Imported Upstream version 0.4upstream/0.4

16 files changed, 0 insertions, 2246 deletions

diff --git a/compute/ARMComputeEx/src/runtime/NEON/NEFunctionsEx.cpp b/compute/ARMComputeEx/src/runtime/NEON/NEFunctionsEx.cpp
deleted file mode 100644
index 80fbf359d..000000000
--- a/compute/ARMComputeEx/src/runtime/NEON/NEFunctionsEx.cpp
+++ /dev/null
@@ -1,20 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT 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/NEON/NEFunctionsEx.h"
-
-// NOTE This empty file aims to validate "NEFunctionsEx.h".
-//      DO NOT REMOVE this file.
diff --git a/compute/ARMComputeEx/src/runtime/NEON/functions/NEActivationLayerEx.cpp b/compute/ARMComputeEx/src/runtime/NEON/functions/NEActivationLayerEx.cpp
deleted file mode 100644
index 2752eb6aa..000000000
--- a/compute/ARMComputeEx/src/runtime/NEON/functions/NEActivationLayerEx.cpp
+++ /dev/null
@@ -1,66 +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.
- */
-
-/*
- * Copyright (c) 2017-2019 ARM Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-
-#include "arm_compute/runtime/NEON/functions/NEActivationLayerEx.h"
-
-#include "arm_compute/core/NEON/kernels/NEActivationLayerKernelEx.h"
-#include "arm_compute/runtime/IRuntimeContext.h"
-#include "support/MemorySupport.h"
-
-namespace arm_compute
-{
-NEActivationLayerEx::NEActivationLayerEx(IRuntimeContext *ctx) // NOLINT
-    : INESimpleFunctionNoBorder(ctx)
-{
-}
-void NEActivationLayerEx::configure(ITensor *input, ITensor *output,
-                                    ActivationLayerInfo activation_info)
-{
-  auto k = support::cpp14::make_unique<NEActivationLayerKernelEx>();
-  k->configure(input, output, activation_info);
-  _kernel = std::move(k);
-}
-
-Status NEActivationLayerEx::validate(const ITensorInfo *input, const ITensorInfo *output,
-                                     const ActivationLayerInfo &act_info)
-{
-  return NEActivationLayerKernelEx::validate(input, output, act_info);
-}
-} // namespace arm_compute
diff --git a/compute/ARMComputeEx/src/runtime/NEON/functions/NEBinaryLogicalOperation.cpp b/compute/ARMComputeEx/src/runtime/NEON/functions/NEBinaryLogicalOperation.cpp
deleted file mode 100644
index 2fc94b267..000000000
--- a/compute/ARMComputeEx/src/runtime/NEON/functions/NEBinaryLogicalOperation.cpp
+++ /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.
- */
-
-/*
- * Copyright (c) 2018-2019 ARM Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-
-#include "arm_compute/runtime/NEON/functions/NEBinaryLogicalOperation.h"
-#include <arm_compute/core/NEON/kernels/NEBinaryLogicalOperationKernel.h>
-
-#include "arm_compute/core/ITensor.h"
-#include "support/MemorySupport.h"
-
-#include <utility>
-
-namespace arm_compute
-{
-
-template <BinaryLogicalOperation COP>
-void NEBinaryLogicalOperationStatic<COP>::configure(ITensor *input1, ITensor *input2,
-                                                    ITensor *output)
-{
-  auto k = support::cpp14::make_unique<NEBinaryLogicalOperationKernel>();
-  k->configure(COP, input1, input2, output);
-  _kernel = std::move(k);
-}
-
-template <BinaryLogicalOperation COP>
-Status NEBinaryLogicalOperationStatic<COP>::validate(const ITensorInfo *input1,
-                                                     const ITensorInfo *input2,
-                                                     const ITensorInfo *output)
-{
-  return NEBinaryLogicalOperationKernel::validate(COP, input1, input2, output);
-}
-
-void NEBinaryLogicalOperation::configure(ITensor *input1, ITensor *input2, ITensor *output,
-                                         BinaryLogicalOperation op)
-{
-  auto k = support::cpp14::make_unique<NEBinaryLogicalOperationKernel>();
-  k->configure(op, input1, input2, output);
-  _kernel = std::move(k);
-}
-
-Status NEBinaryLogicalOperation::validate(const ITensorInfo *input1, const ITensorInfo *input2,
-                                          const ITensorInfo *output, BinaryLogicalOperation op)
-{
-  return NEBinaryLogicalOperationKernel::validate(op, input1, input2, output);
-}
-
-// Supported Specializations
-template class NEBinaryLogicalOperationStatic<BinaryLogicalOperation::AND>;
-template class NEBinaryLogicalOperationStatic<BinaryLogicalOperation::OR>;
-} // namespace arm_compute
diff --git a/compute/ARMComputeEx/src/runtime/NEON/functions/NECastBool.cpp b/compute/ARMComputeEx/src/runtime/NEON/functions/NECastBool.cpp
deleted file mode 100644
index 6ad3e1b12..000000000
--- a/compute/ARMComputeEx/src/runtime/NEON/functions/NECastBool.cpp
+++ /dev/null
@@ -1,57 +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.
- */
-
-/*
- * Copyright (c) 2016-2020 ARM Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#include "arm_compute/runtime/NEON/functions/NECastBool.h"
-
-#include "arm_compute/core/NEON/kernels/NECastBoolKernel.h"
-#include "support/MemorySupport.h"
-
-using namespace arm_compute;
-
-void NECastBool::configure(const ITensor *input, ITensor *output)
-{
-  auto k = arm_compute::support::cpp14::make_unique<NECastBoolKernel>();
-  k->configure(input, output);
-  _kernel = std::move(k);
-}
-
-Status NECastBool::validate(const ITensorInfo *input, const ITensorInfo *output)
-{
-  return NECastBoolKernel::validate(input, output);
-}
diff --git a/compute/ARMComputeEx/src/runtime/NEON/functions/NEEmbeddingLookup.cpp b/compute/ARMComputeEx/src/runtime/NEON/functions/NEEmbeddingLookup.cpp
deleted file mode 100644
index e0ab3e025..000000000
--- a/compute/ARMComputeEx/src/runtime/NEON/functions/NEEmbeddingLookup.cpp
+++ /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.
- */
-
-/*
- * Copyright (c) 2016-2018 ARM Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-
-#include "arm_compute/runtime/NEON/functions/NEEmbeddingLookup.h"
-
-#include "arm_compute/core/NEON/kernels/NEEmbeddingLookupKernel.h"
-#include "support/MemorySupport.h"
-
-using namespace arm_compute;
-
-void NEEmbeddingLookup::configure(const ITensor *input, ITensor *output, const ITensor *lookups)
-{
-  auto k = support::cpp14::make_unique<NEEmbeddingLookupKernel>();
-  k->configure(input, output, lookups);
-  _kernel = std::move(k);
-}
diff --git a/compute/ARMComputeEx/src/runtime/NEON/functions/NEFullyConnectedHybridLayer.cpp b/compute/ARMComputeEx/src/runtime/NEON/functions/NEFullyConnectedHybridLayer.cpp
deleted file mode 100644
index a123439d9..000000000
--- a/compute/ARMComputeEx/src/runtime/NEON/functions/NEFullyConnectedHybridLayer.cpp
+++ /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.
