diff --git a/src/backends/backendsCommon/test/DynamicBackendTests.hpp b/src/backends/backendsCommon/test/DynamicBackendTests.hpp
index 0d98804..6a673c6 100644
--- a/src/backends/backendsCommon/test/DynamicBackendTests.hpp
+++ b/src/backends/backendsCommon/test/DynamicBackendTests.hpp
@@ -1,5 +1,5 @@
 //
-// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017,2022 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 
@@ -1469,8 +1469,6 @@
         { outputInfo }
     };
     convolution2dQueueDescriptor.m_Inputs.push_back(nullptr);
-    auto weights = std::make_unique<ScopedTensorHandle>(weightInfo);
-    convolution2dQueueDescriptor.m_Weight = weights.get();
 
     // Create a convolution workload with the dummy settings
     auto workload = referenceWorkloadFactory->CreateWorkload(LayerType::Convolution2d,
diff --git a/src/backends/backendsCommon/test/IsLayerSupportedTestImpl.hpp b/src/backends/backendsCommon/test/IsLayerSupportedTestImpl.hpp
index 18f11a5..fb7a027 100644
--- a/src/backends/backendsCommon/test/IsLayerSupportedTestImpl.hpp
+++ b/src/backends/backendsCommon/test/IsLayerSupportedTestImpl.hpp
@@ -1,5 +1,5 @@
 //
-// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017,2022 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 #pragma once
@@ -242,10 +242,6 @@
         desc.m_StrideX = 1;
         desc.m_StrideY = 1;
         m_Layer = dummyGraph.AddLayer<ConvolutionLayerType>(desc, "");
-        m_Layer->m_Weight = std::make_unique<armnn::ScopedTensorHandle>(
-            armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
-        m_Layer->m_Bias = std::make_unique<armnn::ScopedTensorHandle>(
-            armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
     }
 
     ~DummyConvolutionLayer()
@@ -268,10 +264,28 @@
 {
 };
 
-template<>
+// Note: When m_Weight and m_Bias are removed from TransposeConvolution, Transpose can use DummyConvolutionLayer
+template <>
 struct DummyLayer<armnn::TransposeConvolution2dLayer>
-    : public DummyConvolutionLayer<armnn::TransposeConvolution2dLayer>
 {
+    DummyLayer()
+    {
+        typename armnn::TransposeConvolution2dLayer::DescriptorType desc;
+        desc.m_StrideX = 1;
+        desc.m_StrideY = 1;
+        m_Layer = dummyGraph.AddLayer<armnn::TransposeConvolution2dLayer>(desc, "");
+        m_Layer->m_Weight = std::make_unique<armnn::ScopedTensorHandle>(
+            armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
+        m_Layer->m_Bias = std::make_unique<armnn::ScopedTensorHandle>(
+            armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
+    }
+
+    ~DummyLayer()
+    {
+        dummyGraph.EraseLayer(m_Layer);
+    }
+
+    armnn::TransposeConvolution2dLayer* m_Layer;
 };
 
 template<>
@@ -518,8 +532,6 @@
     {
         armnn::FullyConnectedLayer::DescriptorType desc;
         m_Layer = dummyGraph.AddLayer<armnn::FullyConnectedLayer>(desc, "");
-        m_Layer->m_Weight = std::make_unique<armnn::ScopedTensorHandle>(
-            armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
     }
 
     ~DummyLayer()
@@ -767,12 +779,6 @@
     return layer.GetNumOutputSlots();
 }
 
-template<>
-unsigned int GetNumInputs<armnn::LayerType::Concat>(const armnn::Layer& layer)
-{
-    IgnoreUnused(layer);
-    return 2;
-}
 
 // Tests that the IsLayerSupported() function returns the correct value.
 // We determined the correct value by *trying* to create the relevant workload and seeing if it matches what we expect.
diff --git a/src/backends/backendsCommon/test/LayerReleaseConstantDataTest.cpp b/src/backends/backendsCommon/test/LayerReleaseConstantDataTest.cpp
index abfb621..b7b5145 100644
--- a/src/backends/backendsCommon/test/LayerReleaseConstantDataTest.cpp
+++ b/src/backends/backendsCommon/test/LayerReleaseConstantDataTest.cpp
@@ -1,5 +1,5 @@
 //
-// Copyright © 2017 Arm Ltd. All rights reserved.
+// Copyright © 2017,2022 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 
@@ -12,8 +12,6 @@
 
 #include <doctest/doctest.h>
 
-#include <utility>
-
 using namespace armnn;
 using namespace std;
 
@@ -84,29 +82,15 @@
     layerDesc.m_StrideY = 4;
     layerDesc.m_BiasEnabled = true;
 
-    Convolution2dLayer* const layer = graph.AddLayer<Convolution2dLayer>(layerDesc, "layer");
+    auto* const convolutionLayer = graph.AddLayer<Convolution2dLayer>(layerDesc, "convolution");
+    auto* const weightsLayer = graph.AddLayer<ConstantLayer>("weights");
+    auto* const biasLayer = graph.AddLayer<ConstantLayer>("bias");
 
-    layer->m_Weight = std::make_unique<ScopedTensorHandle>(TensorInfo({ 2, 3, 5, 3 },
-                                                                      armnn::DataType::Float32));
-    layer->m_Bias = std::make_unique<ScopedTensorHandle>
-        (TensorInfo({ 2 }, GetBiasDataType(armnn::DataType::Float32)));
+    TensorInfo weightsInfo = TensorInfo({ 2, 3, 5, 3 }, armnn::DataType::Float32, 1.0, 0.0, true);
+    TensorInfo biasInfo = TensorInfo({ 2 }, GetBiasDataType(armnn::DataType::Float32), 1.0, 0.0, true);
 
-    layer->m_Weight->Allocate();
-    layer->m_Bias->Allocate();
-
-    ConstantLayer* weightsLayer = graph.AddLayer<ConstantLayer>("Weights");
-    ConstantLayer* biasLayer = graph.AddLayer<ConstantLayer>("Bias");
-
-    weightsLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(TensorInfo({ 2, 3, 5, 3 },
-                                                                      armnn::DataType::Float32));
-
-    biasLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(
-        TensorInfo({2}, GetBiasDataType(armnn::DataType::Float32)));
-
-    TensorInfo weightsInfo = weightsLayer->m_LayerOutput->GetTensorInfo();
-    weightsInfo.SetConstant();
-    TensorInfo biasInfo = biasLayer->m_LayerOutput->GetTensorInfo();
-    biasInfo.SetConstant();
+    weightsLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(weightsInfo);
+    biasLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(biasInfo);
 
     weightsLayer->GetOutputSlot(0).SetTensorInfo(weightsInfo);
     biasLayer->GetOutputSlot(0).SetTensorInfo(biasInfo);
@@ -116,21 +100,21 @@
     Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
 
