diff --git a/1.0/ArmnnDriver.hpp b/1.0/ArmnnDriver.hpp
index 560b0d3..a048973 100644
--- a/1.0/ArmnnDriver.hpp
+++ b/1.0/ArmnnDriver.hpp
@@ -9,67 +9,62 @@
 
 #include "ArmnnDevice.hpp"
 #include "ArmnnDriverImpl.hpp"
+#include "HalPolicy.hpp"
+
 #include "../ArmnnDriverImpl.hpp"
 
 #include <log/log.h>
 
 namespace armnn_driver
 {
-namespace V1_0
+namespace hal_1_0
 {
 
-class ArmnnDriver : public ArmnnDevice, public ::android::hardware::neuralnetworks::V1_0::IDevice
+class ArmnnDriver : public ArmnnDevice, public V1_0::IDevice
 {
 public:
     ArmnnDriver(DriverOptions options)
         : ArmnnDevice(std::move(options))
     {
-        ALOGV("V1_0::ArmnnDriver::ArmnnDriver()");
+        ALOGV("hal_1_0::ArmnnDriver::ArmnnDriver()");
     }
     ~ArmnnDriver() {}
 
 public:
-    Return<void> getCapabilities(
-            ::android::hardware::neuralnetworks::V1_0::IDevice::getCapabilities_cb cb) override
+    Return<void> getCapabilities(V1_0::IDevice::getCapabilities_cb cb) override
     {
-        ALOGV("V1_0::ArmnnDriver::getCapabilities()");
+        ALOGV("hal_1_0::ArmnnDriver::getCapabilities()");
 
-        return V1_0::ArmnnDriverImpl::getCapabilities(m_Runtime,
-                                                      cb);
+        return hal_1_0::ArmnnDriverImpl::getCapabilities(m_Runtime, cb);
     }
 
-    Return<void> getSupportedOperations(
-            const ::android::hardware::neuralnetworks::V1_0::Model& model,
-            ::android::hardware::neuralnetworks::V1_0::IDevice::getSupportedOperations_cb cb) override
+    Return<void> getSupportedOperations(const V1_0::Model& model,
+                                        V1_0::IDevice::getSupportedOperations_cb cb) override
     {
-        ALOGV("V1_0::ArmnnDriver::getSupportedOperations()");
+        ALOGV("hal_1_0::ArmnnDriver::getSupportedOperations()");
 
-        return armnn_driver::ArmnnDriverImpl<HalVersion_1_0>::getSupportedOperations(m_Runtime,
-                                                                                     m_Options,
-                                                                                     model,
-                                                                                     cb);
+        return armnn_driver::ArmnnDriverImpl<HalPolicy>::getSupportedOperations(m_Runtime, m_Options, model, cb);
     }
 
-    Return<ErrorStatus> prepareModel(
-            const ::android::hardware::neuralnetworks::V1_0::Model& model,
-            const android::sp<IPreparedModelCallback>& cb) override
+    Return<ErrorStatus> prepareModel(const V1_0::Model& model,
+                                     const android::sp<IPreparedModelCallback>& cb) override
     {
-        ALOGV("V1_0::ArmnnDriver::prepareModel()");
+        ALOGV("hal_1_0::ArmnnDriver::prepareModel()");
 
-        return armnn_driver::ArmnnDriverImpl<HalVersion_1_0>::prepareModel(m_Runtime,
-                                                                           m_ClTunedParameters,
-                                                                           m_Options,
-                                                                           model,
-                                                                           cb);
+        return armnn_driver::ArmnnDriverImpl<HalPolicy>::prepareModel(m_Runtime,
+                                                                      m_ClTunedParameters,
+                                                                      m_Options,
+                                                                      model,
+                                                                      cb);
     }
 
     Return<DeviceStatus> getStatus() override
     {
-        ALOGV("V1_0::ArmnnDriver::getStatus()");
+        ALOGV("hal_1_0::ArmnnDriver::getStatus()");
 