- */
-
-/*
- * Copyright (c) 2017-2019 ARM Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-
-#include "arm_compute/runtime/NEON/functions/NEFullyConnectedHybridLayer.h"
-
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/Size2D.h"
-#include "arm_compute/core/Validate.h"
-#include "arm_compute/core/utils/misc/ShapeCalculator.h"
-#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
-#include "arm_compute/runtime/NEON/NEScheduler.h"
-
-#include <algorithm>
-#include <cmath>
-
-using namespace arm_compute;
-using namespace arm_compute::misc::shape_calculator;
-
-namespace
-{
-Status validate_mm(const ITensorInfo &input, const ITensorInfo &weights, const ITensorInfo &output)
-{
-  ARM_COMPUTE_RETURN_ON_ERROR(
-      NEGEMMLowpMatrixMultiplyCore::validate(&input, &weights, nullptr, &output));
-
-  return Status{};
-}
-} // namespace
-
-void NEFullyConnectedHybridLayerReshapeWeights::configure(const ITensor *input, ITensor *output)
-{
-  auto k = support::cpp14::make_unique<NETransposeKernel>();
-  k->configure(input, output);
-  _kernel = std::move(k);
-}
-
-Status NEFullyConnectedHybridLayerReshapeWeights::validate(const ITensorInfo *input,
-                                                           const ITensorInfo *output)
-{
-  return NETransposeKernel::validate(input, output);
-}
-
-NEFullyConnectedHybridLayer::NEFullyConnectedHybridLayer(
-    std::shared_ptr<IMemoryManager> memory_manager)
-    : _memory_group(std::move(memory_manager)), _reshape_weights_function(), _quant_input_kernel(),
-      _mm_gemmlowp(), _accumulate_biases_kernel(), _reshape_weights_output(), _quantized_input(),
-      _scale_factor(), _original_weights(nullptr), _are_weights_reshaped(false),
-      _accumulate_biases(false), _is_prepared(false)
-{
-}
-
-void NEFullyConnectedHybridLayer::configure_mm(const ITensor *input, const ITensor *weights,
-                                               ITensor *output)
-{
-  ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != weights->info()->dimension(1));
-
-  // Configure gemmlowp function
-  _mm_gemmlowp.configure(input, weights, nullptr, output);
-}
-
-void NEFullyConnectedHybridLayer::configure(const ITensor *input, const ITensor *weights,
-                                            const ITensor *biases, ITensor *output,
-                                            FullyConnectedLayerInfo fc_info)
-{
-  ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
-
-  // Perform validate step
-  ARM_COMPUTE_ERROR_THROW_ON(NEFullyConnectedHybridLayer::validate(
-      input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, output->info(),
-      fc_info));
-
-  _are_weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true;
-  _accumulate_biases = false;
-  _original_weights = weights;
-
-  // Configure accumulate biases kernel for non quantized asymmetric types
-  if (biases != nullptr)
-  {
-    _accumulate_biases = true;
-
-    // Configure accumulate biases kernel
-    _accumulate_biases_kernel.configure(output, biases);
-  }
-
-  // With the Fully Connected layer we can have 4 different cases:
-  //  1) Convolution layer -> Fully Connected layer without batches
-  //  2) Fully Connected layer -> Fully Connected layer without batches
-  //  3) Convolution layer -> Fully Connected layer with batches
-  //  4) Fully Connected layer -> Fully Connected layer with batches
-
-  const ITensor *weights_to_use = weights;
-
-  // Check if we have a fully connected layer with batches
-  const bool is_batched_fc_layer = output->info()->dimension(1) > 1;
-  bool _is_fc_after_conv;
-  if (is_batched_fc_layer)
-  {
-    _is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) &&
-                        (std::equal(input->info()->tensor_shape().cbegin() + 3,
-                                    input->info()->tensor_shape().cend(),
-                                    output->info()->tensor_shape().cbegin() + 1));
-  }
-  else
-  {
-    _is_fc_after_conv = input->info()->num_dimensions() > 1 && input->info()->dimension(1) > 1;
-  }
-  ARM_COMPUTE_ERROR_ON_MSG(_is_fc_after_conv,
-                           "NEFullyConnectedHybridLayer does not support after conv");
-  (void)_is_fc_after_conv;
-
-  // Reshape weights if needed
-  if (!_are_weights_reshaped)
-  {
-    // Reshape the weights
-    _reshape_weights_output.allocator()->init(
-        weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(
-            compute_transposed_shape(*weights->info())));
-    _reshape_weights_function.configure(weights_to_use, &_reshape_weights_output);
-    weights_to_use = &_reshape_weights_output;
-  }
-
-  // Quantize input
-  _quantized_input.allocator()->init(
-      input->info()->clone()->set_is_resizable(true).reset_padding().set_data_type(
-          DataType::QASYMM8_SIGNED));
-  _scale_factor.allocator()->init(
-      TensorInfo(TensorShape{output->info()->dimension(1)}, 1, DataType::F32));
-  _quant_input_kernel.configure(input, &_quantized_input, &_scale_factor);
-
-  // GEMM
-  _gemmlowp_output.allocator()->init(
-      output->info()->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32));
-  configure_mm(&_quantized_input, weights_to_use, &_gemmlowp_output);
-
-  // Multiply scale
-  _multiply_scale_kernel.configure(&_gemmlowp_output, &_scale_factor, output,
-                                   weights->info()->quantization_info().uniform().scale);
-
-  _are_weights_reshaped = _are_weights_reshaped || fc_info.retain_internal_weights;
-
-  _quantized_input.allocator()->allocate();
-  _scale_factor.allocator()->allocate();
-  _gemmlowp_output.allocator()->allocate();
-}
-
-Status NEFullyConnectedHybridLayer::validate(const ITensorInfo *input, const ITensorInfo *weights,
-                                             const ITensorInfo *biases, const ITensorInfo *output,
-                                             FullyConnectedLayerInfo fc_info)
-{
-  ARM_COMPUTE_UNUSED(fc_info.retain_internal_weights);
-  ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
-  ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
-  ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QASYMM8_SIGNED);
-  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
-  ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 2);
-  ARM_COMPUTE_RETURN_ERROR_ON(output->num_dimensions() > 2);
-
-  bool weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true;
-
-  const ITensorInfo &reshaped_weights =
-      TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(
-          compute_transposed_shape(*weights)));
-
-  // Configure accumulate biases kernel for non quantized asymmetric types
-  if (biases != nullptr)
-  {
-    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
-    ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixAccumulateBiasesKernel::validate(output, biases));
-  }
-
-  // With the Fully Connected layer we can have 4 different cases:
-  //  1) Convolution layer -> Fully Connected layer without batches
-  //  2) Fully Connected layer -> Fully Connected layer without batches
-  //  3) Convolution layer -> Fully Connected layer with batches
-  //  4) Fully Connected layer -> Fully Connected layer with batches
-
-  const ITensorInfo *weights_to_use = weights;
-
-  if (!weights_reshaped)
-  {
-    // Validate reshape weights kernel
-    ARM_COMPUTE_RETURN_ON_ERROR(
-        NEFullyConnectedHybridLayerReshapeWeights::validate(weights_to_use, &reshaped_weights));
-    weights_to_use = &reshaped_weights;
-  }
-
-  // Fully Connected layer after a Fully Connected Layer without batches
-  ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != weights_to_use->dimension(1));
-
-  // Validate quantization kernel
-  const ITensorInfo &quantized_input =
-      TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_data_type(
-          DataType::QASYMM8_SIGNED));
-  const ITensorInfo &scale_factor = TensorInfo(TensorShape{output->dimension(1)}, 1, DataType::F32);
-  ARM_COMPUTE_RETURN_ON_ERROR(
-      NEQuantizationSymmetricKernel::validate(input, &quantized_input, &scale_factor));
-
-  const ITensorInfo &gemmlowp_output = TensorInfo(
-      output->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32));
-  // Validate matrix multiply kernel
-  ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(quantized_input, *weights_to_use, gemmlowp_output));
-
-  ARM_COMPUTE_RETURN_ON_ERROR(NEMultiplyScaleFactorKernel::validate(
-      &gemmlowp_output, &scale_factor, output, weights->quantization_info().uniform().scale));
-
-  return Status{};
-}
-
-void NEFullyConnectedHybridLayer::run()
-{
-  prepare();
-
-  MemoryGroupResourceScope scope_mg(_memory_group);
-
-  // Quantize input
-  NEScheduler::get().schedule(&_quant_input_kernel, Window::DimY);
-
-  // Run matrix multiply
-  _mm_gemmlowp.run();
-
-  // Multiply scale factor
-  NEScheduler::get().schedule(&_multiply_scale_kernel, Window::DimY);
-
-  // Accumulate biases if provided
-  if (_accumulate_biases)
-  {
-    NEScheduler::get().schedule(&_accumulate_biases_kernel, Window::DimY);
-  }
