diff --git a/1.0/HalPolicy.cpp b/1.0/HalPolicy.cpp
index 5f42107..8acb0d4 100644
--- a/1.0/HalPolicy.cpp
+++ b/1.0/HalPolicy.cpp
@@ -25,9 +25,11 @@
         case V1_0::OperationType::CONCATENATION:
             return ConvertConcatenation(operation, model, data);
         case V1_0::OperationType::CONV_2D:
-            return ConvertConv2d(operation, model, data);
+            return ValidateConv2dParameters(operation)
+                    && ConvertConv2d<Operand, OperandType, Operation, Model>(operation, model, data);
         case V1_0::OperationType::DEPTHWISE_CONV_2D:
-            return ConvertDepthwiseConv2d(operation, model, data);
+            return ValidateDepthwiseConv2dParameters(operation)
+                    && ConvertDepthwiseConv2d<Operand, OperandType, Operation, Model>(operation, model, data);
         case V1_0::OperationType::DEQUANTIZE:
             return ConvertDequantize(operation, model, data);
         case V1_0::OperationType::FLOOR:
@@ -68,10 +70,28 @@
     }
 }
 
+bool HalPolicy::ValidateConv2dParameters(const Operation &operation)
+{
+    if (operation.inputs.size() != 10 && operation.inputs.size() != 7)
+    {
+        return Fail("%s: Unsupported number of operation inputs", __func__);
+    }
+    return true;
+}
+
+bool HalPolicy::ValidateDepthwiseConv2dParameters(const Operation &operation)
+{
+    if (operation.inputs.size() != 11 && operation.inputs.size() != 8)
+    {
+        return Fail("%s: Unsupported number of operation inputs", __func__);
+    }
+    return true;
+}
+
 bool HalPolicy::ConvertAdd(const Operation& operation, const Model& model, ConversionData& data)
 {
-    LayerInputHandle input0 = ConvertToLayerInputHandle(operation, 0, model, data);
-    LayerInputHandle input1 = ConvertToLayerInputHandle(operation, 1, model, data);
+    LayerInputHandle input0 = ConvertToLayerInputHandle<Operand>(operation, 0, model, data);
+    LayerInputHandle input1 = ConvertToLayerInputHandle<Operand>(operation, 1, model, data);
 
     if (!input0.IsValid() || !input1.IsValid())
     {
@@ -81,12 +101,12 @@
     // The FuseActivation parameter is always the input index 2
     // and it should be optional
     ActivationFn activationFunction;
-    if (!GetOptionalInputActivation(operation, 2, activationFunction, model, data))
+    if (!GetOptionalInputActivation<Operand, OperandType>(operation, 2, activationFunction, model, data))
     {
         return Fail("%s: Operation has invalid inputs", __func__);
     }
 
-    const Operand* outputOperand = GetOutputOperand(operation, 0, model);
+    const Operand* outputOperand = GetOutputOperand<Operand>(operation, 0, model);
     if (!outputOperand)
     {
         return false;
@@ -113,7 +133,7 @@
     if (endLayer != nullptr)
     {
         BroadcastTensor(input0, input1, startLayer, *data.m_Network);
-        return SetupAndTrackLayerOutputSlot(operation, 0, *endLayer, model, data);
+        return SetupAndTrackLayerOutputSlot<Operand>(operation, 0, *endLayer, model, data);
     }
     else
     {
@@ -123,7 +143,7 @@
 
 bool HalPolicy::ConvertAveragePool2d(const Operation& operation, const Model& model, ConversionData& data)
 {
-    return ConvertPooling2d(operation, __func__, armnn::PoolingAlgorithm::Average, model, data);
+    return ConvertPooling2d<Operand, OperandType>(operation, __func__, armnn::PoolingAlgorithm::Average, model, data);
 }
 
 bool HalPolicy::ConvertConcatenation(const Operation& operation, const Model& model, ConversionData& data)
@@ -138,12 +158,12 @@
     const std::size_t numInputTensors = operation.inputs.size() - 1;
 
     int32_t concatDim;
-    if (!GetInputScalar(operation, numInputTensors, OperandType::INT32, concatDim, model, data))
+    if (!GetInputScalar<Operand, OperandType>(operation, numInputTensors, OperandType::INT32, concatDim, model, data))
     {
         return Fail("%s: Operation has invalid inputs", __func__);
     }
 
-    const Operand* const outputOperand = GetOutputOperand(operation, 0, model);
+    const Operand* const outputOperand = GetOutputOperand<Operand>(operation, 0, model);
     if (!outputOperand)
     {
         return Fail("%s: Operation has no outputs", __func__);
@@ -179,14 +199,14 @@
 
     for (uint32_t i = 0; i < numInputTensors; ++i)
     {
-        const Operand* const operand = GetInputOperand(operation, i, model);
+        const Operand* const operand = GetInputOperand<Operand>(operation, i, model);
         if (!operand)
         {
             return Fail("%s: Operation has invalid inputs", __func__);
         }
 
         armnn::TensorShape operandShape     = GetTensorShapeForOperand(*operand);
-        LayerInputHandle operandInputHandle = ConvertToLayerInputHandle(operation, i, model, data);
+        LayerInputHandle operandInputHandle = ConvertToLayerInputHandle<Operand>(operation, i, model, data);
 
         if (operandShape.GetNumDimensions() == 0)
         {
@@ -346,250 +366,19 @@
         );
     }
 