-        return armnn_driver::ArmnnDriverImpl<HalVersion_1_0>::getStatus();
+        return armnn_driver::ArmnnDriverImpl<HalPolicy>::getStatus();
     }
 };
 
-} // armnn_driver::namespace V1_0
-} // namespace armnn_driver
+} // namespace hal_1_0
+} // namespace armnn_driver
\ No newline at end of file
diff --git a/1.0/ArmnnDriverImpl.cpp b/1.0/ArmnnDriverImpl.cpp
index c7c0f7e..a35bb0e 100644
--- a/1.0/ArmnnDriverImpl.cpp
+++ b/1.0/ArmnnDriverImpl.cpp
@@ -8,33 +8,27 @@
 
 #include <log/log.h>
 
-using namespace std;
-using namespace android;
-using namespace android::nn;
-using namespace android::hardware;
-
 namespace
 {
 
-const char *g_Float32PerformanceExecTimeName = "ArmNN.float32Performance.execTime";
-const char *g_Float32PerformancePowerUsageName = "ArmNN.float32Performance.powerUsage";
-const char *g_Quantized8PerformanceExecTimeName = "ArmNN.quantized8Performance.execTime";
+const char *g_Float32PerformanceExecTimeName      = "ArmNN.float32Performance.execTime";
+const char *g_Float32PerformancePowerUsageName    = "ArmNN.float32Performance.powerUsage";
+const char *g_Quantized8PerformanceExecTimeName   = "ArmNN.quantized8Performance.execTime";
 const char *g_Quantized8PerformancePowerUsageName = "ArmNN.quantized8Performance.powerUsage";
 
 } // anonymous namespace
 
 namespace armnn_driver
 {
-namespace V1_0
+namespace hal_1_0
 {
 
-Return<void> ArmnnDriverImpl::getCapabilities(
-        const armnn::IRuntimePtr& runtime,
-        neuralnetworks::V1_0::IDevice::getCapabilities_cb cb)
+Return<void> ArmnnDriverImpl::getCapabilities(const armnn::IRuntimePtr& runtime,
+                                              V1_0::IDevice::getCapabilities_cb cb)
 {
-    ALOGV("V1_0::ArmnnDriverImpl::getCapabilities()");
+    ALOGV("hal_1_0::ArmnnDriverImpl::getCapabilities()");
 
-    neuralnetworks::V1_0::Capabilities capabilities;
+    V1_0::Capabilities capabilities;
     if (runtime)
     {
         capabilities.float32Performance.execTime =
@@ -53,9 +47,9 @@
     }
     else
     {
-        capabilities.float32Performance.execTime = 0;
-        capabilities.float32Performance.powerUsage = 0;
-        capabilities.quantized8Performance.execTime = 0;
+        capabilities.float32Performance.execTime      = 0;
+        capabilities.float32Performance.powerUsage    = 0;
+        capabilities.quantized8Performance.execTime   = 0;
         capabilities.quantized8Performance.powerUsage = 0;
 
         cb(ErrorStatus::DEVICE_UNAVAILABLE, capabilities);
@@ -64,5 +58,5 @@
     return Void();
 }
 
-} // namespace armnn_driver::V1_0
-} // namespace armnn_driver
+} // namespace hal_1_0
+} // namespace armnn_driver
\ No newline at end of file
diff --git a/1.0/ArmnnDriverImpl.hpp b/1.0/ArmnnDriverImpl.hpp
index a6af74d..7f033e0 100644
--- a/1.0/ArmnnDriverImpl.hpp
+++ b/1.0/ArmnnDriverImpl.hpp
@@ -11,18 +11,18 @@
 
 #include <armnn/ArmNN.hpp>
 
+namespace V1_0 = ::android::hardware::neuralnetworks::V1_0;
+
 namespace armnn_driver
 {
-namespace V1_0
+namespace hal_1_0
 {
 
 class ArmnnDriverImpl
 {
 public:
-    static Return<void> getCapabilities(
-            const armnn::IRuntimePtr& runtime,
-            ::android::hardware::neuralnetworks::V1_0::IDevice::getCapabilities_cb cb);
+    static Return<void> getCapabilities(const armnn::IRuntimePtr& runtime, V1_0::IDevice::getCapabilities_cb cb);
 };
 