-}
-
-void NEFullyConnectedHybridLayer::prepare()
-{
-  if (!_is_prepared)
-  {
-    ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
-
-    auto release_unused = [](Tensor *w) {
-      if (!w->is_used())
-      {
-        w->allocator()->free();
-      }
-    };
-
-    // Reshape of the weights (happens only once)
-    if (!_are_weights_reshaped)
-    {
-      // Run reshape weights kernel and mark weights as unused
-      _reshape_weights_output.allocator()->allocate();
-      _reshape_weights_function.run();
-
-      _are_weights_reshaped = true;
-      // We can not release _original_weights because it can be used in other nodes
-    }
-
-    // Prepare GEMM prepare and release unused weights
-    _mm_gemmlowp.prepare();
-
-    // Release reshaped weights if unused
-    release_unused(&_reshape_weights_output);
-
-    _is_prepared = true;
-  }
-}
diff --git a/compute/ARMComputeEx/src/runtime/NEON/functions/NEFullyConnectedLayerEx.cpp b/compute/ARMComputeEx/src/runtime/NEON/functions/NEFullyConnectedLayerEx.cpp
deleted file mode 100644
index cb7557a5a..000000000
--- a/compute/ARMComputeEx/src/runtime/NEON/functions/NEFullyConnectedLayerEx.cpp
+++ /dev/null
@@ -1,494 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * Copyright (c) 2017-2019 ARM Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-
-#include "arm_compute/runtime/NEON/functions/NEFullyConnectedLayerEx.h"
-
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/Size2D.h"
-#include "arm_compute/core/Validate.h"
-#include "arm_compute/core/utils/misc/ShapeCalculator.h"
-#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
-#include "arm_compute/runtime/NEON/NEScheduler.h"
-
-#include <algorithm>
-#include <cmath>
-
-using namespace arm_compute;
-using namespace arm_compute::misc::shape_calculator;
-
-namespace
-{
-Status validate_mm(const ITensorInfo &input, const ITensorInfo &weights, const ITensorInfo &output)
-{
-  if (is_data_type_quantized_asymmetric(input.data_type()))
-  {
-    // Since we need negative offsets for computing convolution, we need to change
-    // QuantizationInfo()
-    // Extract and negate input and weights offset
-    const QuantizationInfo input_quantization_info(input.quantization_info().uniform().scale,
-                                                   -input.quantization_info().uniform().offset);
-    const QuantizationInfo weights_quantization_info(weights.quantization_info().uniform().scale,
-                                                     -weights.quantization_info().uniform().offset);
-
-    // Validate gemmlowp function
-    ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixMultiplyCore::validate(
-        &input.clone()->set_quantization_info(input_quantization_info),
-        &weights.clone()->set_quantization_info(weights_quantization_info), nullptr, &output));
-  }
-  else
-  {
-    ARM_COMPUTE_RETURN_ON_ERROR(NEGEMM::validate(
-        &input, &weights, nullptr, &output, 1.f, 0.0f,
-        GEMMInfo(false, false, false /* Reshape weights only for the first run */)));
-  }
-
-  return Status{};
-}
-} // namespace
-
-NEFullyConnectedLayerEx::NEFullyConnectedLayerEx(std::shared_ptr<IMemoryManager> memory_manager)
-    : _memory_group(std::move(memory_manager)), _flatten_kernel(), _convert_weights(),
-      _reshape_weights_function(), _mm_gemm(), _mm_gemmlowp(), _gemmlowp_output_stage(),
-      _accumulate_biases_kernel(), _flatten_output(), _gemmlowp_output(),
-      _converted_weights_output(), _reshape_weights_output(), _original_weights(nullptr),
-      _are_weights_converted(true), _are_weights_reshaped(false), _is_fc_after_conv(false),
-      _accumulate_biases(false), _is_quantized(false), _is_prepared(false)
-{
-}
-
-void NEFullyConnectedLayerEx::configure_mm(const ITensor *input, const ITensor *weights,
-                                           ITensor *output)
-{
-  if (_is_quantized)
-  {
-    // Since we need negative offsets for computing convolution, we need to change
-    // QuantizationInfo()
-    // Extract and negate input and weights offset
-    const QuantizationInfo input_quantization_info = input->info()->quantization_info();
-    const QuantizationInfo weights_quantization_info = weights->info()->quantization_info();
-
-    input->info()->set_quantization_info(QuantizationInfo(
-        input_quantization_info.uniform().scale, -input_quantization_info.uniform().offset));
-    weights->info()->set_quantization_info(QuantizationInfo(
-        weights_quantization_info.uniform().scale, -weights_quantization_info.uniform().offset));
-
-    // Configure gemmlowp function
-    _mm_gemmlowp.configure(input, weights, nullptr, output);
-
-    // Revert back QuantizatioInfo as input and weights could be used in other fully connected
-    // layers
-    input->info()->set_quantization_info(input_quantization_info);
-    weights->info()->set_quantization_info(weights_quantization_info);
-  }
-  else
-  {
-    // Configure matrix multiply kernel
-    _mm_gemm.configure(input, weights, nullptr, output, 1.f, 0.0f,
-                       GEMMInfo(false, false, false /* Reshape weights only for the first run */));
-  }
-}
-
-void NEFullyConnectedLayerEx::configure_conv_fc(const ITensor *input, const ITensor *weights,
-                                                ITensor *output)
-{
-  ARM_COMPUTE_ERROR_ON(
-      (weights->info()->dimension(1) !=
-       (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2))));
-
-  // If the fully connected layer is called after a convolution layer, the input tensor must be
-  // linearized
-
-  // Initialize output tensor for flatten
-  TensorShape shape_flatten = compute_flatten_shape(input->info());
-  _flatten_output.allocator()->init(
-      input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(
-          shape_flatten));
-
-  // Configure flatten kernel
-  _memory_group.manage(&_flatten_output);
-  _flatten_kernel.configure(input, &_flatten_output);
-
-  // Configure matrix multiply kernel
-  configure_mm(&_flatten_output, weights, output);
-
-  // Allocate the output tensor for flatten once all the configure methods have been called
-  _flatten_output.allocator()->allocate();
-}
-
-void NEFullyConnectedLayerEx::configure_fc_fc(const ITensor *input, const ITensor *weights,
-                                              ITensor *output)
-{
-  ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != weights->info()->dimension(1));
-
-  // Configure matrix multiply kernel
-  configure_mm(input, weights, output);
-}
-
-void NEFullyConnectedLayerEx::configure(const ITensor *input, const ITensor *weights,
-                                        const ITensor *biases, ITensor *output,
-                                        FullyConnectedLayerInfo fc_info)
-{
-  ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
-
-  // Perform validate step
-  ARM_COMPUTE_ERROR_THROW_ON(NEFullyConnectedLayerEx::validate(
-      input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, output->info(),
-      fc_info));
-
-  _are_weights_converted = true;
-  _are_weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true;
-  _is_fc_after_conv = true;
-  _accumulate_biases = false;
-  _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type());
-  _original_weights = weights;
-
-  // Configure gemmlowp output
-  if (_is_quantized)
-  {
-    _gemmlowp_output.allocator()->init(
-        output->info()->clone()->set_is_resizable(true).reset_padding().set_data_type(
-            DataType::S32));
-  }
-
-  // Configure accumulate biases kernel for non quantized asymmetric types
-  if (biases != nullptr && !_is_quantized)
-  {
-    _accumulate_biases = true;
-
-    // Configure accumulate biases kernel
-    _accumulate_biases_kernel.configure(output, biases);
-  }
-
-  // With the Fully Connected layer we can have 4 different cases:
-  //  1) Convolution layer -> Fully Connected layer without batches
-  //  2) Fully Connected layer -> Fully Connected layer without batches
-  //  3) Convolution layer -> Fully Connected layer with batches
-  //  4) Fully Connected layer -> Fully Connected layer with batches
-
-  const ITensor *weights_to_use = weights;
-
-  // Check if we have a fully connected layer with batches
-  const bool is_batched_fc_layer = output->info()->dimension(1) > 1;
-  if (is_batched_fc_layer)
-  {
-    _is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) &&
-                        (std::equal(input->info()->tensor_shape().cbegin() + 3,
-                                    input->info()->tensor_shape().cend(),
-                                    output->info()->tensor_shape().cbegin() + 1));
-  }
-  else
-  {
-    _is_fc_after_conv = input->info()->num_dimensions() > 1;
-  }
-
-  // Reshape weights if needed
-  if (!_are_weights_reshaped)
-  {
-    // Reshape the weights
-    _reshape_weights_function.configure(weights, &_reshape_weights_output);
-    weights_to_use = &_reshape_weights_output;
-  }
-
-  // Convert weights if needed