-    return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data);
-}
-
-bool HalPolicy::ConvertConv2d(const Operation& operation, const Model& model, ConversionData& data)
-{
-    LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data);
-    if (!input.IsValid())
-    {
-        return Fail("%s: Operation has invalid inputs", __func__);
-    }
-
-    const Operand* output = GetOutputOperand(operation, 0, model);
-    if (!output)
-    {
-        return Fail("%s: Could not read output 0", __func__);
-    }
-
-    const armnn::TensorInfo& inputInfo  = input.GetTensorInfo();
-    const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
-
-    // ArmNN does not currently support non-fixed weights or bias
-    const ConstTensorPin weightsPin = ConvertOperationInputToConstTensorPin(operation, 1, model, data);
-    const ConstTensorPin biasPin    = ConvertOperationInputToConstTensorPin(operation, 2, model, data);
-
-    if (!weightsPin.IsValid() || !biasPin.IsValid())
-    {
-        return Fail("%s: Operation has invalid inputs", __func__);
-    }
-
-    armnn::ConstTensor weights = weightsPin.GetConstTensor();
-    armnn::ConstTensor bias = biasPin.GetConstTensor();
-    SanitizeBiasQuantizationScale(bias.GetInfo(), weights.GetInfo(), inputInfo);
-
-    armnn::Convolution2dDescriptor desc;
-    desc.m_DataLayout = armnn::DataLayout::NHWC;
-    ActivationFn activation;
-
-    if (operation.inputs.size() == 10)
-    {
-        if (!GetInputScalar(operation, 3, OperandType::INT32, desc.m_PadLeft, model, data)   ||
-            !GetInputScalar(operation, 4, OperandType::INT32, desc.m_PadRight, model, data)  ||
-            !GetInputScalar(operation, 5, OperandType::INT32, desc.m_PadTop, model, data)    ||
-            !GetInputScalar(operation, 6, OperandType::INT32, desc.m_PadBottom, model, data) ||
-            !GetInputScalar(operation, 7, OperandType::INT32, desc.m_StrideX, model, data)   ||
-            !GetInputScalar(operation, 8, OperandType::INT32, desc.m_StrideY, model, data)   ||
-            !GetInputActivationFunction(operation, 9, activation, model, data))
-        {
-            return Fail("%s: Operation has invalid inputs", __func__);
-        }
-    }
-    else if (operation.inputs.size() == 7)
-    {
-        android::nn::PaddingScheme paddingScheme;
-        if (!GetInputPaddingScheme(operation, 3, paddingScheme, model, data)               ||
-            !GetInputScalar(operation, 4, OperandType::INT32, desc.m_StrideX, model, data) ||
-            !GetInputScalar(operation, 5, OperandType::INT32, desc.m_StrideY, model, data) ||
-            !GetInputActivationFunction(operation, 6, activation, model, data))
-        {
-            return Fail("%s: Operation has invalid inputs", __func__);
-        }
-
-        const uint32_t kernelX = weights.GetShape()[2];
-        const uint32_t kernelY = weights.GetShape()[1];
-        const uint32_t inputX  = inputInfo.GetShape()[2];
-        const uint32_t inputY  = inputInfo.GetShape()[1];
-
-        CalcPadding(inputX, kernelX, desc.m_StrideX, desc.m_PadLeft, desc.m_PadRight, paddingScheme);
-        CalcPadding(inputY, kernelY, desc.m_StrideY, desc.m_PadTop, desc.m_PadBottom, paddingScheme);
-    }
-    else
-    {
-        return Fail("%s: Unsupported number of operation inputs", __func__);
-    }
-
-    desc.m_BiasEnabled = true;
-    armnn::Optional<armnn::TensorInfo> biases(bias.GetInfo());
-
-    if (!IsLayerSupportedForAnyBackend(__func__,
-                                       armnn::IsConvolution2dSupported,
-                                       data.m_Backends,
-                                       inputInfo,
-                                       outputInfo,
-                                       desc,
-                                       weights.GetInfo(),
-                                       biases))
-    {
-        return false;
-    }
-
-    armnn::IConnectableLayer* startLayer =
-            data.m_Network->AddConvolution2dLayer(desc, weights, armnn::Optional<armnn::ConstTensor>(bias));
-
-    if (!startLayer)
-    {
-        return Fail("%s: AddConvolution2dLayer failed", __func__);
-    }
-
-    armnn::IConnectableLayer* endLayer = ProcessActivation(outputInfo, activation, startLayer, data);
-
-    if (!endLayer)
-    {
-        return Fail("%s: ProcessActivation failed", __func__);
-    }
-
-    input.Connect(startLayer->GetInputSlot(0));
-
-    return SetupAndTrackLayerOutputSlot(operation, 0, *endLayer, model, data);
-}
-
-bool HalPolicy::ConvertDepthwiseConv2d(const Operation& operation, const Model& model, ConversionData& data)
-{
-    LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data);
-    if (!input.IsValid())
-    {
-        return Fail("%s: Operation has invalid inputs", __func__);
-    }
-
-    const Operand* output = GetOutputOperand(operation, 0, model);
-    if (!output)
-    {
-        return Fail("%s: Could not read output 0", __func__);
-    }
-
