOptionsBuilder &);
-  flatbuffers::Offset<SplitOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SplitOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SplitOptions> CreateSplitOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                                                            int32_t num_splits = 0)
-{
-  SplitOptionsBuilder builder_(_fbb);
-  builder_.add_num_splits(num_splits);
-  return builder_.Finish();
-}
-
-struct SplitVOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_NUM_SPLITS = 4
-  };
-  int32_t num_splits() const { return GetField<int32_t>(VT_NUM_SPLITS, 0); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_NUM_SPLITS) &&
-           verifier.EndTable();
-  }
-};
-
-struct SplitVOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_num_splits(int32_t num_splits)
-  {
-    fbb_.AddElement<int32_t>(SplitVOptions::VT_NUM_SPLITS, num_splits, 0);
-  }
-  explicit SplitVOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SplitVOptionsBuilder &operator=(const SplitVOptionsBuilder &);
-  flatbuffers::Offset<SplitVOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SplitVOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SplitVOptions> CreateSplitVOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                                                              int32_t num_splits = 0)
-{
-  SplitVOptionsBuilder builder_(_fbb);
-  builder_.add_num_splits(num_splits);
-  return builder_.Finish();
-}
-
-struct StridedSliceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_BEGIN_MASK = 4,
-    VT_END_MASK = 6,
-    VT_ELLIPSIS_MASK = 8,
-    VT_NEW_AXIS_MASK = 10,
-    VT_SHRINK_AXIS_MASK = 12
-  };
-  int32_t begin_mask() const { return GetField<int32_t>(VT_BEGIN_MASK, 0); }
-  int32_t end_mask() const { return GetField<int32_t>(VT_END_MASK, 0); }
-  int32_t ellipsis_mask() const { return GetField<int32_t>(VT_ELLIPSIS_MASK, 0); }
-  int32_t new_axis_mask() const { return GetField<int32_t>(VT_NEW_AXIS_MASK, 0); }
-  int32_t shrink_axis_mask() const { return GetField<int32_t>(VT_SHRINK_AXIS_MASK, 0); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_BEGIN_MASK) &&
-           VerifyField<int32_t>(verifier, VT_END_MASK) &&
-           VerifyField<int32_t>(verifier, VT_ELLIPSIS_MASK) &&
-           VerifyField<int32_t>(verifier, VT_NEW_AXIS_MASK) &&
-           VerifyField<int32_t>(verifier, VT_SHRINK_AXIS_MASK) && verifier.EndTable();
-  }
-};
-
-struct StridedSliceOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_begin_mask(int32_t begin_mask)
-  {
-    fbb_.AddElement<int32_t>(StridedSliceOptions::VT_BEGIN_MASK, begin_mask, 0);
-  }
-  void add_end_mask(int32_t end_mask)
-  {
-    fbb_.AddElement<int32_t>(StridedSliceOptions::VT_END_MASK, end_mask, 0);
-  }
-  void add_ellipsis_mask(int32_t ellipsis_mask)
-  {
-    fbb_.AddElement<int32_t>(StridedSliceOptions::VT_ELLIPSIS_MASK, ellipsis_mask, 0);
-  }
-  void add_new_axis_mask(int32_t new_axis_mask)
-  {
-    fbb_.AddElement<int32_t>(StridedSliceOptions::VT_NEW_AXIS_MASK, new_axis_mask, 0);
-  }
-  void add_shrink_axis_mask(int32_t shrink_axis_mask)
-  {
-    fbb_.AddElement<int32_t>(StridedSliceOptions::VT_SHRINK_AXIS_MASK, shrink_axis_mask, 0);
-  }
-  explicit StridedSliceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  StridedSliceOptionsBuilder &operator=(const StridedSliceOptionsBuilder &);
-  flatbuffers::Offset<StridedSliceOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<StridedSliceOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<StridedSliceOptions>
-CreateStridedSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, int32_t begin_mask = 0,
-                          int32_t end_mask = 0, int32_t ellipsis_mask = 0,
-                          int32_t new_axis_mask = 0, int32_t shrink_axis_mask = 0)
-{
-  StridedSliceOptionsBuilder builder_(_fbb);
-  builder_.add_shrink_axis_mask(shrink_axis_mask);
-  builder_.add_new_axis_mask(new_axis_mask);
-  builder_.add_ellipsis_mask(ellipsis_mask);
-  builder_.add_end_mask(end_mask);
-  builder_.add_begin_mask(begin_mask);
-  return builder_.Finish();
-}
-
-struct LogSoftmaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct LogSoftmaxOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit LogSoftmaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  LogSoftmaxOptionsBuilder &operator=(const LogSoftmaxOptionsBuilder &);
-  flatbuffers::Offset<LogSoftmaxOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<LogSoftmaxOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<LogSoftmaxOptions>
-CreateLogSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  LogSoftmaxOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct CastOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_IN_DATA_TYPE = 4,
-    VT_OUT_DATA_TYPE = 6
-  };
-  TensorType in_data_type() const
-  {
-    return static_cast<TensorType>(GetField<int8_t>(VT_IN_DATA_TYPE, 0));
-  }
-  TensorType out_data_type() const
-  {
-    return static_cast<TensorType>(GetField<int8_t>(VT_OUT_DATA_TYPE, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_IN_DATA_TYPE) &&
-           VerifyField<int8_t>(verifier, VT_OUT_DATA_TYPE) && verifier.EndTable();
-  }
-};
-
-struct CastOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_in_data_type(TensorType in_data_type)
-  {
-    fbb_.AddElement<int8_t>(CastOptions::VT_IN_DATA_TYPE, static_cast<int8_t>(in_data_type), 0);
-  }
-  void add_out_data_type(TensorType out_data_type)
-  {
-    fbb_.AddElement<int8_t>(CastOptions::VT_OUT_DATA_TYPE, static_cast<int8_t>(out_data_type), 0);
-  }
-  explicit CastOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  CastOptionsBuilder &operator=(const CastOptionsBuilder &);
-  flatbuffers::Offset<CastOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<CastOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<CastOptions>
-CreateCastOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                  TensorType in_data_type = TensorType_FLOAT32,
-                  TensorType out_data_type = TensorType_FLOAT32)
-{
-  CastOptionsBuilder builder_(_fbb);
-  builder_.add_out_data_type(out_data_type);
-  builder_.add_in_data_type(in_data_type);
-  return builder_.Finish();
-}
-
-struct DequantizeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct DequantizeOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit DequantizeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  DequantizeOptionsBuilder &operator=(const DequantizeOptionsBuilder &);
-  flatbuffers::Offset<DequantizeOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<DequantizeOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<DequantizeOptions>
-CreateDequantizeOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  DequantizeOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct MaximumMinimumOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct MaximumMinimumOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit MaximumMinimumOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  MaximumMinimumOptionsBuilder &operator=(const MaximumMinimumOptionsBuilder &);
-  flatbuffers::Offset<MaximumMinimumOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<MaximumMinimumOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<MaximumMinimumOptions>
-CreateMaximumMinimumOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  MaximumMinimumOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct TileOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct TileOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit TileOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  TileOptionsBuilder &operator=(const TileOptionsBuilder &);
-  flatbuffers::Offset<TileOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<TileOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<TileOptions> CreateTileOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  TileOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct ArgMaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_OUTPUT_TYPE = 4
-  };
-  TensorType output_type() const
-  {
-    return static_cast<TensorType>(GetField<int8_t>(VT_OUTPUT_TYPE, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_OUTPUT_TYPE) &&
-           verifier.EndTable();
-  }
-};
-
-struct ArgMaxOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_output_type(TensorType output_type)
-  {
-    fbb_.AddElement<int8_t>(ArgMaxOptions::VT_OUTPUT_TYPE, static_cast<int8_t>(output_type), 0);
-  }
-  explicit ArgMaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  ArgMaxOptionsBuilder &operator=(const ArgMaxOptionsBuilder &);
-  flatbuffers::Offset<ArgMaxOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ArgMaxOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ArgMaxOptions>
-CreateArgMaxOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                    TensorType output_type = TensorType_FLOAT32)
-{
-  ArgMaxOptionsBuilder builder_(_fbb);
-  builder_.add_output_type(output_type);
-  return builder_.Finish();
-}
-
-struct ArgMinOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_OUTPUT_TYPE = 4
-  };
-  TensorType output_type() const
-  {
-    return static_cast<TensorType>(GetField<int8_t>(VT_OUTPUT_TYPE, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_OUTPUT_TYPE) &&
-           verifier.EndTable();
-  }
-};
-
-struct ArgMinOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_output_type(TensorType output_type)
-  {
-    fbb_.AddElement<int8_t>(ArgMinOptions::VT_OUTPUT_TYPE, static_cast<int8_t>(output_type), 0);
-  }
-  explicit ArgMinOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  ArgMinOptionsBuilder &operator=(const ArgMinOptionsBuilder &);
-  flatbuffers::Offset<ArgMinOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ArgMinOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ArgMinOptions>
-CreateArgMinOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                    TensorType output_type = TensorType_FLOAT32)
-{
-  ArgMinOptionsBuilder builder_(_fbb);
-  builder_.add_output_type(output_type);
-  return builder_.Finish();
-}
-
-struct GreaterOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct GreaterOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit GreaterOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  GreaterOptionsBuilder &operator=(const GreaterOptionsBuilder &);
-  flatbuffers::Offset<GreaterOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<GreaterOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<GreaterOptions>
-CreateGreaterOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  GreaterOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct GreaterEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct GreaterEqualOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit GreaterEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  GreaterEqualOptionsBuilder &operator=(const GreaterEqualOptionsBuilder &);
-  flatbuffers::Offset<GreaterEqualOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<GreaterEqualOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<GreaterEqualOptions>
-CreateGreaterEqualOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  GreaterEqualOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct LessOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct LessOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit LessOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  LessOptionsBuilder &operator=(const LessOptionsBuilder &);
-  flatbuffers::Offset<LessOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<LessOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<LessOptions> CreateLessOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  LessOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct LessEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct LessEqualOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit LessEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  LessEqualOptionsBuilder &operator=(const LessEqualOptionsBuilder &);
-  flatbuffers::Offset<LessEqualOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<LessEqualOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<LessEqualOptions>
-CreateLessEqualOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  LessEqualOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct NegOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct NegOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit NegOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  NegOptionsBuilder &operator=(const NegOptionsBuilder &);
-  flatbuffers::Offset<NegOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<NegOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<NegOptions> CreateNegOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  NegOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct SelectOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct SelectOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit SelectOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SelectOptionsBuilder &operator=(const SelectOptionsBuilder &);
-  flatbuffers::Offset<SelectOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SelectOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SelectOptions> CreateSelectOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  SelectOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct SliceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct SliceOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit SliceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SliceOptionsBuilder &operator=(const SliceOptionsBuilder &);
-  flatbuffers::Offset<SliceOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SliceOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SliceOptions> CreateSliceOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  SliceOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct TransposeConvOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_PADDING = 4,
-    VT_STRIDE_W = 6,
-    VT_STRIDE_H = 8
-  };
-  Padding padding() const { return static_cast<Padding>(GetField<int8_t>(VT_PADDING, 0)); }
-  int32_t stride_w() const { return GetField<int32_t>(VT_STRIDE_W, 0); }
-  int32_t stride_h() const { return GetField<int32_t>(VT_STRIDE_H, 0); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_PADDING) &&
-           VerifyField<int32_t>(verifier, VT_STRIDE_W) &&
-           VerifyField<int32_t>(verifier, VT_STRIDE_H) && verifier.EndTable();
-  }
-};
-
-struct TransposeConvOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_padding(Padding padding)
-  {
-    fbb_.AddElement<int8_t>(TransposeConvOptions::VT_PADDING, static_cast<int8_t>(padding), 0);
-  }
-  void add_stride_w(int32_t stride_w)
-  {
-    fbb_.AddElement<int32_t>(TransposeConvOptions::VT_STRIDE_W, stride_w, 0);
-  }
-  void add_stride_h(int32_t stride_h)
-  {
-    fbb_.AddElement<int32_t>(TransposeConvOptions::VT_STRIDE_H, stride_h, 0);
-  }
-  explicit TransposeConvOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  TransposeConvOptionsBuilder &operator=(const TransposeConvOptionsBuilder &);
-  flatbuffers::Offset<TransposeConvOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<TransposeConvOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<TransposeConvOptions>
-CreateTransposeConvOptions(flatbuffers::FlatBufferBuilder &_fbb, Padding padding = Padding_SAME,
-                           int32_t stride_w = 0, int32_t stride_h = 0)
-{
-  TransposeConvOptionsBuilder builder_(_fbb);
-  builder_.add_stride_h(stride_h);
-  builder_.add_stride_w(stride_w);
-  builder_.add_padding(padding);
-  return builder_.Finish();
-}
-
-struct ExpandDimsOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct ExpandDimsOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit ExpandDimsOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  ExpandDimsOptionsBuilder &operator=(const ExpandDimsOptionsBuilder &);
-  flatbuffers::Offset<ExpandDimsOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ExpandDimsOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ExpandDimsOptions>
-CreateExpandDimsOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  ExpandDimsOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct SparseToDenseOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_VALIDATE_INDICES = 4
-  };
-  bool validate_indices() const { return GetField<uint8_t>(VT_VALIDATE_INDICES, 0) != 0; }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<uint8_t>(verifier, VT_VALIDATE_INDICES) &&
-           verifier.EndTable();
-  }
-};
-
-struct SparseToDenseOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_validate_indices(bool validate_indices)
-  {
-    fbb_.AddElement<uint8_t>(SparseToDenseOptions::VT_VALIDATE_INDICES,
-                             static_cast<uint8_t>(validate_indices), 0);
-  }
-  explicit SparseToDenseOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SparseToDenseOptionsBuilder &operator=(const SparseToDenseOptionsBuilder &);
-  flatbuffers::Offset<SparseToDenseOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SparseToDenseOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SparseToDenseOptions>
-CreateSparseToDenseOptions(flatbuffers::FlatBufferBuilder &_fbb, bool validate_indices = false)
-{
-  SparseToDenseOptionsBuilder builder_(_fbb);
-  builder_.add_validate_indices(validate_indices);
-  return builder_.Finish();
-}
-
-struct EqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct EqualOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit EqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  EqualOptionsBuilder &operator=(const EqualOptionsBuilder &);
-  flatbuffers::Offset<EqualOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<EqualOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<EqualOptions> CreateEqualOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  EqualOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct NotEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct NotEqualOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit NotEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  NotEqualOptionsBuilder &operator=(const NotEqualOptionsBuilder &);
-  flatbuffers::Offset<NotEqualOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<NotEqualOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<NotEqualOptions>
-CreateNotEqualOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  NotEqualOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct ShapeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_OUT_TYPE = 4
-  };
-  TensorType out_type() const { return static_cast<TensorType>(GetField<int8_t>(VT_OUT_TYPE, 0)); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_OUT_TYPE) &&
-           verifier.EndTable();
-  }
-};
-
-struct ShapeOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_out_type(TensorType out_type)
-  {
-    fbb_.AddElement<int8_t>(ShapeOptions::VT_OUT_TYPE, static_cast<int8_t>(out_type), 0);
-  }
-  explicit ShapeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  ShapeOptionsBuilder &operator=(const ShapeOptionsBuilder &);
-  flatbuffers::Offset<ShapeOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ShapeOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ShapeOptions>
-CreateShapeOptions(flatbuffers::FlatBufferBuilder &_fbb, TensorType out_type = TensorType_FLOAT32)
-{
-  ShapeOptionsBuilder builder_(_fbb);
-  builder_.add_out_type(out_type);
-  return builder_.Finish();
-}
-
-struct PowOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct PowOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit PowOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  PowOptionsBuilder &operator=(const PowOptionsBuilder &);
-  flatbuffers::Offset<PowOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<PowOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<PowOptions> CreatePowOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  PowOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct FakeQuantOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_MIN = 4,
-    VT_MAX = 6,
-    VT_NUM_BITS = 8,
-    VT_NARROW_RANGE = 10
-  };
-  float min() const { return GetField<float>(VT_MIN, 0.0f); }
-  float max() const { return GetField<float>(VT_MAX, 0.0f); }
-  int32_t num_bits() const { return GetField<int32_t>(VT_NUM_BITS, 0); }
-  bool narrow_range() const { return GetField<uint8_t>(VT_NARROW_RANGE, 0) != 0; }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<float>(verifier, VT_MIN) &&
-           VerifyField<float>(verifier, VT_MAX) && VerifyField<int32_t>(verifier, VT_NUM_BITS) &&
-           VerifyField<uint8_t>(verifier, VT_NARROW_RANGE) && verifier.EndTable();
-  }
-};
-
-struct FakeQuantOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_min(float min) { fbb_.AddElement<float>(FakeQuantOptions::VT_MIN, min, 0.0f); }
-  void add_max(float max) { fbb_.AddElement<float>(FakeQuantOptions::VT_MAX, max, 0.0f); }
-  void add_num_bits(int32_t num_bits)
-  {
-    fbb_.AddElement<int32_t>(FakeQuantOptions::VT_NUM_BITS, num_bits, 0);
-  }
-  void add_narrow_range(bool narrow_range)
-  {
-    fbb_.AddElement<uint8_t>(FakeQuantOptions::VT_NARROW_RANGE, static_cast<uint8_t>(narrow_range),
-                             0);
-  }
-  explicit FakeQuantOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  FakeQuantOptionsBuilder &operator=(const FakeQuantOptionsBuilder &);
-  flatbuffers::Offset<FakeQuantOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<FakeQuantOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<FakeQuantOptions>
-CreateFakeQuantOptions(flatbuffers::FlatBufferBuilder &_fbb, float min = 0.0f, float max = 0.0f,
-                       int32_t num_bits = 0, bool narrow_range = false)
-{
-  FakeQuantOptionsBuilder builder_(_fbb);
-  builder_.add_num_bits(num_bits);
-  builder_.add_max(max);
-  builder_.add_min(min);
-  builder_.add_narrow_range(narrow_range);
-  return builder_.Finish();
-}
-
-struct PackOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_VALUES_COUNT = 4,
-    VT_AXIS = 6
-  };
-  int32_t values_count() const { return GetField<int32_t>(VT_VALUES_COUNT, 0); }
-  int32_t axis() const { return GetField<int32_t>(VT_AXIS, 0); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_VALUES_COUNT) &&
-           VerifyField<int32_t>(verifier, VT_AXIS) && verifier.EndTable();
-  }
-};
-
-struct PackOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_values_count(int32_t values_count)
-  {
-    fbb_.AddElement<int32_t>(PackOptions::VT_VALUES_COUNT, values_count, 0);
-  }
-  void add_axis(int32_t axis) { fbb_.AddElement<int32_t>(PackOptions::VT_AXIS, axis, 0); }
-  explicit PackOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  PackOptionsBuilder &operator=(const PackOptionsBuilder &);
-  flatbuffers::Offset<PackOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<PackOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<PackOptions>
-CreatePackOptions(flatbuffers::FlatBufferBuilder &_fbb, int32_t values_count = 0, int32_t axis = 0)
-{
-  PackOptionsBuilder builder_(_fbb);
-  builder_.add_axis(axis);
-  builder_.add_values_count(values_count);
-  return builder_.Finish();
-}
-
-struct LogicalOrOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct LogicalOrOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit LogicalOrOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  LogicalOrOptionsBuilder &operator=(const LogicalOrOptionsBuilder &);
-  flatbuffers::Offset<LogicalOrOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<LogicalOrOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<LogicalOrOptions>
-CreateLogicalOrOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  LogicalOrOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct OneHotOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_AXIS = 4
-  };
-  int32_t axis() const { return GetField<int32_t>(VT_AXIS, 0); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_AXIS) &&
-           verifier.EndTable();
-  }
-};
-
-struct OneHotOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_axis(int32_t axis) { fbb_.AddElement<int32_t>(OneHotOptions::VT_AXIS, axis, 0); }
-  explicit OneHotOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  OneHotOptionsBuilder &operator=(const OneHotOptionsBuilder &);
-  flatbuffers::Offset<OneHotOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<OneHotOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<OneHotOptions> CreateOneHotOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                                                              int32_t axis = 0)
-{
-  OneHotOptionsBuilder builder_(_fbb);
-  builder_.add_axis(axis);
-  return builder_.Finish();
-}
-
-struct AbsOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct AbsOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit AbsOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  AbsOptionsBuilder &operator=(const AbsOptionsBuilder &);
-  flatbuffers::Offset<AbsOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<AbsOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<AbsOptions> CreateAbsOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  AbsOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct LogicalAndOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct LogicalAndOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit LogicalAndOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  LogicalAndOptionsBuilder &operator=(const LogicalAndOptionsBuilder &);
-  flatbuffers::Offset<LogicalAndOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<LogicalAndOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<LogicalAndOptions>
-CreateLogicalAndOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  LogicalAndOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct LogicalNotOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct LogicalNotOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit LogicalNotOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  LogicalNotOptionsBuilder &operator=(const LogicalNotOptionsBuilder &);
-  flatbuffers::Offset<LogicalNotOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<LogicalNotOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<LogicalNotOptions>
-CreateLogicalNotOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  LogicalNotOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct UnpackOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_NUM = 4,
-    VT_AXIS = 6
-  };
-  int32_t num() const { return GetField<int32_t>(VT_NUM, 0); }
-  int32_t axis() const { return GetField<int32_t>(VT_AXIS, 0); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_NUM) &&
-           VerifyField<int32_t>(verifier, VT_AXIS) && verifier.EndTable();
-  }
-};
-
-struct UnpackOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_num(int32_t num) { fbb_.AddElement<int32_t>(UnpackOptions::VT_NUM, num, 0); }
-  void add_axis(int32_t axis) { fbb_.AddElement<int32_t>(UnpackOptions::VT_AXIS, axis, 0); }
-  explicit UnpackOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  UnpackOptionsBuilder &operator=(const UnpackOptionsBuilder &);
-  flatbuffers::Offset<UnpackOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<UnpackOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<UnpackOptions> CreateUnpackOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                                                              int32_t num = 0, int32_t axis = 0)
-{
-  UnpackOptionsBuilder builder_(_fbb);
-  builder_.add_axis(axis);
-  builder_.add_num(num);
-  return builder_.Finish();
-}
-
-struct FloorDivOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct FloorDivOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit FloorDivOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  FloorDivOptionsBuilder &operator=(const FloorDivOptionsBuilder &);
-  flatbuffers::Offset<FloorDivOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<FloorDivOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<FloorDivOptions>
-CreateFloorDivOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  FloorDivOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct SquareOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct SquareOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit SquareOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SquareOptionsBuilder &operator=(const SquareOptionsBuilder &);
-  flatbuffers::Offset<SquareOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SquareOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SquareOptions> CreateSquareOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  SquareOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct ZerosLikeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct ZerosLikeOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit ZerosLikeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  ZerosLikeOptionsBuilder &operator=(const ZerosLikeOptionsBuilder &);
-  flatbuffers::Offset<ZerosLikeOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ZerosLikeOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ZerosLikeOptions>
-CreateZerosLikeOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  ZerosLikeOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct FillOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct FillOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit FillOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  FillOptionsBuilder &operator=(const FillOptionsBuilder &);
-  flatbuffers::Offset<FillOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<FillOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<FillOptions> CreateFillOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  FillOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct FloorModOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct FloorModOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit FloorModOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  FloorModOptionsBuilder &operator=(const FloorModOptionsBuilder &);
-  flatbuffers::Offset<FloorModOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<FloorModOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<FloorModOptions>
-CreateFloorModOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  FloorModOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct RangeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct RangeOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit RangeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  RangeOptionsBuilder &operator=(const RangeOptionsBuilder &);
-  flatbuffers::Offset<RangeOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<RangeOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<RangeOptions> CreateRangeOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  RangeOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct LeakyReluOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_ALPHA = 4
-  };
-  float alpha() const { return GetField<float>(VT_ALPHA, 0.0f); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<float>(verifier, VT_ALPHA) &&
-           verifier.EndTable();
-  }
-};
-
-struct LeakyReluOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_alpha(float alpha) { fbb_.AddElement<float>(LeakyReluOptions::VT_ALPHA, alpha, 0.0f); }
-  explicit LeakyReluOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  LeakyReluOptionsBuilder &operator=(const LeakyReluOptionsBuilder &);
-  flatbuffers::Offset<LeakyReluOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<LeakyReluOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<LeakyReluOptions>
-CreateLeakyReluOptions(flatbuffers::FlatBufferBuilder &_fbb, float alpha = 0.0f)
-{
-  LeakyReluOptionsBuilder builder_(_fbb);
-  builder_.add_alpha(alpha);
-  return builder_.Finish();
-}
-
-struct SquaredDifferenceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && verifier.EndTable();
-  }
-};
-
-struct SquaredDifferenceOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit SquaredDifferenceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SquaredDifferenceOptionsBuilder &operator=(const SquaredDifferenceOptionsBuilder &);
-  flatbuffers::Offset<SquaredDifferenceOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SquaredDifferenceOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SquaredDifferenceOptions>
-CreateSquaredDifferenceOptions(flatbuffers::FlatBufferBuilder &_fbb)
-{
-  SquaredDifferenceOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-struct MirrorPadOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_MODE = 4
-  };
-  MirrorPadMode mode() const { return static_cast<MirrorPadMode>(GetField<int8_t>(VT_MODE, 0)); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_MODE) &&
-           verifier.EndTable();
-  }
-};
-
-struct MirrorPadOptionsBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_mode(MirrorPadMode mode)
-  {
-    fbb_.AddElement<int8_t>(MirrorPadOptions::VT_MODE, static_cast<int8_t>(mode), 0);
-  }
-  explicit MirrorPadOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  MirrorPadOptionsBuilder &operator=(const MirrorPadOptionsBuilder &);
-  flatbuffers::Offset<MirrorPadOptions> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<MirrorPadOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<MirrorPadOptions>
-CreateMirrorPadOptions(flatbuffers::FlatBufferBuilder &_fbb,
-                       MirrorPadMode mode = MirrorPadMode_REFLECT)
-{
-  MirrorPadOptionsBuilder builder_(_fbb);
-  builder_.add_mode(mode);
-  return builder_.Finish();
-}
-
-struct OperatorCode FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_BUILTIN_CODE = 4,
-    VT_CUSTOM_CODE = 6,
-    VT_VERSION = 8
-  };
-  BuiltinOperator builtin_code() const
-  {
-    return static_cast<BuiltinOperator>(GetField<int8_t>(VT_BUILTIN_CODE, 0));
-  }
-  const flatbuffers::String *custom_code() const
-  {
-    return GetPointer<const flatbuffers::String *>(VT_CUSTOM_CODE);
-  }
-  int32_t version() const { return GetField<int32_t>(VT_VERSION, 1); }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_BUILTIN_CODE) &&
-           VerifyOffset(verifier, VT_CUSTOM_CODE) && verifier.VerifyString(custom_code()) &&
-           VerifyField<int32_t>(verifier, VT_VERSION) && verifier.EndTable();
-  }
-};
-
-struct OperatorCodeBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_builtin_code(BuiltinOperator builtin_code)
-  {
-    fbb_.AddElement<int8_t>(OperatorCode::VT_BUILTIN_CODE, static_cast<int8_t>(builtin_code), 0);
-  }
-  void add_custom_code(flatbuffers::Offset<flatbuffers::String> custom_code)
-  {
-    fbb_.AddOffset(OperatorCode::VT_CUSTOM_CODE, custom_code);
-  }
-  void add_version(int32_t version)
-  {
-    fbb_.AddElement<int32_t>(OperatorCode::VT_VERSION, version, 1);
-  }
-  explicit OperatorCodeBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  OperatorCodeBuilder &operator=(const OperatorCodeBuilder &);
-  flatbuffers::Offset<OperatorCode> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<OperatorCode>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<OperatorCode>
-CreateOperatorCode(flatbuffers::FlatBufferBuilder &_fbb,
-                   BuiltinOperator builtin_code = BuiltinOperator_ADD,
-                   flatbuffers::Offset<flatbuffers::String> custom_code = 0, int32_t version = 1)
-{
-  OperatorCodeBuilder builder_(_fbb);
-  builder_.add_version(version);
-  builder_.add_custom_code(custom_code);
-  builder_.add_builtin_code(builtin_code);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<OperatorCode>
-CreateOperatorCodeDirect(flatbuffers::FlatBufferBuilder &_fbb,
-                         BuiltinOperator builtin_code = BuiltinOperator_ADD,
-                         const char *custom_code = nullptr, int32_t version = 1)
-{
-  return neurun_tflite::CreateOperatorCode(
-      _fbb, builtin_code, custom_code ? _fbb.CreateString(custom_code) : 0, version);
-}
-
-struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_OPCODE_INDEX = 4,
-    VT_INPUTS = 6,
-    VT_OUTPUTS = 8,
-    VT_BUILTIN_OPTIONS_TYPE = 10,
-    VT_BUILTIN_OPTIONS = 12,
-    VT_CUSTOM_OPTIONS = 14,
-    VT_CUSTOM_OPTIONS_FORMAT = 16,
-    VT_MUTATING_VARIABLE_INPUTS = 18
-  };
-  uint32_t opcode_index() const { return GetField<uint32_t>(VT_OPCODE_INDEX, 0); }
-  const flatbuffers::Vector<int32_t> *inputs() const
-  {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_INPUTS);
-  }
-  const flatbuffers::Vector<int32_t> *outputs() const
-  {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_OUTPUTS);
-  }
-  BuiltinOptions builtin_options_type() const
-  {
-    return static_cast<BuiltinOptions>(GetField<uint8_t>(VT_BUILTIN_OPTIONS_TYPE, 0));
-  }
-  const void *builtin_options() const { return GetPointer<const void *>(VT_BUILTIN_OPTIONS); }
-  template <typename T> const T *builtin_options_as() const;
-  const Conv2DOptions *builtin_options_as_Conv2DOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_Conv2DOptions
-               ? static_cast<const Conv2DOptions *>(builtin_options())
-               : nullptr;
-  }
-  const DepthwiseConv2DOptions *builtin_options_as_DepthwiseConv2DOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_DepthwiseConv2DOptions
-               ? static_cast<const DepthwiseConv2DOptions *>(builtin_options())
-               : nullptr;
-  }
-  const ConcatEmbeddingsOptions *builtin_options_as_ConcatEmbeddingsOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_ConcatEmbeddingsOptions
-               ? static_cast<const ConcatEmbeddingsOptions *>(builtin_options())
-               : nullptr;
-  }
-  const LSHProjectionOptions *builtin_options_as_LSHProjectionOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_LSHProjectionOptions
-               ? static_cast<const LSHProjectionOptions *>(builtin_options())
-               : nullptr;
-  }
-  const Pool2DOptions *builtin_options_as_Pool2DOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_Pool2DOptions
-               ? static_cast<const Pool2DOptions *>(builtin_options())
-               : nullptr;
-  }
-  const SVDFOptions *builtin_options_as_SVDFOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_SVDFOptions
-               ? static_cast<const SVDFOptions *>(builtin_options())
-               : nullptr;
-  }
-  const RNNOptions *builtin_options_as_RNNOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_RNNOptions
-               ? static_cast<const RNNOptions *>(builtin_options())
-               : nullptr;
-  }
-  const FullyConnectedOptions *builtin_options_as_FullyConnectedOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_FullyConnectedOptions
-               ? static_cast<const FullyConnectedOptions *>(builtin_options())
-               : nullptr;
-  }
-  const SoftmaxOptions *builtin_options_as_SoftmaxOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_SoftmaxOptions
-               ? static_cast<const SoftmaxOptions *>(builtin_options())
-               : nullptr;
-  }
-  const ConcatenationOptions *builtin_options_as_ConcatenationOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_ConcatenationOptions
-               ? static_cast<const ConcatenationOptions *>(builtin_options())
-               : nullptr;
-  }
-  const AddOptions *builtin_options_as_AddOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_AddOptions
-               ? static_cast<const AddOptions *>(builtin_options())
-               : nullptr;
-  }
-  const L2NormOptions *builtin_options_as_L2NormOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_L2NormOptions
-               ? static_cast<const L2NormOptions *>(builtin_options())
-               : nullptr;
-  }
-  const LocalResponseNormalizationOptions *
-  builtin_options_as_LocalResponseNormalizationOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_LocalResponseNormalizationOptions
-               ? static_cast<const LocalResponseNormalizationOptions *>(builtin_options())
-               : nullptr;
-  }
-  const LSTMOptions *builtin_options_as_LSTMOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_LSTMOptions
-               ? static_cast<const LSTMOptions *>(builtin_options())
-               : nullptr;
-  }
-  const ResizeBilinearOptions *builtin_options_as_ResizeBilinearOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_ResizeBilinearOptions
-               ? static_cast<const ResizeBilinearOptions *>(builtin_options())
-               : nullptr;
-  }
-  const CallOptions *builtin_options_as_CallOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_CallOptions
-               ? static_cast<const CallOptions *>(builtin_options())
-               : nullptr;
-  }
-  const ReshapeOptions *builtin_options_as_ReshapeOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_ReshapeOptions
-               ? static_cast<const ReshapeOptions *>(builtin_options())
-               : nullptr;
-  }
-  const SkipGramOptions *builtin_options_as_SkipGramOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_SkipGramOptions
-               ? static_cast<const SkipGramOptions *>(builtin_options())
-               : nullptr;
-  }
-  const SpaceToDepthOptions *builtin_options_as_SpaceToDepthOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_SpaceToDepthOptions
-               ? static_cast<const SpaceToDepthOptions *>(builtin_options())
-               : nullptr;
-  }
-  const EmbeddingLookupSparseOptions *builtin_options_as_EmbeddingLookupSparseOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_EmbeddingLookupSparseOptions
-               ? static_cast<const EmbeddingLookupSparseOptions *>(builtin_options())
-               : nullptr;
-  }
-  const MulOptions *builtin_options_as_MulOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_MulOptions
-               ? static_cast<const MulOptions *>(builtin_options())
-               : nullptr;
-  }
-  const PadOptions *builtin_options_as_PadOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_PadOptions
-               ? static_cast<const PadOptions *>(builtin_options())
-               : nullptr;
-  }
-  const GatherOptions *builtin_options_as_GatherOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_GatherOptions
-               ? static_cast<const GatherOptions *>(builtin_options())
-               : nullptr;
-  }
-  const BatchToSpaceNDOptions *builtin_options_as_BatchToSpaceNDOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_BatchToSpaceNDOptions
-               ? static_cast<const BatchToSpaceNDOptions *>(builtin_options())
-               : nullptr;
-  }
-  const SpaceToBatchNDOptions *builtin_options_as_SpaceToBatchNDOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_SpaceToBatchNDOptions
-               ? static_cast<const SpaceToBatchNDOptions *>(builtin_options())
-               : nullptr;
-  }
-  const TransposeOptions *builtin_options_as_TransposeOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_TransposeOptions
-               ? static_cast<const TransposeOptions *>(builtin_options())
-               : nullptr;
-  }
-  const ReducerOptions *builtin_options_as_ReducerOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_ReducerOptions
-               ? static_cast<const ReducerOptions *>(builtin_options())
-               : nullptr;
-  }
-  const SubOptions *builtin_options_as_SubOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_SubOptions
-               ? static_cast<const SubOptions *>(builtin_options())
-               : nullptr;
-  }
-  const DivOptions *builtin_options_as_DivOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_DivOptions
-               ? static_cast<const DivOptions *>(builtin_options())
-               : nullptr;
-  }
-  const SqueezeOptions *builtin_options_as_SqueezeOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_SqueezeOptions
-               ? static_cast<const SqueezeOptions *>(builtin_options())
-               : nullptr;
-  }
-  const SequenceRNNOptions *builtin_options_as_SequenceRNNOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_SequenceRNNOptions
-               ? static_cast<const SequenceRNNOptions *>(builtin_options())
-               : nullptr;
-  }
-  const StridedSliceOptions *builtin_options_as_StridedSliceOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_StridedSliceOptions
-               ? static_cast<const StridedSliceOptions *>(builtin_options())
-               : nullptr;
-  }
-  const ExpOptions *builtin_options_as_ExpOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_ExpOptions
-               ? static_cast<const ExpOptions *>(builtin_options())
-               : nullptr;
-  }
-  const TopKV2Options *builtin_options_as_TopKV2Options() const
-  {
-    return builtin_options_type() == BuiltinOptions_TopKV2Options
-               ? static_cast<const TopKV2Options *>(builtin_options())
-               : nullptr;
-  }
-  const SplitOptions *builtin_options_as_SplitOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_SplitOptions
-               ? static_cast<const SplitOptions *>(builtin_options())
-               : nullptr;
-  }
-  const LogSoftmaxOptions *builtin_options_as_LogSoftmaxOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_LogSoftmaxOptions
-               ? static_cast<const LogSoftmaxOptions *>(builtin_options())
-               : nullptr;
-  }
-  const CastOptions *builtin_options_as_CastOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_CastOptions
-               ? static_cast<const CastOptions *>(builtin_options())
-               : nullptr;
-  }
-  const DequantizeOptions *builtin_options_as_DequantizeOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_DequantizeOptions
-               ? static_cast<const DequantizeOptions *>(builtin_options())
-               : nullptr;
-  }
-  const MaximumMinimumOptions *builtin_options_as_MaximumMinimumOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_MaximumMinimumOptions
-               ? static_cast<const MaximumMinimumOptions *>(builtin_options())
-               : nullptr;
-  }
-  const ArgMaxOptions *builtin_options_as_ArgMaxOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_ArgMaxOptions
-               ? static_cast<const ArgMaxOptions *>(builtin_options())
-               : nullptr;
-  }
-  const LessOptions *builtin_options_as_LessOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_LessOptions
-               ? static_cast<const LessOptions *>(builtin_options())
-               : nullptr;
-  }
-  const NegOptions *builtin_options_as_NegOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_NegOptions
-               ? static_cast<const NegOptions *>(builtin_options())
-               : nullptr;
-  }
-  const PadV2Options *builtin_options_as_PadV2Options() const
-  {
-    return builtin_options_type() == BuiltinOptions_PadV2Options
-               ? static_cast<const PadV2Options *>(builtin_options())
-               : nullptr;
-  }
-  const GreaterOptions *builtin_options_as_GreaterOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_GreaterOptions
-               ? static_cast<const GreaterOptions *>(builtin_options())
-               : nullptr;
-  }
-  const GreaterEqualOptions *builtin_options_as_GreaterEqualOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_GreaterEqualOptions
-               ? static_cast<const GreaterEqualOptions *>(builtin_options())
-               : nullptr;
-  }
-  const LessEqualOptions *builtin_options_as_LessEqualOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_LessEqualOptions
-               ? static_cast<const LessEqualOptions *>(builtin_options())
-               : nullptr;
-  }
-  const SelectOptions *builtin_options_as_SelectOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_SelectOptions
-               ? static_cast<const SelectOptions *>(builtin_options())
-               : nullptr;
-  }
-  const SliceOptions *builtin_options_as_SliceOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_SliceOptions
-               ? static_cast<const SliceOptions *>(builtin_options())
-               : nullptr;
-  }
-  const TransposeConvOptions *builtin_options_as_TransposeConvOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_TransposeConvOptions
-               ? static_cast<const TransposeConvOptions *>(builtin_options())
-               : nullptr;
-  }
-  const SparseToDenseOptions *builtin_options_as_SparseToDenseOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_SparseToDenseOptions
-               ? static_cast<const SparseToDenseOptions *>(builtin_options())
-               : nullptr;
-  }
-  const TileOptions *builtin_options_as_TileOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_TileOptions
-               ? static_cast<const TileOptions *>(builtin_options())
-               : nullptr;
-  }
-  const ExpandDimsOptions *builtin_options_as_ExpandDimsOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_ExpandDimsOptions
-               ? static_cast<const ExpandDimsOptions *>(builtin_options())
-               : nullptr;
-  }
-  const EqualOptions *builtin_options_as_EqualOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_EqualOptions
-               ? static_cast<const EqualOptions *>(builtin_options())
-               : nullptr;
-  }
-  const NotEqualOptions *builtin_options_as_NotEqualOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_NotEqualOptions
-               ? static_cast<const NotEqualOptions *>(builtin_options())
-               : nullptr;
-  }
-  const ShapeOptions *builtin_options_as_ShapeOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_ShapeOptions
-               ? static_cast<const ShapeOptions *>(builtin_options())
-               : nullptr;
-  }
-  const PowOptions *builtin_options_as_PowOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_PowOptions
-               ? static_cast<const PowOptions *>(builtin_options())
-               : nullptr;
-  }
-  const ArgMinOptions *builtin_options_as_ArgMinOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_ArgMinOptions
-               ? static_cast<const ArgMinOptions *>(builtin_options())
-               : nullptr;
-  }
-  const FakeQuantOptions *builtin_options_as_FakeQuantOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_FakeQuantOptions
-               ? static_cast<const FakeQuantOptions *>(builtin_options())
-               : nullptr;
-  }
-  const PackOptions *builtin_options_as_PackOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_PackOptions
-               ? static_cast<const PackOptions *>(builtin_options())
-               : nullptr;
-  }
-  const LogicalOrOptions *builtin_options_as_LogicalOrOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_LogicalOrOptions
-               ? static_cast<const LogicalOrOptions *>(builtin_options())
-               : nullptr;
-  }
-  const OneHotOptions *builtin_options_as_OneHotOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_OneHotOptions
-               ? static_cast<const OneHotOptions *>(builtin_options())
-               : nullptr;
-  }
-  const LogicalAndOptions *builtin_options_as_LogicalAndOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_LogicalAndOptions
-               ? static_cast<const LogicalAndOptions *>(builtin_options())
-               : nullptr;
-  }
-  const LogicalNotOptions *builtin_options_as_LogicalNotOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_LogicalNotOptions
-               ? static_cast<const LogicalNotOptions *>(builtin_options())
-               : nullptr;
-  }
-  const UnpackOptions *builtin_options_as_UnpackOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_UnpackOptions
-               ? static_cast<const UnpackOptions *>(builtin_options())
-               : nullptr;
-  }
-  const FloorDivOptions *builtin_options_as_FloorDivOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_FloorDivOptions
-               ? static_cast<const FloorDivOptions *>(builtin_options())
-               : nullptr;
-  }
-  const SquareOptions *builtin_options_as_SquareOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_SquareOptions
-               ? static_cast<const SquareOptions *>(builtin_options())
-               : nullptr;
-  }
-  const ZerosLikeOptions *builtin_options_as_ZerosLikeOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_ZerosLikeOptions
-               ? static_cast<const ZerosLikeOptions *>(builtin_options())
-               : nullptr;
-  }
-  const FillOptions *builtin_options_as_FillOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_FillOptions
-               ? static_cast<const FillOptions *>(builtin_options())