-  if (_is_fc_after_conv && (input->info()->data_layout() != fc_info.weights_trained_layout))
-  {
-    // Convert weights
-    _convert_weights.configure(weights_to_use, &_converted_weights_output,
-                               input->info()->tensor_shape(), fc_info.weights_trained_layout);
-
-    weights_to_use = &_converted_weights_output;
-    _are_weights_converted = false;
-  }
-
-  ITensor *tmp_output = (_is_quantized) ? &_gemmlowp_output : output;
-  if (_is_fc_after_conv)
-  {
-    // Fully Connected layer after a Convolution Layer without batches
-    configure_conv_fc(input, weights_to_use, tmp_output);
-  }
-  else
-  {
-    // Fully Connected layer after a Fully Connected Layer without batches
-    configure_fc_fc(input, weights_to_use, tmp_output);
-  }
-
-  // Configure output stage for asymmetric quantized types
-  if (_is_quantized)
-  {
-    float multiplier = input->info()->quantization_info().uniform().scale *
-                       weights->info()->quantization_info().uniform().scale /
-                       output->info()->quantization_info().uniform().scale;
-    int output_multiplier;
-    int output_shift;
-    quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier,
-                                                               &output_shift);
-    _gemmlowp_output_stage.configure(&_gemmlowp_output, biases, output, output_multiplier,
-                                     output_shift,
-                                     output->info()->quantization_info().uniform().offset);
-    _gemmlowp_output.allocator()->allocate();
-  }
-
-  _are_weights_reshaped = _are_weights_reshaped || fc_info.retain_internal_weights;
-}
-
-Status NEFullyConnectedLayerEx::validate(const ITensorInfo *input, const ITensorInfo *weights,
-                                         const ITensorInfo *biases, const ITensorInfo *output,
-                                         FullyConnectedLayerInfo fc_info)
-{
-  ARM_COMPUTE_UNUSED(fc_info.retain_internal_weights);
-  ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
-  ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16,
-                                                       DataType::F32);
-  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
-  ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 2);
-
-  bool weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true;
-  bool is_fc_after_conv = true;
-  bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
-
-  const ITensorInfo &flatten_input =
-      TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(
-          compute_flatten_shape(input)));
-  const ITensorInfo &reshaped_weights =
-      TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(
-          compute_transposed_shape(*weights)));
-  const ITensorInfo &converted_weights =
-      weights_reshaped ? TensorInfo(weights->clone()->set_is_resizable(true).reset_padding())
-                       : TensorInfo(*reshaped_weights.clone());
-  const ITensorInfo &gemmlowp_output = TensorInfo(
-      output->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32));
-
-  // Configure accumulate biases kernel for non quantized asymmetric types
-  if (biases != nullptr && !is_quantized)
-  {
-    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
-    ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixAccumulateBiasesKernel::validate(output, biases));
-  }
-
-  // With the Fully Connected layer we can have 4 different cases:
-  //  1) Convolution layer -> Fully Connected layer without batches
-  //  2) Fully Connected layer -> Fully Connected layer without batches
-  //  3) Convolution layer -> Fully Connected layer with batches
-  //  4) Fully Connected layer -> Fully Connected layer with batches
-
-  const ITensorInfo *input_to_use = input;
-  const ITensorInfo *weights_to_use = weights;
-  const ITensorInfo *tmp_output = (is_quantized) ? &gemmlowp_output : output;
-
-  // Check if we have a fully connected layer with batches
-  const bool is_batched_fc_layer = output->dimension(1) > 1;
-
-  if (is_batched_fc_layer)
-  {
-    is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) &&
-                       (std::equal(input->tensor_shape().cbegin() + 3, input->tensor_shape().cend(),
-                                   output->tensor_shape().cbegin() + 1));
-  }
-  else
-  {
-    is_fc_after_conv = input->num_dimensions() > 1;
-  }
-
-  if (!weights_reshaped)
-  {
-    // Validate reshape weights kernel
-    ARM_COMPUTE_RETURN_ON_ERROR(
-        NEFullyConnectedLayerReshapeWeights::validate(weights, &reshaped_weights));
-    weights_to_use = &reshaped_weights;
-  }
-
-  if (is_fc_after_conv && (input->data_layout() != fc_info.weights_trained_layout))
-  {
-    // Validate convert weights kernel
-    ARM_COMPUTE_RETURN_ON_ERROR(NEConvertFullyConnectedWeights::validate(
-        weights_to_use, &converted_weights, input->tensor_shape(), fc_info.weights_trained_layout));
-    weights_to_use = &converted_weights;
-  }
-
-  if (is_fc_after_conv)
-  {
-    // Fully Connected layer after a Convolution Layer without batches
-    ARM_COMPUTE_RETURN_ERROR_ON(
-        (weights_to_use->dimension(1) !=
-         (input->dimension(0) * input->dimension(1) * input->dimension(2))));
-
-    // Validate flatten kernel
-    ARM_COMPUTE_RETURN_ON_ERROR(NEFlattenLayerKernel::validate(input, &flatten_input));
-    input_to_use = &flatten_input;
-  }
-  else
-  {
-    // Fully Connected layer after a Fully Connected Layer without batches
-    ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != weights_to_use->dimension(1));
-  }
-  // Validate matrix multiply kernel
-  ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(*input_to_use, *weights_to_use, *tmp_output));
-
-  // Validate output stage for asymmetric quantized types
-  if (is_quantized)
-  {
-    ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(
-        &gemmlowp_output, biases, output));
-  }
-
-  return Status{};
-}
-
-void NEFullyConnectedLayerEx::run()
-{
-  if (!_is_prepared)
-  {
-    if (!_are_weights_reshaped)
-      _reshape_weights_output.allocator()->allocate();
-    if (!_are_weights_converted)
-      _converted_weights_output.allocator()->allocate();
-    _is_prepared = true;
-  }
-
-  {
-    ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
-
-    // Reshape of the weights
-    if (!_are_weights_reshaped)
-    {
-      _reshape_weights_function.run();
-    }
-
-    // Convert weights if needed
-    if (!_are_weights_converted)
-    {
-      _convert_weights.run();
-    }
-
-    // Prepare GEMM prepare
-    if (!_is_quantized)
-    {
-      _mm_gemm.prepare();
-    }
-  }
-
-  MemoryGroupResourceScope scope_mg(_memory_group);
-
-  // Linearize input if it comes from a convolutional layer
-  if (_is_fc_after_conv)
-  {
-    NEScheduler::get().schedule(&_flatten_kernel, Window::DimY);
-  }
-
-  // Run matrix multiply
-  if (_is_quantized)
-  {
-    _mm_gemmlowp.run();
-  }
-  else
-  {
-    _mm_gemm.run();
-  }
-
-  // Accumulate biases if provided
-  if (_is_quantized)
-  {
-    _gemmlowp_output_stage.run();
-  }
-  else
-  {
-    if (_accumulate_biases)
-    {
-      NEScheduler::get().schedule(&_accumulate_biases_kernel, Window::DimY);
-    }
-  }
-}
-
-void NEFullyConnectedLayerEx::prepare()
-{
-#if 0 // TODO Remove this block
-  if (!_is_prepared)
-  {
-    ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
-
-    auto release_unused = [](Tensor *w) {
-      if (!w->is_used())
-      {
-        w->allocator()->free();
-      }
-    };
-
-    // Pointer to current weights
-    const ITensor *cur_weights = _original_weights;
-
-    // Reshape of the weights (happens only once)
-    if (!_are_weights_reshaped)
-    {
-      // Run reshape weights kernel and mark weights as unused
-      _reshape_weights_output.allocator()->allocate();
-      _reshape_weights_function.run();
-
-      cur_weights->mark_as_unused();
-      cur_weights = &_reshape_weights_output;
-      _are_weights_reshaped = true;
-    }
-
-    // Convert weights if needed (happens only once)
-    if (!_are_weights_converted)
-    {
-      _converted_weights_output.allocator()->allocate();
-      _convert_weights.run();
-
-      cur_weights->mark_as_unused();
-      _are_weights_converted = true;
-    }
-
-    // Release reshaped weights if unused
-    release_unused(&_reshape_weights_output);
-
-    // Prepare GEMM prepare and release unused weights
-    if (!_is_quantized)
-    {
-      _mm_gemm.prepare();
-    }
-
-    // Release converted weights if unused
-    release_unused(&_reshape_weights_output);
-    release_unused(&_converted_weights_output);
-
-    _is_prepared = true;
-  }
-#endif
-}
diff --git a/compute/ARMComputeEx/src/runtime/NEON/functions/NEFullyConnectedReshapingLayer.cpp b/compute/ARMComputeEx/src/runtime/NEON/functions/NEFullyConnectedReshapingLayer.cpp
deleted file mode 100644
index dc6c78478..000000000
--- a/compute/ARMComputeEx/src/runtime/NEON/functions/NEFullyConnectedReshapingLayer.cpp
+++ /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.