-    const armnn::TensorInfo& inputInfo  = input.GetTensorInfo();
-    const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
-
-    // ArmNN does not currently support non-fixed weights or bias
-
-    // Find the shape of the weights tensor. In AndroidNN this will be [ 1, H, W, I * M ]
-    const Operand* weightsOperand = GetInputOperand(operation, 1, model);
-
-    if (weightsOperand == nullptr)
-    {
-        return Fail("%s: Operand is invalid", __func__);
-    }
-
-    // Reinterpret weight data as [ H, W, I, M ]
-    armnn::TensorShape weightsShape({ weightsOperand->dimensions[1],
-                                      weightsOperand->dimensions[2],
-                                      inputInfo.GetShape()[3],
-                                      weightsOperand->dimensions[3] / inputInfo.GetShape()[3] });
-
-    // Swizzle weight data [ H, W, I, M ] -> [ M, I, H, W ]
-    const armnn::PermutationVector HWIMToMIHW = { 2U, 3U, 1U, 0U };
-
-    const ConstTensorPin weightsPin = ConvertOperationInputToConstTensorPin(operation, 1, model, data,
-                                                                            HWIMToMIHW, &weightsShape);
-
-    // Bias is a 1D tensor
-    const ConstTensorPin biasPin = ConvertOperationInputToConstTensorPin(operation, 2, model, data);
-
-    if (!weightsPin.IsValid() || !biasPin.IsValid())
-    {
-        return Fail("%s: Operation has invalid inputs", __func__);
-    }
-
-    armnn::ConstTensor weights = weightsPin.GetConstTensor();
-    armnn::ConstTensor bias = biasPin.GetConstTensor();
-    SanitizeBiasQuantizationScale(bias.GetInfo(), weights.GetInfo(), inputInfo);
-
-    armnn::DepthwiseConvolution2dDescriptor desc;
-    desc.m_DataLayout = armnn::DataLayout::NHWC;
-    ActivationFn activation;
-
-    if (operation.inputs.size() == 11)
-    {
-        if (!GetInputScalar(operation, 3, OperandType::INT32, desc.m_PadLeft, model, data)   ||
-            !GetInputScalar(operation, 4, OperandType::INT32, desc.m_PadRight, model, data)  ||
-            !GetInputScalar(operation, 5, OperandType::INT32, desc.m_PadTop, model, data)    ||
-            !GetInputScalar(operation, 6, OperandType::INT32, desc.m_PadBottom, model, data) ||
-            !GetInputScalar(operation, 7, OperandType::INT32, desc.m_StrideX, model, data)   ||
-            !GetInputScalar(operation, 8, OperandType::INT32, desc.m_StrideY, model, data)   ||
-            !GetInputActivationFunction(operation,  10, activation, model, data))
-        {
-            return Fail("%s: Operation has invalid inputs", __func__);
-        }
-    }
-    else if (operation.inputs.size() == 8)
-    {
-        android::nn::PaddingScheme paddingScheme;
-        if (!GetInputPaddingScheme(operation, 3, paddingScheme, model, data)               ||
-            !GetInputScalar(operation, 4, OperandType::INT32, desc.m_StrideX, model, data) ||
-            !GetInputScalar(operation, 5, OperandType::INT32, desc.m_StrideY, model, data) ||
-            !GetInputActivationFunction(operation, 7, activation, model, data))
-        {
-            return Fail("%s: Operation has invalid inputs", __func__);
-        }
-
-        const uint32_t kernelX = weights.GetShape()[3];
-        const uint32_t kernelY = weights.GetShape()[2];
-        const uint32_t inputX  = inputInfo.GetShape()[2];
-        const uint32_t inputY  = inputInfo.GetShape()[1];
-
-        CalcPadding(inputX, kernelX, desc.m_StrideX, desc.m_PadLeft, desc.m_PadRight, paddingScheme);
-        CalcPadding(inputY, kernelY, desc.m_StrideY, desc.m_PadTop, desc.m_PadBottom, paddingScheme);
-    }
-    else
-    {
-        return Fail("%s: Unsupported number of operation inputs", __func__);
-    }
-
-    desc.m_BiasEnabled = true;
-    armnn::Optional<armnn::TensorInfo> biases(bias.GetInfo());
-
-    if (!IsLayerSupportedForAnyBackend(__func__,
-                                       armnn::IsDepthwiseConvolutionSupported,
-                                       data.m_Backends,
-                                       inputInfo,
-                                       outputInfo,
-                                       desc,
-                                       weights.GetInfo(),
-                                       biases))
-    {
-        return false;
-    }
-
-    armnn::IConnectableLayer* startLayer =
-            data.m_Network->AddDepthwiseConvolution2dLayer(desc, weights, armnn::Optional<armnn::ConstTensor>(bias));
-    if (!startLayer)
-    {
-        return Fail("%s: AddDepthwiseConvolution2dLayer failed", __func__);
-    }
-
-    armnn::IConnectableLayer* endLayer = ProcessActivation(outputInfo, activation, startLayer, data);
-    if (!endLayer)
-    {
-        return Fail("%s: ProcessActivation failed", __func__);
-    }
-
-    input.Connect(startLayer->GetInputSlot(0));
-
-    return SetupAndTrackLayerOutputSlot(operation, 0, *endLayer, model, data);
+    return SetupAndTrackLayerOutputSlot<Operand>(operation, 0, *layer, model, data);
 }
 
