IVGCVSW-7209 Remove deprecated code due to be removed in 23.02

* Removed weights and bias from Convolution, DepthwiseConv & FullyConnected
  layers
* Removed the weight and bias ConstTensorHandles from the QueueDescriptors
* Updated Workloads to take tensors from WorkloadInfo rather than the
  QueueDescriptors
* Removed unused RedirectMembersToConstantInputs optimization and tests.

Signed-off-by: Teresa Charlin <teresa.charlinreyes@arm.com>
Signed-off-by: Mike Kelly <mike.kelly@arm.com>
Change-Id: I9ffcdc4a1c0dff725539dd69fc435b700bd98a56

19 files changed, 102 insertions, 529 deletions

diff --git a/src/armnn/LoadedNetwork.cpp b/src/armnn/LoadedNetwork.cpp
index 40fbde8ac8..7b24fd77b8 100644
--- a/src/armnn/LoadedNetwork.cpp
+++ b/src/armnn/LoadedNetwork.cpp
@@ -421,7 +421,7 @@ LoadedNetwork::LoadedNetwork(std::unique_ptr<IOptimizedNetwork> net,
                             ConstWorkloads.push_back(m_WorkloadQueue.back().get());
                         }
                     }
-                    // release the constant data in the layer..
+                    // release the constant data in the layer.
                     layer->ReleaseConstantData();
                     break;
                 }
diff --git a/src/armnn/Network.cpp b/src/armnn/Network.cpp
index a61624fb0a..158142f48e 100644
--- a/src/armnn/Network.cpp
+++ b/src/armnn/Network.cpp
@@ -1,5 +1,5 @@
 //
-// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017,2022 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 
@@ -1714,9 +1714,6 @@ IOptimizedNetworkPtr Optimize(const Graph& inGraph,
         ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "Optimizer_ConvertConstants");
         Optimizer::Pass(optGraph, MakeOptimizations(ConvertConstantsFloatToHalf()));
         Optimizer::Pass(optGraph, MakeOptimizations(ConvertConstantsHalfToFloat()));
-
-        // Once the constants are converted we can now safely call RedirectMembersToConstantInputs
-        Optimizer::Pass(optGraph, MakeOptimizations(RedirectMembersToConstantInputs()));
     }
 
     // This must occur after all topological changes to the graph and any redirection of variables
@@ -1860,82 +1857,6 @@ IConnectableLayer* NetworkImpl::AddFullyConnectedLayer(const FullyConnectedDescr
     return m_Graph->AddLayer<FullyConnectedLayer>(fullyConnectedDescriptor, name);
 }
 
-IConnectableLayer* NetworkImpl::AddFullyConnectedLayer(const FullyConnectedDescriptor& fullyConnectedDescriptor,
-                                                       const Optional<ConstTensor>& weights,
-                                                       const Optional<ConstTensor>& biases,
-                                                       const char* name)
-{
-    ConstantLayer* weightsLayer = nullptr;
-    ConstantLayer* biasLayer    = nullptr;
-    unsigned int   numInputs    = fullyConnectedDescriptor.GetNumInputs();
-
-    // Add a constant layer for weights
-    if (weights.has_value())
-    {
-        weightsLayer = m_Graph->AddLayer<ConstantLayer>("Weights");
-        weightsLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(weights.value());
-
-        TensorInfo weightsInfo = weightsLayer->m_LayerOutput->GetTensorInfo();
-        weightsInfo.SetConstant();
-
-        weightsLayer->GetOutputSlot(0).SetTensorInfo(weightsInfo);
-    }
-    else if (fullyConnectedDescriptor.m_ConstantWeights)
-    {
-        throw InvalidArgumentException("AddFullyConnectedLayer: Constant weights tensor is empty.");
-    }
-
-    // Add a constant layer for biases
-    if (biases.has_value() && fullyConnectedDescriptor.m_BiasEnabled)
-    {
-        biasLayer = m_Graph->AddLayer<ConstantLayer>("Biases");
-        biasLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(biases.value());
-
-        TensorInfo biasInfo = biasLayer->m_LayerOutput->GetTensorInfo();
-        biasInfo.SetConstant();
-
-        biasLayer->GetOutputSlot(0).SetTensorInfo(biasInfo);
-    }
-
-    if (numInputs < 2)
-    {
-        throw InvalidArgumentException("AddFullyConnectedLayer: Requires at least 2 input tensors: Input, Weights");
-    }
-
-    auto layer = m_Graph->AddLayer<FullyConnectedLayer>(fullyConnectedDescriptor, name);
-
-    if (weightsLayer)
-    {
-        // Connect weights layer
-        weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1));
-    }
-
-    if ( fullyConnectedDescriptor.m_BiasEnabled && numInputs == 3 )
-    {
-        if (biasLayer)
-        {
-            // Connect bias layer
-            biasLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2));
-        }
-    }
-    else if ( !fullyConnectedDescriptor.m_BiasEnabled && numInputs == 2 )
-    {
-        // Bias is disabled
-        layer->m_Bias = nullptr;
-    }
-    else
-    {
-        throw InvalidArgumentException(fmt::format(
-                "AddFullyConnectedLayer: Value mismatch. When bias is enabled in the "
-                "descriptor the number of inputs is expected to be 3 otherwise 2. "
-                "BiasEnabled={}, numInputs={}",
-                fullyConnectedDescriptor.m_BiasEnabled,
-                numInputs));
-    }
-
-    return layer;
-}
-
 IConnectableLayer* NetworkImpl::AddConcatLayer(const ConcatDescriptor& concatDescriptor,
                                            const char* name)
 {
@@ -1948,32 +1869,6 @@ IConnectableLayer* NetworkImpl::AddConvolution2dLayer(const Convolution2dDescrip
     return m_Graph->AddLayer<Convolution2dLayer>(convolution2dDescriptor, name);
 }
 
-IConnectableLayer* NetworkImpl::AddConvolution2dLayer(const Convolution2dDescriptor& convolution2dDescriptor,
-                                                      const ConstTensor& weights,
-                                                      const Optional<ConstTensor>& biases,
-                                                      const char* name)
-{
-    auto layer = m_Graph->AddLayer<Convolution2dLayer>(convolution2dDescriptor, name);
-    // Add a constant layer for weights
-    ConstantLayer* weightsLayer = m_Graph->AddLayer<ConstantLayer>("Weights");
-    auto weightsTensorHandle = std::make_shared<ScopedTensorHandle>(weights);
-    weightsLayer->m_LayerOutput = weightsTensorHandle;
-    layer->m_Weight = weightsTensorHandle;
-    weightsLayer->GetOutputSlot(0).SetTensorInfo(weightsLayer->m_LayerOutput->GetTensorInfo());
-    weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1));
-    // Add a constant layer for biases
-    if (biases.has_value() && convolution2dDescriptor.m_BiasEnabled)
-    {
-        ConstantLayer* biasLayer = m_Graph->AddLayer<ConstantLayer>("Bias");
-        auto biasTensorHandle = std::make_shared<ScopedTensorHandle>(biases.value());
-        biasLayer->m_LayerOutput = biasTensorHandle;
-        layer->m_Bias = biasTensorHandle;
-        biasLayer->GetOutputSlot(0).SetTensorInfo(biasLayer->m_LayerOutput->GetTensorInfo());
-        biasLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2));
-    }
-    return layer;
-}
-
 IConnectableLayer* NetworkImpl::AddConvertFp16ToFp32Layer(const char* name)
 {
     return m_Graph->AddLayer<ConvertFp16ToFp32Layer>(name);
@@ -2003,38 +1898,6 @@ IConnectableLayer* NetworkImpl::AddDepthwiseConvolution2dLayer(
     return m_Graph->AddLayer<DepthwiseConvolution2dLayer>(convolution2dDescriptor, name);
 }
 