     // connect up
-    Connect(input, layer, TensorInfo({ 2, 3, 8, 16 }, armnn::DataType::Float32));
-    weightsLayer->GetOutputSlot().Connect(layer->GetInputSlot(1));
-    biasLayer->GetOutputSlot().Connect(layer->GetInputSlot(2));
-    Connect(layer, output, TensorInfo({ 2, 2, 2, 10 }, armnn::DataType::Float32));
+    Connect(input, convolutionLayer, TensorInfo({ 2, 3, 8, 16 }, armnn::DataType::Float32));
+    weightsLayer->GetOutputSlot().Connect(convolutionLayer->GetInputSlot(1));
+    biasLayer->GetOutputSlot().Connect(convolutionLayer->GetInputSlot(2));
+    Connect(convolutionLayer, output, TensorInfo({ 2, 2, 2, 10 }, armnn::DataType::Float32));
 
     // check the constants that they are not NULL
     CHECK(weightsLayer->m_LayerOutput != nullptr);
     CHECK(biasLayer->m_LayerOutput != nullptr);
 
-    // free up the constants..
-    layer->ReleaseConstantData();
+    // free up the constants.
+    convolutionLayer->ReleaseConstantData();
 
-    // check the constants that they are NULL now
-    CHECK(weightsLayer->m_LayerOutput == nullptr);
-    CHECK(biasLayer->m_LayerOutput == nullptr);
+    // check the constants that they are still not NULL
+    CHECK(weightsLayer->m_LayerOutput != nullptr);
+    CHECK(biasLayer->m_LayerOutput != nullptr);
 }
 
 TEST_CASE("ReleaseDepthwiseConvolution2dLayerConstantDataTest")
@@ -147,33 +131,39 @@
     layerDesc.m_StrideY         = 4;
     layerDesc.m_BiasEnabled     = true;
 
-    DepthwiseConvolution2dLayer* const layer = graph.AddLayer<DepthwiseConvolution2dLayer>(layerDesc, "layer");
+    auto* const convolutionLayer = graph.AddLayer<DepthwiseConvolution2dLayer>(layerDesc, "convolution");
+    auto* const weightsLayer = graph.AddLayer<ConstantLayer>("weights");
+    auto* const biasLayer = graph.AddLayer<ConstantLayer>("bias");
 
-    layer->m_Weight = std::make_unique<ScopedTensorHandle>(
-        TensorInfo({3, 3, 5, 3}, DataType::Float32));
-    layer->m_Bias = std::make_unique<ScopedTensorHandle>(
-        TensorInfo({9}, DataType::Float32));
-    layer->m_Weight->Allocate();
-    layer->m_Bias->Allocate();
+    TensorInfo weightsInfo = TensorInfo({ 3, 3, 5, 3 }, armnn::DataType::Float32, 1.0, 0.0, true);
+    TensorInfo biasInfo = TensorInfo({ 9 }, GetBiasDataType(armnn::DataType::Float32), 1.0, 0.0, true);
+
+    weightsLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(weightsInfo);
+    biasLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(biasInfo);
+
+    weightsLayer->GetOutputSlot(0).SetTensorInfo(weightsInfo);
+    biasLayer->GetOutputSlot(0).SetTensorInfo(biasInfo);
 
     // create extra layers
     Layer* const input = graph.AddLayer<InputLayer>(0, "input");
     Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
 
     // connect up
-    Connect(input, layer, TensorInfo({2, 3, 8, 16}, armnn::DataType::Float32));
-    Connect(layer, output, TensorInfo({2, 9, 2, 10}, armnn::DataType::Float32));
+    Connect(input, convolutionLayer, TensorInfo({2, 3, 8, 16}, armnn::DataType::Float32));
+    weightsLayer->GetOutputSlot().Connect(convolutionLayer->GetInputSlot(1));
+    biasLayer->GetOutputSlot().Connect(convolutionLayer->GetInputSlot(2));
+    Connect(convolutionLayer, output, TensorInfo({2, 9, 2, 10}, armnn::DataType::Float32));
 
     // check the constants that they are not NULL
-    CHECK(layer->m_Weight != nullptr);
-    CHECK(layer->m_Bias != nullptr);
+    CHECK(weightsLayer->m_LayerOutput != nullptr);
+    CHECK(biasLayer->m_LayerOutput != nullptr);
 
-    // free up the constants..
-    layer->ReleaseConstantData();
+    // free up the constants.
+    convolutionLayer->ReleaseConstantData();
 
-    // check the constants that they are NULL now
-    CHECK(layer->m_Weight == nullptr);
-    CHECK(layer->m_Bias == nullptr);
+    // check the constants that they are still not NULL
+    CHECK(weightsLayer->m_LayerOutput != nullptr);
+    CHECK(biasLayer->m_LayerOutput != nullptr);
 }
 
 TEST_CASE("ReleaseFullyConnectedLayerConstantDataTest")
@@ -185,36 +175,42 @@
     layerDesc.m_BiasEnabled = true;
     layerDesc.m_TransposeWeightMatrix = true;
 
-    FullyConnectedLayer* const layer = graph.AddLayer<FullyConnectedLayer>(layerDesc, "layer");
+    auto* const fullyConnectedLayer = graph.AddLayer<FullyConnectedLayer>(layerDesc, "layer");
+    auto* const weightsLayer = graph.AddLayer<ConstantLayer>("weights");
+    auto* const biasLayer = graph.AddLayer<ConstantLayer>("bias");
 
     float inputsQScale = 1.0f;
     float outputQScale = 2.0f;
 
-    layer->m_Weight = std::make_unique<ScopedTensorHandle>(
-        TensorInfo({7, 20}, DataType::QAsymmU8, inputsQScale, 0));
-    layer->m_Bias = std::make_unique<ScopedTensorHandle>(
-        TensorInfo({7}, GetBiasDataType(DataType::QAsymmU8), inputsQScale));
-    layer->m_Weight->Allocate();
-    layer->m_Bias->Allocate();
+    TensorInfo weightsInfo = TensorInfo({ 7, 20 }, DataType::QAsymmU8, inputsQScale, 0.0, true);
+    TensorInfo biasInfo = TensorInfo({ 7 }, GetBiasDataType(DataType::QAsymmU8), inputsQScale, 0.0, true);
+
+    weightsLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(weightsInfo);
+    biasLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(biasInfo);
+
+    weightsLayer->GetOutputSlot(0).SetTensorInfo(weightsInfo);
+    biasLayer->GetOutputSlot(0).SetTensorInfo(biasInfo);
 