 bool HalPolicy::ConvertDequantize(const Operation& operation, const Model& model, ConversionData& data)
 {
-    LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data);
+    LayerInputHandle input = ConvertToLayerInputHandle<Operand>(operation, 0, model, data);
 
     if (!input.IsValid())
     {
         return Fail("%s: Operation has invalid input", __func__);
     }
 
-    const Operand* const outputOperand = GetOutputOperand(operation, 0, model);
+    const Operand* const outputOperand = GetOutputOperand<Operand>(operation, 0, model);
     if (!outputOperand)
     {
         return Fail("%s: Operation has invalid outputs", __func__);
@@ -608,18 +397,18 @@
     assert(layer != nullptr);
     input.Connect(layer->GetInputSlot(0));
 
-    return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data);
+    return SetupAndTrackLayerOutputSlot<Operand>(operation, 0, *layer, model, data);
 }
 
 bool HalPolicy::ConvertFloor(const Operation& operation, const Model& model, ConversionData& data)
 {
-    LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data);
+    LayerInputHandle input = ConvertToLayerInputHandle<Operand>(operation, 0, model, data);
     if (!input.IsValid())
     {
         return Fail("%s: Operation has invalid inputs", __func__);
     }
 
-    const Operand* const outputOperand = GetOutputOperand(operation, 0, model);
+    const Operand* const outputOperand = GetOutputOperand<Operand>(operation, 0, model);
     if (!outputOperand)
     {
         return Fail("%s: Operation has invalid outputs", __func__);
@@ -638,18 +427,18 @@
     assert(layer != nullptr);
     input.Connect(layer->GetInputSlot(0));
 
-    return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data);
+    return SetupAndTrackLayerOutputSlot<Operand>(operation, 0, *layer, model, data);
 }
 
 bool HalPolicy::ConvertFullyConnected(const Operation& operation, const Model& model, ConversionData& data)
 {
-    LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data);
+    LayerInputHandle input = ConvertToLayerInputHandle<Operand>(operation, 0, model, data);
     if (!input.IsValid())
     {
         return Fail("%s: Operation has invalid inputs", __func__);
     }
 
-    const Operand* output = GetOutputOperand(operation, 0, model);
+    const Operand* output = GetOutputOperand<Operand>(operation, 0, model);
     if (!output)
     {
         return Fail("%s: Could not read output 0", __func__);
@@ -659,8 +448,8 @@
     const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
 
     // ArmNN does not currently support non-fixed weights or bias
-    ConstTensorPin weightsPin = ConvertOperationInputToConstTensorPin(operation, 1, model, data); // 2D
-    ConstTensorPin biasPin    = ConvertOperationInputToConstTensorPin(operation, 2, model, data);    // 1D
+    ConstTensorPin weightsPin = ConvertOperationInputToConstTensorPin<Operand>(operation, 1, model, data); // 2D
+    ConstTensorPin biasPin    = ConvertOperationInputToConstTensorPin<Operand>(operation, 2, model, data);    // 1D
 
     if (!weightsPin.IsValid() || !biasPin.IsValid())
     {
@@ -682,7 +471,7 @@
     SanitizeBiasQuantizationScale(bias.GetInfo(), weights.GetInfo(), reshapedInfo);
 
     ActivationFn activationFunction;
-    if (!GetInputActivationFunction(operation, 3, activationFunction, model, data))
+    if (!GetInputActivationFunction<Operand, OperandType>(operation, 3, activationFunction, model, data))
     {
         return Fail("%s: Operation has invalid inputs", __func__);
     }
@@ -725,7 +514,7 @@
             input.Connect(startLayer->GetInputSlot(0));
         }
 
-        return SetupAndTrackLayerOutputSlot(operation, 0, *endLayer, model, data);
+        return SetupAndTrackLayerOutputSlot<Operand>(operation, 0, *endLayer, model, data);
     }
     else
     {
@@ -737,13 +526,13 @@
                                                   const Model& model,
                                                   ConversionData& data)
 {
-    LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data);
+    LayerInputHandle input = ConvertToLayerInputHandle<Operand>(operation, 0, model, data);
     if (!input.IsValid())
     {
         return Fail("%s: Operation has invalid inputs", __func__);
     }
 
-    const Operand* output = GetOutputOperand(operation, 0, model);
+    const Operand* output = GetOutputOperand<Operand>(operation, 0, model);
     if (!output)
     {
         return Fail("%s: Could not read output 0", __func__);
@@ -759,10 +548,10 @@
     descriptor.m_NormMethodType = armnn::NormalizationAlgorithmMethod::LocalBrightness;
 
     if (!input.IsValid() ||
-        !GetInputScalar(operation, 1, OperandType::INT32, descriptor.m_NormSize, model, data) ||
-        !GetInputFloat32(operation, 2, descriptor.m_K, model, data) ||
-        !GetInputFloat32(operation, 3, descriptor.m_Alpha, model, data) ||
-        !GetInputFloat32(operation, 4, descriptor.m_Beta, model, data))
+        !GetInputScalar<Operand, OperandType>(operation, 1, OperandType::INT32, descriptor.m_NormSize, model, data) ||
+        !GetInputFloat32<Operand, OperandType>(operation, 2, descriptor.m_K, model, data) ||
+        !GetInputFloat32<Operand, OperandType>(operation, 3, descriptor.m_Alpha, model, data) ||
+        !GetInputFloat32<Operand, OperandType>(operation, 4, descriptor.m_Beta, model, data))
     {
         return Fail("%s: Operation has invalid inputs", __func__);
     }
@@ -786,7 +575,7 @@
     assert(layer != nullptr);
     input.Connect(layer->GetInputSlot(0));
 