-IConnectableLayer* NetworkImpl::AddDepthwiseConvolution2dLayer(
-    const DepthwiseConvolution2dDescriptor& convolution2dDescriptor,
-    const ConstTensor& weights,
-    const Optional<ConstTensor>& biases,
-    const char* name)
-{
-    auto layer = m_Graph->AddLayer<DepthwiseConvolution2dLayer>(convolution2dDescriptor, name);
-
-    // Add a constant layer for weights
-    ConstantLayer* weightsLayer = m_Graph->AddLayer<ConstantLayer>("Weights");
-    auto weightsTensorHandle = std::make_shared<ScopedTensorHandle>(weights);
-    weightsLayer->m_LayerOutput = weightsTensorHandle;
-    layer->m_Weight = weightsTensorHandle;
-
-    weightsLayer->GetOutputSlot(0).SetTensorInfo(weightsLayer->m_LayerOutput->GetTensorInfo());
-    weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1));
-
-    // Add a constant layer for biases
-    if (biases.has_value() && convolution2dDescriptor.m_BiasEnabled)
-    {
-        ConstantLayer* biasLayer = m_Graph->AddLayer<ConstantLayer>("Bias");
-        auto biasTensorHandle = std::make_shared<ScopedTensorHandle>(biases.value());
-        biasLayer->m_LayerOutput = biasTensorHandle;
-        layer->m_Bias = biasTensorHandle;
-
-        biasLayer->GetOutputSlot(0).SetTensorInfo(biasLayer->m_LayerOutput->GetTensorInfo());
-        biasLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2));
-    }
-
-    return layer;
-}
-
 IConnectableLayer* NetworkImpl::AddDetectionPostProcessLayer(const armnn::DetectionPostProcessDescriptor& descriptor,
                                                          const ConstTensor& anchors, const char* name)
 {
diff --git a/src/armnn/Network.hpp b/src/armnn/Network.hpp
index 5ca16e2968..a37a4be218 100644
--- a/src/armnn/Network.hpp
+++ b/src/armnn/Network.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
@@ -76,23 +76,6 @@ public:
     IConnectableLayer* AddConvolution2dLayer(const Convolution2dDescriptor& convolution2dDescriptor,
                                              const char* name = nullptr);
 
-    ARMNN_DEPRECATED_MSG_REMOVAL_DATE("This AddConvolution2dLayer overload is deprecated", "23.02")
-    IConnectableLayer* AddConvolution2dLayer(const Convolution2dDescriptor& convolution2dDescriptor,
-                                             const ConstTensor& weights,
-                                             const Optional<ConstTensor>& biases,
-                                             const char* name = nullptr);
-
-    ARMNN_DEPRECATED_MSG_REMOVAL_DATE("This AddConvolution2dLayer overload is deprecated", "23.02")
-    IConnectableLayer* AddConvolution2dLayer(const Convolution2dDescriptor& convolution2dDescriptor,
-                                             const ConstTensor& weights,
-                                             const char* name = nullptr);
-
-    ARMNN_DEPRECATED_MSG_REMOVAL_DATE("This AddConvolution2dLayer overload is deprecated", "23.02")
-    IConnectableLayer* AddConvolution2dLayer(const Convolution2dDescriptor& convolution2dDescriptor,
-                                             const ConstTensor& weights,
-                                             const ConstTensor& biases,
-                                             const char* name = nullptr);
-
     IConnectableLayer* AddConvolution3dLayer(const Convolution3dDescriptor& convolution3dDescriptor,
                                              const char* name = nullptr);
 
@@ -101,23 +84,14 @@ public:
     IConnectableLayer* AddDepthToSpaceLayer(const DepthToSpaceDescriptor& depthToSpaceDescriptor,
                                             const char* name = nullptr);
 
-    IConnectableLayer* AddDepthwiseConvolution2dLayer(
-        const DepthwiseConvolution2dDescriptor& convolution2dDescriptor,
-        const char* name = nullptr);
-
-    ARMNN_DEPRECATED_MSG("This AddDepthwiseConvolution2dLayer overload is deprecated")
-    IConnectableLayer* AddDepthwiseConvolution2dLayer(
-        const DepthwiseConvolution2dDescriptor& convolution2dDescriptor,
-        const ConstTensor& weights,
-        const Optional<ConstTensor>& biases,
-        const char* name = nullptr);
+    IConnectableLayer* AddDepthwiseConvolution2dLayer(const DepthwiseConvolution2dDescriptor& convolution2dDescriptor,
+                                                      const char* name = nullptr);
 
     IConnectableLayer* AddDequantizeLayer(const char* name = nullptr);
 
-    IConnectableLayer* AddDetectionPostProcessLayer(
-        const DetectionPostProcessDescriptor& descriptor,
-        const ConstTensor& anchors,
-        const char* name = nullptr);
+    IConnectableLayer* AddDetectionPostProcessLayer(const DetectionPostProcessDescriptor& descriptor,
+                                                    const ConstTensor& anchors,
+                                                    const char* name = nullptr);
 
     IConnectableLayer* AddDivisionLayer(const char* name = nullptr);
 
@@ -134,11 +108,6 @@ public:
     IConnectableLayer* AddFullyConnectedLayer(const FullyConnectedDescriptor& fullyConnectedDescriptor,
                                               const char* name = nullptr);
 
-    IConnectableLayer* AddFullyConnectedLayer(const FullyConnectedDescriptor& fullyConnectedDescriptor,
-                                              const Optional<ConstTensor>& weights,
-                                              const Optional<ConstTensor>& biases,
-                                              const char* name = nullptr);
-
     IConnectableLayer* AddGatherLayer(const GatherDescriptor& gatherDescriptor,
                                       const char* name = nullptr);
 
diff --git a/src/armnn/layers/Convolution2dLayer.cpp b/src/armnn/layers/Convolution2dLayer.cpp
index d0233976c4..e06b45acb0 100644
--- a/src/armnn/layers/Convolution2dLayer.cpp
+++ b/src/armnn/layers/Convolution2dLayer.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
 //
 