- */
-
-#include "arm_compute/runtime/NEON/functions/NEFullyConnectedReshapingLayer.h"
-
-#include <arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h>
-#include <arm_compute/runtime/NEON/functions/NEFullyConnectedHybridLayer.h>
-#include <arm_compute/runtime/NEON/functions/NEFullyConnectedLayerEx.h>
-
-using namespace arm_compute;
-
-void NEFullyConnectedReshapingLayer::configure(const arm_compute::ITensor *input,
-                                               const arm_compute::ITensor *weights,
-                                               const arm_compute::ITensor *biases,
-                                               arm_compute::ITensor *output, bool needs_reshape,
-                                               const arm_compute::TensorShape &reshape,
-                                               KernelType kernel_type)
-{
-  _input = input;
-  _weights = weights;
-  _biases = biases;
-  _output = output;
-  _needs_reshape = needs_reshape;
-
-  const ITensor *input_to_use = input;
-  if (_needs_reshape)
-  {
-    // reshape
-    auto_init_if_empty(*_neon_buffer.info(), _input->info()->clone()->set_tensor_shape(reshape));
-    _neon_reshape.configure(_input, &_neon_buffer);
-    input_to_use = &_neon_buffer;
-  }
-
-  _neon_fc = [&]() {
-    if (kernel_type == KernelType::GENERAL)
-    {
-      auto fc = new arm_compute::NEFullyConnectedLayerEx{_memory_manager};
-      fc->configure(input_to_use, _weights, _biases, _output);
-      return std::unique_ptr<arm_compute::IFunction>(fc);
-    }
-    else
-    {
-      assert(kernel_type == KernelType::PREPROCESSED_WEIGHTS);
-
-      bool is_hybrid = input->info()->data_type() == DataType::F32 &&
-                       (weights->info()->data_type() == DataType::S8 ||
-                        weights->info()->data_type() == DataType::QASYMM8_SIGNED);
-
-      if (is_hybrid)
-      {
-        auto fc = new arm_compute::NEFullyConnectedHybridLayer{_memory_manager};
-        ITensorInfo *weights_info = const_cast<ITensorInfo *>(_weights->info());
-        const auto orgin_weights_data_type = weights_info->data_type();
-        weights_info->set_data_type(DataType::QASYMM8_SIGNED);
-        fc->configure(input_to_use, _weights, _biases, _output);
-        weights_info->set_data_type(orgin_weights_data_type);
-        return std::unique_ptr<arm_compute::IFunction>(fc);
-      }
-      else
-      {
-        auto fc = new arm_compute::NEFullyConnectedLayer{_memory_manager};
-        fc->configure(input_to_use, _weights, _biases, _output);
-        return std::unique_ptr<arm_compute::IFunction>(fc);
-      }
-    }
-  }();
-
-  // NOTE _neon_buffer is inaccessible from outside, and thus it is safe to invoke allocate here.
-  if (_needs_reshape)
-  {
-    _neon_buffer.allocator()->allocate();
-  }
-}
-
-void NEFullyConnectedReshapingLayer::run(void)
-{
-  if (_needs_reshape)
-    _neon_reshape.run();
-
-  _neon_fc->run();
-}
-
-void NEFullyConnectedReshapingLayer::prepare(void) { _neon_fc->prepare(); }
diff --git a/compute/ARMComputeEx/src/runtime/NEON/functions/NEGatherEx.cpp b/compute/ARMComputeEx/src/runtime/NEON/functions/NEGatherEx.cpp
deleted file mode 100644
index 433c35d58..000000000
--- a/compute/ARMComputeEx/src/runtime/NEON/functions/NEGatherEx.cpp
+++ /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.
- */
-
-/*
- * Copyright (c) 2019 ARM Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-
-#include "arm_compute/runtime/NEON/functions/NEGatherEx.h"
-
-#include "arm_compute/core/NEON/kernels/NEGatherKernelEx.h"
-#include "support/MemorySupport.h"
-
-#include <utility>
-
-namespace arm_compute
-{
-void NEGatherEx::configure(const ITensor *input, const ITensor *indices, ITensor *output, int axis)
-{
-  auto k = support::cpp14::make_unique<NEGatherKernelEx>();
-  k->configure(input, indices, output, axis);
-  _kernel = std::move(k);
-}
-
-Status NEGatherEx::validate(const ITensorInfo *input, const ITensorInfo *indices,
-                            const ITensorInfo *output, int axis)
-{
-  return NEGatherKernelEx::validate(input, indices, output, axis);
-}
-
-} // namespace arm_compute
diff --git a/compute/ARMComputeEx/src/runtime/NEON/functions/NEHashtableLookup.cpp b/compute/ARMComputeEx/src/runtime/NEON/functions/NEHashtableLookup.cpp
deleted file mode 100644
index 52d58accf..000000000
--- a/compute/ARMComputeEx/src/runtime/NEON/functions/NEHashtableLookup.cpp
+++ /dev/null
@@ -1,61 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * Copyright (c) 2016-2018 ARM Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-
-#include "arm_compute/runtime/NEON/functions/NEHashtableLookup.h"
-
-#include "arm_compute/core/NEON/kernels/NEHashtableLookupKernel.h"
-#include "support/MemorySupport.h"
-
-using namespace arm_compute;
-
-void NEHashtableLookup::configure(const ITensor *lookups, const ITensor *keys, const ITensor *input,
-                                  ITensor *output, ITensor *hits)
-{
-  auto k = support::cpp14::make_unique<NEHashtableLookupKernel>();
-  k->configure(lookups, keys, input, output, hits);
-  _kernel = std::move(k);
-}
-
-Status NEHashtableLookup::validate(const ITensorInfo *lookups, const ITensorInfo *keys,
-                                   const ITensorInfo *input, const ITensorInfo *output,
-                                   const ITensorInfo *hits)
-{
-  return NEHashtableLookupKernel::validate(lookups, keys, input, output, hits);
-}
diff --git a/compute/ARMComputeEx/src/runtime/NEON/functions/NEInstanceNormalizationLayerEx.cpp b/compute/ARMComputeEx/src/runtime/NEON/functions/NEInstanceNormalizationLayerEx.cpp
deleted file mode 100644
index 16d74e62d..000000000
--- a/compute/ARMComputeEx/src/runtime/NEON/functions/NEInstanceNormalizationLayerEx.cpp
+++ /dev/null
@@ -1,113 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * Copyright (c) 2019 ARM Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-
-#include "arm_compute/runtime/NEON/functions/NEInstanceNormalizationLayerEx.h"
-
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/runtime/NEON/NEScheduler.h"
-
-namespace arm_compute
-{
-NEInstanceNormalizationLayerEx::NEInstanceNormalizationLayerEx(
-    std::shared_ptr<IMemoryManager> memory_manager)
-    : _memory_group(std::move(memory_manager)), _normalization_kernel(), _is_nchw(false),
-      _permute_input(), _permute_output(), _permuted_input(), _permuted_output()
-{
-}
-
-void NEInstanceNormalizationLayerEx::configure(ITensor *input, ITensor *output, ITensor *gamma,
-                                               ITensor *beta, float epsilon)
-{
-  const DataLayout data_layout = input->info()->data_layout();
-
-  // Configure Kernels
-  _is_nchw = data_layout == DataLayout::NCHW;
-
-  if (!_is_nchw)
-  {
-    _memory_group.manage(&_permuted_input);
-    _memory_group.manage(&_permuted_output);
-
-    // Configure the function to transform the input tensor from NHWC -> NCHW
-    _permute_input.configure(input, &_permuted_input, PermutationVector(1U, 2U, 0U));
-    _permuted_input.info()->set_data_layout(DataLayout::NCHW);
-
-    _normalization_kernel.configure(&_permuted_input, &_permuted_output, gamma, beta, epsilon);
-    _permuted_output.info()->set_data_layout(DataLayout::NCHW);
-
-    _permute_output.configure(&_permuted_output, output != nullptr ? output : input,
-                              PermutationVector(2U, 0U, 1U));
-    _permuted_input.allocator()->allocate();
-    _permuted_output.allocator()->allocate();
-  }
-  else
-  {
-    _normalization_kernel.configure(input, output, gamma, beta, epsilon);
-  }
-}
-
-Status NEInstanceNormalizationLayerEx::validate(const ITensorInfo *input, const ITensorInfo *output,
-                                                const ITensorInfo *gamma, const ITensorInfo *beta,
-                                                float epsilon)
-{
-  return NEInstanceNormalizationLayerKernelEx::validate(
-      &input->clone()->set_data_layout(DataLayout::NCHW),
-      &output->clone()->set_data_layout(DataLayout::NCHW), gamma, beta, epsilon);
-}
-
-void NEInstanceNormalizationLayerEx::run()
-{
-  MemoryGroupResourceScope scope_mg(_memory_group);
-
-  // Permute input
-  if (!_is_nchw)
-  {
-    _permute_input.run();
-  }
-
-  NEScheduler::get().schedule(&_normalization_kernel, Window::DimZ);
-
-  // Permute output
-  if (!_is_nchw)
-  {
-    _permute_output.run();
-  }
-}
-} // namespace arm_compute
diff --git a/compute/ARMComputeEx/src/runtime/NEON/functions/NEOneHot.cpp b/compute/ARMComputeEx/src/runtime/NEON/functions/NEOneHot.cpp
deleted file mode 100644
index 275c55024..000000000
--- a/compute/ARMComputeEx/src/runtime/NEON/functions/NEOneHot.cpp
+++ /dev/null
@@ -1,59 +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.