-               : nullptr;
-  }
-  const BidirectionalSequenceLSTMOptions *
-  builtin_options_as_BidirectionalSequenceLSTMOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_BidirectionalSequenceLSTMOptions
-               ? static_cast<const BidirectionalSequenceLSTMOptions *>(builtin_options())
-               : nullptr;
-  }
-  const BidirectionalSequenceRNNOptions *builtin_options_as_BidirectionalSequenceRNNOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_BidirectionalSequenceRNNOptions
-               ? static_cast<const BidirectionalSequenceRNNOptions *>(builtin_options())
-               : nullptr;
-  }
-  const UnidirectionalSequenceLSTMOptions *
-  builtin_options_as_UnidirectionalSequenceLSTMOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_UnidirectionalSequenceLSTMOptions
-               ? static_cast<const UnidirectionalSequenceLSTMOptions *>(builtin_options())
-               : nullptr;
-  }
-  const FloorModOptions *builtin_options_as_FloorModOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_FloorModOptions
-               ? static_cast<const FloorModOptions *>(builtin_options())
-               : nullptr;
-  }
-  const RangeOptions *builtin_options_as_RangeOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_RangeOptions
-               ? static_cast<const RangeOptions *>(builtin_options())
-               : nullptr;
-  }
-  const ResizeNearestNeighborOptions *builtin_options_as_ResizeNearestNeighborOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_ResizeNearestNeighborOptions
-               ? static_cast<const ResizeNearestNeighborOptions *>(builtin_options())
-               : nullptr;
-  }
-  const LeakyReluOptions *builtin_options_as_LeakyReluOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_LeakyReluOptions
-               ? static_cast<const LeakyReluOptions *>(builtin_options())
-               : nullptr;
-  }
-  const SquaredDifferenceOptions *builtin_options_as_SquaredDifferenceOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_SquaredDifferenceOptions
-               ? static_cast<const SquaredDifferenceOptions *>(builtin_options())
-               : nullptr;
-  }
-  const MirrorPadOptions *builtin_options_as_MirrorPadOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_MirrorPadOptions
-               ? static_cast<const MirrorPadOptions *>(builtin_options())
-               : nullptr;
-  }
-  const AbsOptions *builtin_options_as_AbsOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_AbsOptions
-               ? static_cast<const AbsOptions *>(builtin_options())
-               : nullptr;
-  }
-  const SplitVOptions *builtin_options_as_SplitVOptions() const
-  {
-    return builtin_options_type() == BuiltinOptions_SplitVOptions
-               ? static_cast<const SplitVOptions *>(builtin_options())
-               : nullptr;
-  }
-  const flatbuffers::Vector<uint8_t> *custom_options() const
-  {
-    return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_CUSTOM_OPTIONS);
-  }
-  CustomOptionsFormat custom_options_format() const
-  {
-    return static_cast<CustomOptionsFormat>(GetField<int8_t>(VT_CUSTOM_OPTIONS_FORMAT, 0));
-  }
-  const flatbuffers::Vector<uint8_t> *mutating_variable_inputs() const
-  {
-    return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_MUTATING_VARIABLE_INPUTS);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<uint32_t>(verifier, VT_OPCODE_INDEX) &&
-           VerifyOffset(verifier, VT_INPUTS) && verifier.VerifyVector(inputs()) &&
-           VerifyOffset(verifier, VT_OUTPUTS) && verifier.VerifyVector(outputs()) &&
-           VerifyField<uint8_t>(verifier, VT_BUILTIN_OPTIONS_TYPE) &&
-           VerifyOffset(verifier, VT_BUILTIN_OPTIONS) &&
-           VerifyBuiltinOptions(verifier, builtin_options(), builtin_options_type()) &&
-           VerifyOffset(verifier, VT_CUSTOM_OPTIONS) && verifier.VerifyVector(custom_options()) &&
-           VerifyField<int8_t>(verifier, VT_CUSTOM_OPTIONS_FORMAT) &&
-           VerifyOffset(verifier, VT_MUTATING_VARIABLE_INPUTS) &&
-           verifier.VerifyVector(mutating_variable_inputs()) && verifier.EndTable();
-  }
-};
-
-template <> inline const Conv2DOptions *Operator::builtin_options_as<Conv2DOptions>() const
-{
-  return builtin_options_as_Conv2DOptions();
-}
-
-template <>
-inline const DepthwiseConv2DOptions *Operator::builtin_options_as<DepthwiseConv2DOptions>() const
-{
-  return builtin_options_as_DepthwiseConv2DOptions();
-}
-
-template <>
-inline const ConcatEmbeddingsOptions *Operator::builtin_options_as<ConcatEmbeddingsOptions>() const
-{
-  return builtin_options_as_ConcatEmbeddingsOptions();
-}
-
-template <>
-inline const LSHProjectionOptions *Operator::builtin_options_as<LSHProjectionOptions>() const
-{
-  return builtin_options_as_LSHProjectionOptions();
-}
-
-template <> inline const Pool2DOptions *Operator::builtin_options_as<Pool2DOptions>() const
-{
-  return builtin_options_as_Pool2DOptions();
-}
-
-template <> inline const SVDFOptions *Operator::builtin_options_as<SVDFOptions>() const
-{
-  return builtin_options_as_SVDFOptions();
-}
-
-template <> inline const RNNOptions *Operator::builtin_options_as<RNNOptions>() const
-{
-  return builtin_options_as_RNNOptions();
-}
-
-template <>
-inline const FullyConnectedOptions *Operator::builtin_options_as<FullyConnectedOptions>() const
-{
-  return builtin_options_as_FullyConnectedOptions();
-}
-
-template <> inline const SoftmaxOptions *Operator::builtin_options_as<SoftmaxOptions>() const
-{
-  return builtin_options_as_SoftmaxOptions();
-}
-
-template <>
-inline const ConcatenationOptions *Operator::builtin_options_as<ConcatenationOptions>() const
-{
-  return builtin_options_as_ConcatenationOptions();
-}
-
-template <> inline const AddOptions *Operator::builtin_options_as<AddOptions>() const
-{
-  return builtin_options_as_AddOptions();
-}
-
-template <> inline const L2NormOptions *Operator::builtin_options_as<L2NormOptions>() const
-{
-  return builtin_options_as_L2NormOptions();
-}
-
-template <>
-inline const LocalResponseNormalizationOptions *
-Operator::builtin_options_as<LocalResponseNormalizationOptions>() const
-{
-  return builtin_options_as_LocalResponseNormalizationOptions();
-}
-
-template <> inline const LSTMOptions *Operator::builtin_options_as<LSTMOptions>() const
-{
-  return builtin_options_as_LSTMOptions();
-}
-
-template <>
-inline const ResizeBilinearOptions *Operator::builtin_options_as<ResizeBilinearOptions>() const
-{
-  return builtin_options_as_ResizeBilinearOptions();
-}
-
-template <> inline const CallOptions *Operator::builtin_options_as<CallOptions>() const
-{
-  return builtin_options_as_CallOptions();
-}
-
-template <> inline const ReshapeOptions *Operator::builtin_options_as<ReshapeOptions>() const
-{
-  return builtin_options_as_ReshapeOptions();
-}
-
-template <> inline const SkipGramOptions *Operator::builtin_options_as<SkipGramOptions>() const
-{
-  return builtin_options_as_SkipGramOptions();
-}
-
-template <>
-inline const SpaceToDepthOptions *Operator::builtin_options_as<SpaceToDepthOptions>() const
-{
-  return builtin_options_as_SpaceToDepthOptions();
-}
-
-template <>
-inline const EmbeddingLookupSparseOptions *
-Operator::builtin_options_as<EmbeddingLookupSparseOptions>() const
-{
-  return builtin_options_as_EmbeddingLookupSparseOptions();
-}
-
-template <> inline const MulOptions *Operator::builtin_options_as<MulOptions>() const
-{
-  return builtin_options_as_MulOptions();
-}
-
-template <> inline const PadOptions *Operator::builtin_options_as<PadOptions>() const
-{
-  return builtin_options_as_PadOptions();
-}
-
-template <> inline const GatherOptions *Operator::builtin_options_as<GatherOptions>() const
-{
-  return builtin_options_as_GatherOptions();
-}
-
-template <>
-inline const BatchToSpaceNDOptions *Operator::builtin_options_as<BatchToSpaceNDOptions>() const
-{
-  return builtin_options_as_BatchToSpaceNDOptions();
-}
-
-template <>
-inline const SpaceToBatchNDOptions *Operator::builtin_options_as<SpaceToBatchNDOptions>() const
-{
-  return builtin_options_as_SpaceToBatchNDOptions();
-}
-
-template <> inline const TransposeOptions *Operator::builtin_options_as<TransposeOptions>() const
-{
-  return builtin_options_as_TransposeOptions();
-}
-
-template <> inline const ReducerOptions *Operator::builtin_options_as<ReducerOptions>() const
-{
-  return builtin_options_as_ReducerOptions();
-}
-
-template <> inline const SubOptions *Operator::builtin_options_as<SubOptions>() const
-{
-  return builtin_options_as_SubOptions();
-}
-
-template <> inline const DivOptions *Operator::builtin_options_as<DivOptions>() const
-{
-  return builtin_options_as_DivOptions();
-}
-
-template <> inline const SqueezeOptions *Operator::builtin_options_as<SqueezeOptions>() const
-{
-  return builtin_options_as_SqueezeOptions();
-}
-
-template <>
-inline const SequenceRNNOptions *Operator::builtin_options_as<SequenceRNNOptions>() const
-{
-  return builtin_options_as_SequenceRNNOptions();
-}
-
-template <>
-inline const StridedSliceOptions *Operator::builtin_options_as<StridedSliceOptions>() const
-{
-  return builtin_options_as_StridedSliceOptions();
-}
-
-template <> inline const ExpOptions *Operator::builtin_options_as<ExpOptions>() const
-{
-  return builtin_options_as_ExpOptions();
-}
-
-template <> inline const TopKV2Options *Operator::builtin_options_as<TopKV2Options>() const
-{
-  return builtin_options_as_TopKV2Options();
-}
-
-template <> inline const SplitOptions *Operator::builtin_options_as<SplitOptions>() const
-{
-  return builtin_options_as_SplitOptions();
-}
-
-template <> inline const LogSoftmaxOptions *Operator::builtin_options_as<LogSoftmaxOptions>() const
-{
-  return builtin_options_as_LogSoftmaxOptions();
-}
-
-template <> inline const CastOptions *Operator::builtin_options_as<CastOptions>() const
-{
-  return builtin_options_as_CastOptions();
-}
-
-template <> inline const DequantizeOptions *Operator::builtin_options_as<DequantizeOptions>() const
-{
-  return builtin_options_as_DequantizeOptions();
-}
-
-template <>
-inline const MaximumMinimumOptions *Operator::builtin_options_as<MaximumMinimumOptions>() const
-{
-  return builtin_options_as_MaximumMinimumOptions();
-}
-
-template <> inline const ArgMaxOptions *Operator::builtin_options_as<ArgMaxOptions>() const
-{
-  return builtin_options_as_ArgMaxOptions();
-}
-
-template <> inline const LessOptions *Operator::builtin_options_as<LessOptions>() const
-{
-  return builtin_options_as_LessOptions();
-}
-
-template <> inline const NegOptions *Operator::builtin_options_as<NegOptions>() const
-{
-  return builtin_options_as_NegOptions();
-}
-
-template <> inline const PadV2Options *Operator::builtin_options_as<PadV2Options>() const
-{
-  return builtin_options_as_PadV2Options();
-}
-
-template <> inline const GreaterOptions *Operator::builtin_options_as<GreaterOptions>() const
-{
-  return builtin_options_as_GreaterOptions();
-}
-
-template <>
-inline const GreaterEqualOptions *Operator::builtin_options_as<GreaterEqualOptions>() const
-{
-  return builtin_options_as_GreaterEqualOptions();
-}
-
-template <> inline const LessEqualOptions *Operator::builtin_options_as<LessEqualOptions>() const
-{
-  return builtin_options_as_LessEqualOptions();
-}
-
-template <> inline const SelectOptions *Operator::builtin_options_as<SelectOptions>() const
-{
-  return builtin_options_as_SelectOptions();
-}
-
-template <> inline const SliceOptions *Operator::builtin_options_as<SliceOptions>() const
-{
-  return builtin_options_as_SliceOptions();
-}
-
-template <>
-inline const TransposeConvOptions *Operator::builtin_options_as<TransposeConvOptions>() const
-{
-  return builtin_options_as_TransposeConvOptions();
-}
-
-template <>
-inline const SparseToDenseOptions *Operator::builtin_options_as<SparseToDenseOptions>() const
-{
-  return builtin_options_as_SparseToDenseOptions();
-}
-
-template <> inline const TileOptions *Operator::builtin_options_as<TileOptions>() const
-{
-  return builtin_options_as_TileOptions();
-}
-
-template <> inline const ExpandDimsOptions *Operator::builtin_options_as<ExpandDimsOptions>() const
-{
-  return builtin_options_as_ExpandDimsOptions();
-}
-
-template <> inline const EqualOptions *Operator::builtin_options_as<EqualOptions>() const
-{
-  return builtin_options_as_EqualOptions();
-}
-
-template <> inline const NotEqualOptions *Operator::builtin_options_as<NotEqualOptions>() const
-{
-  return builtin_options_as_NotEqualOptions();
-}
-
-template <> inline const ShapeOptions *Operator::builtin_options_as<ShapeOptions>() const
-{
-  return builtin_options_as_ShapeOptions();
-}
-
-template <> inline const PowOptions *Operator::builtin_options_as<PowOptions>() const
-{
-  return builtin_options_as_PowOptions();
-}
-
-template <> inline const ArgMinOptions *Operator::builtin_options_as<ArgMinOptions>() const
-{
-  return builtin_options_as_ArgMinOptions();
-}
-
-template <> inline const FakeQuantOptions *Operator::builtin_options_as<FakeQuantOptions>() const
-{
-  return builtin_options_as_FakeQuantOptions();
-}
-
-template <> inline const PackOptions *Operator::builtin_options_as<PackOptions>() const
-{
-  return builtin_options_as_PackOptions();
-}
-
-template <> inline const LogicalOrOptions *Operator::builtin_options_as<LogicalOrOptions>() const
-{
-  return builtin_options_as_LogicalOrOptions();
-}
-
-template <> inline const OneHotOptions *Operator::builtin_options_as<OneHotOptions>() const
-{
-  return builtin_options_as_OneHotOptions();
-}
-
-template <> inline const LogicalAndOptions *Operator::builtin_options_as<LogicalAndOptions>() const
-{
-  return builtin_options_as_LogicalAndOptions();
-}
-
-template <> inline const LogicalNotOptions *Operator::builtin_options_as<LogicalNotOptions>() const
-{
-  return builtin_options_as_LogicalNotOptions();
-}
-
-template <> inline const UnpackOptions *Operator::builtin_options_as<UnpackOptions>() const
-{
-  return builtin_options_as_UnpackOptions();
-}
-
-template <> inline const FloorDivOptions *Operator::builtin_options_as<FloorDivOptions>() const
-{
-  return builtin_options_as_FloorDivOptions();
-}
-
-template <> inline const SquareOptions *Operator::builtin_options_as<SquareOptions>() const
-{
-  return builtin_options_as_SquareOptions();
-}
-
-template <> inline const ZerosLikeOptions *Operator::builtin_options_as<ZerosLikeOptions>() const
-{
-  return builtin_options_as_ZerosLikeOptions();
-}
-
-template <> inline const FillOptions *Operator::builtin_options_as<FillOptions>() const
-{
-  return builtin_options_as_FillOptions();
-}
-
-template <>
-inline const BidirectionalSequenceLSTMOptions *
-Operator::builtin_options_as<BidirectionalSequenceLSTMOptions>() const
-{
-  return builtin_options_as_BidirectionalSequenceLSTMOptions();
-}
-
-template <>
-inline const BidirectionalSequenceRNNOptions *
-Operator::builtin_options_as<BidirectionalSequenceRNNOptions>() const
-{
-  return builtin_options_as_BidirectionalSequenceRNNOptions();
-}
-
-template <>
-inline const UnidirectionalSequenceLSTMOptions *
-Operator::builtin_options_as<UnidirectionalSequenceLSTMOptions>() const
-{
-  return builtin_options_as_UnidirectionalSequenceLSTMOptions();
-}
-
-template <> inline const FloorModOptions *Operator::builtin_options_as<FloorModOptions>() const
-{
-  return builtin_options_as_FloorModOptions();
-}
-
-template <> inline const RangeOptions *Operator::builtin_options_as<RangeOptions>() const
-{
-  return builtin_options_as_RangeOptions();
-}
-
-template <>
-inline const ResizeNearestNeighborOptions *
-Operator::builtin_options_as<ResizeNearestNeighborOptions>() const
-{
-  return builtin_options_as_ResizeNearestNeighborOptions();
-}
-
-template <> inline const LeakyReluOptions *Operator::builtin_options_as<LeakyReluOptions>() const
-{
-  return builtin_options_as_LeakyReluOptions();
-}
-
-template <>
-inline const SquaredDifferenceOptions *
-Operator::builtin_options_as<SquaredDifferenceOptions>() const
-{
-  return builtin_options_as_SquaredDifferenceOptions();
-}
-
-template <> inline const MirrorPadOptions *Operator::builtin_options_as<MirrorPadOptions>() const
-{
-  return builtin_options_as_MirrorPadOptions();
-}
-
-template <> inline const AbsOptions *Operator::builtin_options_as<AbsOptions>() const
-{
-  return builtin_options_as_AbsOptions();
-}
-
-template <> inline const SplitVOptions *Operator::builtin_options_as<SplitVOptions>() const
-{
-  return builtin_options_as_SplitVOptions();
-}
-
-struct OperatorBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_opcode_index(uint32_t opcode_index)
-  {
-    fbb_.AddElement<uint32_t>(Operator::VT_OPCODE_INDEX, opcode_index, 0);
-  }
-  void add_inputs(flatbuffers::Offset<flatbuffers::Vector<int32_t>> inputs)
-  {
-    fbb_.AddOffset(Operator::VT_INPUTS, inputs);
-  }
-  void add_outputs(flatbuffers::Offset<flatbuffers::Vector<int32_t>> outputs)
-  {
-    fbb_.AddOffset(Operator::VT_OUTPUTS, outputs);
-  }
-  void add_builtin_options_type(BuiltinOptions builtin_options_type)
-  {
-    fbb_.AddElement<uint8_t>(Operator::VT_BUILTIN_OPTIONS_TYPE,
-                             static_cast<uint8_t>(builtin_options_type), 0);
-  }
-  void add_builtin_options(flatbuffers::Offset<void> builtin_options)
-  {
-    fbb_.AddOffset(Operator::VT_BUILTIN_OPTIONS, builtin_options);
-  }
-  void add_custom_options(flatbuffers::Offset<flatbuffers::Vector<uint8_t>> custom_options)
-  {
-    fbb_.AddOffset(Operator::VT_CUSTOM_OPTIONS, custom_options);
-  }
-  void add_custom_options_format(CustomOptionsFormat custom_options_format)
-  {
-    fbb_.AddElement<int8_t>(Operator::VT_CUSTOM_OPTIONS_FORMAT,
-                            static_cast<int8_t>(custom_options_format), 0);
-  }
-  void add_mutating_variable_inputs(
-      flatbuffers::Offset<flatbuffers::Vector<uint8_t>> mutating_variable_inputs)
-  {
-    fbb_.AddOffset(Operator::VT_MUTATING_VARIABLE_INPUTS, mutating_variable_inputs);
-  }
-  explicit OperatorBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  OperatorBuilder &operator=(const OperatorBuilder &);
-  flatbuffers::Offset<Operator> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<Operator>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<Operator>
-CreateOperator(flatbuffers::FlatBufferBuilder &_fbb, uint32_t opcode_index = 0,
-               flatbuffers::Offset<flatbuffers::Vector<int32_t>> inputs = 0,
-               flatbuffers::Offset<flatbuffers::Vector<int32_t>> outputs = 0,
-               BuiltinOptions builtin_options_type = BuiltinOptions_NONE,
-               flatbuffers::Offset<void> builtin_options = 0,
-               flatbuffers::Offset<flatbuffers::Vector<uint8_t>> custom_options = 0,
-               CustomOptionsFormat custom_options_format = CustomOptionsFormat_FLEXBUFFERS,
-               flatbuffers::Offset<flatbuffers::Vector<uint8_t>> mutating_variable_inputs = 0)
-{
-  OperatorBuilder builder_(_fbb);
-  builder_.add_mutating_variable_inputs(mutating_variable_inputs);
-  builder_.add_custom_options(custom_options);
-  builder_.add_builtin_options(builtin_options);
-  builder_.add_outputs(outputs);
-  builder_.add_inputs(inputs);
-  builder_.add_opcode_index(opcode_index);
-  builder_.add_custom_options_format(custom_options_format);
-  builder_.add_builtin_options_type(builtin_options_type);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<Operator>
-CreateOperatorDirect(flatbuffers::FlatBufferBuilder &_fbb, uint32_t opcode_index = 0,
-                     const std::vector<int32_t> *inputs = nullptr,
-                     const std::vector<int32_t> *outputs = nullptr,
-                     BuiltinOptions builtin_options_type = BuiltinOptions_NONE,
-                     flatbuffers::Offset<void> builtin_options = 0,
-                     const std::vector<uint8_t> *custom_options = nullptr,
-                     CustomOptionsFormat custom_options_format = CustomOptionsFormat_FLEXBUFFERS,
-                     const std::vector<uint8_t> *mutating_variable_inputs = nullptr)
-{
-  return neurun_tflite::CreateOperator(
-      _fbb, opcode_index, inputs ? _fbb.CreateVector<int32_t>(*inputs) : 0,
-      outputs ? _fbb.CreateVector<int32_t>(*outputs) : 0, builtin_options_type, builtin_options,
-      custom_options ? _fbb.CreateVector<uint8_t>(*custom_options) : 0, custom_options_format,
-      mutating_variable_inputs ? _fbb.CreateVector<uint8_t>(*mutating_variable_inputs) : 0);
-}
-
-struct SubGraph FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_TENSORS = 4,
-    VT_INPUTS = 6,
-    VT_OUTPUTS = 8,
-    VT_OPERATORS = 10,
-    VT_NAME = 12
-  };
-  const flatbuffers::Vector<flatbuffers::Offset<Tensor>> *tensors() const
-  {
-    return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<Tensor>> *>(VT_TENSORS);
-  }
-  const flatbuffers::Vector<int32_t> *inputs() const
-  {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_INPUTS);
-  }
-  const flatbuffers::Vector<int32_t> *outputs() const
-  {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_OUTPUTS);
-  }
-  const flatbuffers::Vector<flatbuffers::Offset<Operator>> *operators() const
-  {
-    return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<Operator>> *>(VT_OPERATORS);
-  }
-  const flatbuffers::String *name() const
-  {
-    return GetPointer<const flatbuffers::String *>(VT_NAME);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_TENSORS) &&
-           verifier.VerifyVector(tensors()) && verifier.VerifyVectorOfTables(tensors()) &&
-           VerifyOffset(verifier, VT_INPUTS) && verifier.VerifyVector(inputs()) &&
-           VerifyOffset(verifier, VT_OUTPUTS) && verifier.VerifyVector(outputs()) &&
-           VerifyOffset(verifier, VT_OPERATORS) && verifier.VerifyVector(operators()) &&
-           verifier.VerifyVectorOfTables(operators()) && VerifyOffset(verifier, VT_NAME) &&
-           verifier.VerifyString(name()) && verifier.EndTable();
-  }
-};
-
-struct SubGraphBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_tensors(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<Tensor>>> tensors)
-  {
-    fbb_.AddOffset(SubGraph::VT_TENSORS, tensors);
-  }
-  void add_inputs(flatbuffers::Offset<flatbuffers::Vector<int32_t>> inputs)
-  {
-    fbb_.AddOffset(SubGraph::VT_INPUTS, inputs);
-  }
-  void add_outputs(flatbuffers::Offset<flatbuffers::Vector<int32_t>> outputs)
-  {
-    fbb_.AddOffset(SubGraph::VT_OUTPUTS, outputs);
-  }
-  void
-  add_operators(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<Operator>>> operators)
-  {
-    fbb_.AddOffset(SubGraph::VT_OPERATORS, operators);
-  }
-  void add_name(flatbuffers::Offset<flatbuffers::String> name)
-  {
-    fbb_.AddOffset(SubGraph::VT_NAME, name);
-  }
-  explicit SubGraphBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  SubGraphBuilder &operator=(const SubGraphBuilder &);
-  flatbuffers::Offset<SubGraph> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SubGraph>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SubGraph> CreateSubGraph(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<Tensor>>> tensors = 0,
-    flatbuffers::Offset<flatbuffers::Vector<int32_t>> inputs = 0,
-    flatbuffers::Offset<flatbuffers::Vector<int32_t>> outputs = 0,
-    flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<Operator>>> operators = 0,
-    flatbuffers::Offset<flatbuffers::String> name = 0)
-{
-  SubGraphBuilder builder_(_fbb);
-  builder_.add_name(name);
-  builder_.add_operators(operators);
-  builder_.add_outputs(outputs);
-  builder_.add_inputs(inputs);
-  builder_.add_tensors(tensors);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<SubGraph>
-CreateSubGraphDirect(flatbuffers::FlatBufferBuilder &_fbb,
-                     const std::vector<flatbuffers::Offset<Tensor>> *tensors = nullptr,
-                     const std::vector<int32_t> *inputs = nullptr,
-                     const std::vector<int32_t> *outputs = nullptr,
-                     const std::vector<flatbuffers::Offset<Operator>> *operators = nullptr,
-                     const char *name = nullptr)
-{
-  return neurun_tflite::CreateSubGraph(
-      _fbb, tensors ? _fbb.CreateVector<flatbuffers::Offset<Tensor>>(*tensors) : 0,
-      inputs ? _fbb.CreateVector<int32_t>(*inputs) : 0,
-      outputs ? _fbb.CreateVector<int32_t>(*outputs) : 0,
-      operators ? _fbb.CreateVector<flatbuffers::Offset<Operator>>(*operators) : 0,
-      name ? _fbb.CreateString(name) : 0);
-}
-
-struct Buffer FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_DATA = 4
-  };
-  const flatbuffers::Vector<uint8_t> *data() const
-  {
-    return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_DATA);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_DATA) &&
-           verifier.VerifyVector(data()) && verifier.EndTable();
-  }
-};
-
-struct BufferBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_data(flatbuffers::Offset<flatbuffers::Vector<uint8_t>> data)
-  {
-    fbb_.AddOffset(Buffer::VT_DATA, data);
-  }
-  explicit BufferBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  BufferBuilder &operator=(const BufferBuilder &);
-  flatbuffers::Offset<Buffer> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<Buffer>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<Buffer>
-CreateBuffer(flatbuffers::FlatBufferBuilder &_fbb,
-             flatbuffers::Offset<flatbuffers::Vector<uint8_t>> data = 0)
-{
-  BufferBuilder builder_(_fbb);
-  builder_.add_data(data);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<Buffer> CreateBufferDirect(flatbuffers::FlatBufferBuilder &_fbb,
-                                                      const std::vector<uint8_t> *data = nullptr)
-{
-  return neurun_tflite::CreateBuffer(_fbb, data ? _fbb.CreateVector<uint8_t>(*data) : 0);
-}
-
-struct Model FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table
-{
-  enum
-  {
-    VT_VERSION = 4,
-    VT_OPERATOR_CODES = 6,
-    VT_SUBGRAPHS = 8,
-    VT_DESCRIPTION = 10,
-    VT_BUFFERS = 12,
-    VT_METADATA_BUFFER = 14
-  };
-  uint32_t version() const { return GetField<uint32_t>(VT_VERSION, 0); }
-  const flatbuffers::Vector<flatbuffers::Offset<OperatorCode>> *operator_codes() const
-  {
-    return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<OperatorCode>> *>(