@@ -48,18 +48,8 @@ void Convolution2dLayer::SerializeLayerParameters(ParameterStringifyFunction& fn
 
 std::unique_ptr<IWorkload> Convolution2dLayer::CreateWorkload(const IWorkloadFactory& factory) const
 {
-    // on this level constant data should not be released..
     ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "Convolution2dLayer_CreateWorkload");
     Convolution2dQueueDescriptor descriptor;
-    if (m_Weight)
-    {
-        descriptor.m_Weight = m_Weight.get();
-    }
-    if (m_Param.m_BiasEnabled && m_Bias)
-    {
-        descriptor.m_Bias = m_Bias.get();
-    }
-
     SetAdditionalInfo(descriptor);
 
     return factory.CreateWorkload(LayerType::Convolution2d, descriptor, PrepInfoAndDesc(descriptor));
@@ -68,14 +58,6 @@ std::unique_ptr<IWorkload> Convolution2dLayer::CreateWorkload(const IWorkloadFac
 Convolution2dLayer* Convolution2dLayer::Clone(Graph& graph) const
 {
     auto layer = CloneBase<Convolution2dLayer>(graph, m_Param, GetName());
-
-    layer->m_Weight = m_Weight ? m_Weight : nullptr;
-
-    if (layer->m_Param.m_BiasEnabled)
-    {
-        layer->m_Bias = m_Bias ? m_Bias : nullptr;
-    }
-
     return std::move(layer);
 }
 
@@ -140,14 +122,7 @@ void Convolution2dLayer::ValidateTensorShapesFromInputs()
 Layer::ConstantTensors Convolution2dLayer::GetConstantTensorsByRef()
 {
     Layer::ConstantTensors tensors = GetConnectedConstantAsInputTensors();
-
-    if (!tensors.empty())
-    {
-        return tensors;
-    }
-
-    // For API stability DO NOT ALTER order and add new members to the end of vector
-    return {m_Weight, m_Bias};
+    return tensors;
 }
 
 void Convolution2dLayer::ExecuteStrategy(IStrategy& strategy) const
diff --git a/src/armnn/layers/Convolution2dLayer.hpp b/src/armnn/layers/Convolution2dLayer.hpp
index 02ae05f83b..f7e4dec72f 100644
--- a/src/armnn/layers/Convolution2dLayer.hpp
+++ b/src/armnn/layers/Convolution2dLayer.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
@@ -16,13 +16,6 @@ class Convolution2dLayer : public LayerWithParameters<Convolution2dDescriptor>
 {
 public:
 
-    /// A unique pointer to store Weight values.
-    /// @Note: Deprecated. Removal date is 23.02. Weights are stored in ConstantLayers now.
-    std::shared_ptr<ConstTensorHandle> m_Weight;
-    /// A unique pointer to store Bias values.
-    /// @Note: Deprecated. Removal date is 23.02. Bias are stored in ConstantLayers now.
-    std::shared_ptr<ConstTensorHandle> m_Bias;
-
     /// Makes a workload for the Convolution2d type.
     /// @param [in] graph The graph where this layer can be found.
     /// @param [in] factory The workload factory which will create the workload.
@@ -48,6 +41,10 @@ public:
 
     void SerializeLayerParameters(ParameterStringifyFunction& fn) const override;
 
+    /// This layer does not have any data stored, weights and bias are now stored in constant layers.
+    /// We do not want to release the data in the constant layer, that is why we override with an empty function.
+    void ReleaseConstantData() override {}
+
 protected:
     /// Constructor to create a Convolution2dLayer.
     /// @param [in] param Convolution2dDescriptor to configure the convolution2d operation.
@@ -57,8 +54,8 @@ protected:
     /// Default destructor
     ~Convolution2dLayer() = default;
 
-    /// @Note Deprecated. GetConstantTensorsByRef is deprecated. m_Weights and m_Bias
-    ///                   should be connected to layer as Constant Layers instead."
+    /// Retrieve the handles to the constant values connected to the layer.
+    /// @return A vector of the constant tensors connected to the layer.
     ConstantTensors GetConstantTensorsByRef() override;
 };
 
diff --git a/src/armnn/layers/DepthwiseConvolution2dLayer.cpp b/src/armnn/layers/DepthwiseConvolution2dLayer.cpp
index dcd800e367..4c97437a1c 100644
--- a/src/armnn/layers/DepthwiseConvolution2dLayer.cpp
+++ b/src/armnn/layers/DepthwiseConvolution2dLayer.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
 //
 
@@ -50,16 +50,6 @@ void DepthwiseConvolution2dLayer::SerializeLayerParameters(ParameterStringifyFun
 std::unique_ptr<IWorkload> DepthwiseConvolution2dLayer::CreateWorkload(const IWorkloadFactory& factory) const
 {
     DepthwiseConvolution2dQueueDescriptor descriptor;
-
-    if (m_Weight)
-    {
-        descriptor.m_Weight = m_Weight.get();
-    }
-    if (m_Param.m_BiasEnabled && m_Bias)
-    {
-        descriptor.m_Bias = m_Bias.get();
-    }
-
     SetAdditionalInfo(descriptor);
 
     return factory.CreateWorkload(LayerType::DepthwiseConvolution2d, descriptor, PrepInfoAndDesc(descriptor));
@@ -68,13 +58,6 @@ std::unique_ptr<IWorkload> DepthwiseConvolution2dLayer::CreateWorkload(const IWo
 DepthwiseConvolution2dLayer* DepthwiseConvolution2dLayer::Clone(Graph& graph) const
 {
     auto layer      = CloneBase<DepthwiseConvolution2dLayer>(graph, m_Param, GetName());
-    layer->m_Weight = m_Weight ? m_Weight : nullptr;
-
-    if (layer->m_Param.m_BiasEnabled)
-    {
-        layer->m_Bias = m_Bias ? m_Bias : nullptr;
-    }
-
     return std::move(layer);
 }
 
@@ -143,14 +126,7 @@ void DepthwiseConvolution2dLayer::ValidateTensorShapesFromInputs()
 Layer::ConstantTensors DepthwiseConvolution2dLayer::GetConstantTensorsByRef()
 {
     Layer::ConstantTensors tensors = GetConnectedConstantAsInputTensors();
-
-    if (!tensors.empty())
-    {
-        return tensors;
-    }
-
-    // For API stability DO NOT ALTER order and add new members to the end of vector
-    return {m_Weight, m_Bias};
+    return tensors;
 }
 
 void DepthwiseConvolution2dLayer::ExecuteStrategy(IStrategy& strategy) const
diff --git a/src/armnn/layers/DepthwiseConvolution2dLayer.hpp b/src/armnn/layers/DepthwiseConvolution2dLayer.hpp
index baae7f122a..ef7410f1d3 100644
--- a/src/armnn/layers/DepthwiseConvolution2dLayer.hpp
+++ b/src/armnn/layers/DepthwiseConvolution2dLayer.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
@@ -15,12 +15,6 @@ class ScopedTensorHandle;
 class DepthwiseConvolution2dLayer : public LayerWithParameters<DepthwiseConvolution2dDescriptor>
 {
 public:
-    /// A unique pointer to store Weight values.
-    /// @Note Deprecated. Removal date is 23.02. Weights are stored in ConstantLayers now.
-    std::shared_ptr<ConstTensorHandle> m_Weight;
-    /// A unique pointer to store Bias values.
-    /// @Note Deprecated. Removal date is 23.02. Bias are stored in ConstantLayers now.
-    std::shared_ptr<ConstTensorHandle> m_Bias;
 
     /// Makes a workload for the DepthwiseConvolution2d type.
     /// @param [in] graph The graph where this layer can be found.
@@ -47,6 +41,10 @@ public:
 
     void SerializeLayerParameters(ParameterStringifyFunction& fn) const override;
 