     // create extra layers
     Layer* const input = graph.AddLayer<InputLayer>(0, "input");
     Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
 
     // connect up
-    Connect(input, layer, TensorInfo({3, 1, 4, 5}, DataType::QAsymmU8, inputsQScale));
-    Connect(layer, output, TensorInfo({3, 7}, DataType::QAsymmU8, outputQScale));
+    Connect(input, fullyConnectedLayer, TensorInfo({ 3, 1, 4, 5 }, DataType::QAsymmU8, inputsQScale));
+    weightsLayer->GetOutputSlot().Connect(fullyConnectedLayer->GetInputSlot(1));
+    biasLayer->GetOutputSlot().Connect(fullyConnectedLayer->GetInputSlot(2));
+    Connect(fullyConnectedLayer, output, TensorInfo({ 3, 7 }, DataType::QAsymmU8, outputQScale));
 
     // check the constants that they are not NULL
-    CHECK(layer->m_Weight != nullptr);
-    CHECK(layer->m_Bias != nullptr);
+    CHECK(weightsLayer->m_LayerOutput != nullptr);
+    CHECK(biasLayer->m_LayerOutput != nullptr);
 
-    // free up the constants..
-    layer->ReleaseConstantData();
+    // free up the constants.
+    fullyConnectedLayer->ReleaseConstantData();
 
-    // check the constants that they are NULL now
-    CHECK(layer->m_Weight == nullptr);
-    CHECK(layer->m_Bias == nullptr);
+    // check the constants that they are still not NULL
+    CHECK(weightsLayer->m_LayerOutput != nullptr);
+    CHECK(biasLayer->m_LayerOutput != nullptr);
 }
 
 }
diff --git a/src/backends/backendsCommon/test/OptimizeSubgraphViewTests.cpp b/src/backends/backendsCommon/test/OptimizeSubgraphViewTests.cpp
index 6735469..997fe98 100644
--- a/src/backends/backendsCommon/test/OptimizeSubgraphViewTests.cpp
+++ b/src/backends/backendsCommon/test/OptimizeSubgraphViewTests.cpp
@@ -1,5 +1,5 @@
 //
-// Copyright © 2017 Arm Ltd. All rights reserved.
+// Copyright © 2017,2022 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 
@@ -94,13 +94,10 @@
                                         LayerNameToLayerMap& layersInGraph,
                                         const Convolution2dDescriptor& convolutionDescriptor,
                                         const std::string& layerName,
-                                        const TensorInfo& weightInfo,
-                                        const TensorInfo& biasInfo,
                                         const TensorInfo& outputInfo)
 {
     Convolution2dLayer* const convLayer = graph.AddLayer<Convolution2dLayer>(convolutionDescriptor, layerName.c_str());
     CHECK(convLayer);
-    SetWeightAndBias(convLayer, weightInfo, biasInfo);
     convLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);
     layersInGraph.insert(std::make_pair(convLayer->GetName(), convLayer));
     return convLayer;
@@ -335,11 +332,11 @@
     // Construct the graph
     Layer* const inputLayer = AddInputLayer(graph, "input layer", inputInfo);
     Convolution2dLayer* const convLayer = AddConvolutionLayer(graph, layersInGraph, convolutionDescriptor,
-                                                              "conv layer", weightInfo, biasInfo, outputInfo);
+                                                              "conv layer", outputInfo);
 
   ConstantLayer* const weightsLayer =
-      AddConstantLayer(graph, layersInGraph, "Weights Layer", constWeightsTensor, outputInfo);
-  ConstantLayer* const biasLayer = AddConstantLayer(graph, layersInGraph, "Bias Layer", constBiasTensor, outputInfo);
+      AddConstantLayer(graph, layersInGraph, "Weights Layer", constWeightsTensor, weightInfo);
+  ConstantLayer* const biasLayer = AddConstantLayer(graph, layersInGraph, "Bias Layer", constBiasTensor, biasInfo);
 
     Layer* const outputLayer = AddOutputLayer(graph, "output layer");
 
@@ -373,7 +370,6 @@
     std::vector<float> biasVector(16);
     ConstTensor constBiasTensor(biasInfo, biasVector);
 
-
     Convolution2dDescriptor convolutionDescriptor;
     convolutionDescriptor.m_StrideX     = 1;
     convolutionDescriptor.m_StrideY     = 1;
@@ -383,40 +379,40 @@
     // Construct the graph
     Layer* const inputLayer = AddInputLayer(graph, "input layer", inputInfo);
     Convolution2dLayer* const conv1Layer = AddConvolutionLayer(graph, layersInGraph, convolutionDescriptor,
-                                                               "conv1 layer", weightInfo, biasInfo, outputInfo);
+                                                               "conv1 layer", outputInfo);
     ConstantLayer* const weightsLayer1 =
-        AddConstantLayer(graph, layersInGraph, "Weights Layer 1", constWeightsTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Weights Layer 1", constWeightsTensor, weightInfo);
     ConstantLayer* const biasLayer1 =
-        AddConstantLayer(graph, layersInGraph, "Bias Layer 1", constBiasTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Bias Layer 1", constBiasTensor, biasInfo);
 
     Convolution2dLayer* const conv2Layer = AddConvolutionLayer(graph, layersInGraph, convolutionDescriptor,
-                                                               "conv2 layer", weightInfo, biasInfo, outputInfo);
+                                                               "conv2 layer", outputInfo);
     ConstantLayer* const weightsLayer2 =
-        AddConstantLayer(graph, layersInGraph, "Weights Layer 2", constWeightsTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Weights Layer 2", constWeightsTensor, weightInfo);
     ConstantLayer* const biasLayer2 =
-        AddConstantLayer(graph, layersInGraph, "Bias Layer 2", constBiasTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Bias Layer 2", constBiasTensor, biasInfo);
 
 
     Convolution2dLayer* const conv3Layer = AddConvolutionLayer(graph, layersInGraph, convolutionDescriptor,
-                                                               "conv3 layer", weightInfo, biasInfo, outputInfo);
+                                                               "conv3 layer", outputInfo);
     ConstantLayer* const weightsLayer3 =
-        AddConstantLayer(graph, layersInGraph, "Weights Layer 3", constWeightsTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Weights Layer 3", constWeightsTensor, weightInfo);
     ConstantLayer* const biasLayer3 =
-        AddConstantLayer(graph, layersInGraph, "Bias Layer 3", constBiasTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Bias Layer 3", constBiasTensor, biasInfo);
 