- */
-
-/*
- * Copyright (c) 2019-2020 Arm Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#include "arm_compute/runtime/NEON/functions/NEOneHot.h"
-#include "arm_compute/core/NEON/kernels/NEOneHotKernel.h"
-#include "support/MemorySupport.h"
-#include <utility>
-namespace arm_compute
-{
-void NEOneHot::configure(const ITensor *indices, const ITensor *depth, const ITensor *on_value,
-                         const ITensor *off_value, ITensor *output, int axis)
-{
-  auto k = arm_compute::support::cpp14::make_unique<NEOneHotKernel>();
-  k->configure(indices, depth, on_value, off_value, output, axis);
-  _kernel = std::move(k);
-}
-Status NEOneHot::validate(const ITensorInfo *indices, const ITensorInfo *depth,
-                          const ITensorInfo *on_value, const ITensorInfo *off_value,
-                          const ITensorInfo *output, int axis)
-{
-  return NEOneHotKernel::validate(indices, depth, on_value, off_value, output, axis);
-}
-} // namespace arm_compute
diff --git a/compute/ARMComputeEx/src/runtime/NEON/functions/NEReduceOperation.cpp b/compute/ARMComputeEx/src/runtime/NEON/functions/NEReduceOperation.cpp
deleted file mode 100644
index aedb537e9..000000000
--- a/compute/ARMComputeEx/src/runtime/NEON/functions/NEReduceOperation.cpp
+++ /dev/null
@@ -1,182 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * Copyright (c) 2018-2019 ARM Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-
-#include "arm_compute/runtime/NEON/functions/NEReduceOperation.h"
-
-#include "arm_compute/core/CPP/Validate.h"
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/runtime/NEON/NEScheduler.h"
-#include "arm_compute/core/TensorInfo.h"
-#include "arm_compute/runtime/Tensor.h"
-
-using namespace arm_compute;
-
-NEReduceOperation::NEReduceOperation(std::shared_ptr<IMemoryManager> memory_manager)
-    : _memory_group(std::move(memory_manager)), _reduction_kernels(), _reduced_outs(), _reshape(),
-      _reduction_ops(), _keep_dims()
-{
-}
-
-Status NEReduceOperation::validate(const ITensorInfo *input, const Coordinates &reduction_axis,
-                                   bool keep_dims, const ITensorInfo *output, ReduceOperation op)
-{
-  ARM_COMPUTE_UNUSED(keep_dims);
-  ARM_COMPUTE_UNUSED(op);
-  ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
-  ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input);
-  ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16,
-                                                       DataType::F32);
-  ARM_COMPUTE_RETURN_ERROR_ON(reduction_axis.num_dimensions() > input->num_dimensions());
-
-  TensorShape out_shape = input->tensor_shape();
-  const unsigned int reduction_ops = reduction_axis.num_dimensions();
-  const int input_dims = input->num_dimensions();
-  Coordinates axis_local = reduction_axis;
-
-  // Convert negative axis
-  for (unsigned int i = 0; i < reduction_ops; ++i)
-  {
-    axis_local[i] = wrap_around(axis_local[i], input_dims);
-  }
-
-  std::sort(axis_local.begin(), axis_local.begin() + reduction_ops);
-  for (unsigned int i = 0; i < reduction_ops; ++i)
-  {
-    ARM_COMPUTE_RETURN_ERROR_ON(axis_local[i] > 3);
-    ARM_COMPUTE_RETURN_ERROR_ON(static_cast<unsigned int>(axis_local[i]) >
-                                input->num_dimensions() - 1);
-    if (output->total_size() > 0 && keep_dims)
-    {
-      ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(axis_local[i]) != 1);
-    }
-    if (keep_dims)
-    {
-      out_shape.set(axis_local[i], 1);
-    }
-    else
-    {
-      out_shape.remove_dimension(axis_local[i] - i);
-    }
-  }
-  const TensorInfo out_info = input->clone()->set_tensor_shape(out_shape);
-  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &out_info);
-
-  return Status{};
-}
-
-void NEReduceOperation::configure(ITensor *input, const Coordinates &reduction_axis, bool keep_dims,
-                                  ITensor *output, ReduceOperation op)
-{
-  ARM_COMPUTE_ERROR_ON_NULLPTR(input);
-
-  _reduction_ops = reduction_axis.num_dimensions();
-  _reduction_kernels.resize(_reduction_ops);
-  _reduced_outs.resize(_reduction_ops - (keep_dims ? 1 : 0));
-  _keep_dims = keep_dims;
-
-  Coordinates axis_local = reduction_axis;
-  const int input_dims = input->info()->num_dimensions();
-  const unsigned int reduction_ops = reduction_axis.num_dimensions();
-
-  // Convert negative axis
-  for (unsigned int i = 0; i < reduction_ops; ++i)
-  {
-    axis_local[i] = wrap_around(axis_local[i], input_dims);
-  }
-
-  // Perform reduction for every axis
-  for (unsigned int i = 0; i < _reduction_ops; ++i)
-  {
-    TensorShape out_shape =
-        i == 0 ? input->info()->tensor_shape() : (&_reduced_outs[i - 1])->info()->tensor_shape();
-    out_shape.set(axis_local[i], 1);
-    auto in = (i == 0) ? input : (&_reduced_outs[i - 1]);
-
-    if (i == _reduction_ops - 1 && keep_dims)
-    {
-      _reduction_kernels[i].configure(in, output, axis_local[i], op);
-    }
-    else
-    {
-      _reduced_outs[i].allocator()->init(TensorInfo(out_shape, input->info()->num_channels(),
-                                                    input->info()->data_type(),
-                                                    input->info()->quantization_info()));
-      _memory_group.manage(&_reduced_outs[i]);
-      _reduction_kernels[i].configure(in, &_reduced_outs[i], axis_local[i], op);
-    }
-  }
-
-  // Allocate intermediate tensors
-  for (unsigned int i = 0; i < _reduction_ops - (keep_dims ? 1 : 0); ++i)
-  {
-    _reduced_outs[i].allocator()->allocate();
-  }
-
-  // Configure reshape layer if we want to drop the dimensions
-  if (!keep_dims)
-  {
-    TensorShape out_shape = input->info()->tensor_shape();
-
-    // We have to sort the reduction axis vectors in order for remove_dimension
-    // to work properly
-    std::sort(axis_local.begin(), axis_local.begin() + _reduction_ops);
-    for (unsigned int i = 0; i < _reduction_ops; ++i)
-    {
-      out_shape.remove_dimension(axis_local[i] - i);
-    }
-    auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(out_shape));
-    _reshape.configure(&_reduced_outs[_reduction_ops - 1], output);
-  }
-}
-
-void NEReduceOperation::run()
-{
-  MemoryGroupResourceScope scope_mg(_memory_group);
-
-  for (unsigned int i = 0; i < _reduction_ops; ++i)
-  {
-    _reduction_kernels[i].run();
-  }
-
-  if (!_keep_dims)
-  {
-    _reshape.run();
-  }
-}
diff --git a/compute/ARMComputeEx/src/runtime/NEON/functions/NEReduceSum.cpp b/compute/ARMComputeEx/src/runtime/NEON/functions/NEReduceSum.cpp
deleted file mode 100644
index 26a887912..000000000
--- a/compute/ARMComputeEx/src/runtime/NEON/functions/NEReduceSum.cpp
+++ /dev/null
@@ -1,181 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * Copyright (c) 2018-2019 ARM Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-
-#include "arm_compute/runtime/NEON/functions/NEReduceSum.h"
-
-#include "arm_compute/core/CPP/Validate.h"
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/runtime/NEON/NEScheduler.h"
-
-using namespace arm_compute;
-
-NEReduceSum::NEReduceSum(std::shared_ptr<IMemoryManager> memory_manager)
-    : _memory_group(std::move(memory_manager)), _reduction_kernels(), _reduced_outs(), _reshape(),
-      _reduction_ops(), _keep_dims()
-{
-}
-
-Status NEReduceSum::validate(const ITensorInfo *input, const Coordinates &reduction_axis,
-                             bool keep_dims, const ITensorInfo *output)
-{
-  ARM_COMPUTE_UNUSED(keep_dims);
-  ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
-  ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input);
-  ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16,
-                                                       DataType::F32);
-  ARM_COMPUTE_RETURN_ERROR_ON(reduction_axis.num_dimensions() > input->num_dimensions());
-
-  TensorShape out_shape = input->tensor_shape();
-  const unsigned int reduction_ops = reduction_axis.num_dimensions();
-  const int input_dims = input->num_dimensions();
-  Coordinates axis_local = reduction_axis;
-
-  // Convert negative axis
-  for (unsigned int i = 0; i < reduction_ops; ++i)
-  {