+    /// This layer does not have any data stored, weights and bias are now stored in constant layers.
+    /// We do not want to release the data in the constant layer, that is why we override with an empty function.
+    void ReleaseConstantData() override {}
+
 protected:
     /// Constructor to create a DepthwiseConvolution2dLayer.
     /// @param [in] param DepthwiseConvolution2dDescriptor to configure the depthwise convolution2d.
@@ -56,10 +54,8 @@ protected:
     /// Default destructor
     ~DepthwiseConvolution2dLayer() = default;
 
-    /// Retrieve the handles to the constant values stored by the layer.
-    /// @return A vector of the constant tensors stored by this layer.
-    /// @Note Deprecated. GetConstantTensorsByRef is deprecated. m_Weights and m_Bias
-    ///                   should be connected to layer as Constant Layers instead."
+    /// Retrieve the handles to the constant values connected to the layer.
+    /// @return A vector of the constant tensors connected to the layer.
     ConstantTensors GetConstantTensorsByRef() override;
 };
 
diff --git a/src/armnn/layers/FullyConnectedLayer.cpp b/src/armnn/layers/FullyConnectedLayer.cpp
index c20bc8d167..05c53961e3 100644
--- a/src/armnn/layers/FullyConnectedLayer.cpp
+++ b/src/armnn/layers/FullyConnectedLayer.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
 //
 #include "FullyConnectedLayer.hpp"
@@ -22,27 +22,13 @@ FullyConnectedLayer::FullyConnectedLayer(const FullyConnectedDescriptor& param,
 std::unique_ptr<IWorkload> FullyConnectedLayer::CreateWorkload(const IWorkloadFactory& factory) const
 {
     FullyConnectedQueueDescriptor descriptor;
-    if (m_Weight)
-    {
-        descriptor.m_Weight = m_Weight.get();
-    }
-    if (m_Param.m_BiasEnabled && m_Bias)
-    {
-        descriptor.m_Bias = m_Bias.get();
-    }
     SetAdditionalInfo(descriptor);
-
     return factory.CreateWorkload(LayerType::FullyConnected, descriptor, PrepInfoAndDesc(descriptor));
 }
 
 FullyConnectedLayer* FullyConnectedLayer::Clone(Graph& graph) const
 {
     auto layer = CloneBase<FullyConnectedLayer>(graph, m_Param, GetName());
-    layer->m_Weight = m_Weight ? m_Weight : nullptr;
-    if (layer->m_Param.m_BiasEnabled)
-    {
-        layer->m_Bias = m_Bias ? m_Bias : nullptr;
-    }
     return std::move(layer);
 }
 
@@ -78,14 +64,7 @@ void FullyConnectedLayer::ValidateTensorShapesFromInputs()
 Layer::ConstantTensors FullyConnectedLayer::GetConstantTensorsByRef()
 {
     Layer::ConstantTensors tensors = GetConnectedConstantAsInputTensors();
-
-    if (!tensors.empty())
-    {
-        return tensors;
-    }
-
-    // For API stability DO NOT ALTER order and add new members to the end of vector
-    return {m_Weight, m_Bias};
+    return tensors;
 }
 
 void FullyConnectedLayer::ExecuteStrategy(IStrategy& strategy) const
diff --git a/src/armnn/layers/FullyConnectedLayer.hpp b/src/armnn/layers/FullyConnectedLayer.hpp
index 07f4a936f9..f3ca696b62 100644
--- a/src/armnn/layers/FullyConnectedLayer.hpp
+++ b/src/armnn/layers/FullyConnectedLayer.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
@@ -15,12 +15,6 @@ class ScopedTensorHandle;
 class FullyConnectedLayer : public LayerWithParameters<FullyConnectedDescriptor>
 {
 public:
-    /// A unique pointer to store Weight values.
-    /// @Note: Deprecated. Removal date is 23.02. Weights are stored in ConstantLayers now.
-    std::shared_ptr<ConstTensorHandle> m_Weight;
-    /// A unique pointer to store Bias values.
-    /// @Note: Deprecated. Removal date is 23.02. Bias are stored in ConstantLayers now.
-    std::shared_ptr<ConstTensorHandle> m_Bias;
 
     /// Makes a workload for the FullyConnected type.
     /// @param [in] graph The graph where this layer can be found.
@@ -45,6 +39,10 @@ public:
 
     void ExecuteStrategy(IStrategy& strategy) const override;
 
+    /// This layer does not have any data stored, weights and bias are now stored in constant layers.
+    /// We do not want to release the data in the constant layer, that is why we override with an empty function.
+    void ReleaseConstantData() override {}
+
 protected:
     /// Constructor to create a FullyConnectedLayer.
     /// @param [in] param FullyConnectedDescriptor to configure the fully connected operation.
diff --git a/src/armnn/optimizations/All.hpp b/src/armnn/optimizations/All.hpp
index a11dec9446..0e67516193 100644
--- a/src/armnn/optimizations/All.hpp
+++ b/src/armnn/optimizations/All.hpp
@@ -20,6 +20,5 @@
 #include "PermuteAsReshape.hpp"
 #include "PermuteAndBatchToSpaceAsDepthToSpace.hpp"
 #include "PermuteDepthwiseConv2dWeights.hpp"
-#include "RedirectMembersToConstantInputs.hpp"
 #include "SquashEqualSiblings.hpp"
 #include "TransposeAsReshape.hpp"
\ No newline at end of file
diff --git a/src/armnn/optimizations/FoldPadIntoLayer2d.hpp b/src/armnn/optimizations/FoldPadIntoLayer2d.hpp
index 7a50c4ac06..874749fda9 100644
--- a/src/armnn/optimizations/FoldPadIntoLayer2d.hpp
+++ b/src/armnn/optimizations/FoldPadIntoLayer2d.hpp
@@ -1,5 +1,5 @@
 //
-// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017,2022 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 
@@ -196,21 +196,14 @@ public:
         if (newConv2dLayer != nullptr)
         {
             const auto conv2dLayer = PolymorphicDowncast<Convolution2dLayer*>(&connection.GetOwningLayer());
-            // Copy weights and bias to the new convolution layer
             ARMNN_ASSERT_MSG(newConv2dLayer->GetInputSlot(1).GetConnection() != nullptr,
                              "FoldPadIntoConvolution2d: New convolution layer is missing connection to weights layer");
 