     Convolution2dLayer* const conv4Layer = AddConvolutionLayer(graph, layersInGraph, convolutionDescriptor,
-                                                               "conv4 layer", weightInfo, biasInfo, outputInfo);
+                                                               "conv4 layer", outputInfo);
     ConstantLayer* const weightsLayer4 =
-        AddConstantLayer(graph, layersInGraph, "Weights Layer 4", constWeightsTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Weights Layer 4", constWeightsTensor, weightInfo);
     ConstantLayer* const biasLayer4 =
-        AddConstantLayer(graph, layersInGraph, "Bias Layer 4", constBiasTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Bias Layer 4", constBiasTensor, biasInfo);
 
     Convolution2dLayer* const conv5Layer = AddConvolutionLayer(graph, layersInGraph, convolutionDescriptor,
-                                                               "conv5 layer", weightInfo, biasInfo, outputInfo);
+                                                               "conv5 layer", outputInfo);
     ConstantLayer* const weightsLayer5 =
-        AddConstantLayer(graph, layersInGraph, "Weights Layer 5", constWeightsTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Weights Layer 5", constWeightsTensor, weightInfo);
     ConstantLayer* const biasLayer5 =
-        AddConstantLayer(graph, layersInGraph, "Bias Layer 5", constBiasTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Bias Layer 5", constBiasTensor, biasInfo);
 
 
     Layer* const outputLayer = AddOutputLayer(graph, "output layer");
@@ -504,26 +500,26 @@
     // Construct the graph
     Layer* const inputLayer = AddInputLayer(graph, "input layer", inputInfo);
     ConstantLayer* const weightsLayer1 =
-        AddConstantLayer(graph, layersInGraph, "Weights Layer 1", constWeightsTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Weights Layer 1", constWeightsTensor, weightInfo);
 
     ConstantLayer* const biasLayer1 =
-        AddConstantLayer(graph, layersInGraph, "Bias Layer 1", constBiasTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Bias Layer 1", constBiasTensor, biasInfo);
 
     Convolution2dLayer* const conv1Layer = AddConvolutionLayer(graph, layersInGraph, convolutionDescriptor,
-                                                               "conv1 layer", weightInfo, biasInfo, outputInfo);
+                                                               "conv1 layer", outputInfo);
     Pooling2dLayer* const pooling1Layer = AddPoolingLayer(graph, layersInGraph, poolingDescriptor,
                                                           "pooling1 layer", outputInfo);
     Pooling2dLayer* const pooling2Layer = AddPoolingLayer(graph, layersInGraph, poolingDescriptor,
                                                           "pooling2 layer", outputInfo);
 
     ConstantLayer* const weightsLayer2 =
-        AddConstantLayer(graph, layersInGraph, "Weights Layer 2", constWeightsTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Weights Layer 2", constWeightsTensor, weightInfo);
 
     ConstantLayer* const biasLayer2 =
-        AddConstantLayer(graph, layersInGraph, "Bias Layer 2", constBiasTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Bias Layer 2", constBiasTensor, biasInfo);
 
     Convolution2dLayer* const conv2Layer = AddConvolutionLayer(graph, layersInGraph, convolutionDescriptor,
-                                                               "conv2 layer", weightInfo, biasInfo, outputInfo);
+                                                               "conv2 layer", outputInfo);
     Pooling2dLayer* const pooling3Layer = AddPoolingLayer(graph, layersInGraph, poolingDescriptor,
                                                           "pooling3 layer", outputInfo);
     Layer* const outputLayer = AddOutputLayer(graph, "output layer");
@@ -581,14 +577,13 @@
     Layer* const inputLayer = AddInputLayer(graph, "input layer", inputInfo);
 
     ConstantLayer* const weightsLayer =
-        AddConstantLayer(graph, layersInGraph, "Weights Layer unoptimizable", constWeightsTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Weights Layer unoptimizable", constWeightsTensor, weightInfo);
 
     ConstantLayer* const biasLayer =
-        AddConstantLayer(graph, layersInGraph, "Bias Layer unoptimizable", constBiasTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Bias Layer unoptimizable", constBiasTensor, biasInfo);
 
     Convolution2dLayer* const convLayer = AddConvolutionLayer(graph, layersInGraph, convolutionDescriptor,
-                                                               "conv layer unoptimizable", weightInfo, biasInfo,
-                                                               outputInfo);
+                                                               "conv layer unoptimizable", outputInfo);
     Layer* const outputLayer = AddOutputLayer(graph, "output layer");
 
     // Connect the network
@@ -631,46 +626,36 @@
     Layer* const inputLayer = AddInputLayer(graph, "input layer", inputInfo);
 
     ConstantLayer* const weightsLayer1 =
-        AddConstantLayer(graph, layersInGraph, "Weights Layer 1", constWeightsTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Weights Layer 1", constWeightsTensor, weightInfo);
     ConstantLayer* const biasLayer1 =
-        AddConstantLayer(graph, layersInGraph, "Bias Layer 1", constBiasTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Bias Layer 1", constBiasTensor, biasInfo);
     ConstantLayer* const weightsLayer2 =
-        AddConstantLayer(graph, layersInGraph, "Weights Layer 2 unoptimizable", constWeightsTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Weights Layer 2 unoptimizable", constWeightsTensor, weightInfo);
     ConstantLayer* const biasLayer2 =
-        AddConstantLayer(graph, layersInGraph, "Bias Layer 2 unoptimizable", constBiasTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Bias Layer 2 unoptimizable", constBiasTensor, biasInfo);
     ConstantLayer* const weightsLayer3 =
-        AddConstantLayer(graph, layersInGraph, "Weights Layer 3", constWeightsTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Weights Layer 3", constWeightsTensor, weightInfo);
     ConstantLayer* const biasLayer3 =
-        AddConstantLayer(graph, layersInGraph, "Bias Layer 3", constBiasTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Bias Layer 3", constBiasTensor, biasInfo);
     ConstantLayer* const weightsLayer4 =
-        AddConstantLayer(graph, layersInGraph, "Weights Layer 4 unoptimizable", constWeightsTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Weights Layer 4 unoptimizable", constWeightsTensor, weightInfo);
     ConstantLayer* const biasLayer4 =
-        AddConstantLayer(graph, layersInGraph, "Bias Layer 4 unoptimizable", constBiasTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Bias Layer 4 unoptimizable", constBiasTensor, biasInfo);
     ConstantLayer* const weightsLayer5 =
-        AddConstantLayer(graph, layersInGraph, "Weights Layer 5", constWeightsTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Weights Layer 5", constWeightsTensor, weightInfo);
     ConstantLayer* const biasLayer5 =
-        AddConstantLayer(graph, layersInGraph, "Bias Layer 5", constBiasTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Bias Layer 5", constBiasTensor, biasInfo);
 