-        VT_OPERATOR_CODES);
-  }
-  const flatbuffers::Vector<flatbuffers::Offset<SubGraph>> *subgraphs() const
-  {
-    return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<SubGraph>> *>(VT_SUBGRAPHS);
-  }
-  const flatbuffers::String *description() const
-  {
-    return GetPointer<const flatbuffers::String *>(VT_DESCRIPTION);
-  }
-  const flatbuffers::Vector<flatbuffers::Offset<Buffer>> *buffers() const
-  {
-    return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<Buffer>> *>(VT_BUFFERS);
-  }
-  const flatbuffers::Vector<int32_t> *metadata_buffer() const
-  {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_METADATA_BUFFER);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const
-  {
-    return VerifyTableStart(verifier) && VerifyField<uint32_t>(verifier, VT_VERSION) &&
-           VerifyOffset(verifier, VT_OPERATOR_CODES) && verifier.VerifyVector(operator_codes()) &&
-           verifier.VerifyVectorOfTables(operator_codes()) &&
-           VerifyOffset(verifier, VT_SUBGRAPHS) && verifier.VerifyVector(subgraphs()) &&
-           verifier.VerifyVectorOfTables(subgraphs()) && VerifyOffset(verifier, VT_DESCRIPTION) &&
-           verifier.VerifyString(description()) && VerifyOffset(verifier, VT_BUFFERS) &&
-           verifier.VerifyVector(buffers()) && verifier.VerifyVectorOfTables(buffers()) &&
-           VerifyOffset(verifier, VT_METADATA_BUFFER) && verifier.VerifyVector(metadata_buffer()) &&
-           verifier.EndTable();
-  }
-};
-
-struct ModelBuilder
-{
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_version(uint32_t version) { fbb_.AddElement<uint32_t>(Model::VT_VERSION, version, 0); }
-  void add_operator_codes(
-      flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<OperatorCode>>> operator_codes)
-  {
-    fbb_.AddOffset(Model::VT_OPERATOR_CODES, operator_codes);
-  }
-  void
-  add_subgraphs(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<SubGraph>>> subgraphs)
-  {
-    fbb_.AddOffset(Model::VT_SUBGRAPHS, subgraphs);
-  }
-  void add_description(flatbuffers::Offset<flatbuffers::String> description)
-  {
-    fbb_.AddOffset(Model::VT_DESCRIPTION, description);
-  }
-  void add_buffers(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<Buffer>>> buffers)
-  {
-    fbb_.AddOffset(Model::VT_BUFFERS, buffers);
-  }
-  void add_metadata_buffer(flatbuffers::Offset<flatbuffers::Vector<int32_t>> metadata_buffer)
-  {
-    fbb_.AddOffset(Model::VT_METADATA_BUFFER, metadata_buffer);
-  }
-  explicit ModelBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
-  {
-    start_ = fbb_.StartTable();
-  }
-  ModelBuilder &operator=(const ModelBuilder &);
-  flatbuffers::Offset<Model> Finish()
-  {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<Model>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<Model> CreateModel(
-    flatbuffers::FlatBufferBuilder &_fbb, uint32_t version = 0,
-    flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<OperatorCode>>> operator_codes = 0,
-    flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<SubGraph>>> subgraphs = 0,
-    flatbuffers::Offset<flatbuffers::String> description = 0,
-    flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<Buffer>>> buffers = 0,
-    flatbuffers::Offset<flatbuffers::Vector<int32_t>> metadata_buffer = 0)
-{
-  ModelBuilder builder_(_fbb);
-  builder_.add_metadata_buffer(metadata_buffer);
-  builder_.add_buffers(buffers);
-  builder_.add_description(description);
-  builder_.add_subgraphs(subgraphs);
-  builder_.add_operator_codes(operator_codes);
-  builder_.add_version(version);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<Model>
-CreateModelDirect(flatbuffers::FlatBufferBuilder &_fbb, uint32_t version = 0,
-                  const std::vector<flatbuffers::Offset<OperatorCode>> *operator_codes = nullptr,
-                  const std::vector<flatbuffers::Offset<SubGraph>> *subgraphs = nullptr,
-                  const char *description = nullptr,
-                  const std::vector<flatbuffers::Offset<Buffer>> *buffers = nullptr,
-                  const std::vector<int32_t> *metadata_buffer = nullptr)
-{
-  return neurun_tflite::CreateModel(
-      _fbb, version,
-      operator_codes ? _fbb.CreateVector<flatbuffers::Offset<OperatorCode>>(*operator_codes) : 0,
-      subgraphs ? _fbb.CreateVector<flatbuffers::Offset<SubGraph>>(*subgraphs) : 0,
-      description ? _fbb.CreateString(description) : 0,
-      buffers ? _fbb.CreateVector<flatbuffers::Offset<Buffer>>(*buffers) : 0,
-      metadata_buffer ? _fbb.CreateVector<int32_t>(*metadata_buffer) : 0);
-}
-
-inline bool VerifyQuantizationDetails(flatbuffers::Verifier &verifier, const void *obj,
-                                      QuantizationDetails type)
-{
-  switch (type)
-  {
-    case QuantizationDetails_NONE:
-    {
-      return true;
-    }
-    case QuantizationDetails_CustomQuantization:
-    {
-      auto ptr = reinterpret_cast<const CustomQuantization *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    default:
-      return false;
-  }
-}
-
-inline bool
-VerifyQuantizationDetailsVector(flatbuffers::Verifier &verifier,
-                                const flatbuffers::Vector<flatbuffers::Offset<void>> *values,
-                                const flatbuffers::Vector<uint8_t> *types)
-{
-  if (!values || !types)
-    return !values && !types;
-  if (values->size() != types->size())
-    return false;
-  for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i)
-  {
-    if (!VerifyQuantizationDetails(verifier, values->Get(i),
-                                   types->GetEnum<QuantizationDetails>(i)))
-    {
-      return false;
-    }
-  }
-  return true;
-}
-
-inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj,
-                                 BuiltinOptions type)
-{
-  switch (type)
-  {
-    case BuiltinOptions_NONE:
-    {
-      return true;
-    }
-    case BuiltinOptions_Conv2DOptions:
-    {
-      auto ptr = reinterpret_cast<const Conv2DOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_DepthwiseConv2DOptions:
-    {
-      auto ptr = reinterpret_cast<const DepthwiseConv2DOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ConcatEmbeddingsOptions:
-    {
-      auto ptr = reinterpret_cast<const ConcatEmbeddingsOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_LSHProjectionOptions:
-    {
-      auto ptr = reinterpret_cast<const LSHProjectionOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_Pool2DOptions:
-    {
-      auto ptr = reinterpret_cast<const Pool2DOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SVDFOptions:
-    {
-      auto ptr = reinterpret_cast<const SVDFOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_RNNOptions:
-    {
-      auto ptr = reinterpret_cast<const RNNOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_FullyConnectedOptions:
-    {
-      auto ptr = reinterpret_cast<const FullyConnectedOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SoftmaxOptions:
-    {
-      auto ptr = reinterpret_cast<const SoftmaxOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ConcatenationOptions:
-    {
-      auto ptr = reinterpret_cast<const ConcatenationOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_AddOptions:
-    {
-      auto ptr = reinterpret_cast<const AddOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_L2NormOptions:
-    {
-      auto ptr = reinterpret_cast<const L2NormOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_LocalResponseNormalizationOptions:
-    {
-      auto ptr = reinterpret_cast<const LocalResponseNormalizationOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_LSTMOptions:
-    {
-      auto ptr = reinterpret_cast<const LSTMOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ResizeBilinearOptions:
-    {
-      auto ptr = reinterpret_cast<const ResizeBilinearOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_CallOptions:
-    {
-      auto ptr = reinterpret_cast<const CallOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ReshapeOptions:
-    {
-      auto ptr = reinterpret_cast<const ReshapeOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SkipGramOptions:
-    {
-      auto ptr = reinterpret_cast<const SkipGramOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SpaceToDepthOptions:
-    {
-      auto ptr = reinterpret_cast<const SpaceToDepthOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_EmbeddingLookupSparseOptions:
-    {
-      auto ptr = reinterpret_cast<const EmbeddingLookupSparseOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_MulOptions:
-    {
-      auto ptr = reinterpret_cast<const MulOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_PadOptions:
-    {
-      auto ptr = reinterpret_cast<const PadOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_GatherOptions:
-    {
-      auto ptr = reinterpret_cast<const GatherOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_BatchToSpaceNDOptions:
-    {
-      auto ptr = reinterpret_cast<const BatchToSpaceNDOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SpaceToBatchNDOptions:
-    {
-      auto ptr = reinterpret_cast<const SpaceToBatchNDOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_TransposeOptions:
-    {
-      auto ptr = reinterpret_cast<const TransposeOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ReducerOptions:
-    {
-      auto ptr = reinterpret_cast<const ReducerOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SubOptions:
-    {
-      auto ptr = reinterpret_cast<const SubOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_DivOptions:
-    {
-      auto ptr = reinterpret_cast<const DivOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SqueezeOptions:
-    {
-      auto ptr = reinterpret_cast<const SqueezeOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SequenceRNNOptions:
-    {
-      auto ptr = reinterpret_cast<const SequenceRNNOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_StridedSliceOptions:
-    {
-      auto ptr = reinterpret_cast<const StridedSliceOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ExpOptions:
-    {
-      auto ptr = reinterpret_cast<const ExpOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_TopKV2Options:
-    {
-      auto ptr = reinterpret_cast<const TopKV2Options *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SplitOptions:
-    {
-      auto ptr = reinterpret_cast<const SplitOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_LogSoftmaxOptions:
-    {
-      auto ptr = reinterpret_cast<const LogSoftmaxOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_CastOptions:
-    {
-      auto ptr = reinterpret_cast<const CastOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_DequantizeOptions:
-    {
-      auto ptr = reinterpret_cast<const DequantizeOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_MaximumMinimumOptions:
-    {
-      auto ptr = reinterpret_cast<const MaximumMinimumOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ArgMaxOptions:
-    {
-      auto ptr = reinterpret_cast<const ArgMaxOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_LessOptions:
-    {
-      auto ptr = reinterpret_cast<const LessOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_NegOptions:
-    {
-      auto ptr = reinterpret_cast<const NegOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_PadV2Options:
-    {
-      auto ptr = reinterpret_cast<const PadV2Options *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_GreaterOptions:
-    {
-      auto ptr = reinterpret_cast<const GreaterOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_GreaterEqualOptions:
-    {
-      auto ptr = reinterpret_cast<const GreaterEqualOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_LessEqualOptions:
-    {
-      auto ptr = reinterpret_cast<const LessEqualOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SelectOptions:
-    {
-      auto ptr = reinterpret_cast<const SelectOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SliceOptions:
-    {
-      auto ptr = reinterpret_cast<const SliceOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_TransposeConvOptions:
-    {
-      auto ptr = reinterpret_cast<const TransposeConvOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SparseToDenseOptions:
-    {
-      auto ptr = reinterpret_cast<const SparseToDenseOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_TileOptions:
-    {
-      auto ptr = reinterpret_cast<const TileOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ExpandDimsOptions:
-    {
-      auto ptr = reinterpret_cast<const ExpandDimsOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_EqualOptions:
-    {
-      auto ptr = reinterpret_cast<const EqualOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_NotEqualOptions:
-    {
-      auto ptr = reinterpret_cast<const NotEqualOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ShapeOptions:
-    {
-      auto ptr = reinterpret_cast<const ShapeOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_PowOptions:
-    {
-      auto ptr = reinterpret_cast<const PowOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ArgMinOptions:
-    {
-      auto ptr = reinterpret_cast<const ArgMinOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_FakeQuantOptions:
-    {
-      auto ptr = reinterpret_cast<const FakeQuantOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_PackOptions:
-    {
-      auto ptr = reinterpret_cast<const PackOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_LogicalOrOptions:
-    {
-      auto ptr = reinterpret_cast<const LogicalOrOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_OneHotOptions:
-    {
-      auto ptr = reinterpret_cast<const OneHotOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_LogicalAndOptions:
-    {
-      auto ptr = reinterpret_cast<const LogicalAndOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_LogicalNotOptions:
-    {
-      auto ptr = reinterpret_cast<const LogicalNotOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_UnpackOptions:
-    {
-      auto ptr = reinterpret_cast<const UnpackOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_FloorDivOptions:
-    {
-      auto ptr = reinterpret_cast<const FloorDivOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SquareOptions:
-    {
-      auto ptr = reinterpret_cast<const SquareOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ZerosLikeOptions:
-    {
-      auto ptr = reinterpret_cast<const ZerosLikeOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_FillOptions:
-    {
-      auto ptr = reinterpret_cast<const FillOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_BidirectionalSequenceLSTMOptions:
-    {
-      auto ptr = reinterpret_cast<const BidirectionalSequenceLSTMOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_BidirectionalSequenceRNNOptions:
-    {
-      auto ptr = reinterpret_cast<const BidirectionalSequenceRNNOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_UnidirectionalSequenceLSTMOptions:
-    {
-      auto ptr = reinterpret_cast<const UnidirectionalSequenceLSTMOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_FloorModOptions:
-    {
-      auto ptr = reinterpret_cast<const FloorModOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_RangeOptions:
-    {
-      auto ptr = reinterpret_cast<const RangeOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ResizeNearestNeighborOptions:
-    {
-      auto ptr = reinterpret_cast<const ResizeNearestNeighborOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_LeakyReluOptions:
-    {
-      auto ptr = reinterpret_cast<const LeakyReluOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SquaredDifferenceOptions:
-    {
-      auto ptr = reinterpret_cast<const SquaredDifferenceOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_MirrorPadOptions:
-    {
-      auto ptr = reinterpret_cast<const MirrorPadOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_AbsOptions:
-    {
-      auto ptr = reinterpret_cast<const AbsOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SplitVOptions:
-    {
-      auto ptr = reinterpret_cast<const SplitVOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    default:
-      return false;
-  }
-}
-
-inline bool VerifyBuiltinOptionsVector(flatbuffers::Verifier &verifier,
-                                       const flatbuffers::Vector<flatbuffers::Offset<void>> *values,
-                                       const flatbuffers::Vector<uint8_t> *types)
-{
-  if (!values || !types)
-    return !values && !types;
-  if (values->size() != types->size())
-    return false;
-  for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i)
-  {
-    if (!VerifyBuiltinOptions(verifier, values->Get(i), types->GetEnum<BuiltinOptions>(i)))
-    {
-      return false;
-    }
-  }
-  return true;
-}
-
-inline const neurun_tflite::Model *GetModel(const void *buf)
-{
-  return flatbuffers::GetRoot<neurun_tflite::Model>(buf);
-}
-
-inline const neurun_tflite::Model *GetSizePrefixedModel(const void *buf)
-{
-  return flatbuffers::GetSizePrefixedRoot<neurun_tflite::Model>(buf);
-}
-
-inline const char *ModelIdentifier() { return "TFL3"; }
-
-inline bool ModelBufferHasIdentifier(const void *buf)
-{
-  return flatbuffers::BufferHasIdentifier(buf, ModelIdentifier());
-}
-
-inline bool VerifyModelBuffer(flatbuffers::Verifier &verifier)
-{
-  return verifier.VerifyBuffer<neurun_tflite::Model>(ModelIdentifier());
-}
-
-inline bool VerifySizePrefixedModelBuffer(flatbuffers::Verifier &verifier)
-{
-  return verifier.VerifySizePrefixedBuffer<neurun_tflite::Model>(ModelIdentifier());
-}
-
-inline const char *ModelExtension() { return "tflite"; }
-
-inline void FinishModelBuffer(flatbuffers::FlatBufferBuilder &fbb,
-                              flatbuffers::Offset<neurun_tflite::Model> root)
-{
-  fbb.Finish(root, ModelIdentifier());
-}
-
-inline void FinishSizePrefixedModelBuffer(flatbuffers::FlatBufferBuilder &fbb,
-                                          flatbuffers::Offset<neurun_tflite::Model> root)
-{
-  fbb.FinishSizePrefixed(root, ModelIdentifier());
-}
-
-} // namespace neurun_tflite
-
-#endif // FLATBUFFERS_GENERATED_SCHEMA_NEURUN_TFLITE_H_
diff --git a/runtime/neurun/frontend/tflite/tflite_schema.fbs b/runtime/neurun/frontend/tflite/tflite_schema.fbs
deleted file mode 100644
index ede4dfa3a..000000000
--- a/runtime/neurun/frontend/tflite/tflite_schema.fbs
+++ /dev/null
@@ -1,795 +0,0 @@
-// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-// Revision History
-// Version 0: Initial version.
-// Version 1: Add subgraphs to schema.
-// Version 2: Rename operators to conform to NN API.
-// Version 3: Move buffer data from Model.Subgraph.Tensors to Model.Buffers.
-
-// Change namespace to neurun_tflite
-namespace neurun_tflite;
-
-// This corresponds to the version.
-file_identifier "TFL3";
-// File extension of any written files.
-file_extension "tflite";
-
-// IMPORTANT: All new members of tables, enums and unions must be added at the
-// end to ensure backwards compatibility.
-
-// The type of data stored in a tensor.
-enum TensorType : byte {
-  FLOAT32 = 0,
-  FLOAT16 = 1,
-  INT32 = 2,
-  UINT8 = 3,
-  INT64 = 4,
-  STRING = 5,
-  BOOL = 6,
-  INT16 = 7,
-  COMPLEX64 = 8,
-  INT8 = 9,
-}
-
-// Custom quantization parameters for experimenting with new quantization
-// techniques.
-table CustomQuantization {
-  custom:[ubyte] (force_align: 16);
-}
-
-// Represents a specific quantization technique's parameters.
-union QuantizationDetails {
-  CustomQuantization,
-}
-
-// Parameters for converting a quantized tensor back to float.
-table QuantizationParameters {
-  // These four parameters are the asymmetric linear quantization parameters.
-  // Given a quantized value q, the corresponding float value f should be:
-  //   f = scale * (q - zero_point)
-  // For other quantization types, the QuantizationDetails below is used.
-  min:[float];  // For importing back into tensorflow.
-  max:[float];  // For importing back into tensorflow.
-  scale:[float];  // For dequantizing the tensor's values.
-  zero_point:[long];
-
-  // If this is not none, the quantization parameters above are ignored and the
-  // value of the QuantizationDetails union below should be used.
-  details:QuantizationDetails;
-}
-
-table Tensor {
-  // The tensor shape. The meaning of each entry is operator-specific but
-  // builtin ops use: [batch size, height, width, number of channels] (That's
-  // Tensorflow's NHWC).
-  shape:[int];
-  type:TensorType;
-  // An index that refers to the buffers table at the root of the model. Or,
-  // if there is no data buffer associated (i.e. intermediate results), then
-  // this is 0 (which refers to an always existent empty buffer).
-  //
-  // The data_buffer itself is an opaque container, with the assumption that the
-  // target device is little-endian. In addition, all builtin operators assume
-  // the memory is ordered such that if `shape` is [4, 3, 2], then index
-  // [i, j, k] maps to data_buffer[i*3*2 + j*2 + k].
-  buffer:uint;
-  name:string;  // For debugging and importing back into tensorflow.
-  quantization:QuantizationParameters;  // Optional.
-
-  is_variable:bool = false;
-}
-
-// A list of builtin operators. Builtin operators are slightly faster than custom
-// ones, but not by much. Moreover, while custom operators accept an opaque
-// object containing configuration parameters, builtins have a predetermined
-// set of acceptable options.
-enum BuiltinOperator : byte {
-  ADD = 0,
-  AVERAGE_POOL_2D = 1,
-  CONCATENATION = 2,
-  CONV_2D = 3,
-  DEPTHWISE_CONV_2D = 4,
-  // DEPTH_TO_SPACE = 5,
-  DEQUANTIZE = 6,
-  EMBEDDING_LOOKUP = 7,
-  FLOOR = 8,
-  FULLY_CONNECTED = 9,
-  HASHTABLE_LOOKUP = 10,
-  L2_NORMALIZATION = 11,
-  L2_POOL_2D = 12,
-  LOCAL_RESPONSE_NORMALIZATION = 13,
-  LOGISTIC = 14,
-  LSH_PROJECTION = 15,
-  LSTM = 16,
-  MAX_POOL_2D = 17,
-  MUL = 18,
-  RELU = 19,
-  // NOTE(aselle): RELU_N1_TO_1 used to be called RELU1, but it was renamed
-  // since different model developers use RELU1 in different ways. Never
-  // create another op called RELU1.
-  RELU_N1_TO_1 = 20,
-  RELU6 = 21,
-  RESHAPE = 22,
-  RESIZE_BILINEAR = 23,
-  RNN = 24,
-  SOFTMAX = 25,
-  SPACE_TO_DEPTH = 26,
-  SVDF = 27,
-  TANH = 28,
-  // TODO(aselle): Consider rename to CONCATENATE_EMBEDDINGS
-  CONCAT_EMBEDDINGS = 29,
-  SKIP_GRAM = 30,
-  CALL = 31,
-  CUSTOM = 32,
-  EMBEDDING_LOOKUP_SPARSE = 33,
-  PAD = 34,
-  UNIDIRECTIONAL_SEQUENCE_RNN = 35,
-  GATHER = 36,
-  BATCH_TO_SPACE_ND = 37,
-  SPACE_TO_BATCH_ND = 38,
-  TRANSPOSE = 39,
-  MEAN = 40,
-  SUB = 41,
-  DIV = 42,
-  SQUEEZE = 43,
-  UNIDIRECTIONAL_SEQUENCE_LSTM = 44,
-  STRIDED_SLICE = 45,
-  BIDIRECTIONAL_SEQUENCE_RNN = 46,
-  EXP = 47,
-  TOPK_V2 = 48,
-  SPLIT = 49,
-  LOG_SOFTMAX = 50,
-  // DELEGATE is a special op type for the operations which are delegated to
-  // other backends.
-  // WARNING: Experimental interface, subject to change
-  DELEGATE = 51,
-  BIDIRECTIONAL_SEQUENCE_LSTM = 52,
-  CAST = 53,
-  PRELU = 54,
-  MAXIMUM = 55,
-  ARG_MAX = 56,
-  MINIMUM = 57,
-  LESS = 58,
-  NEG = 59,
-  PADV2 = 60,
-  GREATER = 61,
-  GREATER_EQUAL = 62,
-  LESS_EQUAL = 63,
-  SELECT = 64,
-  SLICE = 65,
-  SIN = 66,
-  TRANSPOSE_CONV = 67,
-  SPARSE_TO_DENSE = 68,
-  TILE = 69,
-  EXPAND_DIMS = 70,
-  EQUAL = 71,
-  NOT_EQUAL = 72,
-  LOG = 73,
-  SUM = 74,
-  SQRT = 75,
-  RSQRT = 76,
-  SHAPE = 77,
-  POW = 78,
-  ARG_MIN = 79,
-  FAKE_QUANT = 80,
-  REDUCE_PROD = 81,
-  REDUCE_MAX = 82,
-  PACK = 83,
-  LOGICAL_OR = 84,
-  ONE_HOT = 85,
-  LOGICAL_AND = 86,
-  LOGICAL_NOT = 87,
-  UNPACK = 88,
-  REDUCE_MIN = 89,
-  FLOOR_DIV = 90,
-  REDUCE_ANY = 91,
-  SQUARE = 92,
-  ZEROS_LIKE = 93,
-  FILL = 94,
-  FLOOR_MOD = 95,
-  RANGE = 96,
-  RESIZE_NEAREST_NEIGHBOR = 97,
-  LEAKY_RELU = 98,
-  SQUARED_DIFFERENCE = 99,
-  MIRROR_PAD = 100,
-  ABS = 101,
-  SPLIT_V = 102,
-}
-
-// Options for the builtin operators.
-union BuiltinOptions {
-  Conv2DOptions,
-  DepthwiseConv2DOptions,
-  ConcatEmbeddingsOptions,
-  LSHProjectionOptions,
-  Pool2DOptions,
-  SVDFOptions,
-  RNNOptions,
-  FullyConnectedOptions,
-  SoftmaxOptions,
-  ConcatenationOptions,
-  AddOptions,
-  L2NormOptions,
-  LocalResponseNormalizationOptions,
-  LSTMOptions,
-  ResizeBilinearOptions,
-  CallOptions,
-  ReshapeOptions,
-  SkipGramOptions,
-  SpaceToDepthOptions,
-  EmbeddingLookupSparseOptions,
-  MulOptions,
-  PadOptions,
-  GatherOptions,
-  BatchToSpaceNDOptions,
-  SpaceToBatchNDOptions,
-  TransposeOptions,
-  ReducerOptions,
-  SubOptions,
-  DivOptions,
-  SqueezeOptions,
-  SequenceRNNOptions,
-  StridedSliceOptions,
-  ExpOptions,
-  TopKV2Options,
-  SplitOptions,
-  LogSoftmaxOptions,
-  CastOptions,
-  DequantizeOptions,
-  MaximumMinimumOptions,
-  ArgMaxOptions,
-  LessOptions,
-  NegOptions,
-  PadV2Options,
-  GreaterOptions,
-  GreaterEqualOptions,
-  LessEqualOptions,
-  SelectOptions,
-  SliceOptions,
-  TransposeConvOptions,
-  SparseToDenseOptions,
-  TileOptions,
-  ExpandDimsOptions,
-  EqualOptions,
-  NotEqualOptions,
-  ShapeOptions,
-  PowOptions,
-  ArgMinOptions,
-  FakeQuantOptions,
-  PackOptions,
-  LogicalOrOptions,
-  OneHotOptions,
-  LogicalAndOptions,
-  LogicalNotOptions,
-  UnpackOptions,
-  FloorDivOptions,
-  SquareOptions,
-  ZerosLikeOptions,
-  FillOptions,
-  BidirectionalSequenceLSTMOptions,
-  BidirectionalSequenceRNNOptions,
-  UnidirectionalSequenceLSTMOptions,
-  FloorModOptions,
-  RangeOptions,
-  ResizeNearestNeighborOptions,
-  LeakyReluOptions,
-  SquaredDifferenceOptions,
-  MirrorPadOptions,
-  AbsOptions,
-  SplitVOptions,
-}
-
-enum Padding : byte { SAME, VALID }
-
-enum ActivationFunctionType : byte {
-  NONE = 0,
-  RELU = 1,
-  RELU_N1_TO_1 = 2,
-  RELU6 = 3,
-  TANH = 4,
-  SIGN_BIT = 5,
-}
-
-table Conv2DOptions {
-  padding:Padding;
-  stride_w:int;
-  stride_h:int;
-  fused_activation_function:ActivationFunctionType;
-  dilation_w_factor:int = 1;
-  dilation_h_factor:int = 1;
-}
-
-table Pool2DOptions {
-  padding:Padding;
-  stride_w:int;
-  stride_h:int;
-  filter_width:int;
-  filter_height:int;
-  fused_activation_function:ActivationFunctionType;
-}
-
-table DepthwiseConv2DOptions {
-  // Parameters for DepthwiseConv version 1 or above.