-            // Deprecated. Removal date is 23.02.
-            newConv2dLayer->m_Weight = std::move(conv2dLayer->m_Weight);
-
             if (conv2dLayer->GetParameters().m_BiasEnabled)
             {
                 ARMNN_ASSERT_MSG(newConv2dLayer->GetInputSlot(2).GetConnection() != nullptr,
                                  "FoldPadIntoConvolution2d: New convolution layer is missing "
                                  "connection to bias layer.");
-
-                // Deprecated. Removal date is 23.02.
-                newConv2dLayer->m_Bias = std::move(conv2dLayer->m_Bias);
             }
         }
     }
@@ -230,24 +223,18 @@ public:
         if (newConv2dLayer != nullptr)
         {
             const auto conv2dLayer = PolymorphicDowncast<DepthwiseConvolution2dLayer*>(&connection.GetOwningLayer());
-            // Copy weights and bias to the new convolution layer
             ARMNN_ASSERT_MSG(newConv2dLayer->GetInputSlot(1).GetConnection() != nullptr,
-            "FoldPadIntoDepthwiseConvolution2d: New convolution layer is missing connection to weights layer");
-
-            // Deprecated. Removal date is 23.02.
-            newConv2dLayer->m_Weight = std::move(conv2dLayer->m_Weight);
+                             "FoldPadIntoDepthwiseConvolution2d: New convolution layer is missing "
+                             "connection to weights layer");
 
             if (conv2dLayer->GetParameters().m_BiasEnabled)
             {
                 ARMNN_ASSERT_MSG(newConv2dLayer->GetInputSlot(2).GetConnection() != nullptr,
                                  "FoldPadIntoConvolution2d: New convolution layer is missing "
                                  "connection to bias layer.");
-                // Deprecated. Removal date is 23.02.
-                newConv2dLayer->m_Bias = std::move(conv2dLayer->m_Bias);
             }
         }
     }
-
 protected:
     FoldPadIntoDepthwiseConvolution2dImpl() =  default;
     ~FoldPadIntoDepthwiseConvolution2dImpl() = default;
diff --git a/src/armnn/optimizations/FuseBatchNorm.hpp b/src/armnn/optimizations/FuseBatchNorm.hpp
index bca0c7d00a..88ac97cd0c 100644
--- a/src/armnn/optimizations/FuseBatchNorm.hpp
+++ b/src/armnn/optimizations/FuseBatchNorm.hpp
@@ -1,5 +1,5 @@
 //
-// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2020,2022 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 
@@ -167,8 +167,6 @@ public:
             auto& newConv2dLayer = *graph.InsertNewLayer<ConvLayer>(base.GetInputSlot(0),
                                                                     convDescriptor,
                                                                     name.c_str());
-            newConv2dLayer.m_Weight = std::make_unique<ScopedTensorHandle>(fusedWeightsTensor);
-            newConv2dLayer.m_Bias = std::make_unique<ScopedTensorHandle>(ConstTensor(fusedBiasTensor));
 
             // Connect weights and bias from old to new Conv2d layer
             // This optimization will always have 3 input slots on the Conv2d base layer
@@ -177,7 +175,7 @@ public:
                 // Remove old connection and connect to new layer2d
                 weightLayer->GetOutputSlot(0).Disconnect(base.GetInputSlot(1));
                 weightLayer->GetOutputSlot(0).Connect(newConv2dLayer.GetInputSlot(1));
-                weightLayer->m_LayerOutput = newConv2dLayer.m_Weight;
+                weightLayer->m_LayerOutput = std::make_unique<ScopedTensorHandle>(fusedWeightsTensor);
 
                 // Move bias const layers as normal if it was enabled before the optimisation
                 ConstantLayer* biasLayer;
@@ -198,7 +196,7 @@ public:
                     biasLayer->GetOutputSlot(0).SetTensorInfo(fusedBiasTensor.GetInfo());
                     biasLayer->GetOutputSlot(0).Connect(newConv2dLayer.GetInputSlot(2));
                 }
-                biasLayer->m_LayerOutput = newConv2dLayer.m_Bias;
+                biasLayer->m_LayerOutput = std::make_unique<ScopedTensorHandle>(ConstTensor(fusedBiasTensor));
             }
 
 
diff --git a/src/armnn/optimizations/RedirectMembersToConstantInputs.hpp b/src/armnn/optimizations/RedirectMembersToConstantInputs.hpp
deleted file mode 100644
index a2bad710e6..0000000000
--- a/src/armnn/optimizations/RedirectMembersToConstantInputs.hpp
+++ /dev/null
@@ -1,90 +0,0 @@
-//
-// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#pragma once
-
-#include "Optimization.hpp"
-
-#include <armnn/utility/IgnoreUnused.hpp>
-#include <armnn/utility/PolymorphicDowncast.hpp>
-
-namespace armnn
-{
-namespace optimizations
-{
-
-class RedirectMembersToConstantInputsImpl
-{
-public:
-    /// Search for layers with ConstantLayers as inputs. If the inputs are constant redirect the layers member
-    /// variable for ConstTensors (e.g. m_weights) to the data stored in the ConstantLayer it is connected to.
-    void Run(Graph& graph, Layer& layer) const
-    {
-        IgnoreUnused(graph);
-
-        switch (layer.GetType())
-        {
-            case LayerType::BatchNormalization:
-                break;
-            case LayerType::Convolution2d:
-                RedirectWeightsAndBiases<Convolution2dLayer>(&layer);
-                break;
-            case LayerType::DepthwiseConvolution2d:
-                RedirectWeightsAndBiases<DepthwiseConvolution2dLayer>(&layer);
-                break;
-            case LayerType::DetectionPostProcess:
-                break;
-            case LayerType::FullyConnected:
-                RedirectWeightsAndBiases<FullyConnectedLayer>(&layer);
-                break;
-            case LayerType::Lstm:
-                break;
-            case LayerType::TransposeConvolution2d:
-                break;
-            default:
-                break;
-        }
-    }
-
-protected:
-    RedirectMembersToConstantInputsImpl()  = default;
-    ~RedirectMembersToConstantInputsImpl() = default;
-
-private:
-    template <typename LayerT>
-    static LayerT* RedirectWeightsAndBiases(Layer* layer)
-    {
-        LayerT* layerPtr = PolymorphicDowncast<LayerT*>(layer);
-
-        // Loop through input slots to check for constant weights and biases layers.
-        // Weights index = 1, Biases index = 2.
-        for (unsigned int inputSlotIndex = 1; inputSlotIndex != layerPtr->GetNumInputSlots(); ++inputSlotIndex)
-        {
-            OutputSlot* outputSlot = layerPtr->GetInputSlot(inputSlotIndex).GetConnectedOutputSlot();
-            // Debug layers should not be inserted in optimize process yet
-            ARMNN_ASSERT(outputSlot->GetOwningLayer().GetType() != LayerType::Debug);
-            if (outputSlot->GetOwningLayer().GetType() == LayerType::Constant)
-            {
-                // Get constant layer and redirect base layer member variables.
-                ConstantLayer& constantLayer = dynamic_cast<ConstantLayer&>(outputSlot->GetOwningLayer());
-                if (inputSlotIndex == 1)
-                {
-                    layerPtr->m_Weight = constantLayer.m_LayerOutput;
-                }
-                else if (inputSlotIndex == 2)
-                {
-                    layerPtr->m_Bias = constantLayer.m_LayerOutput;
-                }
-            }
-        }
-
-        return layerPtr;
-    }
-};
-
-using RedirectMembersToConstantInputs = OptimizeForType<Layer, RedirectMembersToConstantInputsImpl>;
-
-} // namespace optimizations
-} // namespace armnn
diff --git a/src/armnn/test/OptimizerTests.cpp b/src/armnn/test/OptimizerTests.cpp
index b78863dddc..f83900404b 100644
--- a/src/armnn/test/OptimizerTests.cpp
+++ b/src/armnn/test/OptimizerTests.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
 //
 
@@ -441,16 +441,15 @@ void CreateConvolution2dGraph(Graph &graph, const unsigned int* inputShape,
     Layer* input = graph.AddLayer<InputLayer>(0, "input");
     input->GetOutputSlot().SetTensorInfo(inputInfo);
 