-} // namespace armnn_driver::V1_0
+} // namespace hal_1_0
 } // namespace armnn_driver
diff --git a/1.0/HalPolicy.cpp b/1.0/HalPolicy.cpp
new file mode 100644
index 0000000..d3c6dba
--- /dev/null
+++ b/1.0/HalPolicy.cpp
@@ -0,0 +1,1360 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "HalPolicy.hpp"
+
+namespace armnn_driver
+{
+namespace hal_1_0
+{
+
+bool HalPolicy::ConvertOperation(const Operation& operation, const Model& model, ConversionData& data)
+{
+    switch (operation.type)
+    {
+        case V1_0::OperationType::ADD:
+            return ConvertAdd(operation, model, data);
+        case V1_0::OperationType::AVERAGE_POOL_2D:
+            return ConvertAveragePool2d(operation, model, data);
+        case V1_0::OperationType::CONCATENATION:
+            return ConvertConcatenation(operation, model, data);
+        case V1_0::OperationType::CONV_2D:
+            return ConvertConv2d(operation, model, data);
+        case V1_0::OperationType::DEPTHWISE_CONV_2D:
+            return ConvertDepthwiseConv2d(operation, model, data);
+        case V1_0::OperationType::FLOOR:
+            return ConvertFloor(operation, model, data);
+        case V1_0::OperationType::FULLY_CONNECTED:
+            return ConvertFullyConnected(operation, model, data);
+        case V1_0::OperationType::LOCAL_RESPONSE_NORMALIZATION:
+            return ConvertLocalResponseNormalization(operation, model, data);
+        case V1_0::OperationType::LOGISTIC:
+            return ConvertLogistic(operation, model, data);
+        case V1_0::OperationType::LSTM:
+            return ConvertLstm(operation, model, data);
+        case V1_0::OperationType::L2_NORMALIZATION:
+            return ConvertL2Normalization(operation, model, data);
+        case V1_0::OperationType::L2_POOL_2D:
+            return ConvertL2Pool2d(operation, model, data);
+        case V1_0::OperationType::MAX_POOL_2D:
+            return ConvertMaxPool2d(operation, model, data);
+        case V1_0::OperationType::MUL:
+            return ConvertMul(operation, model, data);
+        case V1_0::OperationType::RELU:
+            return ConvertReLu(operation, model, data);
+        case V1_0::OperationType::RELU1:
+            return ConvertReLu1(operation, model, data);
+        case V1_0::OperationType::RELU6:
+            return ConvertReLu6(operation, model, data);
+        case V1_0::OperationType::SOFTMAX:
+            return ConvertSoftmax(operation, model, data);
+        case V1_0::OperationType::TANH:
+            return ConvertTanH(operation, model, data);
+        case V1_0::OperationType::RESHAPE:
+            return ConvertReshape(operation, model, data);
+        case V1_0::OperationType::RESIZE_BILINEAR:
+            return ConvertResizeBilinear(operation, model, data);
+        default:
+            return Fail("%s: Operation type %s not supported in ArmnnDriver",
+                        __func__, toString(operation.type).c_str());
+    }
+}
+
+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);
+
+    if (!input0.IsValid() || !input1.IsValid())
+    {
+        return Fail("%s: Operation has invalid inputs", __func__);
+    }
+
+    // The FuseActivation parameter is always the input index 2
+    // and it should be optional
+    ActivationFn activationFunction;
+    if (!GetOptionalInputActivation(operation, 2, activationFunction, model, data))
+    {
+        return Fail("%s: Operation has invalid inputs", __func__);
+    }
+
+    const Operand* outputOperand = GetOutputOperand(operation, 0, model);
+    if (!outputOperand)
+    {
+        return false;
+    }
+
+    const armnn::TensorInfo outInfo = GetTensorInfoForOperand(*outputOperand);
+
+    if (!IsLayerSupported(__func__,
+                          armnn::IsAdditionSupported,