-    return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data);
+    return SetupAndTrackLayerOutputSlot<Operand>(operation, 0, *layer, model, data);
 }
 
 bool HalPolicy::ConvertLogistic(const Operation& operation, const Model& model, ConversionData& data)
@@ -794,7 +583,7 @@
     armnn::ActivationDescriptor desc;
     desc.m_Function = armnn::ActivationFunction::Sigmoid;
 
-    return ConvertToActivation(operation, __func__, desc, model, data);
+    return ConvertToActivation<Operand>(operation, __func__, desc, model, data);
 }
 
 bool HalPolicy::ConvertLstm(const Operation& operation, const Model& model, ConversionData& data)
@@ -802,19 +591,19 @@
     // Inputs:
     // 00: The input: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, input_size], where
     //      “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input.
-    LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data);
+    LayerInputHandle input = ConvertToLayerInputHandle<Operand>(operation, 0, model, data);
     if (!input.IsValid())
     {
         return Fail("%s: Could not read input 0: input", __func__);
     }
     // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size].
-    LayerInputHandle outputStateIn = ConvertToLayerInputHandle(operation, 18, model, data);
+    LayerInputHandle outputStateIn = ConvertToLayerInputHandle<Operand>(operation, 18, model, data);
     if (!outputStateIn.IsValid())
     {
         return Fail("%s: Could not read input 18: outputStateIn", __func__);
     }
     // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units].
-    LayerInputHandle cellStateIn = ConvertToLayerInputHandle(operation, 19, model, data);
+    LayerInputHandle cellStateIn = ConvertToLayerInputHandle<Operand>(operation, 19, model, data);
     if (!cellStateIn.IsValid())
     {
         return Fail("%s: Could not read input 19: cellStateIn", __func__);
@@ -823,29 +612,33 @@
     // Get the mandatory input tensors:
     // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
     //     [num_units, input_size].
-    const ConstTensorPin inputToForgetWeightsPin = ConvertOperationInputToConstTensorPin(operation, 2, model, data);
+    const ConstTensorPin inputToForgetWeightsPin = ConvertOperationInputToConstTensorPin<Operand>(operation, 2, model,
+            data);
     // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units, input_size].
-    const ConstTensorPin inputToCellWeightsPin = ConvertOperationInputToConstTensorPin(operation, 3, model, data);
+    const ConstTensorPin inputToCellWeightsPin = ConvertOperationInputToConstTensorPin<Operand>(operation, 3, model,
+            data);
     // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
     //     [num_units, input_size].
-    const ConstTensorPin inputToOutputWeightsPin = ConvertOperationInputToConstTensorPin(operation, 4, model, data);
+    const ConstTensorPin inputToOutputWeightsPin = ConvertOperationInputToConstTensorPin<Operand>(operation, 4, model,
+            data);
     // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
     //     [num_units, output_size].
-    const ConstTensorPin recurrentToForgetWeightsPin =
-            ConvertOperationInputToConstTensorPin(operation, 6, model, data);
+    const ConstTensorPin recurrentToForgetWeightsPin = ConvertOperationInputToConstTensorPin<Operand>(operation, 6,
+            model, data);
     // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
     //     [num_units, output_size].
-    const ConstTensorPin recurrentToCellWeightsPin = ConvertOperationInputToConstTensorPin(operation, 7, model, data);
+    const ConstTensorPin recurrentToCellWeightsPin = ConvertOperationInputToConstTensorPin<Operand>(operation, 7, model,
+            data);
     // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
     //     [num_units, output_size].
     const ConstTensorPin recurrentToOutputWeightsPin =
-            ConvertOperationInputToConstTensorPin(operation, 8, model, data);
+            ConvertOperationInputToConstTensorPin<Operand>(operation, 8, model, data);
     // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
-    const ConstTensorPin forgetGateBiasPin = ConvertOperationInputToConstTensorPin(operation, 13, model, data);
+    const ConstTensorPin forgetGateBiasPin = ConvertOperationInputToConstTensorPin<Operand>(operation, 13, model, data);
     // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
-    const ConstTensorPin cellBiasPin = ConvertOperationInputToConstTensorPin(operation, 14, model, data);
+    const ConstTensorPin cellBiasPin = ConvertOperationInputToConstTensorPin<Operand>(operation, 14, model, data);
     // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
-    const ConstTensorPin outputGateBiasPin = ConvertOperationInputToConstTensorPin(operation, 15, model, data);
+    const ConstTensorPin outputGateBiasPin = ConvertOperationInputToConstTensorPin<Operand>(operation, 15, model, data);
 