-  padding:Padding;
-  stride_w:int;
-  stride_h:int;
-  depth_multiplier:int;
-  fused_activation_function:ActivationFunctionType;
-  // Parameters for DepthwiseConv version 2 or above.
-  dilation_w_factor:int = 1;
-  dilation_h_factor:int = 1;
-}
-
-table ConcatEmbeddingsOptions {
-  num_channels:int;
-  num_columns_per_channel:[int];
-  embedding_dim_per_channel:[int]; // This could be inferred from parameters.
-}
-
-enum LSHProjectionType: byte {
-  UNKNOWN = 0,
-  SPARSE = 1,
-  DENSE = 2,
-}
-
-table LSHProjectionOptions {
-  type: LSHProjectionType;
-}
-
-table SVDFOptions {
-  rank:int;
-  fused_activation_function:ActivationFunctionType;
-}
-
-// An implementation of TensorFlow RNNCell.
-table RNNOptions {
-  fused_activation_function:ActivationFunctionType;
-}
-
-// An implementation of TensorFlow dynamic_rnn with RNNCell.
-table SequenceRNNOptions {
-  time_major:bool;
-  fused_activation_function:ActivationFunctionType;
-}
-
-// An implementation of TensorFlow bidrectional_dynamic_rnn with RNNCell.
-table BidirectionalSequenceRNNOptions {
-  time_major:bool;
-  fused_activation_function:ActivationFunctionType;
-  merge_outputs: bool;
-}
-
-enum FullyConnectedOptionsWeightsFormat: byte {
-  DEFAULT = 0,
-  SHUFFLED4x16INT8 = 1,
-}
-
-// An implementation of TensorFlow fully_connected (a.k.a Dense) layer.
-table FullyConnectedOptions {
-  // Parameters for FullyConnected version 1 or above.
-  fused_activation_function:ActivationFunctionType;
-
-  // Parameters for FullyConnected version 2 or above.
-  weights_format:FullyConnectedOptionsWeightsFormat = DEFAULT;
-}
-
-table SoftmaxOptions {
-  beta: float;
-}
-
-// An implementation of TensorFlow concat.
-table ConcatenationOptions {
-  axis:int;
-  fused_activation_function:ActivationFunctionType;
-}
-
-table AddOptions {
-  fused_activation_function:ActivationFunctionType;
-}
-
-table MulOptions {
-  fused_activation_function:ActivationFunctionType;
-}
-
-table L2NormOptions {
-  fused_activation_function:ActivationFunctionType;
-}
-
-table LocalResponseNormalizationOptions {
-  radius:int;
-  bias:float;
-  alpha:float;
-  beta:float;
-}
-
-enum LSTMKernelType : byte {
-  // Full LSTM kernel which supports peephole and projection.
-  FULL = 0,
-  // Basic LSTM kernels. Equivalent to TensorFlow BasicLSTMCell.
-  BASIC = 1,
-}
-
-// An implementation of TensorFlow LSTMCell and CoupledInputForgetGateLSTMCell
-table LSTMOptions {
-  // Parameters for LSTM version 1 or above.
-  fused_activation_function:ActivationFunctionType;
-  cell_clip: float; // Optional, 0.0 means no clipping
-  proj_clip: float; // Optional, 0.0 means no clipping
-
-  // Parameters for LSTM version 2 or above.
-  // Basic kernel is only supported in version 2 or above.
-  kernel_type: LSTMKernelType = FULL;
-}
-
-// An implementation of TensorFlow dynamic_rnn with LSTMCell.
-table UnidirectionalSequenceLSTMOptions {
-  fused_activation_function:ActivationFunctionType;
-  cell_clip: float; // Optional, 0.0 means no clipping
-  proj_clip: float; // Optional, 0.0 means no clipping
-
-  // If true then first dimension is sequence, otherwise batch.
-  time_major:bool;
-}
-
-table BidirectionalSequenceLSTMOptions {
-  fused_activation_function:ActivationFunctionType;
-  cell_clip: float; // Optional, 0.0 means no clipping
-  proj_clip: float; // Optional, 0.0 means no clipping
-
-  // If true, store the outputs of both directions into the first output.
-  merge_outputs: bool;
-}
-
-table ResizeBilinearOptions {
-  new_height: int (deprecated);
-  new_width: int (deprecated);
-  align_corners: bool;
-}
-
-table ResizeNearestNeighborOptions {
-  align_corners: bool;
-}
-
-// A call operation options
-table CallOptions {
-  // The subgraph index that needs to be called.
-  subgraph:uint;
-}
-
-table PadOptions {
-}
-
-table PadV2Options {
-}
-
-table ReshapeOptions {
-  new_shape:[int];
-}
-
-table SpaceToBatchNDOptions {
-}
-
-table BatchToSpaceNDOptions {
-}
-
-table SkipGramOptions {
-  ngram_size: int;
-  max_skip_size: int;
-  include_all_ngrams: bool;
-}
-
-table SpaceToDepthOptions {
-  block_size: int;
-}
-
-table SubOptions {
-  fused_activation_function:ActivationFunctionType;
-}
-
-table DivOptions {
-  fused_activation_function:ActivationFunctionType;
-}
-
-table TopKV2Options {
-}
-
-enum CombinerType : byte {
-  SUM = 0,
-  MEAN = 1,
-  SQRTN = 2,
-}
-
-table EmbeddingLookupSparseOptions {
-  combiner:CombinerType;
-}
-
-table GatherOptions {
-  axis: int;
-}
-
-table TransposeOptions {
-}
-
-table ExpOptions {
-}
-
-table ReducerOptions {
-  keep_dims: bool;
-}
-
-table SqueezeOptions {
-  squeeze_dims:[int];
-}
-
-table SplitOptions {
-  num_splits: int;
-}
-
-table SplitVOptions {
-  num_splits: int;
-}
-
-table StridedSliceOptions {
-  begin_mask: int;
-  end_mask: int;
-  ellipsis_mask: int;
-  new_axis_mask: int;
-  shrink_axis_mask: int;
-}
-
-table LogSoftmaxOptions {
-}
-
-table CastOptions {
-  in_data_type: TensorType;
-  out_data_type: TensorType;
-}
-
-table DequantizeOptions {
-}
-
-table MaximumMinimumOptions {
-}
-
-table TileOptions {
-}
-
-table ArgMaxOptions {
-  output_type : TensorType;
-}
-
-table ArgMinOptions {
-  output_type : TensorType;
-}
-
-table GreaterOptions {
-}
-
-table GreaterEqualOptions {
-}
-
-table LessOptions {
-}
-
-table LessEqualOptions {
-}
-
-table NegOptions {
-}
-
-table SelectOptions {
-}
-
-table SliceOptions {
-}
-
-table TransposeConvOptions {
-  padding:Padding;
-  stride_w:int;
-  stride_h:int;
-}
-
-table ExpandDimsOptions {
-}
-
-table SparseToDenseOptions {
-  validate_indices:bool;
-}
-
-table EqualOptions {
-}
-
-table NotEqualOptions {
-}
-
-table ShapeOptions {
-  // Optional output type of the operation (int32 or int64). Defaults to int32.
-  out_type : TensorType;
-}
-
-table PowOptions {
-}
-
-table FakeQuantOptions {
-  // Parameters supported by version 1:
-  min:float;
-  max:float;
-  num_bits:int;
-
-  // Parameters supported by version 2:
-  narrow_range:bool;
-}
-
-table PackOptions {
-  values_count:int;
-  axis:int;
-}
-
-table LogicalOrOptions {
-}
-
-table OneHotOptions {
-  axis:int;
-}
-
-table AbsOptions {
-}
-
-
-table LogicalAndOptions {
-}
-
-table LogicalNotOptions {
-}
-
-table UnpackOptions {
-  num:int;
-  axis:int;
-}
-
-table FloorDivOptions {
-}
-
-table SquareOptions {
-}
-
-table ZerosLikeOptions {
-}
-
-table FillOptions {
-}
-
-table FloorModOptions {
-}
-
-table RangeOptions {
-}
-
-table LeakyReluOptions {
-  alpha:float;
-}
-
-table SquaredDifferenceOptions {
-}
-
-enum MirrorPadMode : byte {
-  // Doesn't include borders.
-  REFLECT = 0,
-  // Includes borders.
-  SYMMETRIC = 1,
-}
-
-table MirrorPadOptions {
-  mode:MirrorPadMode;
-}
-
-// An OperatorCode can be an enum value (BuiltinOperator) if the operator is a
-// builtin, or a string if the operator is custom.
-table OperatorCode {
-  builtin_code:BuiltinOperator;
-  custom_code:string;
-
-  // The version of the operator. The version need to be bumped whenever new
-  // parameters are introduced into an op.
-  version:int = 1;
-}
-
-enum CustomOptionsFormat : byte {
-  FLEXBUFFERS = 0,
-}
-
-// An operator takes tensors as inputs and outputs. The type of operation being
-// performed is determined by an index into the list of valid OperatorCodes,
-// while the specifics of each operations is configured using builtin_options
-// or custom_options.
-table Operator {
-  // Index into the operator_codes array. Using an integer here avoids
-  // complicate map lookups.
-  opcode_index:uint;
-
-  // Optional input and output tensors are indicated by -1.
-  inputs:[int];
-  outputs:[int];
-
-  builtin_options:BuiltinOptions;
-  custom_options:[ubyte];
-  custom_options_format:CustomOptionsFormat;
-
-  // A list of booleans indicating the input tensors which are being mutated by
-  // this operator.(e.g. used by RNN and LSTM).
-  // For example, if the "inputs" array refers to 5 tensors and the second and
-  // fifth are mutable variables, then this list will contain
-  // [false, true, false, false, true].
-  //
-  // If the list is empty, no variable is mutated in this operator.
-  // The list either has the same length as `inputs`, or is empty.
-  mutating_variable_inputs:[bool];
-}
-
-// The root type, defining a subgraph, which typically represents an entire
-// model.
-table SubGraph {
-  // A list of all tensors used in this subgraph.
-  tensors:[Tensor];
-
-  // Indices of the tensors that are inputs into this subgraph. Note this is
-  // the list of non-static tensors that feed into the subgraph for inference.
-  inputs:[int];
-
-  // Indices of the tensors that are outputs out of this subgraph. Note this is
-  // the list of output tensors that are considered the product of the
-  // subgraph's inference.
-  outputs:[int];
-
-  // All operators, in execution order.
-  operators:[Operator];
-
-  // Name of this subgraph (used for debugging).
-  name:string;
-}
-
-// Table of raw data buffers (used for constant tensors). Referenced by tensors
-// by index. The generous alignment accommodates mmap-friendly data structures.
-table Buffer {
-  data:[ubyte] (force_align: 16);
-}
-
-table Model {
-  // Version of the schema.
-  version:uint;
-
-  // A list of all operator codes used in this model. This is
-  // kept in order because operators carry an index into this
-  // vector.
-  operator_codes:[OperatorCode];
-
-  // All the subgraphs of the model. The 0th is assumed to be the main
-  // model.
-  subgraphs:[SubGraph];
-
-  // A description of the model.
-  description:string;
-
-  // Buffers of the model.
-  // Note the 0th entry of this array must be an empty buffer (sentinel).
-  // This is a convention so that tensors without a buffer can provide 0 as
-  // their buffer.
-  buffers:[Buffer];
-
-  // Metadata about the model.  Indirects into the existings buffers list.
-  metadata_buffer:[int];
-}
-
-root_type Model;
diff --git a/runtime/neurun/sample/CMakeLists.txt b/runtime/neurun/sample/CMakeLists.txt
deleted file mode 100644
index d853ba634..000000000
--- a/runtime/neurun/sample/CMakeLists.txt
+++ /dev/null
@@ -1 +0,0 @@
-add_subdirectory(minimal)
diff --git a/runtime/neurun/sample/minimal/CMakeLists.txt b/runtime/neurun/sample/minimal/CMakeLists.txt
deleted file mode 100644
index 6f4b02761..000000000
--- a/runtime/neurun/sample/minimal/CMakeLists.txt
+++ /dev/null
@@ -1,10 +0,0 @@
-if(NOT BUILD_MINIMAL_SAMPLE)
-  return()
-endif(NOT BUILD_MINIMAL_SAMPLE)
-
-list(APPEND MINIMAL_SRCS "src/minimal.cc")
-
-add_executable(minimal ${MINIMAL_SRCS})
-target_link_libraries(minimal nnfw-dev pthread dl)
-
-install(TARGETS minimal DESTINATION bin)
diff --git a/runtime/neurun/sample/minimal/README.md b/runtime/neurun/sample/minimal/README.md
deleted file mode 100644
index fecad6fb2..000000000
--- a/runtime/neurun/sample/minimal/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
-# minimal
-
-`minimal` is a simple driver to run `nnpackage` with nnfw API.
-
-It takes `nnpackage` as input. It uses **nnfwAPI** internally.
-
-It assumes model of float32 tensor type as an input.
-
-## Usage
-
-```
-$ ./minimal path_to_nnpackage_directory
-```
diff --git a/runtime/neurun/sample/minimal/src/minimal.cc b/runtime/neurun/sample/minimal/src/minimal.cc
deleted file mode 100644
index 003c8a323..000000000
--- a/runtime/neurun/sample/minimal/src/minimal.cc
+++ /dev/null
@@ -1,67 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "nnfw.h"
-#include <vector>
-
-uint64_t num_elems(const nnfw_tensorinfo *ti)
-{
-  uint64_t n = 1;
-  for (uint32_t i = 0; i < ti->rank; ++i)
-  {
-    n *= ti->dims[i];
-  }
-  return n;
-}
-
-int main(const int argc, char **argv)
-{
-  nnfw_session *session = nullptr;
-  nnfw_create_session(&session);
-
-  // Loading nnpackage
-  nnfw_load_model_from_file(session, argv[1]);
-
-  // Use acl_neon backend for CONV_2D and acl_cl for otherwise.
-  // Note that defalut backend is acl_cl
-  nnfw_set_op_backend(session, "CONV_2D", "acl_neon");
-
-  // Compile model
-  nnfw_prepare(session);
-
-  // Prepare input. Here we just allocate dummy input arrays.
-  std::vector<float> input;
-  nnfw_tensorinfo ti;
-  nnfw_input_tensorinfo(session, 0, &ti); // get first input's info
-  uint32_t input_elements = num_elems(&ti);
-  input.resize(input_elements);
-  // TODO: Please add initialization for your input.
-  nnfw_set_input(session, 0, ti.dtype, input.data(), sizeof(float) * input_elements);
-
-  // Prepare output
-  std::vector<float> output;
-  nnfw_output_tensorinfo(session, 0, &ti); // get first output's info
-  uint32_t output_elements = num_elems(&ti);
-  output.resize(output_elements);
-  nnfw_set_output(session, 0, ti.dtype, output.data(), sizeof(float) * output_elements);
-
-  // Do inference
-  nnfw_run(session);
-
-  // TODO: Please print or compare the output value in your way.
-
-  return 0;
-}
diff --git a/runtime/neurun/test/CMakeLists.txt b/runtime/neurun/test/CMakeLists.txt
deleted file mode 100644
index 815173937..000000000
--- a/runtime/neurun/test/CMakeLists.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-set(TEST_NEURUN test_neurun)
-
-file(GLOB_RECURSE TESTS "*.cc")
-
-add_executable(${TEST_NEURUN} ${TESTS})
-
-target_include_directories(${TEST_NEURUN} PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/../core/src)
-
-target_link_libraries(${TEST_NEURUN} neurun_core)
-target_link_libraries(${TEST_NEURUN} gtest)
-target_link_libraries(${TEST_NEURUN} gtest_main)
-target_link_libraries(${TEST_NEURUN} ${LIB_PTHREAD} dl)
-add_test(${TEST_NEURUN} ${TEST_NEURUN})
-
-install(TARGETS ${TEST_NEURUN} DESTINATION unittest)
diff --git a/runtime/neurun/test/core/backend/ExecTime.test.cc b/runtime/neurun/test/core/backend/ExecTime.test.cc
deleted file mode 100644
index 4b89e64d2..000000000
--- a/runtime/neurun/test/core/backend/ExecTime.test.cc
+++ /dev/null
@@ -1,98 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "backend/ExecTime.h"
-#include "backend/IConfig.h"
-#include "backend/Backend.h"
-#include <gtest/gtest.h>
-#include <string>
-
-namespace
-{
-using namespace neurun;
-using namespace backend;
-
-struct MockConfig : public IConfig
-{
-  std::string id() override { return "b1"; }
-  bool initialize() override { return true; };
-  bool SupportPermutation() override { return false; }
-  bool SupportSubTensorAlloc() override { return false; }
-};
-
-struct MockBackend : public ::neurun::backend::Backend
-{
-  std::shared_ptr<neurun::backend::IConfig> config() const override
-  {
-    return std::make_shared<MockConfig>();
-  }
-  std::unique_ptr<BackendContext>
-  newContext(const ir::Operands &, const std::shared_ptr<custom::IKernelBuilder> &kb) const override
-  {
-    return nullptr;
-  }
-};
-
-TEST(ExecTime, roundtrip_ok)
-{
-  const auto *b = new MockBackend();
-  std::vector<const Backend *> bs = {b};
-  {
-    ExecTime et(bs);
-    et.updateOperationExecTime(b, "op1", true, 100, 100);
-    et.updateOperationExecTime(b, "op1", true, 200, 200);
-    et.updateOperationExecTime(b, "op1", false, 100, 888);
-    et.uploadOperationsExecTime();
-  }
-  {
-    ExecTime et(bs);
-    auto time = et.getOperationExecTime(b, "op1", true, 100);
-    ASSERT_EQ(time, 100);
-    // Check interpolation
-    time = et.getOperationExecTime(b, "op1", true, 150);
-    ASSERT_EQ(time, 150);
-    time = et.getOperationExecTime(b, "op1", false, 100);
-    ASSERT_EQ(time, 888);
-    et.uploadOperationsExecTime();
-  }
-  // clean up
-  EXPECT_EQ(remove("exec_time.json"), 0);
-}
-
-TEST(ExecTime, structure)
-{
-
-  const auto *b = new MockBackend();
-  std::vector<const Backend *> bs = {b};
-  {
-    ExecTime et(bs);
-    et.updateOperationExecTime(b, "op1", true, 100, 100);
-    et.updateOperationExecTime(b, "op1", true, 200, 200);
-    et.uploadOperationsExecTime();
-  }
-  {
-    ExecTime et(bs);
-    auto time = et.getOperationExecTime(b, "op1", true, 100);
-    ASSERT_EQ(time, 100);
-    // Check interpolation
-    time = et.getOperationExecTime(b, "op1", true, 200);
-    ASSERT_EQ(time, 200);
-    et.uploadOperationsExecTime();
-  }
-  // clean up
-  EXPECT_EQ(remove("exec_time.json"), 0);
-}
-} // unnamed namespace
diff --git a/runtime/neurun/test/core/compiler/Scheduler.cc b/runtime/neurun/test/core/compiler/Scheduler.cc
deleted file mode 100644
index a4fbfeb2c..000000000
--- a/runtime/neurun/test/core/compiler/Scheduler.cc
+++ /dev/null
@@ -1,550 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <compiler/HEScheduler.h>
-#include <backend/ExecTime.h>
-#include <backend/IShapeFixer.h>
-
-#include <ir/Shape.h>
-#include <ir/InternalType.h>
-#include <ir/TypeInfo.h>
-#include <ir/DataType.h>
-
-#include <ir/operation/Add.h>
-#include <ir/operation/Sub.h>
-#include <ir/operation/Mul.h>
-#include <ir/operation/FullyConnected.h>
-
-#include <gtest/gtest.h>
-
-namespace
-{
-using namespace neurun;
-using namespace ir;
-using namespace backend;
-using namespace operation;
-
-//
-// Mock backends classes
-//
-
-// Backend could be created without ShapeFixer.
-// But it is used by scheduler to detect which operations are supported by backend.
-struct MockShapeFixer : IShapeFixer
-{
-  void visit(const Add &) override {}
-  void visit(const Sub &) override {}
-  void visit(const Mul &) override {}
-  void visit(const FullyConnected &) override {}
-};
-
-struct MockConfigCPU : public IConfig
-{
-  std::string id() override { return "cpu"; }
-  bool initialize() override { return true; };
-  bool SupportPermutation() override { return false; }
-  bool SupportSubTensorAlloc() override { return false; }
-};
-
-struct MockBackendCPU : public Backend
-{
-  std::shared_ptr<IConfig> config() const override { return std::make_shared<MockConfigCPU>(); }
-  std::unique_ptr<BackendContext>
-  newContext(const Operands &, const std::shared_ptr<custom::IKernelBuilder> &) const override
-  {
-    return std::unique_ptr<BackendContext>(
-        new BackendContext{this, nullptr, nullptr, nullptr, std::make_shared<MockShapeFixer>()});
-  }
-};
-
-struct MockConfigGPU : public IConfig
-{
-  std::string id() override { return "gpu"; }
-  bool initialize() override { return true; };
-  bool SupportPermutation() override { return false; }
-  bool SupportSubTensorAlloc() override { return false; }
-};
-
-struct MockBackendGPU : public Backend
-{
-  std::shared_ptr<IConfig> config() const override { return std::make_shared<MockConfigGPU>(); }
-  std::unique_ptr<BackendContext>
-  newContext(const Operands &, const std::shared_ptr<custom::IKernelBuilder> &) const override
-  {
-    return std::unique_ptr<BackendContext>(
-        new BackendContext{this, nullptr, nullptr, nullptr, std::make_shared<MockShapeFixer>()});
-  }
-};
-
-struct MockConfigNPU : public IConfig
-{
-  std::string id() override { return "npu"; }
-  bool initialize() override { return true; };
-  bool SupportPermutation() override { return false; }
-  bool SupportSubTensorAlloc() override { return false; }
-};
-
-struct MockBackendNPU : public Backend
-{
-  std::shared_ptr<IConfig> config() const override { return std::make_shared<MockConfigNPU>(); }
-  std::unique_ptr<BackendContext>
-  newContext(const Operands &, const std::shared_ptr<custom::IKernelBuilder> &) const override
-  {
-    return std::unique_ptr<BackendContext>(
-        new BackendContext{this, nullptr, nullptr, nullptr, std::make_shared<MockShapeFixer>()});
-  }
-};
-
-//
-// Constants
-//
-
-const int OPERAND_ELEMS = 268203;
-const int OPERAND_SIZE = OPERAND_ELEMS * 4;
-const int OPERATION_SIZE = OPERAND_SIZE * 3;
-
-const std::string LINEAR("Linear");
-const std::string DATAFLOW("Dataflow");
-const std::string PARALLEL("Parallel");
-
-//
-// Helper functions
-//
-
-// Set executor through environment variable
-void setExecutor(const std::string &executor) { setenv("EXECUTOR", executor.c_str(), true); }
-
-// Set profiling mode through environment variable
-void setProfilingMode(const bool value) { setenv("PROFILING_MODE", value ? "1" : "0", true); }
-
-// Calculate operation size by addition sizes of all input and output operands
-uint32_t calcOpSize(const std::shared_ptr<Graph> &graph, const OperationIndex &op_idx)
-{
-  uint32_t size = 0;
-  for (const auto &input : graph->operations().at(op_idx).getInputs())
-    size += graph->operands().at(input).info().total_size();
-  for (const auto &output : graph->operations().at(op_idx).getOutputs())
-    size += graph->operands().at(output).info().total_size();
-  return size;
-}
-
-// Set execution operation time. This method is needed since ExecutionTime has only
-// 'updateOperationExecTime' method.