-    ConstantLayer* weightsLayer = nullptr;
-    weightsLayer = graph.AddLayer<ConstantLayer>("Weights");
+    ConstantLayer* weightsLayer = graph.AddLayer<ConstantLayer>("Weights");
     weightsLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(weights);
     weightsLayer->GetOutputSlot(0).SetTensorInfo(weightsLayer->m_LayerOutput->GetTensorInfo());
 
     Convolution2dLayer* layer = graph.AddLayer<Convolution2dLayer>(desc, "conv2d");
-    layer->m_Weight           = std::make_unique<armnn::ScopedTensorHandle>(weights);
     layer->GetOutputSlot().SetTensorInfo(outputInfo);
 
     Layer* output = graph.AddLayer<OutputLayer>(0, "output");
+
     input->GetOutputSlot().Connect(layer->GetInputSlot(0));
     layer->GetOutputSlot().Connect(output->GetInputSlot(0));
     weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1));
@@ -908,11 +907,10 @@ TEST_CASE("OptimizeForExclusiveConnectionsFuseTest")
     {
         std::vector<float> biasVector = { 11 };
         ConstTensor bias(TensorInfo(1, outputChannelSize, DataType::Float32, 0.0f, 0, true), biasVector);
-        biasLayer =graph.AddLayer<ConstantLayer>("Bias");
+        biasLayer = graph.AddLayer<ConstantLayer>("Bias");
         biasLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(bias);
         biasLayer->GetOutputSlot(0).SetTensorInfo(biasLayer->m_LayerOutput->GetTensorInfo());
         biasLayer->GetOutputSlot(0).Connect(conv->GetInputSlot(2));
-        conv->m_Bias = biasLayer->m_LayerOutput;
     }
 
     // Connect layers
@@ -921,9 +919,6 @@ TEST_CASE("OptimizeForExclusiveConnectionsFuseTest")
     conv->GetOutputSlot(0).Connect(batchNorm->GetInputSlot(0));
     batchNorm->GetOutputSlot(0).Connect(output->GetInputSlot(0));
 
-    // Temporary workaround to ensure the descriptor weights are populated
-    conv->m_Weight = weightsLayer->m_LayerOutput;
-
     if (convolution2dDescriptor.m_BiasEnabled)
     {
         CHECK(6 == graph.GetNumLayers());
@@ -983,22 +978,22 @@ TEST_CASE("OptimizeForExclusiveConnectionsWithoutFuseTest")
     batchNorm->GetOutputSlot(0).Connect(output->GetInputSlot(0));
     conv->GetOutputSlot(0).Connect(output2->GetInputSlot(0));
 
-    CHECK(5 == graph.GetNumLayers());
+    CHECK((5 == graph.GetNumLayers()));
     CHECK(CheckSequence(graph.cbegin(), graph.cend(),
-                             &IsLayerOfType<armnn::InputLayer>,
-                             &IsLayerOfType<armnn::Convolution2dLayer>,
-                             &IsLayerOfType<armnn::BatchNormalizationLayer>,
-                             &IsLayerOfType<armnn::OutputLayer>,
-                             &IsLayerOfType<armnn::OutputLayer>));
+                        &IsLayerOfType<armnn::InputLayer>,
+                        &IsLayerOfType<armnn::Convolution2dLayer>,
+                        &IsLayerOfType<armnn::BatchNormalizationLayer>,
+                        &IsLayerOfType<armnn::OutputLayer>,
+                        &IsLayerOfType<armnn::OutputLayer>));
     // Optimize graph
     armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(FuseBatchNormIntoConvolution2DFloat32()));
 
     CHECK(5 == graph.GetNumLayers());
     CHECK(CheckSequence(graph.cbegin(), graph.cend(),
-                             &IsLayerOfType<armnn::InputLayer>,
-                             &IsLayerOfType<armnn::Convolution2dLayer>,
-                             &IsLayerOfType<armnn::BatchNormalizationLayer>,
-                             &IsLayerOfType<armnn::OutputLayer>,
-                             &IsLayerOfType<armnn::OutputLayer>));
+                        &IsLayerOfType<armnn::InputLayer>,
+                        &IsLayerOfType<armnn::Convolution2dLayer>,
+                        &IsLayerOfType<armnn::BatchNormalizationLayer>,
+                        &IsLayerOfType<armnn::OutputLayer>,
+                        &IsLayerOfType<armnn::OutputLayer>));
 }
 } // Optimizer TestSuite
diff --git a/src/armnn/test/optimizations/ConvertConstantsFloatToHalfTests.cpp b/src/armnn/test/optimizations/ConvertConstantsFloatToHalfTests.cpp
index 34e5f6d3b6..118907e703 100644
--- a/src/armnn/test/optimizations/ConvertConstantsFloatToHalfTests.cpp
+++ b/src/armnn/test/optimizations/ConvertConstantsFloatToHalfTests.cpp
@@ -1,12 +1,12 @@
 //
-// Copyright © 2017 Arm Ltd. All rights reserved.
+// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 
 #include <TestUtils.hpp>
 
-#include <Optimizer.hpp>
 #include <Half.hpp>
+#include <Optimizer.hpp>
 
 #include <doctest/doctest.h>
 
@@ -25,33 +25,38 @@ TEST_CASE("ConvertConstantsFloatToHalfTest")
     // Create const tensor from fp32 data
     unsigned int dims[] = { 4, 1, 1, 1 };
     std::vector<float> floatWeights{ 1.0f, 2.0f, 3.0f, 4.0f };
-    armnn::ConstTensor weights(armnn::TensorInfo(4, dims, armnn::DataType::Float32, 0.0f, 0, true), floatWeights);
+    armnn::TensorInfo weightsInfo = armnn::TensorInfo(4, dims, armnn::DataType::Float32, 0.0f, 0, true);
+    armnn::ConstTensor weights(weightsInfo, floatWeights);
 
     // Create simple test network
     auto input = graph.AddLayer<armnn::InputLayer>(0, "input");
     input->GetOutputSlot().SetTensorInfo(info);
 
     auto fc      = graph.AddLayer<armnn::FullyConnectedLayer>(armnn::FullyConnectedDescriptor(), "fc");
-    fc->m_Weight = std::make_unique<armnn::ScopedTensorHandle>(weights);
     fc->GetOutputSlot().SetTensorInfo(info);
 
+    auto weightsLayer = graph.AddLayer<armnn::ConstantLayer>("weights");
+    weightsLayer->m_LayerOutput = std::make_unique<armnn::ScopedTensorHandle>(weights);
+    weightsLayer->GetOutputSlot().SetTensorInfo(weightsInfo);
+
     auto output = graph.AddLayer<armnn::OutputLayer>(1, "output");
 
     // Connect up the layers
     input->GetOutputSlot().Connect(fc->GetInputSlot(0));
+    weightsLayer->GetOutputSlot().Connect(fc->GetInputSlot(1));
     fc->GetOutputSlot().Connect(output->GetInputSlot(0));
 
     // Check tensor data type before conversion
-    CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::Float32);
+    CHECK(weightsLayer->m_LayerOutput->GetTensorInfo().GetDataType() == armnn::DataType::Float32);
 
     // Run the optimizer
     armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(ConvertConstantsFloatToHalf()));
 
     // Check tensor data type after conversion
-    CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::Float16);
+    CHECK(weightsLayer->m_LayerOutput->GetTensorInfo().GetDataType() == armnn::DataType::Float16);
 