+                          data.m_Compute,
+                          input0.GetTensorInfo(),
+                          input1.GetTensorInfo(),
+                          outInfo))
+    {
+        return false;
+    }
+
+    armnn::IConnectableLayer* const startLayer = data.m_Network->AddAdditionLayer();
+    armnn::IConnectableLayer* const endLayer   = ProcessActivation(outInfo, activationFunction, startLayer, data);
+
+    const armnn::TensorInfo& inputTensorInfo0 = input0.GetTensorInfo();
+    const armnn::TensorInfo& inputTensorInfo1 = input1.GetTensorInfo();
+
+    if (endLayer != nullptr)
+    {
+        BroadcastTensor(input0, input1, startLayer, *data.m_Network);
+        return SetupAndTrackLayerOutputSlot(operation, 0, *endLayer, model, data);
+    }
+    else
+    {
+        return Fail("%s: ProcessActivation failed", __func__);
+    }
+}
+
+bool HalPolicy::ConvertAveragePool2d(const Operation& operation, const Model& model, ConversionData& data)
+{
+    return ConvertPooling2d(operation, __func__, armnn::PoolingAlgorithm::Average, model, data);
+}
+
+bool HalPolicy::ConvertConcatenation(const Operation& operation, const Model& model, ConversionData& data)
+{
+    // The first N (0..N-1) inputs are tensors. The Nth input is the concatenation axis.
+    if (operation.inputs.size() <= 1)
+    {
+        return Fail("%s: Operation has insufficient arguments", __func__);
+    }
+
+    // Get inputs and outputs
+    const std::size_t numInputTensors = operation.inputs.size() - 1;
+
+    int32_t concatDim;
+    if (!GetInputScalar(operation, numInputTensors, OperandType::INT32, concatDim, model, data))
+    {
+        return Fail("%s: Operation has invalid inputs", __func__);
+    }
+
+    const Operand* const outputOperand = GetOutputOperand(operation, 0, model);
+    if (!outputOperand)
+    {
+        return Fail("%s: Operation has no outputs", __func__);
+    }
+
+
+    armnn::TensorInfo  outputInfo  = GetTensorInfoForOperand(*outputOperand);
+    armnn::TensorShape outputShape = outputInfo.GetShape();
+
+    //
+    // handle negative concat dims along the lines of tensorflow as described here:
+    //    https://www.tensorflow.org/api_docs/python/tf/concat
+    // "negative axis refers to axis + rank(values)-th dimension"
+    //
+    if (concatDim < 0)
+    {
+        concatDim += outputShape.GetNumDimensions();
+    }
+
+    if (concatDim >= static_cast<int32_t>(outputShape.GetNumDimensions()) || concatDim < 0)
+    {
+        return Fail("%s: Operation has invalid concat axis: %d", __func__, concatDim);
+    }
+
+    std::vector<LayerInputHandle>   inputHandles;
+    std::vector<armnn::TensorShape> inputShapes;
+
+    inputHandles.reserve(numInputTensors);
+    inputShapes.reserve(numInputTensors);
+
+    bool inputsHaveBeenReshaped        = false;
+    unsigned int tensorDimensionsAdded = 0;
+
+    for (uint32_t i = 0; i < numInputTensors; ++i)
+    {
+        const Operand* const operand = GetInputOperand(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);
+
+        if (operandShape.GetNumDimensions() == 0)
+        {
+            return Fail("%s: Operands with rank 0 are not supported", __func__);
+        }
+
+        if (RequiresReshape(operandShape))
+        {
+            inputsHaveBeenReshaped = true;
+
+            armnn::TensorInfo reshapeInfo = operandInputHandle.GetTensorInfo();
+
+            // Expand the tensor to three dimensions
+            if (operandShape.GetNumDimensions() == 2)
+            {
+                reshapeInfo.SetShape(armnn::TensorShape({1, operandShape[0], operandShape[1]}));
+                tensorDimensionsAdded = 1;
+            }
+            else
+            {
+                reshapeInfo.SetShape(armnn::TensorShape({1, 1, operandShape[0]}));
+                tensorDimensionsAdded = 2;