     if (!inputToForgetWeightsPin.IsValid() ||
         !inputToCellWeightsPin.IsValid() ||
@@ -863,31 +656,31 @@
     // Get the optional input tensors:
     // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
     //     [num_units, input_size], where “num_units” corresponds to the number of cell units.
-    const ConstTensorPin inputToInputWeightsPin = ConvertOperationInputToConstTensorPin(operation, 1, model, data,
-            g_DontPermute, nullptr, true);
+    const ConstTensorPin inputToInputWeightsPin = ConvertOperationInputToConstTensorPin<Operand>(operation, 1, model,
+            data, g_DontPermute, nullptr, true);
     // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
     //     [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e.,
     //     “num_units”), or the second dimension of the “projection_weights”, if defined.
-    const ConstTensorPin recurrentToInputWeightsPin = ConvertOperationInputToConstTensorPin(operation, 5, model, data,
-            g_DontPermute, nullptr, true);
+    const ConstTensorPin recurrentToInputWeightsPin = ConvertOperationInputToConstTensorPin<Operand>(operation, 5,
+            model, data, g_DontPermute, nullptr, true);
     // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
-    const ConstTensorPin cellToInputWeightsPin = ConvertOperationInputToConstTensorPin(operation, 9, model, data,
-            g_DontPermute, nullptr, true);
+    const ConstTensorPin cellToInputWeightsPin = ConvertOperationInputToConstTensorPin<Operand>(operation, 9, model,
+            data, g_DontPermute, nullptr, true);
     // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
-    const ConstTensorPin cellToForgetWeightsPin = ConvertOperationInputToConstTensorPin(operation, 10, model, data,
-            g_DontPermute, nullptr, true);
+    const ConstTensorPin cellToForgetWeightsPin = ConvertOperationInputToConstTensorPin<Operand>(operation, 10, model,
+            data, g_DontPermute, nullptr, true);
     // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
-    const ConstTensorPin cellToOutputWeightsPin = ConvertOperationInputToConstTensorPin(operation, 11, model, data,
-            g_DontPermute, nullptr, true);
+    const ConstTensorPin cellToOutputWeightsPin = ConvertOperationInputToConstTensorPin<Operand>(operation, 11, model,
+            data, g_DontPermute, nullptr, true);
     // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
-    const ConstTensorPin inputGateBiasPin = ConvertOperationInputToConstTensorPin(operation, 12, model, data,
+    const ConstTensorPin inputGateBiasPin = ConvertOperationInputToConstTensorPin<Operand>(operation, 12, model, data,
             g_DontPermute, nullptr, true);
     // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
     //     [output_size, num_units].
-    const ConstTensorPin projectionWeightsPin = ConvertOperationInputToConstTensorPin(operation, 16, model, data,
-            g_DontPermute, nullptr, true);
+    const ConstTensorPin projectionWeightsPin = ConvertOperationInputToConstTensorPin<Operand>(operation, 16, model,
+            data, g_DontPermute, nullptr, true);
     // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size].
-    const ConstTensorPin projectionBiasPin = ConvertOperationInputToConstTensorPin(operation, 17, model, data,
+    const ConstTensorPin projectionBiasPin = ConvertOperationInputToConstTensorPin<Operand>(operation, 17, model, data,
             g_DontPermute, nullptr, true);
 
     if ((!inputToInputWeightsPin.IsValid() && !inputToInputWeightsPin.IsOptional()) ||
@@ -912,9 +705,9 @@
     ActivationFn activation;
     float cellClip;
     float projClip;
-    if (!GetInputActivationFunctionFromTensor(operation, 20, activation, model, data) ||
-        !GetInputScalar(operation, 21, OperandType::FLOAT32, cellClip, model, data) ||
-        !GetInputScalar(operation, 22, OperandType::FLOAT32, projClip, model, data))
+    if (!GetInputActivationFunctionFromTensor<Operand, OperandType>(operation, 20, activation, model, data) ||
+        !GetInputScalar<Operand, OperandType>(operation, 21, OperandType::FLOAT32, cellClip, model, data) ||
+        !GetInputScalar<Operand, OperandType>(operation, 22, OperandType::FLOAT32, projClip, model, data))
     {
         return Fail("%s: Operation has invalid scalar inputs", __func__);
     }
@@ -922,26 +715,26 @@
     // Outputs:
     // 00: The scratch buffer: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units * 4] with
     //     CIFG, or [batch_size, num_units * 3] without CIFG.
-    const Operand* scratchBuffer = GetOutputOperand(operation, 0, model);
+    const Operand* scratchBuffer = GetOutputOperand<Operand>(operation, 0, model);
     if (!scratchBuffer)
     {
         return Fail("%s: Could not read output 0: scratchBuffer", __func__);
     }
     // 01: The output state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size].
-    const Operand* outputStateOut = GetOutputOperand(operation, 1, model);
+    const Operand* outputStateOut = GetOutputOperand<Operand>(operation, 1, model);
     if (!outputStateOut)
     {
         return Fail("%s: Could not read output 1: outputStateOut", __func__);
     }
     // 02: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units].
-    const Operand* cellStateOut = GetOutputOperand(operation, 2, model);
+    const Operand* cellStateOut = GetOutputOperand<Operand>(operation, 2, model);
     if (!cellStateOut)
     {
         return Fail("%s: Could not read output 2: cellStateOut", __func__);
     }
     // 03: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. This is
     //     effectively the same as the current “output state (out)” value.
-    const Operand* output = GetOutputOperand(operation, 3, model);
+    const Operand* output = GetOutputOperand<Operand>(operation, 3, model);
     if (!output)
     {
         return Fail("%s: Could not read output 3: output", __func__);
@@ -1101,21 +894,21 @@
     outputStateIn.Connect(layer->GetInputSlot(1));
     cellStateIn.Connect(layer->GetInputSlot(2));
 