   Convolution2dLayer* const conv1Layer = AddConvolutionLayer(graph, layersInGraph, convolutionDescriptor,
-                                                             "conv1 layer", weightInfo, biasInfo, outputInfo);
-  Convolution2dLayer* const conv2Layer = AddConvolutionLayer(graph,
-                                                             layersInGraph,
-                                                             convolutionDescriptor,
-                                                             "conv2 layer unoptimizable",
-                                                             weightInfo,
-                                                             biasInfo,
-                                                             outputInfo);
+                                                             "conv1 layer", outputInfo);
+  Convolution2dLayer* const conv2Layer = AddConvolutionLayer(graph, layersInGraph, convolutionDescriptor,
+                                                             "conv2 layer unoptimizable", outputInfo);
   Convolution2dLayer* const conv3Layer = AddConvolutionLayer(graph, layersInGraph, convolutionDescriptor,
-                                                             "conv3 layer", weightInfo, biasInfo, outputInfo);
-  Convolution2dLayer* const conv4Layer = AddConvolutionLayer(graph,
-                                                             layersInGraph,
-                                                             convolutionDescriptor,
-                                                             "conv4 layer unoptimizable",
-                                                             weightInfo,
-                                                             biasInfo,
-                                                             outputInfo);
+                                                             "conv3 layer", outputInfo);
+  Convolution2dLayer* const conv4Layer = AddConvolutionLayer(graph, layersInGraph, convolutionDescriptor,
+                                                             "conv4 layer unoptimizable", outputInfo);
   Convolution2dLayer* const conv5Layer = AddConvolutionLayer(graph, layersInGraph, convolutionDescriptor,
-                                                             "conv5 layer", weightInfo, biasInfo, outputInfo);
+                                                             "conv5 layer", outputInfo);
 
   Layer* const outputLayer = AddOutputLayer(graph, "output layer");
 
@@ -747,25 +732,24 @@
     Layer* const input2Layer = AddInputLayer(graph, "input2 layer", inputInfo, 1);
 
     ConstantLayer* const weightsLayer1 =
-        AddConstantLayer(graph, layersInGraph, "Weights Layer 1", constWeightsTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Weights Layer 1", constWeightsTensor, weightInfo);
     ConstantLayer* const biasLayer1 =
-        AddConstantLayer(graph, layersInGraph, "Bias Layer 1", constBiasTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Bias Layer 1", constBiasTensor, biasInfo);
     ConstantLayer* const weightsLayer2 =
-        AddConstantLayer(graph, layersInGraph, "Weights Layer 2 unoptimizable", constWeightsTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Weights Layer 2 unoptimizable", constWeightsTensor, weightInfo);
     ConstantLayer* const biasLayer2 =
-        AddConstantLayer(graph, layersInGraph, "Bias Layer 2 unoptimizable", constBiasTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Bias Layer 2 unoptimizable", constBiasTensor, biasInfo);
     ConstantLayer* const weightsLayer3 =
-        AddConstantLayer(graph, layersInGraph, "Weights Layer 3", constWeightsTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Weights Layer 3", constWeightsTensor, weightInfo);
     ConstantLayer* const biasLayer3 =
-        AddConstantLayer(graph, layersInGraph, "Bias Layer 3", constBiasTensor, outputInfo);
+        AddConstantLayer(graph, layersInGraph, "Bias Layer 3", constBiasTensor, biasInfo);
 
     Convolution2dLayer* const conv1Layer = AddConvolutionLayer(graph, layersInGraph, convolutionDescriptor,
-                                                               "conv1 layer", weightInfo, biasInfo, outputInfo);
+                                                               "conv1 layer", outputInfo);
     Convolution2dLayer* const conv2Layer = AddConvolutionLayer(graph, layersInGraph, convolutionDescriptor,
-                                                               "conv2 layer unoptimizable", weightInfo, biasInfo,
-                                                               outputInfo);
+                                                               "conv2 layer unoptimizable", outputInfo);
     Convolution2dLayer* const conv3Layer = AddConvolutionLayer(graph, layersInGraph, convolutionDescriptor,
-                                                               "conv3 layer", weightInfo, biasInfo, outputInfo);
+                                                               "conv3 layer", outputInfo);
     AdditionLayer* const addLayer = AddAdditionaLayer(graph, layersInGraph, "add layer", outputInfo);
     Layer* const outputLayer = AddOutputLayer(graph, "output layer");
 
diff --git a/src/backends/backendsCommon/test/WorkloadDataValidation.cpp b/src/backends/backendsCommon/test/WorkloadDataValidation.cpp
index fed21eb..2a09f65 100644
--- a/src/backends/backendsCommon/test/WorkloadDataValidation.cpp
+++ b/src/backends/backendsCommon/test/WorkloadDataValidation.cpp
@@ -1,5 +1,5 @@
 //
-// Copyright © 2017 Arm Ltd. All rights reserved.
+// Copyright © 2017,2022 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 
@@ -158,13 +158,8 @@
     FullyConnectedQueueDescriptor invalidData;
     WorkloadInfo                  invalidInfo;
 
-    ScopedTensorHandle weightTensor(weightsDesc);
-    ScopedTensorHandle biasTensor(biasesDesc);
-
     AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr);
     AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr);
-    invalidData.m_Weight = &weightTensor;
-    invalidData.m_Bias = &biasTensor;
     invalidData.m_Parameters.m_BiasEnabled = true;
     invalidData.m_Parameters.m_TransposeWeightMatrix = false;
 
@@ -678,16 +673,10 @@
     AddInputToWorkload(queueDescriptor, workloadInfo, weightInfo, nullptr);
     AddOutputToWorkload(queueDescriptor, workloadInfo, outputInfo, nullptr);
 
-    ScopedTensorHandle weightTensor(weightInfo);
-    queueDescriptor.m_Weight = &weightTensor;
-
     // Test 1: correct per-axis quantization values
     const std::vector<float> biasPerAxisScales1  = { 3.75f, 5.25f };
     const TensorInfo biasInfo1(biasShape, biasType, biasPerAxisScales1, 0);
 