+            }
+
+            armnn::IConnectableLayer& newReshape = AddReshapeLayer(
+                    *data.m_Network,
+                    operandInputHandle,
+                    reshapeInfo
+            );
+
+            // Point to the reshape operation rather then the input operation
+            operandShape = reshapeInfo.GetShape();
+            operandInputHandle = LayerInputHandle(true, &newReshape.GetOutputSlot(0), reshapeInfo);
+        }
+
+        inputShapes.emplace_back(operandShape);
+        inputHandles.emplace_back(operandInputHandle);
+
+        if (!inputHandles.back().IsValid())
+        {
+            return Fail("%s: Operation has invalid inputs", __func__);
+        }
+    }
+
+    BOOST_ASSERT(inputShapes.size() == inputHandles.size());
+
+    if (inputsHaveBeenReshaped)
+    {
+        // Adjust the concatenation dimension by the amount of dimensions added (if any)
+        concatDim += tensorDimensionsAdded;
+
+        // Add extra dimensions to the output shape to reflect the addition of the reshape layers
+        if (tensorDimensionsAdded == 1)
+        {
+            outputShape = armnn::TensorShape({1, outputShape[0], outputShape[1]});
+        }
+        else if (tensorDimensionsAdded == 2)
+        {
+            outputShape = armnn::TensorShape({1, 1, outputShape[0], outputShape[1]});
+        }
+    }
+
+    // Get the pair of permutations required for the concatenation
+    std::pair<armnn::PermutationVector, armnn::PermutationVector> permutationPair =
+            std::make_pair(IdentityPermutation4D, IdentityPermutation4D);
+
+    CreatePermutationParameters(inputShapes[0].GetNumDimensions(), concatDim, permutationPair);
+
+    outputShape = armnnUtils::Permuted(outputShape, permutationPair.first);
+    outputInfo.SetShape(outputShape);
+
+    // this is no-op for identity swizzles, otherwise it replaces both
+    // the handles and shapes with the swizzled layer output handles and shapes
+    SwizzleInputs(*data.m_Network, inputHandles, inputShapes, permutationPair.first);
+
+    // Create an armnn merger layer descriptor - this will also perform validation on the input shapes
+    armnn::OriginsDescriptor mergerDescriptor;
+    try
+    {
+        // The merger descriptor is always created across the only supported concat
+        // dimension, which is 0 or 1
+        mergerDescriptor =
+            armnn::CreateMergerDescriptorForConcatenation(
+                inputShapes.begin(), inputShapes.end(), concatDim);
+    }
+    catch (const armnn::Exception& error)
+    {
+        return Fail("%s: Error preparing merger descriptor. %s", __func__, error.what());
+    }
+
+    // Validate the output shape is correct given the input shapes based on the
+    // only valid concat dimension which is 0 or 1
+    if (!ValidateConcatOutputShape(inputShapes, outputShape, concatDim))
+    {
+        return Fail("%s: Error validating the output shape for concat", __func__);
+    }
+
+    std::vector<const armnn::TensorInfo*> inputTensorInfos;
+    std::transform(inputHandles.begin(), inputHandles.end(), std::back_inserter(inputTensorInfos),
+        [](const LayerInputHandle& h) -> const armnn::TensorInfo*{ return &h.GetTensorInfo(); });
+    if (!IsLayerSupported(__func__,
+                          armnn::IsMergerSupported,
+                          data.m_Compute,
+                          inputTensorInfos,
+                          mergerDescriptor))
+    {
+        return false;
+    }
+
+    armnn::IConnectableLayer* layer = data.m_Network->AddMergerLayer(mergerDescriptor);
+    assert(layer != nullptr);
+    layer->GetOutputSlot(0).SetTensorInfo(outputInfo);
+
+    // Connect inputs to the layer
+    const int numInputSlots = layer->GetNumInputSlots();