-void setOperationExecTime(ExecTime &et, const Backend *backend, const std::string &operation,
-                          bool quant, uint32_t op_size, int64_t time)
-{
-  // You shouldn't set negative time with this method since nnfw JSON deserializer can't read it
-  assert(time > 0);
-  int64_t prev_time = et.getOperationExecTime(backend, operation, quant, op_size);
-  int64_t time_to_set = prev_time == ExecTime::NOT_FOUND ? time : 2 * time - prev_time;
-  et.updateOperationExecTime(backend, operation, quant, op_size, time_to_set);
-  assert(et.getOperationExecTime(backend, operation, quant, op_size) == time);
-}
-
-// Set same execution time for all given backends/operations
-void setOperationsExecutionTime(const std::vector<const Backend *> &backends,
-                                const std::vector<std::string> &op_names,
-                                const std::vector<uint32_t> &op_sizes, int64_t exec_time)
-{
-  assert(op_names.size() == op_sizes.size());
-  ExecTime et(backends);
-  for (int i = 0; i < op_names.size(); ++i)
-  {
-    for (auto &backend : backends)
-      setOperationExecTime(et, backend, op_names[i], false, op_sizes[i], exec_time);
-  }
-  et.uploadOperationsExecTime();
-}
-
-// Set permute time from one backend to another. This method is needed since ExecutionTime has only
-// 'updatePermuteTime' method.
-void setPermutationTime(ExecTime &et, const Backend *from_backend, const Backend *to_backend,
-                        bool quant, uint32_t op_size, int64_t time)
-{
-  // You shouldn't set negative time with this method since nnfw JSON deserializer can't read it
-  assert(time > 0);
-  int64_t prev_time = et.getPermuteTime(from_backend, to_backend, quant, op_size);
-  int64_t time_to_set = prev_time == ExecTime::NOT_FOUND ? time : 2 * time - prev_time;
-  et.updatePermuteTime(from_backend, to_backend, quant, op_size, time_to_set);
-  assert(et.getPermuteTime(from_backend, to_backend, quant, op_size) == time);
-}
-
-// Set same permutation time between all given backends
-void setPermutationsExecutionTime(const std::vector<const Backend *> &backends,
-                                  const int operand_size, const int64_t exec_time)
-{
-  ExecTime et(backends);
-  for (const auto &backend : backends)
-  {
-    for (auto &other_backend : backends)
-    {
-      if (backend == other_backend)
-        continue;
-      setPermutationTime(et, backend, other_backend, false, operand_size, exec_time);
-    }
-  }
-  et.uploadOperationsExecTime();
-}
-
-//
-// Functions for creating graphs
-//
-
-using OIS = OperandIndexSequence;
-
-template <typename NodeT, typename... Types>
-OperationIndex create(std::shared_ptr<Graph> graph, Types &&... args)
-{
-  typename NodeT::Param op_params{Activation::NONE};
-  auto op = nnfw::cpp14::make_unique<NodeT>(std::forward<Types>(args)..., op_params);
-  auto op_idx = graph->addOperation(std::move(op));
-  // For now in scheduler test all operations in tested graphs has same size (for simplicity)
-  assert(calcOpSize(graph, op_idx) == OPERATION_SIZE);
-  return op_idx;
-}
-
-// Create straight graph: Add->Sub->Mul
-std::shared_ptr<Graph> createStraightGraph()
-{
-  auto graph = std::make_shared<Graph>();
-  const TypeInfo float_op(DataType::FLOAT32);
-
-  // Create add node
-  auto add_lhs_idx = graph->addOperand(Shape{OPERAND_ELEMS}, float_op);
-  auto add_rhs_idx = graph->addOperand(Shape{OPERAND_ELEMS}, float_op);
-  auto add_out_idx = graph->addOperand(Shape{OPERAND_ELEMS}, float_op);
-  create<Add>(graph, OIS{add_lhs_idx, add_rhs_idx}, OIS{add_out_idx});
-
-  // Create sub node
-  auto sub_const_idx = graph->addOperand(Shape{OPERAND_ELEMS}, float_op);
-  auto sub_out_idx = graph->addOperand(Shape{OPERAND_ELEMS}, float_op);
-  create<Sub>(graph, OIS{add_out_idx, sub_const_idx}, OIS{sub_out_idx});
-
-  // Create mul node
-  auto mul_const_idx = graph->addOperand(Shape{OPERAND_ELEMS}, float_op);
-  auto mul_out_idx = graph->addOperand(Shape{OPERAND_ELEMS}, float_op);
-  create<Mul>(graph, OIS{sub_out_idx, mul_const_idx}, OIS{mul_out_idx});
-
-  graph->finishBuilding();
-  return graph;
-}
-
-/* Create branched graph:
- *       [Add]
- *      //   \\
- *   [Mul1]  [FC2]
- *     ||     ||
- *   [Mul2]  [FC2]
- *      \\   //
- *       [Sub]
- */
-std::shared_ptr<Graph> createBranchedGraph()
-{
-  auto graph = std::make_shared<Graph>();
-  const TypeInfo float_op(DataType::FLOAT32);
-
-  // Create add node
-  auto add_lhs_idx = graph->addOperand(Shape{OPERAND_ELEMS}, float_op);
-  auto add_rhs_idx = graph->addOperand(Shape{OPERAND_ELEMS}, float_op);
-  auto add_out_idx = graph->addOperand(Shape{OPERAND_ELEMS}, float_op);
-  create<Add>(graph, OIS{add_lhs_idx, add_rhs_idx}, OIS{add_out_idx});
-
-  // Create mul1 node
-  auto mul1_const_idx = graph->addOperand(Shape{OPERAND_ELEMS}, float_op);
-  auto mul1_out_idx = graph->addOperand(Shape{OPERAND_ELEMS}, float_op);
-  create<Mul>(graph, OIS{add_out_idx, mul1_const_idx}, OIS{mul1_out_idx});
-
-  // Create mul2 node
-  auto mul2_const_idx = graph->addOperand(Shape{OPERAND_ELEMS}, float_op);
-  auto mul2_out_idx = graph->addOperand(Shape{OPERAND_ELEMS}, float_op);
-  create<Mul>(graph, OIS{mul1_out_idx, mul2_const_idx}, OIS{mul2_out_idx});
-
-  // Create fc1 node
-  auto fc1_const_idx = graph->addOperand(Shape{OPERAND_ELEMS}, float_op);
-  auto fc1_out_idx = graph->addOperand(Shape{OPERAND_ELEMS}, float_op);
-  create<FullyConnected>(graph, OIS{add_out_idx, fc1_const_idx}, OIS{fc1_out_idx});
-
-  // Create fc2 node
-  auto fc2_const_idx = graph->addOperand(Shape{OPERAND_ELEMS}, float_op);
-  auto fc2_out_idx = graph->addOperand(Shape{OPERAND_ELEMS}, float_op);
-  create<FullyConnected>(graph, OIS{fc1_out_idx, fc2_const_idx}, OIS{fc2_out_idx});
-
-  // Create add2 node
-  auto sub_out_idx = graph->addOperand(Shape{OPERAND_ELEMS}, float_op);
-  create<Sub>(graph, OIS{mul2_out_idx, fc2_out_idx}, OIS{sub_out_idx});
-
-  graph->finishBuilding();
-  return graph;
-}
-
-//
-// Tests setup/teardown
-//
-
-// SetUp/TearDown methods runs before/after each test and performs actions common for each test
-class SchedulerTest : public ::testing::Test
-{
-protected:
-  void SetUp() override
-  {
-    // Initialize mock backends
-    _cpu_backend = new MockBackendCPU();
-    _gpu_backend = new MockBackendGPU();
-    _npu_backend = new MockBackendNPU();
-    _mock_backends = {_cpu_backend, _gpu_backend, _npu_backend};
-
-    // Remove previous profile data if it exists
-    if (!remove("exec_time.json"))
-    {
-      // DO NOTHING (no profile data)
-    }
-
-    // Remember original value of 'EXECUTOR' environment variable
-    char *executor = std::getenv("EXECUTOR");
-    _original_executor = executor == nullptr ? "" : executor;
-
-    // Remember original value of 'PROFILING_MODE' environment variable
-    char *profiling_mode = std::getenv("PROFILING_MODE");
-    _original_profiling_mode = profiling_mode == nullptr ? "" : profiling_mode;
-  }
-
-  void TearDown() override
-  {
-    delete _cpu_backend;
-    delete _gpu_backend;
-    delete _npu_backend;
-    EXPECT_EQ(remove("exec_time.json"), 0);
-    setenv("EXECUTOR", _original_executor.c_str(), true);
-    setenv("PROFILING_MODE", _original_profiling_mode.c_str(), true);
-  }
-
-  const MockBackendCPU *_cpu_backend{nullptr};
-  const MockBackendGPU *_gpu_backend{nullptr};
-  const MockBackendNPU *_npu_backend{nullptr};
-  std::vector<const Backend *> _mock_backends;
-
-  std::string _original_executor;
-  std::string _original_profiling_mode;
-};
-
-class SchedulerTestWithExecutorParam : public SchedulerTest,
-                                       public testing::WithParamInterface<std::string>
-{
-};
-
-//
-// HEScheduler tests
-//
-
-// Test scheduler behavior for straight graph with known execution time of all nodes and permutes.
-TEST_P(SchedulerTestWithExecutorParam, straight_graph_known_exec_time)
-{
-  setExecutor(GetParam());
-
-  // Prepare graph
-  auto graph(createStraightGraph());
-  OperationIndex add_op_idx(0), sub_op_idx(1), mul_op_idx(2);
-
-  // Set default execution and transfer time
-  setPermutationsExecutionTime(_mock_backends, OPERAND_SIZE, 1);
-  setOperationsExecutionTime(_mock_backends, {"Add", "Sub", "Mul"},
-                             {OPERATION_SIZE, OPERATION_SIZE, OPERATION_SIZE}, 1e4);
-
-  // Test 1
-  // Expected behaviour: scheduler assigns different backend to each node
-  {
-    // For each backend reduce execution time of one node
-    ExecTime et(_mock_backends);
-    setOperationExecTime(et, _cpu_backend, "Add", false, OPERATION_SIZE, 1);
-    setOperationExecTime(et, _gpu_backend, "Sub", false, OPERATION_SIZE, 1);
-    setOperationExecTime(et, _npu_backend, "Mul", false, OPERATION_SIZE, 1);
-    et.uploadOperationsExecTime();
-
-    // Test scheduler
-    auto scheduler = compiler::HEScheduler(graph->operands(), _mock_backends, nullptr);
-    const auto br = scheduler.schedule(*graph);
-    ASSERT_EQ(br->getBackend(add_op_idx)->config()->id(), "cpu");
-    ASSERT_EQ(br->getBackend(sub_op_idx)->config()->id(), "gpu");
-    ASSERT_EQ(br->getBackend(mul_op_idx)->config()->id(), "npu");
-  }
-
-  // Test 2
-  // Expected behaviour: scheduler assigns single backend to all nodes because of big transfer time
-  {
-    // Increase transfer time
-    setPermutationsExecutionTime(_mock_backends, OPERAND_SIZE, 1e5);
-
-    // Test scheduler
-    auto scheduler = compiler::HEScheduler(graph->operands(), _mock_backends, nullptr);
-    const auto br = scheduler.schedule(*graph);
-    ASSERT_EQ(br->getBackend(add_op_idx)->config()->id(), "cpu");
-    ASSERT_EQ(br->getBackend(sub_op_idx)->config()->id(), "cpu");
-    ASSERT_EQ(br->getBackend(mul_op_idx)->config()->id(), "cpu");
-  }
-}
-
-// Test scheduler behavior for branched graph with known execution time of all nodes and permutes
-TEST_P(SchedulerTestWithExecutorParam, branched_graph_known_exec_time)
-{
-  const int64_t NPU_ET = 5000;
-  setExecutor(GetParam());
-
-  // Prepare graph
-  auto graph(createBranchedGraph());
-  OperationIndex add_op_idx(0), mul1_op_idx(1), mul2_op_idx(2), fc1_op_idx(3), fc2_op_idx(4),
-      sub_op_idx(5);
-
-  // Set default execution and transfer time
-  setPermutationsExecutionTime(_mock_backends, OPERAND_SIZE, 1000);
-  setOperationsExecutionTime(_mock_backends, {"Add", "Sub", "Mul", "FullyConnected"},
-                             {OPERATION_SIZE, OPERATION_SIZE, OPERATION_SIZE, OPERATION_SIZE}, 1e4);
-
-  // Test 1
-  // Expected behaviour: for dataflow and linear executors scheduler assigns fastest backend to all
-  // nodes, in case of parallel executor scheduler assigns different backends to branches.
-  {
-    // Reduce execution time
-    ExecTime et(_mock_backends);
-    setOperationExecTime(et, _npu_backend, "Add", false, OPERATION_SIZE, NPU_ET);
-    setOperationExecTime(et, _npu_backend, "Mul", false, OPERATION_SIZE, NPU_ET);
-    setOperationExecTime(et, _npu_backend, "Sub", false, OPERATION_SIZE, NPU_ET);
-    setOperationExecTime(et, _npu_backend, "FullyConnected", false, OPERATION_SIZE, NPU_ET);
-    setOperationExecTime(et, _gpu_backend, "Mul", false, OPERATION_SIZE, NPU_ET + 1000);
-    setOperationExecTime(et, _gpu_backend, "FullyConnected", false, OPERATION_SIZE, NPU_ET + 1000);
-    et.uploadOperationsExecTime();
-
-    // Test scheduler
-    auto scheduler = compiler::HEScheduler(graph->operands(), _mock_backends, nullptr);
-    const auto br = scheduler.schedule(*graph);
-
-    std::string branch1_expected_backend("npu"), branch2_expected_backend("npu");
-    if (GetParam() == PARALLEL)
-    {
-      branch1_expected_backend =
-          br->getBackend(mul1_op_idx)->config()->id() == "npu" ? "npu" : "gpu";
-      branch2_expected_backend = branch1_expected_backend == "npu" ? "gpu" : "npu";
-    }
-
-    ASSERT_EQ(br->getBackend(add_op_idx)->config()->id(), "npu");
-    ASSERT_EQ(br->getBackend(mul1_op_idx)->config()->id(), branch1_expected_backend);
-    ASSERT_EQ(br->getBackend(mul2_op_idx)->config()->id(), branch1_expected_backend);
-    ASSERT_EQ(br->getBackend(fc1_op_idx)->config()->id(), branch2_expected_backend);
-    ASSERT_EQ(br->getBackend(fc2_op_idx)->config()->id(), branch2_expected_backend);
-    ASSERT_EQ(br->getBackend(sub_op_idx)->config()->id(), "npu");
-  }
-
-  // Test 2
-  // Expected behaviour: scheduler assigns single backend to all nodes
-  {
-    // Increase execution time for GPU backend
-    ExecTime et(_mock_backends);
-    /* for parallel executor: set a time, that is larger than sum_of_other_branches_nodes_cnt *
-     * npu_exec_time so that npu is prefered: the ith branch will wait for npu until it finishes the
-     * [0;i-1] branches nodes in DFS order. In each branch it goes deep intul doesn't encounter
-     * branching or scheduler assigns another backend to a node*/
-    setOperationExecTime(et, _gpu_backend, "Mul", false, OPERATION_SIZE, NPU_ET * 3 + 1);
-    setOperationExecTime(et, _gpu_backend, "FullyConnected", false, OPERATION_SIZE, NPU_ET * 3 + 1);
-    et.uploadOperationsExecTime();
-
-    // Test scheduler
-    auto scheduler = compiler::HEScheduler(graph->operands(), _mock_backends, nullptr);
-    const auto br = scheduler.schedule(*graph);
-    ASSERT_EQ(br->getBackend(add_op_idx)->config()->id(), "npu");
-    ASSERT_EQ(br->getBackend(mul1_op_idx)->config()->id(), "npu");
-    ASSERT_EQ(br->getBackend(mul2_op_idx)->config()->id(), "npu");
-    ASSERT_EQ(br->getBackend(fc1_op_idx)->config()->id(), "npu");
-    ASSERT_EQ(br->getBackend(fc2_op_idx)->config()->id(), "npu");
-    ASSERT_EQ(br->getBackend(sub_op_idx)->config()->id(), "npu");
-  }
-}
-
-// SchedulerTestWithExecutorParam tests are parameterized with executor name and runs three times -
-// one time for each executor
-INSTANTIATE_TEST_CASE_P(AllExecutors, SchedulerTestWithExecutorParam,
-                        testing::Values(LINEAR, DATAFLOW, PARALLEL));
-
-// Test scheduler behavior for branched graph and enabled profiling mode
-TEST_F(SchedulerTest, branched_graph_profiling_mode)
-{
-  const int ET = 1e5;
-
-  // Turn on profiling mode
-  setProfilingMode(true);
-  setExecutor(DATAFLOW);
-
-  // Prepare graph
-  auto graph(createBranchedGraph());
-  OperationIndex add_op_idx(0), mul1_op_idx(1), mul2_op_idx(2), fc1_op_idx(3), fc2_op_idx(4),
-      sub_op_idx(5);
-
-  // Test 1
-  // Expected behaviour: scheduler assigns backends to nodes with unknown execution time
-  {
-    // Set execution time for all backends/nodes except for cpu/Sub, npu/Mul, gpu/FC
-    ExecTime et(_mock_backends);
-    setOperationExecTime(et, _cpu_backend, "Add", false, OPERATION_SIZE, ET);
-    setOperationExecTime(et, _cpu_backend, "Mul", false, OPERATION_SIZE, ET + 1);
-    setOperationExecTime(et, _cpu_backend, "FullyConnected", false, OPERATION_SIZE, ET);
-    setOperationExecTime(et, _npu_backend, "Add", false, OPERATION_SIZE, ET);
-    setOperationExecTime(et, _npu_backend, "FullyConnected", false, OPERATION_SIZE, ET);
-    setOperationExecTime(et, _npu_backend, "Sub", false, OPERATION_SIZE, ET);
-    setOperationExecTime(et, _gpu_backend, "Add", false, OPERATION_SIZE, ET);
-    setOperationExecTime(et, _gpu_backend, "Mul", false, OPERATION_SIZE, ET + 1);
-    setOperationExecTime(et, _gpu_backend, "Sub", false, OPERATION_SIZE, ET);
-    et.uploadOperationsExecTime();
-
-    // Test scheduler
-    auto scheduler = compiler::HEScheduler(graph->operands(), _mock_backends, nullptr);
-    const auto br = scheduler.schedule(*graph);
-    ASSERT_EQ(br->getBackend(mul1_op_idx)->config()->id(), "npu");
-    ASSERT_EQ(br->getBackend(mul2_op_idx)->config()->id(), "npu");
-    ASSERT_EQ(br->getBackend(fc1_op_idx)->config()->id(), "gpu");
-    ASSERT_EQ(br->getBackend(fc2_op_idx)->config()->id(), "gpu");
-    ASSERT_EQ(br->getBackend(sub_op_idx)->config()->id(), "cpu");
-  }
-
-  // Test 2
-  // Expected behaviour: scheduler shuffling backends, so different backends are assigned to
-  // neighbor nodes
-  {
-    // Set execution time for rest backends/nodes (cpu/Sub, npu/Mul, gpu/FC)
-    ExecTime et(_mock_backends);
-    setOperationExecTime(et, _cpu_backend, "Sub", false, OPERATION_SIZE, ET);
-    setOperationExecTime(et, _npu_backend, "Mul", false, OPERATION_SIZE, ET + 1);
-    setOperationExecTime(et, _gpu_backend, "FullyConnected", false, OPERATION_SIZE, ET);
-    et.uploadOperationsExecTime();
-
-    // Test scheduler
-    auto scheduler = compiler::HEScheduler(graph->operands(), _mock_backends, nullptr);
-    const auto br = scheduler.schedule(*graph);
-    ASSERT_NE(br->getBackend(add_op_idx)->config()->id(),
-              br->getBackend(mul1_op_idx)->config()->id());
-    ASSERT_NE(br->getBackend(add_op_idx)->config()->id(),
-              br->getBackend(fc1_op_idx)->config()->id());
-    ASSERT_NE(br->getBackend(mul1_op_idx)->config()->id(),
-              br->getBackend(mul2_op_idx)->config()->id());
-    ASSERT_NE(br->getBackend(fc1_op_idx)->config()->id(),
-              br->getBackend(fc2_op_idx)->config()->id());
-    ASSERT_NE(br->getBackend(mul2_op_idx)->config()->id(),
-              br->getBackend(sub_op_idx)->config()->id());
-    ASSERT_NE(br->getBackend(fc2_op_idx)->config()->id(),
-              br->getBackend(sub_op_idx)->config()->id());
-  }
-}
-
-// TODO: Add tests with unknown execution and permutation time
-
-} // unnamed namespace
diff --git a/runtime/neurun/test/core/exec/ExecInstance.cc b/runtime/neurun/test/core/exec/ExecInstance.cc
deleted file mode 100644
index 49d561226..000000000
--- a/runtime/neurun/test/core/exec/ExecInstance.cc
+++ /dev/null
@@ -1,307 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <gtest/gtest.h>
-#include <thread>
-
-#include "ir/Graph.h"
-#include "compiler/Compiler.h"
-#include "exec/Execution.h"
-#include "ir/operation/Add.h"
-
-namespace
-{
-
-using namespace neurun::ir;
-
-class CompiledMockUpModel
-{
-public:
-  CompiledMockUpModel()
-  {
-    // Model: two elementwise add operation
-    // model input: lhs, rhs1
-    // model output: second add result (result2)
-    // constant: rhs2
-    // result1 <= (lhs + rhs)
-    // result2 <= (result1 + rhs2)
-    // lhs, rhs1, rh2, result1, result2 shape: {1, 2, 2, 1}
-    // activation: none (constant)
-    graph = std::make_shared<Graph>();
-    // 1st add operands (result1 <= lhs + rhs1)
-    Shape shape{1, 2, 2, 1};
-    TypeInfo type{DataType::FLOAT32};
-    static float rhs2_data[4] = {3, 1, -1, 5};
-    auto operand_lhs = graph->addOperand(shape, type);
-    auto operand_rhs1 = graph->addOperand(shape, type);
-    auto operand_result1 = graph->addOperand(shape, type);
-    auto operand_rhs2 = graph->addOperand(shape, type);
-    auto operand_result2 = graph->addOperand(shape, type);
-    graph->operands()
-        .at(operand_rhs2)
-        .data(nnfw::cpp14::make_unique<CachedData>(reinterpret_cast<const uint8_t *>(&rhs2_data),
-                                                   16));
-    // 2nd add operations (result2 <= result1 + rhs2)
-    operation::Add::Param param1;
-    param1.activation = Activation::NONE;
-    auto input_set1 = OperandIndexSequence{operand_lhs, operand_rhs1};
-    auto output_set1 = OperandIndexSequence{operand_result1};
-    graph->addOperation(nnfw::cpp14::make_unique<operation::Add>(input_set1, output_set1, param1));
-    operation::Add::Param param2;
-    param2.activation = Activation::NONE;
-    auto input_set2 = OperandIndexSequence{operand_result1, operand_rhs2};
-    auto output_set2 = OperandIndexSequence{operand_result2};
-    graph->addOperation(nnfw::cpp14::make_unique<operation::Add>(input_set2, output_set2, param2));
-    // Identify model inputs and outputs
-    graph->addInput(operand_lhs);
-    graph->addInput(operand_rhs1);
-    graph->addOutput(operand_result2);
-    graph->finishBuilding();
-
-    // Compile