-    ScopedTensorHandle biasHandle1(biasInfo1);
-    queueDescriptor.m_Bias = &biasHandle1;
-
     AddInputToWorkload(queueDescriptor, workloadInfo, biasInfo1, nullptr);
 
     CHECK_NOTHROW(queueDescriptor.Validate(workloadInfo));
@@ -729,16 +718,10 @@
     AddInputToWorkload(queueDescriptor, workloadInfo, weightInfo, nullptr);
     AddOutputToWorkload(queueDescriptor, workloadInfo, outputInfo, nullptr);
 
-    ScopedTensorHandle weightTensor(weightInfo);
-    queueDescriptor.m_Weight = &weightTensor;
-
-   // Test 2: wrong per-axis quantization values
+    // Test 2: wrong per-axis quantization values
     const std::vector<float> biasPerAxisScales2 = { 4.00f, 5.00f };
     const TensorInfo biasInfo2(biasShape, biasType, biasPerAxisScales2, 0);
 
-    ScopedTensorHandle biasHandle2(biasInfo2);
-    queueDescriptor.m_Bias = &biasHandle2;
-
     AddInputToWorkload(queueDescriptor, workloadInfo, biasInfo2, nullptr);
 
     CHECK_NOTHROW(queueDescriptor.Validate(workloadInfo));
@@ -781,16 +764,10 @@
     AddInputToWorkload(queueDescriptor, workloadInfo, weightInfo, nullptr);
     AddOutputToWorkload(queueDescriptor, workloadInfo, outputInfo, nullptr);
 
-    ScopedTensorHandle weightTensor(weightInfo);
-    queueDescriptor.m_Weight = &weightTensor;
-
     // Test 3: mismatched number of quantization scales
     const std::vector<float> biasPerAxisScales3 = { 3.75f, 5.25f, 5.25f };
     const TensorInfo biasInfo3(biasShape, biasType, biasPerAxisScales3, 0);
 
-    ScopedTensorHandle biasHandle3(biasInfo3);
-    queueDescriptor.m_Bias = &biasHandle3;
-
     AddInputToWorkload(queueDescriptor, workloadInfo, biasInfo3, nullptr);
 
     CHECK_THROWS_AS(queueDescriptor.Validate(workloadInfo), InvalidArgumentException);
diff --git a/src/backends/backendsCommon/test/layerTests/Conv2dTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/Conv2dTestImpl.cpp
index 1e0adc1..8a2d8c8 100644
--- a/src/backends/backendsCommon/test/layerTests/Conv2dTestImpl.cpp
+++ b/src/backends/backendsCommon/test/layerTests/Conv2dTestImpl.cpp
@@ -1,5 +1,5 @@
 //
-// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017,2022 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 
@@ -313,8 +313,6 @@
 
     armnn::Convolution2dQueueDescriptor data;
     armnn::WorkloadInfo info;
-    armnn::ScopedTensorHandle weightsTensor(kernelDesc);
-    armnn::ScopedTensorHandle biasTensor(biasDesc);
 
     // Permute the kernel if necessary
     std::vector<T> kernel = originalKernel;
@@ -322,12 +320,6 @@
     {
         armnnUtils::Permute(kernelDesc.GetShape(), NCHWToNHWC, originalKernel.data(), kernel.data(), sizeof(T));
     }
-    AllocateAndCopyDataToITensorHandle(&weightsTensor, kernel.data());
-
-    if(biasEnabled)
-    {
-        AllocateAndCopyDataToITensorHandle(&biasTensor, bias.data());
-    }
 
     AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
     AddInputToWorkload(data, info, kernelDesc, weightsHandle.get());
@@ -339,8 +331,6 @@
         biasHandle = tensorHandleFactory.CreateTensorHandle(biasDesc);
         AddInputToWorkload(data, info, biasDesc, biasHandle.get());
     }
-    data.m_Weight = &weightsTensor;
-    data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled - can be a source of bugs.
     data.m_Parameters.m_StrideX = strideX;
     data.m_Parameters.m_StrideY = strideY;
     data.m_Parameters.m_PadLeft = padLeft;
@@ -442,15 +432,13 @@
     std::unique_ptr<armnn::ITensorHandle> weightsHandle = tensorHandleFactory.CreateTensorHandle(kernelDesc);
     std::unique_ptr<armnn::ITensorHandle> biasHandle = nullptr;
 
-    armnn::ScopedTensorHandle weightsTensor(kernelDesc);
-    AllocateAndCopyDataToITensorHandle(&weightsTensor, kernel.data());
+//    armnn::ScopedTensorHandle weightsTensor(kernelDesc);
+//    AllocateAndCopyDataToITensorHandle(&weightsTensor, kernel.data());
 
-    armnn::ScopedTensorHandle biasTensor(biasDesc);
+//    armnn::ScopedTensorHandle biasTensor(biasDesc);
 
     armnn::Convolution2dQueueDescriptor data;
 
-    data.m_Weight = &weightsTensor;
-    data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled - can be a source of bugs.
     data.m_Parameters.m_StrideX = strideX;
     data.m_Parameters.m_StrideY = strideY;
     data.m_Parameters.m_PadLeft = padLeft;
@@ -590,18 +578,16 @@
 
     armnn::Convolution2dQueueDescriptor data;
     armnn::WorkloadInfo info;
-    armnn::ScopedTensorHandle weightsTensor(kernelInfo);
-    armnn::ScopedTensorHandle biasTensor(biasInfo);
-
-    AllocateAndCopyDataToITensorHandle(&weightsTensor, kernelData.data());
-    AllocateAndCopyDataToITensorHandle(&biasTensor, biasData.data());
+//    armnn::ScopedTensorHandle weightsTensor(kernelInfo);
+//    armnn::ScopedTensorHandle biasTensor(biasInfo);
+//
+//    AllocateAndCopyDataToITensorHandle(&weightsTensor, kernelData.data());
+//    AllocateAndCopyDataToITensorHandle(&biasTensor, biasData.data());
 
     AddInputToWorkload(data, info, inputInfo, inputHandle.get());
     AddInputToWorkload(data, info, kernelInfo, weightsHandle.get());
     AddOutputToWorkload(data, info, outputInfo, outputHandle.get());
 
-    data.m_Weight = &weightsTensor;
-    data.m_Bias = &biasTensor;
     data.m_Parameters.m_StrideX = 1;
     data.m_Parameters.m_StrideY = stride;
     data.m_Parameters.m_PadLeft = 0;
@@ -1421,25 +1407,14 @@
     armnn::Convolution2dQueueDescriptor data;
     armnn::WorkloadInfo info;
 