-    axis_local[i] = wrap_around(axis_local[i], input_dims);
-  }
-
-  std::sort(axis_local.begin(), axis_local.begin() + reduction_ops);
-  for (unsigned int i = 0; i < reduction_ops; ++i)
-  {
-    ARM_COMPUTE_RETURN_ERROR_ON(axis_local[i] > 3);
-    ARM_COMPUTE_RETURN_ERROR_ON(static_cast<unsigned int>(axis_local[i]) >
-                                input->num_dimensions() - 1);
-    if (output->total_size() > 0 && keep_dims)
-    {
-      ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(axis_local[i]) != 1);
-    }
-    if (keep_dims)
-    {
-      out_shape.set(axis_local[i], 1);
-    }
-    else
-    {
-      out_shape.remove_dimension(axis_local[i] - i);
-    }
-  }
-  const TensorInfo out_info = input->clone()->set_tensor_shape(out_shape);
-  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &out_info);
-
-  return Status{};
-}
-
-void NEReduceSum::configure(ITensor *input, const Coordinates &reduction_axis, bool keep_dims,
-                            ITensor *output)
-{
-  ARM_COMPUTE_ERROR_ON_NULLPTR(input);
-
-  _reduction_ops = reduction_axis.num_dimensions();
-  _reduction_kernels.resize(_reduction_ops);
-  _reduced_outs.resize(_reduction_ops - (keep_dims ? 1 : 0));
-  _keep_dims = keep_dims;
-
-  Coordinates axis_local = reduction_axis;
-  const int input_dims = input->info()->num_dimensions();
-  const unsigned int reduction_ops = reduction_axis.num_dimensions();
-
-  // Convert negative axis
-  for (unsigned int i = 0; i < reduction_ops; ++i)
-  {
-    axis_local[i] = wrap_around(axis_local[i], input_dims);
-  }
-
-  // Perform reduction for every axis
-  for (unsigned int i = 0; i < _reduction_ops; ++i)
-  {
-    TensorShape out_shape =
-        i == 0 ? input->info()->tensor_shape() : (&_reduced_outs[i - 1])->info()->tensor_shape();
-    out_shape.set(axis_local[i], 1);
-    auto in = (i == 0) ? input : (&_reduced_outs[i - 1]);
-
-    if (i == _reduction_ops - 1 && keep_dims)
-    {
-      _reduction_kernels[i].configure(in, output, axis_local[i], ReductionOperation::SUM);
-    }
-    else
-    {
-      _reduced_outs[i].allocator()->init(TensorInfo(out_shape, input->info()->num_channels(),
-                                                    input->info()->data_type(),
-                                                    input->info()->quantization_info())
-                                             .set_data_layout(input->info()->data_layout()));
-      _memory_group.manage(&_reduced_outs[i]);
-      _reduction_kernels[i].configure(in, &_reduced_outs[i], axis_local[i],
-                                      ReductionOperation::SUM);
-    }
-  }
-
-  // Allocate intermediate tensors
-  for (unsigned int i = 0; i < _reduction_ops - (keep_dims ? 1 : 0); ++i)
-  {
-    _reduced_outs[i].allocator()->allocate();
-  }
-
-  // Configure reshape layer if we want to drop the dimensions
-  if (!keep_dims)
-  {
-    TensorShape out_shape = input->info()->tensor_shape();
-
-    // We have to sort the reduction axis vectors in order for remove_dimension
-    // to work properly
-    std::sort(axis_local.begin(), axis_local.begin() + _reduction_ops);
-    for (unsigned int i = 0; i < _reduction_ops; ++i)
-    {
-      out_shape.remove_dimension(axis_local[i] - i);
-    }
-    auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(out_shape));
-    _reshape.configure(&_reduced_outs[_reduction_ops - 1], output);
-  }
-}
-
-void NEReduceSum::run()
-{
-  MemoryGroupResourceScope scope_mg(_memory_group);
-
-  for (unsigned int i = 0; i < _reduction_ops; ++i)
-  {
-    _reduction_kernels[i].run();
-  }
-
-  if (!_keep_dims)
-  {
-    _reshape.run();
-  }
-}
diff --git a/compute/ARMComputeEx/src/runtime/NEON/functions/NEReductionOperationEx.cpp b/compute/ARMComputeEx/src/runtime/NEON/functions/NEReductionOperationEx.cpp
deleted file mode 100644
index 2aa0d2d4b..000000000
--- a/compute/ARMComputeEx/src/runtime/NEON/functions/NEReductionOperationEx.cpp
+++ /dev/null
@@ -1,173 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * Copyright (c) 2017-2019 ARM Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-
-#include "arm_compute/runtime/NEON/functions/NEReductionOperationEx.h"
-
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/runtime/NEON/NEScheduler.h"
-
-namespace arm_compute
-{
-namespace
-{
-/** Define dimension to split the window
- *
- * @param[in] axis Reduction axis
- *
- * @return The dimension to split the window
- */
-size_t reduction_window_split_dimension(unsigned int axis)
-{
-  switch (axis)
-  {
-    case 0:
-      return Window::DimY;
-    case 1:
-    case 2:
-    case 3:
-      return Window::DimX;
-    default:
-      ARM_COMPUTE_ERROR("Unsupported reduction axis");
-  }
-}
-} // namespace
-
-NEReductionOperationEx::NEReductionOperationEx()
-    : _reduction_kernel(), _fill_border_kernel(), _window_split(0), _reduction_axis()
-{
-}
-
-Status NEReductionOperationEx::validate(const ITensorInfo *input, const ITensorInfo *output,
-                                        unsigned int axis, ReduceOperation op)
-{
-  ARM_COMPUTE_RETURN_ON_ERROR(NEReductionOperationKernelEx::validate(input, output, axis, op));
-
-  return Status{};
-}
-
-void NEReductionOperationEx::configure(ITensor *input, ITensor *output, unsigned int axis,
-                                       ReduceOperation op)
-{
-  ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
-  ARM_COMPUTE_ERROR_THROW_ON(
-      NEReductionOperationEx::validate(input->info(), output->info(), axis, op));
-
-  // Configure reduction kernel
-  _reduction_kernel.configure(input, output, axis, op);
-  _window_split = reduction_window_split_dimension(axis);
-  _reduction_axis = axis;
-
-  if (axis == 0)
-  {
-    // Configure fill border kernel
-    const BorderSize fill_border_size = _reduction_kernel.border_size();
-    PixelValue pixelValue;
-    switch (op)
-    {
-      case ReduceOperation::MIN:
-      {
-        switch (input->info()->data_type())
-        {
-          case DataType::F32:
-          {
-            pixelValue = PixelValue(std::numeric_limits<float>::max());
-            break;
-          }
-          case DataType::F16:
-          {
-            pixelValue = PixelValue(static_cast<half>(65504.0f));
-            break;
-          }
-          case DataType::QASYMM8:
-          {
-            pixelValue =
-                PixelValue(255, input->info()->data_type(), input->info()->quantization_info());
-            break;
-          }
-          default:
-          {
-            ARM_COMPUTE_ERROR("Unsupported DataType");
-          }
-        }
-        break;
-      }
-      case ReduceOperation::MAX:
-      {
-        switch (input->info()->data_type())
-        {
-          case DataType::F32:
-          {
-            pixelValue = PixelValue(-std::numeric_limits<float>::max());
-            break;
-          }
-          case DataType::F16:
-          {
-            pixelValue = PixelValue(static_cast<half>(-65504.0f));
-            break;
-          }
-          case DataType::QASYMM8:
-          {
-            pixelValue =
-                PixelValue(0, input->info()->data_type(), input->info()->quantization_info());
-            break;
-          }
-          default:
-          {
-            ARM_COMPUTE_ERROR("Unsupported DataType");
-          }
-        }
-        break;
-      }
-      default:
-        ARM_COMPUTE_ERROR("Reduction Operation unsupported");
-    }
-    _fill_border_kernel.configure(input, fill_border_size, BorderMode::CONSTANT, pixelValue);
-  }
-}
-
-void NEReductionOperationEx::run()
-{
-  if (_reduction_axis == 0)
-  {
-    NEScheduler::get().schedule(&_fill_border_kernel, Window::DimY);
-  }
-  NEScheduler::get().schedule(&_reduction_kernel, _window_split);
-}
-} // namespace arm_compute
diff --git a/compute/ARMComputeEx/src/runtime/NEON/functions/NETransposeConvLayer.cpp b/compute/ARMComputeEx/src/runtime/NEON/functions/NETransposeConvLayer.cpp
deleted file mode 100644
index aa165cc15..000000000
--- a/compute/ARMComputeEx/src/runtime/NEON/functions/NETransposeConvLayer.cpp
+++ /dev/null
@@ -1,242 +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.