-    auto compiler = new neurun::compiler::Compiler{graph};
-    compiler->compile();
-    compiler->release(executor);
-    delete compiler;
-  }
-
-public:
-  std::shared_ptr<Graph> graph;
-  std::shared_ptr<neurun::exec::IExecutor> executor;
-};
-
-TEST(ExecInstance, simple)
-{
-  auto mockup = CompiledMockUpModel();
-  auto graph = mockup.graph;
-  auto executor = mockup.executor;
-
-  auto input1 = IOIndex{0};
-  auto input2 = IOIndex{1};
-  auto output = IOIndex{0};
-
-  const float input1_buffer[4] = {1, 0, -1, -2};
-  const float input2_buffer[4] = {1, -3, 2, -4};
-  float output_buffer[4] = {};
-  const float output_expected[4] = {5, -2, 0, -1};
-
-  auto execution = new neurun::exec::Execution(executor);
-
-  execution->setInput(input1, reinterpret_cast<const void *>(input1_buffer), 16);
-  execution->setInput(input2, reinterpret_cast<const void *>(input2_buffer), 16);
-  execution->setOutput(output, reinterpret_cast<void *>(output_buffer), 16);
-  execution->execute();
-
-  for (auto i = 0; i < 4; i++)
-  {
-    EXPECT_EQ(output_buffer[i], output_expected[i]);
-  }
-
-  delete execution;
-}
-
-TEST(ExecInstance, twoCompile)
-{
-  auto mockup = CompiledMockUpModel();
-  auto graph = mockup.graph;
-  auto executor1 = mockup.executor;
-  auto execution1 = new neurun::exec::Execution(executor1);
-
-  auto input1 = IOIndex{0};
-  auto input2 = IOIndex{1};
-  auto output = IOIndex{0};
-
-  const float exe1_input1_buffer[4] = {1, 0, -1, -2};
-  const float exe1_input2_buffer[4] = {1, -3, 2, -4};
-  float exe1_output_buffer[4] = {};
-  const float exe1_output_expected[4] = {5, -2, 0, -1};
-
-  execution1->setInput(input1, reinterpret_cast<const void *>(exe1_input1_buffer), 16);
-  execution1->setInput(input2, reinterpret_cast<const void *>(exe1_input2_buffer), 16);
-  execution1->setOutput(output, reinterpret_cast<void *>(exe1_output_buffer), 16);
-
-  // Make new executor: compile again
-  auto compiler = new neurun::compiler::Compiler{graph};
-  compiler->compile();
-  std::shared_ptr<neurun::exec::IExecutor> executor2;
-  compiler->release(executor2);
-  auto execution2 = new neurun::exec::Execution(executor2);
-
-  const float exe2_input1_buffer[4] = {2, 1, -2, 0};
-  const float exe2_input2_buffer[4] = {-3, 3, 1, 2};
-  float exe2_output_buffer[4] = {};
-  const float exe2_output_expected[4] = {2, 5, -2, 7};
-
-  execution2->setInput(input1, reinterpret_cast<const void *>(exe2_input1_buffer), 16);
-  execution2->setInput(input2, reinterpret_cast<const void *>(exe2_input2_buffer), 16);
-  execution2->setOutput(output, reinterpret_cast<void *>(exe2_output_buffer), 16);
-
-  execution1->execute();
-  execution2->execute();
-
-  for (auto i = 0; i < 4; i++)
-  {
-    EXPECT_EQ(exe1_output_buffer[i], exe1_output_expected[i]);
-    EXPECT_EQ(exe2_output_buffer[i], exe2_output_expected[i]);
-  }
-
-  delete compiler;
-  delete execution1;
-  delete execution2;
-}
-
-// Support two initialized execution instance then ordered execution
-TEST(ExecInstance, twoExecution)
-{
-  auto mockup = CompiledMockUpModel();
-  auto executor = mockup.executor;
-  auto input1 = IOIndex{0};
-  auto input2 = IOIndex{1};
-  auto output1 = IOIndex{0};
-
-  const float exe1_input1_buffer[4] = {1, 0, -1, -2};
-  const float exe1_input2_buffer[4] = {1, -3, 2, -4};
-  float exe1_output_buffer[4] = {};
-  const float exe1_output_expected[4] = {5, -2, 0, -1};
-  const float exe2_output_expected[4] = {2, 5, -2, 7};
-
-  auto execution1 = new neurun::exec::Execution(executor);
-  execution1->setInput(input1, reinterpret_cast<const void *>(exe1_input1_buffer), 16);
-  execution1->setInput(input2, reinterpret_cast<const void *>(exe1_input2_buffer), 16);
-  execution1->setOutput(output1, reinterpret_cast<void *>(exe1_output_buffer), 16);
-
-  const float exe2_input1_buffer[4] = {2, 1, -2, 0};
-  const float exe2_input2_buffer[4] = {-3, 3, 1, 2};
-  float exe2_output_buffer[4] = {};
-
-  // Make new execution
-  auto execution2 = new neurun::exec::Execution(executor);
-  execution2->setInput(input1, reinterpret_cast<const void *>(exe2_input1_buffer), 16);
-  execution2->setInput(input2, reinterpret_cast<const void *>(exe2_input2_buffer), 16);
-  execution2->setOutput(output1, reinterpret_cast<void *>(exe2_output_buffer), 16);
-
-  execution1->execute();
-  execution2->execute();
-
-  for (auto i = 0; i < 4; i++)
-  {
-    EXPECT_EQ(exe1_output_buffer[i], exe1_output_expected[i]);
-    EXPECT_EQ(exe2_output_buffer[i], exe2_output_expected[i]);
-  }
-
-  delete execution1;
-  delete execution2;
-}
-
-class Inference
-{
-public:
-  Inference(const float (&input1)[4], const float (&input2)[4], float (&output)[4],
-            std::shared_ptr<neurun::exec::IExecutor> &executor)
-      : _input1{input1}, _input2{input2}, _output{output}, _executor{executor}
-  {
-    // DO NOTHING
-  }
-
-  void inference(void)
-  {
-    auto input1 = IOIndex{0};
-    auto input2 = IOIndex{1};
-    auto output1 = IOIndex{0};
-
-    auto execution = new neurun::exec::Execution(_executor);
-    execution->setInput(input1, reinterpret_cast<const void *>(_input1), 16);
-    execution->setInput(input2, reinterpret_cast<const void *>(_input2), 16);
-    execution->setOutput(output1, reinterpret_cast<void *>(_output), 16);
-
-    execution->execute();
-
-    delete execution;
-  }
-
-private:
-  const float (&_input1)[4];
-  const float (&_input2)[4];
-  float (&_output)[4];
-  std::shared_ptr<neurun::exec::IExecutor> &_executor;
-};
-
-// Support multi-thread execution
-TEST(ExecInstance, twoThreads)
-{
-  auto mockup = CompiledMockUpModel();
-  auto executor = mockup.executor;
-
-  const float exe1_input1_buffer[4] = {1, 0, -1, -2};
-  const float exe1_input2_buffer[4] = {1, -3, 2, -4};
-  float exe1_output_buffer[4] = {};
-  const float exe1_output_expected[4] = {5, -2, 0, -1};
-
-  Inference execution1{exe1_input1_buffer, exe1_input2_buffer, exe1_output_buffer, executor};
-
-  const float exe2_input1_buffer[4] = {2, 1, -2, 0};
-  const float exe2_input2_buffer[4] = {-3, 3, 1, 2};
-  float exe2_output_buffer[4] = {};
-  const float exe2_output_expected[4] = {2, 5, -2, 7};
-
-  Inference execution2{exe2_input1_buffer, exe2_input2_buffer, exe2_output_buffer, executor};
-
-  std::thread t1{&Inference::inference, &execution1};
-  std::thread t2{&Inference::inference, &execution2};
-
-  t1.join();
-  t2.join();
-
-  for (auto i = 0; i < 4; i++)
-  {
-    EXPECT_EQ(exe1_output_buffer[i], exe1_output_expected[i]);
-    EXPECT_EQ(exe2_output_buffer[i], exe2_output_expected[i]);
-  }
-}
-
-// Support asynchronous execution
-TEST(ExecInstance, async)
-{
-  auto mockup = CompiledMockUpModel();
-  auto graph = mockup.graph;
-  auto executor = mockup.executor;
-
-  auto input1 = IOIndex{0};
-  auto input2 = IOIndex{1};
-  auto output = IOIndex{0};
-
-  const float input1_buffer[4] = {1, 0, -1, -2};
-  const float input2_buffer[4] = {1, -3, 2, -4};
-  float output_buffer[4] = {};
-  const float output_expected[4] = {5, -2, 0, -1};
-
-  auto execution = new neurun::exec::Execution(executor);
-
-  execution->setInput(input1, reinterpret_cast<const void *>(input1_buffer), 16);
-  execution->setInput(input2, reinterpret_cast<const void *>(input2_buffer), 16);
-  execution->setOutput(output, reinterpret_cast<void *>(output_buffer), 16);
-  execution->startExecute();
-  execution->waitFinish();
-
-  for (auto i = 0; i < 4; i++)
-  {
-    EXPECT_EQ(output_buffer[i], output_expected[i]);
-  }
-
-  delete execution;
-}
-
-} // namespace
diff --git a/runtime/neurun/test/core/exec/interp/ExecManager.cc b/runtime/neurun/test/core/exec/interp/ExecManager.cc
deleted file mode 100644
index aba64e28f..000000000
--- a/runtime/neurun/test/core/exec/interp/ExecManager.cc
+++ /dev/null
@@ -1,334 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <gtest/gtest.h>
-
-#include <cpp14/memory.h>
-
-#include "ir/Graph.h"
-#include "exec/interp/ExecManager.h"
-#include "exec/Execution.h"
-#include "ir/operation/Add.h"
-
-namespace
-{
-
-using namespace neurun::ir;
-using ExecManager = neurun::exec::interp::ExecManager;
-using Execution = neurun::exec::Execution;
-
-class InterpExecManagerTest : public ::testing::Test
-{
-protected:
-  virtual void SetUp() {}
-  void CreateSimpleModel()
-  {
-    // Model: one elementwise add operation
-    // model input: lhs, rhs
-    // model output: add result
-    // lhs, rhs, result shape: {1, 2, 2, 1}
-    // activation: none (constant)
-    _graph = nnfw::cpp14::make_unique<Graph>();
-
-    // Add operands
-
-    Shape shape{1, 2, 2, 1};
-    TypeInfo type{DataType::INT32};
-    Shape shape_scalar(0);
-    TypeInfo type_scalar{DataType::INT32};
-
-    auto operand_lhs = _graph->addOperand(shape, type);
-    auto operand_rhs = _graph->addOperand(shape, type);
-    auto operand_result = _graph->addOperand(shape, type);
-
-    // Add operations
-
-    operation::Add::Param param;
-    param.activation = Activation::NONE;
-    auto input_set = OperandIndexSequence{operand_lhs, operand_rhs};
-    auto output_set = OperandIndexSequence{operand_result};
-    _graph->addOperation(nnfw::cpp14::make_unique<operation::Add>(input_set, output_set, param));
-
-    // Identify model inputs and outputs
-
-    _graph->getInputs().append(operand_lhs);
-    _graph->getInputs().append(operand_rhs);
-    _graph->getOutputs().append(operand_result);
-
-    _graph->finishBuilding();
-
-    _executor = nnfw::cpp14::make_unique<ExecManager>(*_graph);
-  }
-
-  void CreateTwoStepModel()
-  {
-    // Model: two elementwise add operation
-    // model input: lhs, rhs1
-    // model output: second add result (result2)
-    // constant: rhs2
-    // result1 <= (lhs + rhs)
-    // result2 <= (result1 + rhs2)
-    // lhs, rhs1, rh2, result1, result2 shape: {1, 2, 2, 1}
-    // activation: none (constant)
-    _graph = nnfw::cpp14::make_unique<Graph>();
-
-    // 1st add operands (result1 <= lhs + rhs1)
-
-    Shape shape{1, 2, 2, 1};
-    TypeInfo type{DataType::INT32};
-    Shape shape_scalar(0);
-    TypeInfo type_scalar{DataType::INT32};
-
-    static int32_t rhs2_data[4] = {3, 1, -1, 5};
-
-    auto operand_lhs = _graph->addOperand(shape, type);
-    auto operand_rhs1 = _graph->addOperand(shape, type);
-    auto operand_result1 = _graph->addOperand(shape, type);
-    auto operand_rhs2 = _graph->addOperand(shape, type);
-    auto operand_result2 = _graph->addOperand(shape, type);
-    _graph->operands()
-        .at(operand_rhs2)
-        .data(nnfw::cpp14::make_unique<CachedData>(reinterpret_cast<const uint8_t *>(&rhs2_data),
-                                                   16));
-
-    // 2nd add operations (result2 <= result1 + rhs2)
-
-    operation::Add::Param param1;
-    param1.activation = Activation::NONE;
-    auto input_set1 = OperandIndexSequence{operand_lhs, operand_rhs1};
-    auto output_set1 = OperandIndexSequence{operand_result1};
-    _graph->addOperation(nnfw::cpp14::make_unique<operation::Add>(input_set1, output_set1, param1));
-
-    operation::Add::Param param2;
-    param2.activation = Activation::NONE;
-    auto input_set2 = OperandIndexSequence{operand_result1, operand_rhs2};
-    auto output_set2 = OperandIndexSequence{operand_result2};
-    _graph->addOperation(nnfw::cpp14::make_unique<operation::Add>(input_set2, output_set2, param2));
-
-    // Identify model inputs and outputs
-
-    _graph->getInputs().append(operand_lhs);
-    _graph->getInputs().append(operand_rhs1);
-    _graph->getOutputs().append(operand_result2);
-
-    _graph->finishBuilding();
-
-    _executor = nnfw::cpp14::make_unique<ExecManager>(*_graph);
-  }
-
-  void CreateUnspecifiedDimensionsModel()
-  {
-    // Model: one elementwise add operation
-    // model input: lhs, rhs
-    // model output: add result
-    // lhs, rhs, result shape: {1, unknown, 2, 1}
-    // activation: none (constant)
-    _graph = nnfw::cpp14::make_unique<Graph>();
-
-    // Add operands
-
-    Shape shape{1, 0, 2, 1};
-    TypeInfo type{DataType::INT32};
-    Shape shape_scalar(0);
-    TypeInfo type_scalar{DataType::INT32};
-
-    auto operand_lhs = _graph->addOperand(shape, type);
-    auto operand_rhs = _graph->addOperand(shape, type);
-
-    auto operand_activation = _graph->addOperand(shape_scalar, type_scalar);
-    _graph->operands()
-        .at(operand_activation)
-        .data(nnfw::cpp14::make_unique<CachedData>(
-            reinterpret_cast<const uint8_t *>(&_activation_value), 4));
-
-    auto operand_result = _graph->addOperand(shape, type);
-
-    // Add operations
-
-    operation::Add::Param param;
-    param.activation = Activation::NONE;
-    auto input_set = OperandIndexSequence{operand_lhs, operand_rhs};
-    auto output_set = OperandIndexSequence{operand_result};
-    _graph->addOperation(nnfw::cpp14::make_unique<operation::Add>(input_set, output_set, param));
-
-    // Identify model inputs and outputs
-
-    _graph->getInputs().append(operand_lhs);
-    _graph->getInputs().append(operand_rhs);
-    _graph->getOutputs().append(operand_result);
-
-    _graph->finishBuilding();
-
-    _executor = nnfw::cpp14::make_unique<ExecManager>(*_graph);
-  }
-
-  void createExecution() { _execution = nnfw::cpp14::make_unique<Execution>(_executor); }
-
-  virtual void TearDown() { _executor = nullptr; }
-
-  std::unique_ptr<Graph> _graph{nullptr};
-  std::shared_ptr<ExecManager> _executor{nullptr};
-  std::unique_ptr<Execution> _execution{nullptr};
-  const int32_t _activation_value{0};
-};
-
-TEST_F(InterpExecManagerTest, create_empty)
-{
-  Graph graph;
-  graph.finishBuilding();
-  _executor = nnfw::cpp14::make_unique<ExecManager>(graph);
-  ASSERT_NE(_executor, nullptr);
-}
-
-TEST_F(InterpExecManagerTest, create_simple)
-{
-  CreateSimpleModel();
-  ASSERT_NE(_executor, nullptr);
-}
-
-TEST_F(InterpExecManagerTest, setInput)
-{
-  CreateSimpleModel();
-  createExecution();
-
-  auto input1 = IOIndex{0};
-  const int32_t input1_buffer[4] = {1, 0, -1, -2};
-
-  EXPECT_THROW(_execution->setInput(input1, reinterpret_cast<const void *>(input1_buffer), 4),
-               std::runtime_error);
-  EXPECT_THROW(_execution->setInput(input1, reinterpret_cast<const void *>(input1_buffer), 12),
-               std::runtime_error);
-  EXPECT_NO_THROW(_execution->setInput(input1, reinterpret_cast<const void *>(input1_buffer), 16));
-}
-
-TEST_F(InterpExecManagerTest, setOutput)
-{
-  CreateSimpleModel();
-  createExecution();
-
-  auto output = IOIndex{0};
-  auto output_idx = _graph->getOutputs().at(output);
-
-  int32_t output_buffer[4] = {};
-
-  EXPECT_THROW(_execution->setOutput(output, reinterpret_cast<void *>(output_buffer), 4),
-               std::runtime_error);
-  EXPECT_THROW(_execution->setOutput(output, reinterpret_cast<void *>(output_buffer), 12),
-               std::runtime_error);
-  EXPECT_NO_THROW(_execution->setOutput(output, reinterpret_cast<void *>(output_buffer), 16));
-}
-
-TEST_F(InterpExecManagerTest, setInputForUnspecifiedDimensions)
-{
-  CreateUnspecifiedDimensionsModel();
-  createExecution();
-
-  auto input1 = IOIndex{0};
-  const int32_t input1_buffer[4] = {1, 0, -1, -2};
-
-  TypeInfo operand_type{DataType::INT32};
-  Shape operand_shape{1, 2, 2, 1};
-
-  EXPECT_THROW(_execution->setInput(input1, operand_type, operand_shape,
-                                    reinterpret_cast<const void *>(input1_buffer), 4),
-               std::runtime_error);
-  EXPECT_THROW(_execution->setInput(input1, operand_type, operand_shape,
-                                    reinterpret_cast<const void *>(input1_buffer), 12),
-               std::runtime_error);
-  EXPECT_NO_THROW(_execution->setInput(input1, operand_type, operand_shape,
-                                       reinterpret_cast<const void *>(input1_buffer), 16));
-}
-
-TEST_F(InterpExecManagerTest, setOutputForUnspecifiedDimensions)
-{
-  CreateUnspecifiedDimensionsModel();
-  createExecution();
-
-  auto output = IOIndex{0};
-  auto output_idx = _graph->getOutputs().at(output);
-
-  TypeInfo operand_type{DataType::INT32};
-  Shape operand_shape{1, 2, 2, 1};
-
-  int32_t output_buffer[4] = {};
-
-  EXPECT_THROW(_execution->setOutput(output, operand_type, operand_shape,
-                                     reinterpret_cast<void *>(output_buffer), 4),
-               std::runtime_error);
-  EXPECT_THROW(_execution->setOutput(output, operand_type, operand_shape,
-                                     reinterpret_cast<void *>(output_buffer), 12),
-               std::runtime_error);
-  EXPECT_NO_THROW(_execution->setOutput(output, operand_type, operand_shape,
-                                        reinterpret_cast<void *>(output_buffer), 16));
-}
-
-TEST_F(InterpExecManagerTest, execute)
-{
-  CreateSimpleModel();
-  createExecution();
-
-  auto input1 = IOIndex{0};
-  auto input2 = IOIndex{1};
-  auto input1_idx = _graph->getInputs().at(input1);
-  auto input2_idx = _graph->getInputs().at(input2);
-
-  const int32_t input1_buffer[4] = {1, 0, -1, -2};
-  const int32_t input2_buffer[4] = {1, -3, 2, -4};
-
-  auto output = IOIndex{0};
-  auto output_idx = _graph->getOutputs().at(output);
-
-  int32_t output_buffer[4] = {};
-
-  EXPECT_NO_THROW(_execution->setInput(input1, reinterpret_cast<const void *>(input1_buffer), 16));
-  EXPECT_NO_THROW(_execution->setInput(input2, reinterpret_cast<const void *>(input2_buffer), 16));
-  EXPECT_NO_THROW(_execution->setOutput(output, reinterpret_cast<void *>(output_buffer), 16));
-  EXPECT_NO_THROW(_execution->execute());
-  EXPECT_EQ(output_buffer[0], 2);
-  EXPECT_EQ(output_buffer[1], -3);
-  EXPECT_EQ(output_buffer[2], 1);
-  EXPECT_EQ(output_buffer[3], -6);
-}
-
-TEST_F(InterpExecManagerTest, executeTwoStep)
-{
-  CreateTwoStepModel();
-  createExecution();
-
-  auto input1 = IOIndex{0};
-  auto input2 = IOIndex{1};
-  auto input1_idx = _graph->getInputs().at(input1);
-  auto input2_idx = _graph->getInputs().at(input2);
-
-  const int32_t input1_buffer[4] = {1, 0, -1, -2};
-  const int32_t input2_buffer[4] = {1, -3, 2, -4};
-
-  auto output = IOIndex{0};
-  auto output_idx = _graph->getOutputs().at(output);
-
-  int32_t output_buffer[4] = {};
-
-  EXPECT_NO_THROW(_execution->setInput(input1, reinterpret_cast<const void *>(input1_buffer), 16));
-  EXPECT_NO_THROW(_execution->setInput(input2, reinterpret_cast<const void *>(input2_buffer), 16));
-  EXPECT_NO_THROW(_execution->setOutput(output, reinterpret_cast<void *>(output_buffer), 16));
-  EXPECT_NO_THROW(_execution->execute());
-  EXPECT_EQ(output_buffer[0], 5);
-  EXPECT_EQ(output_buffer[1], -2);
-  EXPECT_EQ(output_buffer[2], 0);
-  EXPECT_EQ(output_buffer[3], -1);
-}
-
-} // namespace
diff --git a/runtime/neurun/test/graph/Graph.cc b/runtime/neurun/test/graph/Graph.cc
deleted file mode 100644
index faf84df9c..000000000
--- a/runtime/neurun/test/graph/Graph.cc
+++ /dev/null
@@ -1,52 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <gtest/gtest.h>
-
-#include "ir/Graph.h"
-
-TEST(Graph, inputs_and_outputs)
-{
-  neurun::ir::Graph graph;
-
-  neurun::ir::OperandIndex index0{0u};
-  neurun::ir::OperandIndex index1{1u};
-
-  graph.addInput({index0});
-  graph.addInput({index1});
-
-  neurun::ir::OperandIndex index10{10u};
-  neurun::ir::OperandIndex index11{11u};
-  neurun::ir::OperandIndex index12{12u};
-
-  graph.addOutput({index10});
-  graph.addOutput({index11});
-  graph.addOutput({index12});
-
-  ASSERT_EQ(graph.getInputs().size(), 2);
-  ASSERT_EQ(graph.getOutputs().size(), 3);
-
-  neurun::ir::IOIndex io_index0{0};
-  neurun::ir::IOIndex io_index1{1};
-  neurun::ir::IOIndex io_index2{2};
-
-  ASSERT_EQ(graph.getInputs().at(io_index0), 0);
-  ASSERT_EQ(graph.getInputs().at(io_index1), 1);
-
-  ASSERT_EQ(graph.getOutputs().at(io_index0), 10);
-  ASSERT_EQ(graph.getOutputs().at(io_index1), 11);
-  ASSERT_EQ(graph.getOutputs().at(io_index2), 12);
-}