     // Check whether data matches expected fp16 data
-    const Half* data = fc->m_Weight->GetConstTensor<Half>();
+    const Half* data = weightsLayer->m_LayerOutput->GetConstTensor<Half>();
     CHECK(data[0] == Half(1.0f));
     CHECK(data[1] == Half(2.0f));
     CHECK(data[2] == Half(3.0f));
@@ -100,12 +105,14 @@ TEST_CASE("ConvertConstantsFloatToHalfTest_constant")
     fcLayer->GetOutputSlot(0).Connect(output->GetInputSlot(0));
 
     // Check tensor data type before conversion
+    CHECK(5 == graph.GetNumLayers());
     CHECK(weights->m_LayerOutput->GetTensorInfo().GetDataType() == armnn::DataType::Float32);
 
     // Run the optimizer
     armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(ConvertConstantsFloatToHalf()));
 
     // Check tensor data type after conversion
+    CHECK(5 == graph.GetNumLayers());
     CHECK(weights->m_LayerOutput->GetTensorInfo().GetDataType() == armnn::DataType::Float16);
 
     // Check whether weights data matches expected fp16 data
diff --git a/src/armnn/test/optimizations/ConvertConstantsHalfToFloatTests.cpp b/src/armnn/test/optimizations/ConvertConstantsHalfToFloatTests.cpp
index 4c453cc799..778d7b0814 100644
--- a/src/armnn/test/optimizations/ConvertConstantsHalfToFloatTests.cpp
+++ b/src/armnn/test/optimizations/ConvertConstantsHalfToFloatTests.cpp
@@ -1,5 +1,5 @@
 //
-// Copyright © 2017 Arm Ltd. All rights reserved.
+// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 
@@ -25,33 +25,38 @@ TEST_CASE("ConvertConstantsHalfToFloatTest")
     std::vector<uint16_t> halfWeights(4);
     armnnUtils::FloatingPointConverter::ConvertFloat32To16(convWeightsData.data(), convWeightsData.size(),
                                                            halfWeights.data());
-    armnn::ConstTensor weights(armnn::TensorInfo(4, dims, armnn::DataType::Float16, 0.0f, 0, true), halfWeights);
+    armnn::TensorInfo weightInfo = armnn::TensorInfo(4, dims, armnn::DataType::Float16, 0.0f, 0, true);
+    armnn::ConstTensor weights(weightInfo, halfWeights);
 
     //Create the simple test network
     auto input = graph.AddLayer<armnn::InputLayer>(0, "input");
     input->GetOutputSlot().SetTensorInfo(info);
 
     auto fc      = graph.AddLayer<armnn::FullyConnectedLayer>(armnn::FullyConnectedDescriptor(), "fc");
-    fc->m_Weight = std::make_unique<armnn::ScopedTensorHandle>(weights);
     fc->GetOutputSlot().SetTensorInfo(info);
 
+    auto weightsLayer = graph.AddLayer<armnn::ConstantLayer>("weights");
+    weightsLayer->m_LayerOutput = std::make_unique<armnn::ScopedTensorHandle>(weights);
+    weightsLayer->GetOutputSlot(0).SetTensorInfo(weightInfo);
+
     auto output = graph.AddLayer<armnn::OutputLayer>(1, "output");
 
     //Connect up the layers
     input->GetOutputSlot().Connect(fc->GetInputSlot(0));
+    weightsLayer->GetOutputSlot().Connect(fc->GetInputSlot(1));
     fc->GetOutputSlot().Connect(output->GetInputSlot(0));
 
     //Test the tensor info is correct.
-    CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::Float16);
+    CHECK(weightsLayer->m_LayerOutput->GetTensorInfo().GetDataType() == armnn::DataType::Float16);
 
     // Run the optimizer
     armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(ConvertConstantsHalfToFloat()));
 
     //Test the tensor info is correct.
-    CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::Float32);
+    CHECK(weightsLayer->m_LayerOutput->GetTensorInfo().GetDataType() == armnn::DataType::Float32);
 
     // Now test the data matches float32 data
-    const float* data = fc->m_Weight->GetConstTensor<float>();
+    const float* data = weightsLayer->m_LayerOutput->GetConstTensor<float>();
     CHECK(1.0f == data[0]);
     CHECK(2.0f == data[1]);
     CHECK(3.0f == data[2]);
diff --git a/src/armnn/test/optimizations/Fp32NetworkToFp16ConverterTests.cpp b/src/armnn/test/optimizations/Fp32NetworkToFp16ConverterTests.cpp
index bc8839948b..0a4a4fafde 100644
--- a/src/armnn/test/optimizations/Fp32NetworkToFp16ConverterTests.cpp
+++ b/src/armnn/test/optimizations/Fp32NetworkToFp16ConverterTests.cpp
@@ -1,5 +1,5 @@
 //
-// Copyright © 2017 Arm Ltd. All rights reserved.
+// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 
@@ -33,14 +33,21 @@ TEST_CASE("Fp32NetworkToFp16OptimizationTest")
     floor->GetOutputSlot().Connect(output->GetInputSlot(0));
 
     CHECK(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<armnn::InputLayer>,
-                             &IsLayerOfType<armnn::FloorLayer>, &IsLayerOfType<armnn::OutputLayer>));
+                                                      &IsLayerOfType<armnn::FloorLayer>,
+                                                      &IsLayerOfType<armnn::OutputLayer>));
 
     // Run the optimizer
     armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(Fp32NetworkToFp16Converter()));
 
     CHECK(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<armnn::InputLayer>,
-                             &IsLayerOfType<armnn::ConvertFp32ToFp16Layer>, &IsLayerOfType<armnn::FloorLayer>,
-                             &IsLayerOfType<armnn::ConvertFp16ToFp32Layer>, &IsLayerOfType<armnn::OutputLayer>));
+                                                      &IsLayerOfType<armnn::ConvertFp32ToFp16Layer>,
+                                                      &IsLayerOfType<armnn::FloorLayer>,
+                                                      &IsLayerOfType<armnn::ConvertFp16ToFp32Layer>,
+                                                      &IsLayerOfType<armnn::OutputLayer>));
+
+    CHECK(floor->GetDataType() == armnn::DataType::Float16);
+    CHECK(floor->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo().GetDataType() == armnn::DataType::Float16);
+    CHECK(floor->GetOutputSlot(0).GetTensorInfo().GetDataType() == armnn::DataType::Float16);
 }
 
 }
\ No newline at end of file
diff --git a/src/armnn/test/optimizations/FuseBatchNormTests.cpp b/src/armnn/test/optimizations/FuseBatchNormTests.cpp
index 54cbbce89f..5cbd17fb6a 100644
--- a/src/armnn/test/optimizations/FuseBatchNormTests.cpp
+++ b/src/armnn/test/optimizations/FuseBatchNormTests.cpp
@@ -1,5 +1,5 @@
 //
-// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 
@@ -27,13 +27,8 @@ public:
 
     static IConnectableLayer *AddConvolution(INetwork *network,
                                              const Convolution2dDescriptor &descriptor,
-                                             const ConstTensor &weights,
-                                             const Optional<ConstTensor> &biases,
                                              const char *name)
     {
-        IgnoreUnused(weights);
-        IgnoreUnused(biases);
-
         return network->AddConvolution2dLayer(descriptor, name);
     }
 