+    assert(static_cast<std::size_t>(numInputSlots) == inputHandles.size());
+    for (int i = 0; i < numInputSlots; ++i)
+    {
+        // connect the input directly to the merge (concat) layer
+        inputHandles[static_cast<unsigned int>(i)].Connect(layer->GetInputSlot(i));
+    }
+
+    // Add permutation layer and connect the output to it, the permutation becomes the output layer
+    armnn::IConnectableLayer& deswizzleLayer = AddPermuteLayer(*data.m_Network,
+                                                               layer->GetOutputSlot(0),
+                                                               permutationPair.second);
+    layer = &deswizzleLayer;
+
+    if (inputsHaveBeenReshaped)
+    {
+        armnn::TensorInfo afterConcatInfo = layer->GetOutputSlot(0).GetTensorInfo();
+
+        // Undo the reshape knowing the amount of dimensions added
+        if (tensorDimensionsAdded == 1)
+        {
+            afterConcatInfo.SetShape(armnn::TensorShape({ afterConcatInfo.GetShape()[1],
+                                                          afterConcatInfo.GetShape()[2] }));
+        }
+        else if (tensorDimensionsAdded == 2)
+        {
+            afterConcatInfo.SetShape(armnn::TensorShape({ afterConcatInfo.GetShape()[2],
+                                                          afterConcatInfo.GetShape()[3] }));
+        }
+
+        layer = &AddReshapeLayer(
+                *data.m_Network,
+                layer->GetOutputSlot(0),
+                afterConcatInfo
+        );
+    }
+
+    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);
+
+    const armnn::TensorInfo swizzledInputInfo  = armnnUtils::Permuted(inputInfo, NHWCToArmNN);
+    const armnn::TensorInfo swizzledOutputInfo = armnnUtils::Permuted(outputInfo, NHWCToArmNN);
+
+    // ArmNN does not currently support non-fixed weights or bias
+    const ConstTensorPin weightsPin = ConvertOperationInputToConstTensorPin(operation, 1, model, data, NHWCToArmNN);
+    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(), swizzledInputInfo);
+
+    armnn::Convolution2dDescriptor desc;
+    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()[3];
+        const uint32_t kernelY = weights.GetShape()[2];
+        const uint32_t inputX  = swizzledInputInfo.GetShape()[3];
+        const uint32_t inputY  = swizzledInputInfo.GetShape()[2];
+
+        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;
+    auto biases = boost::make_optional(bias.GetInfo());
+
+    if (!IsLayerSupported(__func__,
+                          armnn::IsConvolution2dSupported,
+                          data.m_Compute,
+                          swizzledInputInfo,
+                          swizzledOutputInfo,
+                          desc,
+                          weights.GetInfo(),
+                          biases))
+    {
+        return false;
+    }
+
+    armnn::IConnectableLayer* startLayer = data.m_Network->AddConvolution2dLayer(desc, weights, bias);
+    armnn::IConnectableLayer* endLayer = ProcessActivation(swizzledOutputInfo, activation, startLayer, data);
+
+    if (endLayer != nullptr)
+    {
+        armnn::IConnectableLayer& outSwizzleLayer =
+                SwizzleInDeswizzleOut(*data.m_Network, input, *startLayer, *endLayer);
+        return SetupAndTrackLayerOutputSlot(operation, 0, outSwizzleLayer, model, data);
+    }
+    else
+    {
+        return Fail("%s: ProcessActivation failed", __func__);
+    }
+}
+
+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);
+
+    const armnn::TensorInfo swizzledInputInfo  = armnnUtils::Permuted(inputInfo, NHWCToArmNN);
+    const armnn::TensorInfo swizzledOutputInfo = armnnUtils::Permuted(outputInfo, NHWCToArmNN);
+
+    // 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 ]
