1 files changed, 0 insertions, 110 deletions
diff --git a/src/backends/NeonWorkloads/NeonFullyConnectedWorkload.cpp b/src/backends/NeonWorkloads/NeonFullyConnectedWorkload.cpp
deleted file mode 100644
index 8cebb4f48f..0000000000
--- a/src/backends/NeonWorkloads/NeonFullyConnectedWorkload.cpp
+++ /dev/null
@@ -1,110 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include "NeonFullyConnectedWorkload.hpp"
-
-#include <backends/aclCommon/ArmComputeTensorUtils.hpp>
-#include <backends/aclCommon/ArmComputeUtils.hpp>
-#include <backends/CpuTensorHandle.hpp>
-
-namespace armnn
-{
-using namespace armcomputetensorutils;
-
-arm_compute::Status NeonFullyConnectedWorkloadValidate(const TensorInfo& input,
-                                                       const TensorInfo& output,
-                                                       const TensorInfo& weights,
-                                                       const TensorInfo& biases,
-                                                       const FullyConnectedDescriptor& descriptor)
-{
-    const arm_compute::TensorInfo aclInput = BuildArmComputeTensorInfo(input);
-    const arm_compute::TensorInfo aclOutput = BuildArmComputeTensorInfo(output);
-    const arm_compute::TensorInfo aclWeights = BuildArmComputeTensorInfo(weights);
-
-    arm_compute::TensorInfo aclBiases;
-    arm_compute::TensorInfo *optionalAclBiases = nullptr;
-    if (descriptor.m_BiasEnabled)
-    {
-        aclBiases  = BuildArmComputeTensorInfo(biases);
-        optionalAclBiases = &aclBiases;
-    }
-
-    const arm_compute::FullyConnectedLayerInfo fullyConnectedLayerInfo =
-        ConvertFullyConnectedDescriptorToAclFullyConnectedLayerInfo(descriptor);
-
-
-    return arm_compute::NEFullyConnectedLayer::validate(&aclInput,
-                                                        &aclWeights,
-                                                        optionalAclBiases,
-                                                        &aclOutput,
-                                                        fullyConnectedLayerInfo);
-}
-
-NeonFullyConnectedWorkload::NeonFullyConnectedWorkload(const FullyConnectedQueueDescriptor& descriptor,
-    const WorkloadInfo& info, std::shared_ptr<arm_compute::MemoryManagerOnDemand>& memoryManager)
-    : BaseWorkload<FullyConnectedQueueDescriptor>(descriptor, info)
-    , m_FullyConnectedLayer(memoryManager)
-{
-    m_Data.ValidateInputsOutputs("NeonFullyConnectedWorkload", 1, 1);
-
-    arm_compute::ITensor& input = boost::polymorphic_downcast<INeonTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
-    arm_compute::ITensor& output = boost::polymorphic_downcast<INeonTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
-
-    m_WeightsTensor = std::make_unique<arm_compute::Tensor>();
-    BuildArmComputeTensor(*m_WeightsTensor, m_Data.m_Weight->GetTensorInfo());
-
-    if (m_Data.m_Parameters.m_BiasEnabled)
-    {
-        m_BiasesTensor = std::make_unique<arm_compute::Tensor>();
-        BuildArmComputeTensor(*m_BiasesTensor, m_Data.m_Bias->GetTensorInfo());
-    }
-
-    // Construct
-    arm_compute::FullyConnectedLayerInfo fc_info;
-    fc_info.transpose_weights = m_Data.m_Parameters.m_TransposeWeightMatrix;
-    m_FullyConnectedLayer.configure(&input, m_WeightsTensor.get(), m_BiasesTensor.get(), &output, fc_info);
-
-    // Allocate
-    if (m_Data.m_Weight->GetTensorInfo().GetDataType() == DataType::QuantisedAsymm8)
-    {
-        InitialiseArmComputeTensorData(*m_WeightsTensor, m_Data.m_Weight->GetConstTensor<uint8_t>());
-    }
-    else
-    {
-        InitializeArmComputeTensorDataForFloatTypes(*m_WeightsTensor, m_Data.m_Weight);
-    }
-
-    if (m_BiasesTensor)
-    {
-        if (m_Data.m_Bias->GetTensorInfo().GetDataType() == DataType::Signed32)
-        {
-            InitialiseArmComputeTensorData(*m_BiasesTensor, m_Data.m_Bias->GetConstTensor<int32_t>());
-        }
-        else
-        {
-            InitializeArmComputeTensorDataForFloatTypes(*m_BiasesTensor, m_Data.m_Bias);
-        }
-    }
-
-    // Force Compute Library to perform the necessary copying and reshaping, after which
-    // delete all the input tensors that will no longer be needed
-    m_FullyConnectedLayer.prepare();
-    FreeUnusedTensors();
-}
-
-void NeonFullyConnectedWorkload::Execute() const
-{
-    ARMNN_SCOPED_PROFILING_EVENT_NEON("NeonFullyConnectedWorkload_Execute");
-    m_FullyConnectedLayer.run();
-}
-
-void NeonFullyConnectedWorkload::FreeUnusedTensors()
-{
-    FreeTensorIfUnused(m_WeightsTensor);
-    FreeTensorIfUnused(m_BiasesTensor);
-}
-
-} //namespace armnn
-