-    armnn::ScopedTensorHandle weightsTensor(kernelDesc);
-    armnn::ScopedTensorHandle biasTensor(biasDesc);
-
     AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
     AddInputToWorkload(data, info, kernelDesc, weightsHandle.get());
     AddInputToWorkload(data, info, biasDesc, biasHandle.get());
     AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
 
-    // AllocateAndCopyDataToITensorHandle() is required twice for the weights AND biases:
-    // See comment in DepthwiseConvolution2dAsymmetricTestImpl() for reasons.
-    // 1) ScopedTensorHandle (weightsTensor) required for QueueDescriptor (data.m_Weight).
-    // 2) ITensorHandle (converts to Backend TensorHandle) required in RefWorkload for GetTensorInfo() method.
     AllocateAndCopyDataToITensorHandle(weightsHandle.get(), kernel.data());
-    AllocateAndCopyDataToITensorHandle(&weightsTensor, kernel.data());
     AllocateAndCopyDataToITensorHandle(biasHandle.get(), bias.data());
-    AllocateAndCopyDataToITensorHandle(&biasTensor, bias.data());
 
-    data.m_Weight = &weightsTensor;
-    data.m_Bias = &biasTensor;
     data.m_Parameters.m_StrideX = strideX;
     data.m_Parameters.m_StrideY = strideY;
     data.m_Parameters.m_PadLeft = padX;
@@ -1827,15 +1802,6 @@
     armnn::DepthwiseConvolution2dQueueDescriptor data;
     armnn::WorkloadInfo info;
 
-    armnn::ScopedTensorHandle weightsTensor(kernelDesc);
-
-    // AllocateAndCopyDataToITensorHandle() is required twice for the weights AND biases:
-    // 1) ScopedTensorHandle (weightsTensor) required for QueueDescriptor (data.m_Weight).
-    //    Needed in Neon and Cl Workload when permuting. Backend TensorHandle in (2) below will not work.
-    // 2) ITensorHandle (converts to Backend TensorHandle) required in RefWorkload for GetTensorInfo() method.
-    //    Cannot PolymorphicDowncast from ScopedTensorHandle->RefTensorHandle.
-    //    Need to PolymorphicDowncast from ITensorHandle->RefTensorHandle.
-    AllocateAndCopyDataToITensorHandle(&weightsTensor, kernelData.data());
     AllocateAndCopyDataToITensorHandle(weightsHandle.get(), kernelData.data()); // required for ConstantTensor
 
     AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
@@ -1852,8 +1818,6 @@
         AddInputToWorkload(data, info, biasDesc, biasHandle.get());
     }
 
-    data.m_Weight = &weightsTensor;
-    data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled - it can be a source of bugs.
     data.m_Parameters.m_StrideX = strideX;
     data.m_Parameters.m_StrideY = strideY;
     data.m_Parameters.m_PadLeft = padLeft;
@@ -2009,12 +1973,6 @@
     armnn::DepthwiseConvolution2dQueueDescriptor data;
     armnn::WorkloadInfo info;
 
-    armnn::ScopedTensorHandle weightsTensor(kernelDesc);
-    // AllocateAndCopyDataToITensorHandle() is required twice for the weights AND biases:
-    // See comment in DepthwiseConvolution2dAsymmetricTestImpl() for reasons.
-    // 1) ScopedTensorHandle (weightsTensor) required for QueueDescriptor (data.m_Weight).
-    // 2) ITensorHandle (converts to Backend TensorHandle) required in RefWorkload for GetTensorInfo() method.
-    AllocateAndCopyDataToITensorHandle(&weightsTensor, kernelData.data()); // required for QueueDescriptor
     AllocateAndCopyDataToITensorHandle(weightsHandle.get(), kernelData.data()); // required for ConstantTensor
 
     AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
@@ -2031,8 +1989,6 @@
         AddInputToWorkload(data, info, biasDesc, biasHandle.get());
     }
 
-    data.m_Weight = &weightsTensor;
-    data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled.
     data.m_Parameters.m_StrideX = 1;
     data.m_Parameters.m_StrideY = 1;
     data.m_Parameters.m_PadLeft = 0;
@@ -2234,12 +2190,6 @@
     armnn::DepthwiseConvolution2dQueueDescriptor data;
     armnn::WorkloadInfo info;
 
-    armnn::ScopedTensorHandle weightsTensor(kernelDesc);
-    // AllocateAndCopyDataToITensorHandle() is required twice for the weights AND biases:
-    // See comment in DepthwiseConvolution2dAsymmetricTestImpl() for reasons.
-    // 1) ScopedTensorHandle (weightsTensor) required for QueueDescriptor (data.m_Weight).
-    // 2) ITensorHandle (converts to Backend TensorHandle) required in RefWorkload for GetTensorInfo() method.
-    AllocateAndCopyDataToITensorHandle(&weightsTensor, kernelData.data()); // required for QueueDescriptor
     AllocateAndCopyDataToITensorHandle(weightsHandle.get(), kernelData.data()); // required for ConstantTensor
 
     AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
@@ -2256,8 +2206,6 @@
         AddInputToWorkload(data, info, biasDesc, biasHandle.get());
     }
 
-    data.m_Weight = &weightsTensor;
-    data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled.
     data.m_Parameters.m_StrideX = 2;
     data.m_Parameters.m_StrideY = 1;
     data.m_Parameters.m_PadLeft = 0;
@@ -2426,12 +2374,6 @@
     armnn::DepthwiseConvolution2dQueueDescriptor data;
     armnn::WorkloadInfo info;
 
-    armnn::ScopedTensorHandle weightsTensor(kernelDesc);
-    // AllocateAndCopyDataToITensorHandle() is required twice for the weights AND biases:
-    // See comment in DepthwiseConvolution2dAsymmetricTestImpl() for reasons.
-    // 1) ScopedTensorHandle (weightsTensor) required for QueueDescriptor (data.m_Weight).
-    // 2) ITensorHandle (converts to Backend TensorHandle) required in RefWorkload for GetTensorInfo() method.
-    AllocateAndCopyDataToITensorHandle(&weightsTensor, kernelData.data()); // required for QueueDescriptor
     AllocateAndCopyDataToITensorHandle(weightsHandle.get(), kernelData.data()); // required for ConstantTensor
 
     AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
@@ -2448,8 +2390,6 @@
         AddInputToWorkload(data, info, biasDesc, biasHandle.get());
     }
 