- */
-
-/*
- * Copyright (c) 2017-2020 ARM Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#include "arm_compute/runtime/NEON/functions/NETransposeConvLayer.h"
-
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/UtilsEx.h"
-#include "arm_compute/core/Validate.h"
-#include "arm_compute/core/utils/misc/ShapeCalculatorEx.h"
-#include "arm_compute/runtime/NEON/NEScheduler.h"
-
-using namespace arm_compute::misc::shape_calculator;
-
-namespace arm_compute
-{
-
-NETransposeConvLayer::NETransposeConvLayer(std::shared_ptr<IMemoryManager> memory_manager) // NOLINT
-    : _memory_group(std::move(memory_manager)),
-      _conv_f(),
-      _upsample_f(),
-      _flip_weights(),
-      _scaled_output(),
-      _weights_flipped(),
-      _flip_axis(),
-      _original_weights(nullptr),
-      _input(nullptr),
-      _info(),
-      _is_prepared(false)
-{
-}
-
-Status NETransposeConvLayer::validate(const ITensorInfo *input, const ITensorInfo *weights,
-                                      const ITensorInfo *bias, const ITensorInfo *output,
-                                      const PadStrideInfo &info, unsigned int invalid_right,
-                                      unsigned int invalid_bottom)
-{
-  ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
-  ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::F16,
-                                                       DataType::QASYMM8, DataType::QASYMM8_SIGNED);
-  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, input);
-  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(weights, input);
-  const unsigned int width_idx =
-      get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::WIDTH);
-  const unsigned int height_idx =
-      get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::HEIGHT);
-  ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(width_idx) != weights->dimension(height_idx));
-  ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(width_idx) < 1);
-
-  auto out_dims = transposeconv_output_dimensions(
-      input->dimension(width_idx), input->dimension(height_idx), weights->dimension(width_idx),
-      weights->dimension(height_idx), info, invalid_right, invalid_bottom);
-
-  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
-  if (bias != nullptr)
-  {
-    if (is_data_type_quantized_asymmetric(input->data_type()))
-    {
-      ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32);
-    }
-    else
-    {
-      ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
-    }
-  }
-
-  if (output->tensor_shape().total_size() > 0)
-  {
-    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
-
-    const TensorShape output_shape = compute_transposeconv_output_shape(out_dims, *input, *weights);
-
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(Window::DimX) != output_shape.x(),
-                                    "Output's width is invalid.");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(Window::DimY) != output_shape.y(),
-                                    "Output's height is invalid.");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(Window::DimZ) != output_shape.z(),
-                                    "Output's depth is invalid.");
-  }
-
-  unsigned int pad_left = 0;
-  unsigned int pad_right = 0;
-  unsigned int pad_top = 0;
-  unsigned int pad_bottom = 0;
-  const TensorShape scale_out_shape = compute_transposeconv_upsampled_shape(
-      *input, *weights, info, out_dims, invalid_right, invalid_bottom, pad_left, pad_right, pad_top,
-      pad_bottom);
-  TensorInfo scale_out_info(
-      input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(scale_out_shape));
-  const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL);
-
-  const unsigned int batches_idx =
-      get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::BATCHES);
-  const unsigned int channel_idx =
-      get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::CHANNEL);
-  ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(batches_idx) !=
-                              scale_out_info.dimension(batches_idx));
-  ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(channel_idx) !=
-                              scale_out_info.dimension(channel_idx));
-
-  ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayer::validate(&scale_out_info, weights, bias, output,
-                                                           conv_info, WeightsInfo()));
-
-  return Status{};
-}
-
-void NETransposeConvLayer::configure(ITensor *input, const ITensor *weights, const ITensor *bias,
-                                     ITensor *output, const PadStrideInfo &info,
-                                     unsigned int invalid_right, unsigned int invalid_bottom)
-{
-  // Perform validation step
-  ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
-  ARM_COMPUTE_ERROR_THROW_ON(NETransposeConvLayer::validate(
-      input->info(), weights->info(), (bias == nullptr) ? nullptr : bias->info(), output->info(),
-      info, invalid_right, invalid_bottom));
-
-  const DataLayout data_layout = input->info()->data_layout();
-  const unsigned int width_idx =
-      get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
-  const unsigned int height_idx =
-      get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
-  auto out_dims = transposeconv_output_dimensions(
-      input->info()->dimension(width_idx), input->info()->dimension(height_idx),
-      weights->info()->dimension(width_idx), weights->info()->dimension(height_idx), info,
-      invalid_right, invalid_bottom);
-
-  const TensorShape output_shape =
-      compute_transposeconv_output_shape(out_dims, *input->info(), *weights->info());
-
-  _input = input;
-  _original_weights = weights;
-  _info = info;
-  _is_prepared = false;
-
-  unsigned int pad_left = 0;
-  unsigned int pad_right = 0;
-  unsigned int pad_top = 0;
-  unsigned int pad_bottom = 0;
-  const unsigned int stride_x = info.stride().first;
-  const unsigned int stride_y = info.stride().second;
-
-  // Output auto initialization if not yet initialized
-  auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(),
-                     input->info()->quantization_info());
-
-  _flip_axis.allocator()->init(TensorInfo(TensorShape(2U), 1, DataType::U32));
-  _memory_group.manage(&_scaled_output);
-
-  _weights_flipped.allocator()->init(weights->info()->clone()->set_data_layout(data_layout));
-  _flip_weights.configure(weights, &_weights_flipped, &_flip_axis);
-
-  // setup the function to convolve the upscaled output
-  const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL);
-
-  const TensorShape scale_out_shape = compute_transposeconv_upsampled_shape(
-      *input->info(), *weights->info(), info, out_dims, invalid_right, invalid_bottom, pad_left,
-      pad_right, pad_top, pad_bottom);
-
-  const PadStrideInfo upsample_info(stride_x, stride_y, pad_left, pad_right, pad_top, pad_bottom,
-                                    DimensionRoundingType::FLOOR);
-
-  TensorInfo scale_out_info(scale_out_shape, 1, input->info()->data_type(),
-                            input->info()->quantization_info());
-  scale_out_info.set_data_layout(data_layout);
-  _scaled_output.allocator()->init(scale_out_info);
-
-  _upsample_f.configure(input, &_scaled_output, upsample_info);
-
-  _conv_f.configure(&_scaled_output, &_weights_flipped, bias, output, conv_info);
-
-  // Setup flip axis data
-  _flip_axis.allocator()->allocate();
-  auto axis_data = reinterpret_cast<uint32_t *>(_flip_axis.buffer());
-  axis_data[0] = static_cast<uint32_t>(width_idx);
-  axis_data[1] = static_cast<uint32_t>(height_idx);
-
-  _scaled_output.allocator()->allocate();
-}
-
-void NETransposeConvLayer::run()
-{
-  prepare();
-
-  MemoryGroupResourceScope scope_mg(_memory_group);
-
-  _upsample_f.run();
-  _conv_f.run();
-}
-
-void NETransposeConvLayer::prepare()
-{
-  if (!_is_prepared)
-  {
-    ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
-
-    // Run weights flipping and mark original weights tensor as unused
-    _weights_flipped.allocator()->allocate();
-    _flip_weights.run();
-    _original_weights->mark_as_unused();
-
-    // Prepare convolution
-    _conv_f.prepare();
-
-    _is_prepared = true;
-  }
-}
-} // namespace arm_compute