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diff --git a/compiler/enco/frontend/tflite/src/Op/DepthwiseConv2D.cpp b/compiler/enco/frontend/tflite/src/Op/DepthwiseConv2D.cpp
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+++ b/compiler/enco/frontend/tflite/src/Op/DepthwiseConv2D.cpp
@@ -0,0 +1,230 @@
+/*
+ * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "DepthwiseConv2D.h"
+
+#include "Convert.h"
+#include "IRBuilder.h"
+#include "GraphBuilder.h"
+#include "Padding.h"
+#include "Activation.h"
+
+#include <morph/tflite.h>
+
+#include <coco/IR/Module.h>
+#include <coco/IR/KernelLayouts.h>
+#include <coco/IR/FeatureLayouts.h>
+
+#include <nncc/core/ADT/tensor/Shape.h>
+#include <schema_generated.h>
+
+#include <cassert>
+
+using namespace nncc::core::ADT;
+using namespace morph::tflite;
+
+namespace tflimport
+{
+
+bool DepthwiseConv2DGraphBuilder::validate(const tflite::Operator *op) const
+{
+  auto const options = op->builtin_options_as_DepthwiseConv2DOptions();
+
+  if ((options->stride_h() == 0) || (options->stride_w() == 0))
+  {
+    return false;
+  }
+
+  return true;
+}
+
+void DepthwiseConv2DGraphBuilder::build(const tflite::Operator *op,
+                                        GraphBuilderContext *context) const
+{
+  assert(context != nullptr);
+
+  // preparation
+  coco::Module *m = context->m();
+  coco::Data *d = context->d();
+  coco::Block *blk = context->block();
+  TensorContext &tensor_context = context->tensor();
+  TensorBags &bags = context->bags();
+  TflBufferContext &buffer_context = context->buffer();
+  const tflite::SubGraph *graph = context->graph();
+  IndexVector opinputs = as_index_vector(op->inputs());
+  IndexVector opoutputs = as_index_vector(op->outputs());
+
+  // these are fixed in tflite
+  // input index 0 : input feature
+  // input index 1 : kernel
+  // input index 2 : bias (optional)
+  bool hasBias = (opinputs.size() == 3);
+  assert(opinputs.size() == 2 || hasBias);
+  assert(opoutputs.size() == 1);
+
+  int ifm_idx = opinputs.at(0);
+  int ker_idx = opinputs.at(1);
+  int ofm_idx = opoutputs.at(0);
+
+  const tensor::Shape &ifm_shape = tensor_context.shape(ifm_idx);
+  const tensor::Shape &ofm_shape = tensor_context.shape(ofm_idx);
+  tensor::Shape &ker_shape = const_cast<tensor::Shape &>(tensor_context.shape(ker_idx));
+
+  assert(ifm_shape.rank() == 4);
+  assert(ofm_shape.rank() == 4);
+  assert(ker_shape.rank() == 4);
+
+  assert(ker_shape.dim(0) == 1); // value > 1 was not tested. This value seems 1 in DepthwiseConv2D
+  assert(ifm_shape.dim(3) == ofm_shape.dim(3));
+  assert(ofm_shape.dim(3) == ker_shape.dim(3));
+
+  // Create an input feature map object
+  auto *ifm_obj = m->entity()->object()->create<coco::FeatureObject>();
+  auto *ifm_bag = bags.bag(ifm_idx);
+  ifm_obj->bag(ifm_bag);
+  ifm_obj->layout(coco::FeatureLayouts::BHWC::create(as_feature_shape(ifm_shape)));
+
+  // Create an an output feature map object
+  auto *ofm_obj = m->entity()->object()->create<coco::FeatureObject>();
+  auto *ofm_bag = bags.bag(ofm_idx);
+  ofm_obj->bag(ofm_bag);
+  ofm_obj->layout(coco::FeatureLayouts::BHWC::create(as_feature_shape(ofm_shape)));
+
+  // Create an kernel object
+  auto *ker_obj = m->entity()->object()->create<coco::KernelObject>();
+  auto *ker_bag = bags.bag(ker_idx);
+  ker_obj->bag(ker_bag);
+
+  // Adjust tflite kernel shape [1, h, w, channel_out] for coco::Kernel.
