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diff --git a/runtime/neurun/backend/cpu/kernel/FullyConnectedLayer.cc b/runtime/neurun/backend/cpu/kernel/FullyConnectedLayer.cc
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+++ b/runtime/neurun/backend/cpu/kernel/FullyConnectedLayer.cc
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+/*
+ * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "FullyConnectedLayer.h"
+
+#include <cker/operation/FullyConnected.h>
+
+#include "OperationUtils.h"
+
+namespace neurun
+{
+namespace backend
+{
+namespace cpu
+{
+namespace kernel
+{
+
+FullyConnectedLayer::FullyConnectedLayer()
+    : _inputData(), _weightsData(), _biasData(), _outputData(), _inputDescr(), _weightsDescr(),
+      _biasDescr(), _outputDescr(), _activation(ir::Activation::NONE),
+      _inputType(OperandType::FLOAT32)
+{
+  // DO NOTHING
+}
+
+void FullyConnectedLayer::fullyConnectedFloat32()
+{
+  float output_activation_min, output_activation_max;
+  CalculateActivationRangeFloat(_activation, &output_activation_min, &output_activation_max);
+
+  nnfw::cker::FullyConnectedParams op_params;
+  op_params.float_activation_min = output_activation_min;
+  op_params.float_activation_max = output_activation_max;
+
+  nnfw::cker::FullyConnected(op_params, convertToExtendedCkerShape(_inputDescr), _inputData.f,
+                             convertToExtendedCkerShape(_weightsDescr), _weightsData.f,
+                             convertToExtendedCkerShape(_biasDescr), _biasData.f,
+                             convertToExtendedCkerShape(_outputDescr), _outputData.f);
+}
+
+// executionMutex is used to protect concurrent access of non-threadsafe resources
+// like gemmlowp::GemmContext.
+void FullyConnectedLayer::fullyConnectedQuant8()
+{
+  float real_multiplier = 0.0;
+  int32_t output_multiplier = 0;
+  int32_t output_shift = 0;
+  int32_t output_activation_min = 0;
+  int32_t output_activation_max = 0;
+  GetQuantizedConvolutionMultiplier(_inputDescr, _weightsDescr, _biasDescr, _outputDescr,
+                                    &real_multiplier);
+  QuantizeMultiplier(real_multiplier, &output_multiplier, &output_shift);
+  CalculateActivationRangeUint8(_activation, _outputDescr, &output_activation_min,
+                                &output_activation_max);
+
+  nnfw::cker::FullyConnectedParams op_params;
+  op_params.input_offset = -_inputDescr.offset;
+  op_params.weights_offset = -_weightsDescr.offset;
+  op_params.output_offset = _outputDescr.offset;
+  op_params.output_multiplier = output_multiplier;
+  op_params.output_shift = output_shift;
+  op_params.quantized_activation_min = output_activation_min;
+  op_params.quantized_activation_max = output_activation_max;
+
+  nnfw::cker::FullyConnected(op_params, convertToExtendedCkerShape(_inputDescr), _inputData.u8,
+                             convertToExtendedCkerShape(_weightsDescr), _weightsData.u8,
+                             convertToExtendedCkerShape(_biasDescr), _biasData.i32,
+                             convertToExtendedCkerShape(_outputDescr), _outputData.u8);
+}
+
+void FullyConnectedLayer::configure(uint8_t *inputData, const TensorDescriptor inputDescr,
+                                    uint8_t *weightsData, const TensorDescriptor weightsDescr,
+                                    uint8_t *biasData, const TensorDescriptor biasDescr,
+                                    ir::Activation activation, uint8_t *outputData,
+                                    const TensorDescriptor outputDescr)
+{
+  _inputData.u8 = inputData;
+  _inputDescr = inputDescr;
+  _inputType = inputDescr.type;
+  _weightsData.u8 = weightsData;
+  _weightsDescr = weightsDescr;
+  _biasData.u8 = biasData;
+  _biasDescr = biasDescr;
+  _activation = activation;
+  _outputData.u8 = outputData;
+  _outputDescr = outputDescr;
+}
+
+void FullyConnectedLayer::run()
+{
+  if (_inputType == OperandType::FLOAT32)
+  {
+    fullyConnectedFloat32();
+  }
+  else if (_inputType == OperandType::QUANT8_ASYMM)
+  {
+    fullyConnectedQuant8();
+  }
+}
+
+} // namespace kernel
+} // namespace cpu
+} // namespace backend
+} // namespace neurun