-    return (SetupAndTrackLayerOutputSlot(operation, 0, *layer, 0, model, data) &&
-            SetupAndTrackLayerOutputSlot(operation, 1, *layer, 1, model, data) &&
-            SetupAndTrackLayerOutputSlot(operation, 2, *layer, 2, model, data) &&
-            SetupAndTrackLayerOutputSlot(operation, 3, *layer, 3, model, data));
+    return (SetupAndTrackLayerOutputSlot<Operand>(operation, 0, *layer, 0, model, data) &&
+            SetupAndTrackLayerOutputSlot<Operand>(operation, 1, *layer, 1, model, data) &&
+            SetupAndTrackLayerOutputSlot<Operand>(operation, 2, *layer, 2, model, data) &&
+            SetupAndTrackLayerOutputSlot<Operand>(operation, 3, *layer, 3, model, data));
 }
 
 bool HalPolicy::ConvertL2Normalization(const Operation& operation, const Model& model, ConversionData& data)
 {
-    LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data);
+    LayerInputHandle input = ConvertToLayerInputHandle<Operand>(operation, 0, model, data);
     if (!input.IsValid())
     {
         return Fail("%s: Operation has invalid inputs", __func__);
     }
 
-    const Operand* output = GetOutputOperand(operation, 0, model);
+    const Operand* output = GetOutputOperand<Operand>(operation, 0, model);
     if (!output)
     {
         return Fail("%s: Could not read output 0", __func__);
@@ -1141,23 +934,23 @@
     assert(layer != nullptr);
     input.Connect(layer->GetInputSlot(0));
 
-    return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data);
+    return SetupAndTrackLayerOutputSlot<Operand>(operation, 0, *layer, model, data);
 }
 
 bool HalPolicy::ConvertL2Pool2d(const Operation& operation, const Model& model, ConversionData& data)
 {
-    return ConvertPooling2d(operation, __func__, armnn::PoolingAlgorithm::L2, model, data);
+    return ConvertPooling2d<Operand, OperandType>(operation, __func__, armnn::PoolingAlgorithm::L2, model, data);
 }
 
 bool HalPolicy::ConvertMaxPool2d(const Operation& operation, const Model& model, ConversionData& data)
 {
-    return ConvertPooling2d(operation, __func__, armnn::PoolingAlgorithm::Max, model, data);
+    return ConvertPooling2d<Operand, OperandType>(operation, __func__, armnn::PoolingAlgorithm::Max, model, data);
 }
 
 bool HalPolicy::ConvertMul(const Operation& operation, const Model& model, ConversionData& data)
 {
-    LayerInputHandle input0 = ConvertToLayerInputHandle(operation, 0, model, data);
-    LayerInputHandle input1 = ConvertToLayerInputHandle(operation, 1, model, data);
+    LayerInputHandle input0 = ConvertToLayerInputHandle<Operand>(operation, 0, model, data);
+    LayerInputHandle input1 = ConvertToLayerInputHandle<Operand>(operation, 1, model, data);
 
     if (!input0.IsValid() || !input1.IsValid())
     {
@@ -1167,12 +960,12 @@
     // The FuseActivation parameter is always the input index 2
     // and it should be optional
     ActivationFn activationFunction;
-    if (!GetOptionalInputActivation(operation, 2, activationFunction, model, data))
+    if (!GetOptionalInputActivation<Operand, OperandType>(operation, 2, activationFunction, model, data))
     {
         return Fail("%s: Operation has invalid inputs", __func__);
     }
 
-    const Operand* outputOperand = GetOutputOperand(operation, 0, model);
+    const Operand* outputOperand = GetOutputOperand<Operand>(operation, 0, model);
 
     if (outputOperand == nullptr)
     {
@@ -1200,7 +993,7 @@
     if (endLayer != nullptr)
     {
         BroadcastTensor(input0, input1, startLayer, *data.m_Network);
-        return SetupAndTrackLayerOutputSlot(operation, 0, *endLayer, model, data);
+        return SetupAndTrackLayerOutputSlot<Operand>(operation, 0, *endLayer, model, data);
     }
     else
     {
@@ -1213,7 +1006,7 @@
     armnn::ActivationDescriptor desc;
     desc.m_Function = armnn::ActivationFunction::ReLu;
 
-    return ConvertToActivation(operation, __func__, desc, model, data);
+    return ConvertToActivation<Operand>(operation, __func__, desc, model, data);
 }
 
 bool HalPolicy::ConvertReLu1(const Operation& operation, const Model& model, ConversionData& data)
@@ -1223,7 +1016,7 @@
     desc.m_A        = 1.0f;
     desc.m_B        = -1.0f;
 
-    return ConvertToActivation(operation, __func__, desc, model, data);
+    return ConvertToActivation<Operand>(operation, __func__, desc, model, data);
 }
 
 bool HalPolicy::ConvertReLu6(const Operation& operation, const Model& model, ConversionData& data)
@@ -1232,18 +1025,18 @@
     desc.m_Function = armnn::ActivationFunction::BoundedReLu;
     desc.m_A        = 6.0f;
 