+  // coco::Kernel will have kernel.count = channel_out, kernel.depth = 1 ( == ker_shape.dim(0))
+  kernel::Shape new_shape{ker_shape.dim(3), 1, ker_shape.dim(1), ker_shape.dim(2)};
+  ker_obj->layout(coco::KernelLayouts::NHWC::create(new_shape));
+
+  // Create a kernel overlay for the kernel object
+  // TODO : support for other types
+  d->f32()->allocate(ker_bag);
+
+  TflBufferContext::TflBuffer<float> buffer = buffer_context.tensor_buffer<float>(graph, ker_idx);
+
+  auto ker_spn = d->f32()->weight(ker_bag);
+
+  // Copy data from tflBuffer of [1, h, w, channel_out] shape to coco::Data, which will be accessed
+  // by coco::KernelLayouts::NHWC
+  for (auto n = 0; n < new_shape.count(); n++)
+  {
+    auto tfl_c = n;
+    for (auto h = 0; h < new_shape.height(); h++)
+    {
+      for (auto w = 0; w < new_shape.width(); w++)
+      {
+        auto hw = new_shape.height() * new_shape.width();
+        for (auto c = 0; c < new_shape.depth(); c++)
+        {
+          auto tfl_n = c;
+          auto hwc = hw * new_shape.depth();
+          auto wc = new_shape.width() * new_shape.depth();
+
+          ker_spn[n * hwc + h * wc + w * new_shape.depth() + c] =
+              buffer.ptr[tfl_n * hw * new_shape.count() + /* new_shape.count() is old c */
+                         h * new_shape.width() * new_shape.count() + w * new_shape.count() + tfl_c];
+        }
+      }
+    }
+  }
+
+  // Create a Load op
+  auto load = op_builder(m).load(ifm_obj).pop();
+
+  // Create a coco::Conv2D op for DepthwiseConv2D
+  auto coco_dconv2d = m->entity()->op()->create<coco::Conv2D>();
+
+  // populating objects and options such as stride and padding for DepthwiseConv2D
+  coco_dconv2d->ker(ker_obj);
+
+  // setting params passed from TFLITE DepthwiseConv2DOptions
+  auto dconv_params = op->builtin_options_as_DepthwiseConv2DOptions();
+
+  assert(dconv_params->depth_multiplier() == 1); // other depth_multiplier was not tested
+
+  coco_dconv2d->group(ifm_obj->asFeature()->shape().depth());
+
+  coco_dconv2d->stride()->vertical(dconv_params->stride_h());
+  coco_dconv2d->stride()->horizontal(dconv_params->stride_w());
+
+  coco::Padding2D padding = depthwiseConv2D_padding(dconv_params, ifm_shape, ker_shape);
+  coco_dconv2d->pad()->top(padding.top());
+  coco_dconv2d->pad()->bottom(padding.bottom());
+  coco_dconv2d->pad()->left(padding.left());
+  coco_dconv2d->pad()->right(padding.right());
+
+  // Link ops
+  coco_dconv2d->arg(load);
+
+  // Object to store output for DepthwiseConv2D
+  auto *dconv2d_obj = m->entity()->object()->create<coco::FeatureObject>();
+  auto *dconv2d_bag = m->entity()->bag()->create(num_elements(ofm_shape));
+  dconv2d_obj->bag(dconv2d_bag);
+  dconv2d_obj->layout(coco::FeatureLayouts::BHWC::create(as_feature_shape(ofm_shape)));
+
+  // Create an Eval instruction for DepthwiseConv2D
+  auto dconv2d_ins = instr_builder(m).eval(dconv2d_obj, coco_dconv2d);
+
+  // Append the instruction to the block
+  blk->instr()->append(dconv2d_ins);
+
+  // Last Object to make a copy to Output Object
+  coco::FeatureObject *last_obj = dconv2d_obj;
+
+  if (hasBias)
+  {
+    // When there is a bias, use btmp_obj as bias add output
+    // Bias is adding last_obj with bias weight values
+    auto *btmp_obj = m->entity()->object()->create<coco::FeatureObject>();
+    auto *btmp_bag = m->entity()->bag()->create(num_elements(ofm_shape));
+    btmp_obj->bag(btmp_bag);
+    btmp_obj->layout(coco::FeatureLayouts::BHWC::create(ofm_obj->shape()));
+
+    int bias_idx = opinputs.at(2);
+
+    // Create an object for bias
+    auto bias_obj = m->entity()->object()->create<coco::FeatureObject>();
+    coco::Bag *bias_bag = bags.bag(bias_idx);
+    bias_obj->bag(bias_bag);
+    bias_obj->layout(coco::FeatureLayouts::BC::create(ofm_obj->shape()));
+
+    // Create Op of conv2d output (last_obj) + bias values(bias_obj)
+    auto bias_add = op_builder(m).load(last_obj).load(bias_obj).add().pop();
+
+    // Create Instr as bias add result write to btmp_obj
+    auto bias_add_ins = instr_builder(m).eval(btmp_obj, bias_add);
+
+    // Append the instruction
+    blk->instr()->append(bias_add_ins);
+
+    // Update last_obj to btmp_obj
+    last_obj = btmp_obj;
+  }
+
+  // fused activation
+  coco::FeatureObject *act_output =
+      build_activation(dconv_params->fused_activation_function(), blk, last_obj);
+
+  // Create Copy Instr of last_obj to Output Object
+  auto copy_ins = instr_builder(m).copy(ofm_obj, act_output);
+  blk->instr()->append(copy_ins);
+}
+
+} // namespace tflimport