-    data.m_Weight = &weightsTensor;
-    data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled - can be a source of bugs.
     data.m_Parameters.m_StrideX = strideX;
     data.m_Parameters.m_StrideY = strideY;
     data.m_Parameters.m_PadLeft = padLeft;
@@ -3177,25 +3117,14 @@
     armnn::DepthwiseConvolution2dQueueDescriptor data;
     armnn::WorkloadInfo info;
 
-    armnn::ScopedTensorHandle weightsTensor(aclKernelDescriptor);
-    armnn::ScopedTensorHandle biasTensor(biasDesc);
-
     AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
     AddInputToWorkload(data, info, aclKernelDescriptor, weightsHandle.get());
     AddInputToWorkload(data, info, biasDesc, biasHandle.get());
     AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
 
-    // AllocateAndCopyDataToITensorHandle() is required twice for the weights AND biases:
-    // See comment in DepthwiseConvolution2dAsymmetricTestImpl() for reasons.
-    // 1) ScopedTensorHandle (weightsTensor) required for QueueDescriptor (data.m_Weight).
-    // 2) ITensorHandle (converts to Backend TensorHandle) required in RefWorkload for GetTensorInfo() method.
     AllocateAndCopyDataToITensorHandle(weightsHandle.get(), aclKernelData.data());
-    AllocateAndCopyDataToITensorHandle(&weightsTensor, aclKernelData.data());
     AllocateAndCopyDataToITensorHandle(biasHandle.get(), bias.data());
-    AllocateAndCopyDataToITensorHandle(&biasTensor, bias.data());
 
-    data.m_Weight = &weightsTensor;
-    data.m_Bias = &biasTensor;
     data.m_Parameters.m_StrideX = strideX;
     data.m_Parameters.m_StrideY = strideY;
     data.m_Parameters.m_PadLeft = padX;
@@ -3696,16 +3625,14 @@
     std::unique_ptr<armnn::ITensorHandle> biasHandle = nullptr;
 
     WorkloadInfo workloadInfo;
-    ScopedTensorHandle weightTensor(kernelInfo);
-    ScopedTensorHandle biasTensor(biasInfo);
-
-    AllocateAndCopyDataToITensorHandle(&weightTensor, kernelData.data());
-    AllocateAndCopyDataToITensorHandle(&biasTensor, biasData.data());
+//    ScopedTensorHandle weightTensor(kernelInfo);
+//    ScopedTensorHandle biasTensor(biasInfo);
+//
+//    AllocateAndCopyDataToITensorHandle(&weightTensor, kernelData.data());
+//    AllocateAndCopyDataToITensorHandle(&biasTensor, biasData.data());
 
     Convolution2dQueueDescriptor queueDescriptor;
     queueDescriptor.m_Parameters = descriptor;
-    queueDescriptor.m_Weight     = &weightTensor;
-    queueDescriptor.m_Bias       = &biasTensor;
 
     AddInputToWorkload(queueDescriptor, workloadInfo, inputInfo, inputHandle.get());
     AddInputToWorkload(queueDescriptor, workloadInfo, kernelInfo, weightsHandle.get());
@@ -3995,26 +3922,16 @@
 
     DepthwiseConvolution2dQueueDescriptor queueDescriptor;
     WorkloadInfo workloadInfo;
-    ScopedTensorHandle weightTensor(kernelInfo);
-    ScopedTensorHandle biasTensor(biasInfo);
 
     AddInputToWorkload(queueDescriptor, workloadInfo, inputInfo, inputHandle.get());
     AddInputToWorkload(queueDescriptor, workloadInfo, kernelInfo, weightsHandle.get());
     AddOutputToWorkload(queueDescriptor, workloadInfo, outputInfo, outputHandle.get());
     AddInputToWorkload(queueDescriptor, workloadInfo, biasInfo, biasHandle.get());
 
-    // AllocateAndCopyDataToITensorHandle() is required twice for the weights AND biases:
-    // See comment in DepthwiseConvolution2dAsymmetricTestImpl() for reasons.
-    // 1) ScopedTensorHandle (weightsTensor) required for QueueDescriptor (data.m_Weight).
-    // 2) ITensorHandle (converts to Backend TensorHandle) required in RefWorkload for GetTensorInfo() method.
     AllocateAndCopyDataToITensorHandle(weightsHandle.get(), kernelData.data());
-    AllocateAndCopyDataToITensorHandle(&weightTensor, kernelData.data());
     AllocateAndCopyDataToITensorHandle(biasHandle.get(), biasData.data());
-    AllocateAndCopyDataToITensorHandle(&biasTensor, biasData.data());
 
     queueDescriptor.m_Parameters = descriptor;
-    queueDescriptor.m_Weight     = &weightTensor;
-    queueDescriptor.m_Bias       = &biasTensor;
 
     std::unique_ptr<IWorkload> workload = workloadFactory.CreateWorkload(armnn::LayerType::DepthwiseConvolution2d,
                                                                          queueDescriptor,
diff --git a/src/backends/backendsCommon/test/layerTests/FullyConnectedTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/FullyConnectedTestImpl.cpp
index 5555772..f7519a7 100644
--- a/src/backends/backendsCommon/test/layerTests/FullyConnectedTestImpl.cpp
+++ b/src/backends/backendsCommon/test/layerTests/FullyConnectedTestImpl.cpp
@@ -1,5 +1,5 @@
 //
-// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017,2022 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 
@@ -44,20 +44,11 @@
 
     armnn::FullyConnectedQueueDescriptor data;
     armnn::WorkloadInfo info;
-    armnn::ScopedTensorHandle weightsTensor(weightsTensorInfo);
-    armnn::ScopedTensorHandle biasTensor(biasesTensorInfo);
-
-    AllocateAndCopyDataToITensorHandle(&weightsTensor, weights.data());
-    AllocateAndCopyDataToITensorHandle(&biasTensor, bias.data());
 
     AddInputToWorkload(data, info, inputTensorInfo, input0Handle.get());
     AddInputToWorkload(data, info, weightsTensorInfo, input1Handle.get());
     AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
 
-    // Need to set as layer members will be null when creating the workload because the optimization hasn't been run.
-    data.m_Weight = &weightsTensor;
-    data.m_Bias = &biasTensor;
-
     data.m_Parameters.m_BiasEnabled = biasEnabled;
     data.m_Parameters.m_TransposeWeightMatrix = transposeWeights;
     data.m_Parameters.m_ConstantWeights = constantWeights;