-    return ConvertToActivation(operation, __func__, desc, model, data);
+    return ConvertToActivation<Operand>(operation, __func__, desc, model, data);
 }
 
 bool HalPolicy::ConvertSoftmax(const Operation& operation, const Model& model, ConversionData& data)
 {
-    LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data);
+    LayerInputHandle input = ConvertToLayerInputHandle<Operand>(operation, 0, model, data);
     if (!input.IsValid())
     {
         return Fail("%s: Operation has invalid inputs", __func__);
     }
 
-    const Operand* outputOperand = GetOutputOperand(operation, 0, model);
+    const Operand* outputOperand = GetOutputOperand<Operand>(operation, 0, model);
     if (!outputOperand)
     {
         return Fail("%s: Operation has no outputs", __func__);
@@ -1252,7 +1045,7 @@
     const armnn::TensorInfo outInfo = GetTensorInfoForOperand(*outputOperand);
 
     armnn::SoftmaxDescriptor desc;
-    if (!GetInputFloat32(operation, 1, desc.m_Beta, model, data))
+    if (!GetInputFloat32<Operand, OperandType>(operation, 1, desc.m_Beta, model, data))
     {
         return Fail("%s: Operation has invalid inputs", __func__);
     }
@@ -1271,7 +1064,7 @@
     assert(layer != nullptr);
     input.Connect(layer->GetInputSlot(0));
 
-    return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data);
+    return SetupAndTrackLayerOutputSlot<Operand>(operation, 0, *layer, model, data);
 }
 
 bool HalPolicy::ConvertTanH(const Operation& operation, const Model& model, ConversionData& data)
@@ -1281,14 +1074,14 @@
     desc.m_A = 1.0f; // android nn does not support tanH parameters
     desc.m_B = 1.0f; // set to 1.0f for unity scaling
 
-    return ConvertToActivation(operation, __func__, desc, model, data);
+    return ConvertToActivation<Operand>(operation, __func__, desc, model, data);
 }
 
 bool HalPolicy::ConvertReshape(const Operation& operation, const Model& model, ConversionData& data)
 {
-    const Operand* inputOperand = GetInputOperand(operation, 0, model);
-    const Operand* requestedShapeOperand = GetInputOperand(operation, 1, model);
-    const Operand* outputOperand = GetOutputOperand(operation, 0, model);
+    const Operand* inputOperand = GetInputOperand<Operand>(operation, 0, model);
+    const Operand* requestedShapeOperand = GetInputOperand<Operand>(operation, 1, model);
+    const Operand* outputOperand = GetOutputOperand<Operand>(operation, 0, model);
 
     if (inputOperand == nullptr
         || requestedShapeOperand == nullptr
@@ -1305,7 +1098,7 @@
     }
 
     std::vector<int32_t> targetDimensions;
-    if (!GetTensorInt32Values(*requestedShapeOperand, targetDimensions, model, data))
+    if (!GetTensorInt32Values<Operand, OperandType>(*requestedShapeOperand, targetDimensions, model, data))
     {
         return Fail("%s: Could not read values of input 1", __func__);
     }
@@ -1326,7 +1119,7 @@
         return Fail("%s: Shape of output operand does not match resolved requested shape", __func__);
     }
 
-    LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data);
+    LayerInputHandle input = ConvertToLayerInputHandle<Operand>(operation, 0, model, data);
     if (!input.IsValid())
     {
         return Fail("%s: Could not read input 0", __func__);
@@ -1349,18 +1142,18 @@
     assert(layer != nullptr);
     input.Connect(layer->GetInputSlot(0));
 
-    return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data);
+    return SetupAndTrackLayerOutputSlot<Operand>(operation, 0, *layer, model, data);
 }
 
 bool HalPolicy::ConvertResizeBilinear(const Operation& operation, const Model& model, ConversionData& data)
 {
-    LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data);
+    LayerInputHandle input = ConvertToLayerInputHandle<Operand>(operation, 0, model, data);
     if (!input.IsValid())
     {
         return Fail("%s: Could not read input 0", __func__);
     }
 
-    const Operand* output = GetOutputOperand(operation, 0, model);
+    const Operand* output = GetOutputOperand<Operand>(operation, 0, model);
     if (!output)
     {
         return Fail("%s: Could not read output 0", __func__);
@@ -1382,8 +1175,8 @@
     }
 
 
-    if (   !GetInputScalar(operation, 1, OperandType::INT32, desc.m_TargetHeight, model, data)
-        || !GetInputScalar(operation, 2, OperandType::INT32, desc.m_TargetWidth, model, data))
+    if (   !GetInputScalar<Operand, OperandType>(operation, 1, OperandType::INT32, desc.m_TargetHeight, model, data)
+        || !GetInputScalar<Operand, OperandType>(operation, 2, OperandType::INT32, desc.m_TargetWidth, model, data))
     {
         return Fail("%s: Operation has invalid inputs", __func__);
     }
@@ -1395,7 +1188,7 @@
     layer->GetOutputSlot(0).SetTensorInfo(outputInfo);
     input.Connect(layer->GetInputSlot(0));
 
-    return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data);
+    return SetupAndTrackLayerOutputSlot<Operand>(operation, 0, *layer, model, data);
 
 }