@@ -65,12 +60,8 @@ public:
 
     static IConnectableLayer* AddConvolution(INetwork* network,
                                              const DepthwiseConvolution2dDescriptor& descriptor,
-                                             const ConstTensor& weights,
-                                             const Optional<ConstTensor>& biases,
                                              const char* name)
     {
-        IgnoreUnused(weights);
-        IgnoreUnused(biases);
 
         return network->AddDepthwiseConvolution2dLayer(descriptor, name);
     }
@@ -171,8 +162,6 @@ INetworkPtr CreateNetwork(bool depthwise, bool preventFusing)
 
     IConnectableLayer* convLayer      = Conv2dTest::AddConvolution(network.get(),
                                                                    convolution2dDescriptor,
-                                                                   weights,
-                                                                   Optional<ConstTensor>(),
                                                                    "convolution");
 
     IConnectableLayer* batchNormLayer = network->AddBatchNormalizationLayer(batchNormDescriptor,
@@ -243,13 +232,21 @@ void FuseBatchNormIntoConvTest(bool depthwise, float tolerance, armnn::Compute b
         return IsLayerOfType<ConvLayerType>(layer) &&
                (layer->GetNameStr() == "fused-batchNorm-into-convolution");
     };
-
+    auto checkConstant = [ ](const armnn::Layer* const layer) -> bool
+    {
+        const ConstantLayer* constLayer = PolymorphicDowncast<const ConstantLayer*>(layer);
+        auto tensor = ConstTensor(constLayer->m_LayerOutput->GetTensorInfo(),
+                                  constLayer->m_LayerOutput->Map(true));
+        const auto* buffer = static_cast<const T*>(tensor.GetMemoryArea());
+        std::vector<T> vector(buffer, buffer + tensor.GetNumElements());
+        return IsLayerOfType<ConstantLayer>(layer);
+    };
     CHECK(5 == graphFused.GetNumLayers());
     CHECK(CheckSequence(graphFused.cbegin(),
                         graphFused.cend(),
                         &IsLayerOfType<InputLayer>,
-                        &IsLayerOfType<ConstantLayer>,
-                        &IsLayerOfType<ConstantLayer>,
+                        checkConstant,
+                        checkConstant,
                         checkFusedConv2d,
                         &IsLayerOfType<OutputLayer>));
 
diff --git a/src/armnn/test/optimizations/RedirectMembersToConstantInputsTests.cpp b/src/armnn/test/optimizations/RedirectMembersToConstantInputsTests.cpp
deleted file mode 100644
index b3f9ed8780..0000000000
--- a/src/armnn/test/optimizations/RedirectMembersToConstantInputsTests.cpp
+++ /dev/null
@@ -1,85 +0,0 @@
-//
-// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include <TestUtils.hpp>
-
-#include <Optimizer.hpp>
-
-#include <doctest/doctest.h>
-
-TEST_SUITE("Optimizer")
-{
-using namespace armnn::optimizations;
-
-TEST_CASE("RedirectMembersToConstantInputsFullyConnectedTest")
-{
-    armnn::Graph graph;
-
-    const armnn::TensorInfo inputInfo  ({ 1, 2, 2, 3 }, armnn::DataType::Float32);
-    const armnn::TensorInfo outputInfo ({ 1, 2, 2, 3 }, armnn::DataType::Float32);
-    const armnn::TensorInfo weightsInfo({ 4 }, armnn::DataType::Float32, 0.0f, 0, true);
-    const armnn::TensorInfo biasesInfo ({ 2 }, armnn::DataType::Float32, 0.0f, 0, true);
-
-    // Check if isConstant is enabled for weights and biases tensor info.
-    CHECK(weightsInfo.IsConstant());
-    CHECK(biasesInfo.IsConstant());
-
-    armnn::FullyConnectedDescriptor desc;
-    desc.m_BiasEnabled = true;
-    desc.m_ConstantWeights = false;
-
-    // Create the simple test network with Weights and Biases as inputs to a FullyConnected layer.
-    auto input   = graph.AddLayer<armnn::InputLayer>(0, "Input");
-    auto weights = graph.AddLayer<armnn::ConstantLayer>("Weights");
-    auto biases  = graph.AddLayer<armnn::ConstantLayer>("Biases");
-    auto fcLayer = graph.AddLayer<armnn::FullyConnectedLayer>(desc, "FullyConnected");
-    auto output  = graph.AddLayer<armnn::OutputLayer>(1, "Output");
-
-    float expectedWeightsData[] = { 1.0f, 1.0f, 1.0f, 1.0f };
-    float expectedBiasesData[]  = { 2.0f, 2.0f };
-
-    // Set the m_LayerOutput for the optimizer to point to.
-    armnn::ConstTensor weightsTensor(weightsInfo, &expectedWeightsData);
-    armnn::ConstTensor biasesTensor(biasesInfo, &expectedBiasesData);
-    weights->m_LayerOutput = std::make_unique<armnn::ScopedTensorHandle>(weightsTensor);
-    biases->m_LayerOutput  = std::make_unique<armnn::ScopedTensorHandle>(biasesTensor);
-
-    input->GetOutputSlot().SetTensorInfo(inputInfo);
-    weights->GetOutputSlot().SetTensorInfo(weightsInfo);
-    biases->GetOutputSlot().SetTensorInfo(biasesInfo);
-    fcLayer->GetOutputSlot().SetTensorInfo(outputInfo);
-
-    // Connect up the layers
-    input->GetOutputSlot(0).Connect(fcLayer->GetInputSlot(0));
-    weights->GetOutputSlot(0).Connect(fcLayer->GetInputSlot(1));
-    biases->GetOutputSlot(0).Connect(fcLayer->GetInputSlot(2));
-    fcLayer->GetOutputSlot(0).Connect(output->GetInputSlot(0));
-
-    // Member variables should be null before optimization.
-    CHECK(fcLayer->m_Weight == nullptr);
-    CHECK(fcLayer->m_Bias == nullptr);
-
-    // Run the optimizer
-    armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(RedirectMembersToConstantInputs()));
-
-    // Check if member variables are not null and shape is set correctly.
-    CHECK(fcLayer->m_Weight != nullptr);
-    CHECK(fcLayer->m_Bias != nullptr);
-    CHECK(fcLayer->m_Weight->GetTensorInfo().GetShape() == weightsInfo.GetShape());
-    CHECK(fcLayer->m_Bias->GetTensorInfo().GetShape() == biasesInfo.GetShape());
-
-    // Check whether data matches expected float data
-    const float* weightsData = fcLayer->m_Weight->GetConstTensor<float>();
-    CHECK(weightsData[0] == expectedWeightsData[0]);
-    CHECK(weightsData[1] == expectedWeightsData[1]);
-    CHECK(weightsData[2] == expectedWeightsData[2]);
-    CHECK(weightsData[3] == expectedWeightsData[3]);
-
-    const float* biasesData = fcLayer->m_Bias->GetConstTensor<float>();
-    CHECK(biasesData[0] == expectedBiasesData[0]);
-    CHECK(biasesData[1] == expectedBiasesData[1]);
-}
-
-}
\ No newline at end of file