diff --git a/runtime/neurun/test/graph/Index.cc b/runtime/neurun/test/graph/Index.cc
deleted file mode 100644
index c24c2dd5e..000000000
--- a/runtime/neurun/test/graph/Index.cc
+++ /dev/null
@@ -1,34 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <gtest/gtest.h>
-
-#include "util/Index.h"
-
-using Index = ::neurun::util::Index<uint32_t, struct TestTag>;
-
-TEST(Index, index_test)
-{
-  Index idx1{1u};
-  Index idx2{2u};
-  Index idx3{idx1};
-
-  ASSERT_EQ(idx1, 1);
-  ASSERT_EQ(idx1, 1u);
-  ASSERT_EQ(idx1.value(), 1u);
-  ASSERT_NE(idx1, idx2);
-  ASSERT_EQ(idx1, idx3);
-}
diff --git a/runtime/neurun/test/graph/MockNode.h b/runtime/neurun/test/graph/MockNode.h
deleted file mode 100644
index 67f4c049d..000000000
--- a/runtime/neurun/test/graph/MockNode.h
+++ /dev/null
@@ -1,47 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __NEURUN_TEST_GRAPH_MOCK_NODE_H__
-#define __NEURUN_TEST_GRAPH_MOCK_NODE_H__
-
-#include "ir/Operation.h"
-#include "ir/OperandIndexSequence.h"
-
-namespace neurun_test
-{
-namespace ir
-{
-
-class SimpleMock : public neurun::ir::Operation
-{
-public:
-  SimpleMock(const neurun::ir::OperandIndexSequence &inputs,
-             const neurun::ir::OperandIndexSequence &outputs)
-      : Operation{neurun::ir::OperandConstraint::createAny()}
-  {
-    setInputs(inputs);
-    setOutputs(outputs);
-  }
-
-public:
-  void accept(neurun::ir::OperationVisitor &) const override {}
-  neurun::ir::OpCode opcode() const final { return neurun::ir::OpCode::Invalid; }
-};
-
-} // namespace ir
-} // namespace neurun_test
-
-#endif // __NEURUN_TEST_GRAPH_MOCK_NODE_H__
diff --git a/runtime/neurun/test/graph/operand/IndexSet.cc b/runtime/neurun/test/graph/operand/IndexSet.cc
deleted file mode 100644
index 969290fe1..000000000
--- a/runtime/neurun/test/graph/operand/IndexSet.cc
+++ /dev/null
@@ -1,52 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <gtest/gtest.h>
-
-#include "ir/OperandIndexSequence.h"
-
-using neurun::ir::OperandIndex;
-using neurun::ir::OperandIndexSequence;
-
-TEST(graph_OperandIndexSequence, append)
-{
-  OperandIndexSequence iset{0, 2, 4, 8};
-
-  ASSERT_EQ(iset.size(), 4);
-
-  iset.append(OperandIndex{10});
-
-  ASSERT_EQ(iset.size(), 5);
-
-  neurun::ir::IOIndex index1{1};
-  neurun::ir::IOIndex index2{4};
-
-  ASSERT_EQ(iset.at(index1), 2);
-  ASSERT_EQ(iset.at(index2), 10);
-
-  ASSERT_TRUE(iset.contains(OperandIndex{2}));
-  ASSERT_TRUE(iset.contains(OperandIndex{10}));
-  ASSERT_FALSE(iset.contains(OperandIndex{11}));
-}
-
-TEST(graph_OperandIndexSequence, replace)
-{
-  OperandIndexSequence iset{0, 1, 2, 3};
-
-  iset.replace(OperandIndex{1}, OperandIndex{9});
-  ASSERT_FALSE(iset.contains(OperandIndex{1}));
-  ASSERT_TRUE(iset.contains(OperandIndex{9}));
-}
diff --git a/runtime/neurun/test/graph/operand/LayoutSet.cc b/runtime/neurun/test/graph/operand/LayoutSet.cc
deleted file mode 100644
index 7b0dcc9f7..000000000
--- a/runtime/neurun/test/graph/operand/LayoutSet.cc
+++ /dev/null
@@ -1,43 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <gtest/gtest.h>
-
-#include "ir/LayoutSet.h"
-
-using neurun::ir::Layout;
-using neurun::ir::LayoutSet;
-
-TEST(graph_operand_LayoutSet, layout_set_operators)
-{
-  LayoutSet set1{Layout::NCHW};
-  LayoutSet set2{Layout::NHWC};
-  LayoutSet set3 = set1 | set2;
-
-  ASSERT_EQ(set3.size(), 2);
-
-  ASSERT_EQ((set3 - set1).size(), 1);
-  ASSERT_EQ((set3 - set1).contains(Layout::NHWC), true);
-  ASSERT_EQ((set3 - set2).size(), 1);
-  ASSERT_EQ((set3 - set2).contains(Layout::NCHW), true);
-  ASSERT_EQ((set3 - set3).size(), 0);
-
-  ASSERT_EQ((set3 & set1).size(), 1);
-  ASSERT_EQ((set3 & set1).contains(Layout::NCHW), true);
-  ASSERT_EQ((set3 & set2).size(), 1);
-  ASSERT_EQ((set3 & set2).contains(Layout::NHWC), true);
-  ASSERT_EQ((set1 & set2).size(), 0);
-}
diff --git a/runtime/neurun/test/graph/operand/Set.cc b/runtime/neurun/test/graph/operand/Set.cc
deleted file mode 100644
index e30a5b7f7..000000000
--- a/runtime/neurun/test/graph/operand/Set.cc
+++ /dev/null
@@ -1,45 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <gtest/gtest.h>
-
-#include "ir/Operands.h"
-
-TEST(graph_operand_Set, set_test)
-{
-  neurun::ir::Operands set;
-
-  neurun::ir::Shape shape0{1, 2, 3};
-
-  neurun::ir::Shape shape1(4);
-  shape1.dim(0) = 10;
-  shape1.dim(1) = 20;
-  shape1.dim(2) = 30;
-  shape1.dim(3) = 40;
-
-  neurun::ir::TypeInfo type{neurun::ir::DataType::INT32};
-
-  set.emplace(shape0, type);
-  set.emplace(shape1, type);
-
-  ASSERT_EQ(set.exist(neurun::ir::OperandIndex{0u}), true);
-  ASSERT_EQ(set.exist(neurun::ir::OperandIndex{1u}), true);
-  ASSERT_EQ(set.exist(neurun::ir::OperandIndex{2u}), false);
-
-  ASSERT_EQ(set.at(neurun::ir::OperandIndex{0u}).shape().dim(0), 1);
-  ASSERT_EQ(set.at(neurun::ir::OperandIndex{0u}).shape().dim(1), 2);
-  ASSERT_EQ(set.at(neurun::ir::OperandIndex{0u}).shape().dim(2), 3);
-}
diff --git a/runtime/neurun/test/graph/operand/UseDef.cc b/runtime/neurun/test/graph/operand/UseDef.cc
deleted file mode 100644
index c59032824..000000000
--- a/runtime/neurun/test/graph/operand/UseDef.cc
+++ /dev/null
@@ -1,85 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <gtest/gtest.h>
-
-#include "ir/Graph.h"
-#include "ir/verifier/Verifier.h"
-#include "cpp14/memory.h"
-#include "../MockNode.h"
-
-#include <typeindex>
-
-namespace
-{
-
-using IndexSet = neurun::ir::OperandIndexSequence;
-using Mock = neurun_test::ir::SimpleMock;
-
-} // namespace anonymous
-
-TEST(graph_operand_usedef, usedef_test)
-{
-  neurun::ir::Graph graph;
-  neurun::ir::verifier::DAGChecker verifier;
-
-  neurun::ir::Shape shape(3);
-  neurun::ir::TypeInfo type{neurun::ir::DataType::INT32};
-
-  // Model Input/Output
-  auto input_operand = graph.addOperand(shape, type);
-  auto output_operand = graph.addOperand(shape, type);
-
-  graph.addInput(input_operand);
-  graph.addOutput(output_operand);
-
-  // MockNode1
-  auto operand_index1 = graph.addOperand(shape, type);
-  auto mocknode_index1 = graph.addOperation(
-      nnfw::cpp14::make_unique<Mock>(IndexSet{input_operand}, IndexSet{operand_index1}));
-
-  // MockNode2
-  auto operand_index2 = graph.addOperand(shape, type);
-  auto mocknode_index2 = graph.addOperation(
-      nnfw::cpp14::make_unique<Mock>(IndexSet{input_operand}, IndexSet{operand_index2}));
-
-  // MockNode3(two input)
-  auto multiinput_index = graph.addOperation(nnfw::cpp14::make_unique<Mock>(
-      IndexSet{operand_index1, operand_index2}, IndexSet{output_operand}));
-
-  graph.finishBuilding();
-
-  ASSERT_EQ(verifier.verify(graph), true);
-
-  // Check def
-  ASSERT_EQ(graph.operands().at(operand_index1).getDef().contains(mocknode_index1), true);
-  ASSERT_EQ(graph.operands().at(operand_index2).getDef().contains(mocknode_index2), true);
-  ASSERT_EQ(graph.operands().at(output_operand).getDef().contains(multiinput_index), true);
-
-  ASSERT_EQ(graph.operands().at(operand_index1).getDef().contains(mocknode_index2), false);
-  ASSERT_EQ(graph.operands().at(operand_index1).getDef().contains(multiinput_index), false);
-
-  // Check use
-  ASSERT_EQ(graph.operands().at(input_operand).getUses().contains(mocknode_index1), true);
-  ASSERT_EQ(graph.operands().at(input_operand).getUses().contains(mocknode_index2), true);
-  ASSERT_EQ(graph.operands().at(input_operand).getUses().contains(multiinput_index), false);
-  ASSERT_EQ(graph.operands().at(operand_index1).getUses().contains(multiinput_index), true);
-  ASSERT_EQ(graph.operands().at(operand_index2).getUses().contains(multiinput_index), true);
-
-  ASSERT_EQ(graph.operands().at(input_operand).getUses().size(), 2);
-  ASSERT_EQ(graph.operands().at(operand_index1).getUses().size(), 1);
-  ASSERT_EQ(graph.operands().at(output_operand).getUses().size(), 0);
-}
diff --git a/runtime/neurun/test/graph/operation/Set.cc b/runtime/neurun/test/graph/operation/Set.cc
deleted file mode 100644
index fb3d54298..000000000
--- a/runtime/neurun/test/graph/operation/Set.cc
+++ /dev/null
@@ -1,33 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <gtest/gtest.h>
-
-#include "../MockNode.h"
-#include "ir/Operations.h"
-
-using neurun::ir::Operations;
-using neurun::ir::Operation;
-using neurun::ir::OperationIndex;
-
-TEST(graph_operation_Set, operation_test)
-{
-  Operations ops;
-  ops.push(std::unique_ptr<Operation>(new neurun_test::ir::SimpleMock({1, 2, 3, 4}, {5, 6, 7})));
-  OperationIndex idx{0u};
-  ASSERT_EQ(ops.at(idx).getInputs().size(), 4);
-  ASSERT_EQ(ops.at(idx).getOutputs().size(), 3);
-}
diff --git a/runtime/neurun/test/graph/operation/SetIO.cc b/runtime/neurun/test/graph/operation/SetIO.cc
deleted file mode 100644
index 110accfac..000000000
--- a/runtime/neurun/test/graph/operation/SetIO.cc
+++ /dev/null
@@ -1,99 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <gtest/gtest.h>
-
-#include "ir/Graph.h"
-#include "ir/Index.h"
-#include "ir/OperandIndexSequence.h"
-#include "ir/operation/Conv2D.h"
-#include "ir/operation/Concat.h"
-
-#include <cpp14/memory.h>
-
-#include <stdexcept>
-
-using Index = neurun::ir::IOIndex;
-using IndexSet = neurun::ir::OperandIndexSequence;
-
-TEST(graph_operation_setIO, operation_setIO_conv)
-{
-  neurun::ir::Graph graph;
-
-  neurun::ir::Shape shape{3};
-  neurun::ir::TypeInfo type{neurun::ir::DataType::INT32};
-
-  // Add Conv
-  using Graph = neurun::ir::operation::Conv2D;
-
-  auto input_operand = graph.addOperand(shape, type);
-  auto kernel_operand = graph.addOperand(shape, type);
-  auto bias_operand = graph.addOperand(shape, type);
-  IndexSet inputs{input_operand, kernel_operand, bias_operand};
-
-  Graph::Param conv_params;
-  conv_params.padding.type = neurun::ir::PaddingType::SAME;
-  conv_params.stride.horizontal = 1;
-  conv_params.stride.vertical = 1;
-  conv_params.activation = neurun::ir::Activation::NONE;
-
-  auto output_operand = graph.addOperand(shape, type).value();
-  IndexSet outputs{output_operand};
-
-  auto conv = nnfw::cpp14::make_unique<Graph>(inputs, outputs, conv_params);
-
-  ASSERT_NE(conv, nullptr);
-  ASSERT_EQ(conv->getInputs().at(Index{0}).value(), inputs.at(0).value());
-  conv->setInputs({8, 9, 10});
-  ASSERT_NE(conv->getInputs().at(Index{0}).value(), inputs.at(0).value());
-  ASSERT_EQ(conv->getInputs().at(Index{0}).value(), 8);
-}
-
-TEST(graph_operation_setIO, operation_setIO_concat)
-{
-  neurun::ir::Graph graph;
-
-  neurun::ir::Shape shape{3};
-
-  neurun::ir::TypeInfo type{neurun::ir::DataType::INT32};
-
-  using Graph = neurun::ir::operation::Concat;
-
-  // Add Concat
-  IndexSet inputs;
-  for (int i = 0; i < 6; ++i)
-  {
-    inputs.append(graph.addOperand(shape, type));
-  }
-
-  Graph::Param concat_params{0};
-
-  auto output_operand = graph.addOperand(shape, type).value();
-  IndexSet outputs{output_operand};
-
-  auto concat = nnfw::cpp14::make_unique<Graph>(inputs, outputs, concat_params);
-
-  ASSERT_NE(concat, nullptr);
-  ASSERT_EQ(concat->getInputs().size(), 6);
-  ASSERT_EQ(concat->getInputs().at(Index{0}).value(), inputs.at(0).value());
-
-  concat->setInputs({80, 6, 9, 11});
-  ASSERT_EQ(concat->getInputs().size(), 4);
-  ASSERT_NE(concat->getInputs().at(Index{0}).value(), inputs.at(0).value());
-  ASSERT_EQ(concat->getInputs().at(Index{0}).value(), 80);
-  ASSERT_EQ(concat->getInputs().at(Index{2}).value(), 9);
-  ASSERT_THROW(concat->getInputs().at(Index{5}), std::out_of_range);
-}
diff --git a/runtime/neurun/test/graph/verifier/Verifier.cc b/runtime/neurun/test/graph/verifier/Verifier.cc
deleted file mode 100644
index cbda31dfe..000000000
--- a/runtime/neurun/test/graph/verifier/Verifier.cc
+++ /dev/null
@@ -1,49 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <gtest/gtest.h>
-
-#include "ir/Operation.h"
-#include "ir/Graph.h"
-#include "ir/verifier/Verifier.h"
-#include "cpp14/memory.h"
-#include "ir/Operand.h"
-#include "../MockNode.h"
-
-using IndexSet = neurun::ir::OperandIndexSequence;
-using Mock = neurun_test::ir::SimpleMock;
-
-TEST(Verifier, dag_checker)
-{
-  neurun::ir::Graph graph;
-
-  neurun::ir::Shape shape{3};
-  neurun::ir::TypeInfo type{neurun::ir::DataType::INT32};
-
-  auto operand1 = graph.addOperand(shape, type);
-  auto operand2 = graph.addOperand(shape, type);
-
-  graph.addInput(operand1);
-  graph.addOutput(operand2);
-
-  graph.addOperation(nnfw::cpp14::make_unique<Mock>(IndexSet{operand1}, IndexSet{operand2}));
-
-  graph.finishBuilding();
-
-  neurun::ir::verifier::DAGChecker verifier;
-
-  ASSERT_EQ(verifier.verify(graph), true);
-}
diff --git a/runtime/neurun/test/util/ShapeInference.cc b/runtime/neurun/test/util/ShapeInference.cc
deleted file mode 100644
index a8dea3a79..000000000
--- a/runtime/neurun/test/util/ShapeInference.cc
+++ /dev/null
@@ -1,233 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <gtest/gtest.h>
-
-#include "ir/Layout.h"
-#include "util/ShapeInference.h"
-
-using namespace neurun::ir;
-
-TEST(ShapeInference, Elementwise)
-{
-  Shape lhs_shape{1, 299, 299, 3};
-  Shape rhs_shape{3};
-  auto infered_shapes = neurun::shape_inference::inferEltwiseShape(lhs_shape, rhs_shape);
-  auto infered_out_shape = infered_shapes[0];
-
-  ASSERT_EQ(infered_out_shape.rank(), 4);
-  ASSERT_EQ(infered_out_shape.dim(0), 1);
-  ASSERT_EQ(infered_out_shape.dim(1), 299);
-  ASSERT_EQ(infered_out_shape.dim(2), 299);
-  ASSERT_EQ(infered_out_shape.dim(3), 3);
-}
-
-TEST(ShapeInference, IncorrectElementwise)
-{
-  Shape lhs_shape{1, 299, 299, 3};
-  Shape rhs_shape{5, 3};
-  ASSERT_THROW(neurun::shape_inference::inferEltwiseShape(lhs_shape, rhs_shape),
-               std::runtime_error);
-}
-
-TEST(ShapeInference, Pool2DNodeSame)
-{
-  Shape in_shape{10, 6, 12, 20};
-  Stride stride{3, 7};
-  Padding padding{PaddingType::SAME};
-
-  operation::AvgPool2D::Param avg_pool_param{3, 6, stride, padding, Activation::NONE};
-  auto infered_shapes = neurun::shape_inference::inferAvgPoolShape(in_shape, avg_pool_param);
-  auto infered_out_shape = infered_shapes[0];
-
-  ASSERT_EQ(infered_out_shape.rank(), 4);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 20);
-
-  operation::MaxPool2D::Param max_pool_param{3, 6, stride, padding, Activation::NONE};
-  infered_shapes = neurun::shape_inference::inferMaxPoolShape(in_shape, max_pool_param);
-  infered_out_shape = infered_shapes[0];
-
-  ASSERT_EQ(infered_out_shape.rank(), 4);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 20);
-}
-
-TEST(ShapeInference, Pool2DNodeValid)
-{
-  Shape in_shape{10, 6, 12, 20};
-  Stride stride{3, 7};
-  Padding padding{PaddingType::VALID};
-
-  operation::AvgPool2D::Param avg_pool_param{3, 6, stride, padding, Activation::NONE};
-  auto infered_shapes = neurun::shape_inference::inferAvgPoolShape(in_shape, avg_pool_param);
-  auto infered_out_shape = infered_shapes[0];
-
-  ASSERT_EQ(infered_out_shape.rank(), 4);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 1);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 20);
-
-  operation::MaxPool2D::Param max_pool_param{3, 6, stride, padding, Activation::NONE};
-  infered_shapes = neurun::shape_inference::inferMaxPoolShape(in_shape, max_pool_param);
-  infered_out_shape = infered_shapes[0];
-
-  ASSERT_EQ(infered_out_shape.rank(), 4);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 1);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 20);
-}
-
-TEST(ShapeInference, Pool2DNodeExplicit)
-{
-  Shape in_shape{10, 3, 5, 20};
-
-  Stride stride{3, 7};
-  Padding padding{PaddingType::EXPLICIT, {4, 3, 2, 1}};
-
-  operation::AvgPool2D::Param avg_pool_param{3, 6, stride, padding, Activation::NONE};
-  auto infered_shapes = neurun::shape_inference::inferAvgPoolShape(in_shape, avg_pool_param);
-  auto infered_out_shape = infered_shapes[0];
-
-  ASSERT_EQ(infered_out_shape.rank(), 4);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 1);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 20);
-
-  operation::MaxPool2D::Param max_pool_param{3, 6, stride, padding, Activation::NONE};
-  infered_shapes = neurun::shape_inference::inferMaxPoolShape(in_shape, max_pool_param);
-  infered_out_shape = infered_shapes[0];
-
-  ASSERT_EQ(infered_out_shape.rank(), 4);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 1);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 20);
-}
-
-TEST(ShapeInference, Conv2D)
-{
-  Shape in_shape{10, 6, 12, 20};
-  Shape ker_shape{30, 3, 6, 20};
-
-  operation::Conv2D::Param param{Stride{3, 7}, Padding{PaddingType::VALID}, Activation::NONE};
-  auto infered_shapes = neurun::shape_inference::inferConv2DShape(in_shape, ker_shape, param);
-  auto infered_out_shape = infered_shapes[0];
-
-  ASSERT_EQ(infered_out_shape.rank(), 4);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 1);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 30);
-
-  param = operation::Conv2D::Param{Stride{3, 7}, Padding{PaddingType::SAME}, Activation::NONE};
-  infered_shapes = neurun::shape_inference::inferConv2DShape(in_shape, ker_shape, param);
-  infered_out_shape = infered_shapes[0];
-
-  ASSERT_EQ(infered_out_shape.rank(), 4);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 30);
-
-  param = operation::Conv2D::Param{Stride{3, 7}, Padding{PaddingType::EXPLICIT, {4, 3, 2, 1}},
-                                   Activation::NONE};
-  infered_shapes = neurun::shape_inference::inferConv2DShape(in_shape, ker_shape, param);
-  infered_out_shape = infered_shapes[0];
-
-  ASSERT_EQ(infered_out_shape.rank(), 4);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 3);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 30);
-}
-
-TEST(ShapeInference, DepthwiseConv2D)
-{
-  Shape in_shape{10, 6, 12, 20};
-  Shape ker_shape{1, 3, 6, 60};
-
-  operation::DepthwiseConv2D::Param param{Stride{3, 7}, Padding{PaddingType::VALID}, 3,
-                                          Activation::NONE};
-  auto infered_shapes =
-      neurun::shape_inference::inferDepthwiseConv2DShape(in_shape, ker_shape, param);
-  auto infered_out_shape = infered_shapes[0];
-
-  ASSERT_EQ(infered_out_shape.rank(), 4);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 1);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 60);
-
-  param = operation::DepthwiseConv2D::Param{Stride{3, 7}, Padding{PaddingType::SAME}, 3,
-                                            Activation::NONE};
-  infered_shapes = neurun::shape_inference::inferDepthwiseConv2DShape(in_shape, ker_shape, param);
-  infered_out_shape = infered_shapes[0];
-
-  ASSERT_EQ(infered_out_shape.rank(), 4);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 60);
-
-  param = operation::DepthwiseConv2D::Param{
-      Stride{3, 7}, Padding{PaddingType::EXPLICIT, {4, 3, 2, 1}}, 3, Activation::NONE};
-  infered_shapes = neurun::shape_inference::inferDepthwiseConv2DShape(in_shape, ker_shape, param);
-  infered_out_shape = infered_shapes[0];
-
-  ASSERT_EQ(infered_out_shape.rank(), 4);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 3);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 60);
-}
-
-TEST(ShapeInference, Concat)
-{
-  Shape in1{10, 20, 30, 3, 50};
-  Shape in2{10, 20, 30, 2, 50};
-  Shape in3{10, 20, 30, 2, 50};
-
-  operation::Concat::Param param{3};
-  auto infered_shapes = neurun::shape_inference::inferConcatShape({in1, in2, in3}, param);
-  auto infered_out_shape = infered_shapes[0];
-
-  ASSERT_EQ(infered_out_shape.rank(), 5);
-  ASSERT_EQ(infered_out_shape.dim(0), 10);
-  ASSERT_EQ(infered_out_shape.dim(1), 20);
-  ASSERT_EQ(infered_out_shape.dim(2), 30);
-  ASSERT_EQ(infered_out_shape.dim(3), 7);
-  ASSERT_EQ(infered_out_shape.dim(4), 50);
-}
-
-TEST(ShapeInference, FullyConnected)
-{
-  Shape in_shape{3, 4, 5, 6};
-  Shape ker_shape{3, 10};
-  auto infered_shapes = neurun::shape_inference::inferFullyConnectedShape(in_shape, ker_shape);
-  auto infered_out_shape = infered_shapes[0];
-
-  ASSERT_EQ(infered_out_shape.rank(), 2);
-  ASSERT_EQ(infered_out_shape.dim(0), 36);
-  ASSERT_EQ(infered_out_shape.dim(1), 3);
-}