+    // but in ArmNN it needs to be [ M, I, H, W ]
+    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 };
+    ConstTensorPin weightsPin =
+            ConvertOperationInputToConstTensorPin(operation, 1, model, data, HWIMToMIHW, &weightsShape);
+
+    // Bias is a 1D tensor
+    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(), swizzledInputInfo);
+
+    armnn::DepthwiseConvolution2dDescriptor desc;
+    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  = swizzledInputInfo.GetShape()[3];
+        const uint32_t inputY  = swizzledInputInfo.GetShape()[2];
+
+        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;
+    auto biases = boost::make_optional(bias.GetInfo());
+
+    if (!IsLayerSupported(__func__,
+                          armnn::IsDepthwiseConvolutionSupported,
+                          data.m_Compute,
+                          swizzledInputInfo,
+                          swizzledOutputInfo,
+                          desc,
+                          weights.GetInfo(),
+                          biases))
+    {
+        return false;
+    }
+
+    armnn::IConnectableLayer* startLayer = data.m_Network->AddDepthwiseConvolution2dLayer(desc, weights, bias);
+    armnn::IConnectableLayer* endLayer   = ProcessActivation(swizzledOutputInfo, activation, startLayer, data);
+
+    if (endLayer != nullptr)
+    {
+        armnn::IConnectableLayer& outSwizzleLayer =
+                SwizzleInDeswizzleOut(*data.m_Network, input, *startLayer, *endLayer);
+        return SetupAndTrackLayerOutputSlot(operation, 0, outSwizzleLayer, model, data);
+    }
+    else
+    {
+        return Fail("%s: ProcessActivation failed", __func__);
+    }
+}
+
+bool HalPolicy::ConvertFloor(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* const outputOperand = GetOutputOperand(operation, 0, model);
+    if (!outputOperand)
+    {
+        return Fail("%s: Operation has invalid outputs", __func__);
+    }
+
+    if (!IsLayerSupported(__func__,
+                          armnn::IsFloorSupported,
+                          data.m_Compute,
+                          input.GetTensorInfo(),
+                          GetTensorInfoForOperand(*outputOperand)))
+    {
+        return false;
+    }
+
+    armnn::IConnectableLayer* layer = data.m_Network->AddFloorLayer();
+    assert(layer != nullptr);
+    input.Connect(layer->GetInputSlot(0));
+
+    return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data);
+}
+
+bool HalPolicy::ConvertFullyConnected(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
+    ConstTensorPin weightsPin = ConvertOperationInputToConstTensorPin(operation, 1, model, data); // 2D
+    ConstTensorPin biasPin    = ConvertOperationInputToConstTensorPin(operation, 2, model, data);    // 1D
+
+    if (!weightsPin.IsValid() || !biasPin.IsValid())
+    {
+        return Fail("%s: Operation has invalid inputs", __func__);
+    }
+
+    armnn::ConstTensor weights = weightsPin.GetConstTensor();
+    armnn::ConstTensor bias    = biasPin.GetConstTensor();
+
+    armnn::TensorInfo reshapedInfo = inputInfo;
+    if (inputInfo.GetNumDimensions() > 2U)
+    {
+        unsigned int dim0 = inputInfo.GetShape()[0];
+        unsigned int dim1 = inputInfo.GetShape()[1];
+
+        for (unsigned int i = 2U; i < inputInfo.GetNumDimensions(); ++i)
+        {
+            dim1 *= inputInfo.GetShape()[i];
+        }
+
+        unsigned int divisor = weights.GetInfo().GetShape()[1] / dim1;
+        if(dim0 % divisor != 0)
+        {
+            return Fail("%s: Failed to deduce tensor shape", __func__);
+        }
+
+        reshapedInfo.SetShape(armnn::TensorShape({dim0 / divisor, dim1 * divisor}));
+    }
+
+    // ensuring that the bias value is within 1% of the weights input (small float differences can exist)
+    SanitizeBiasQuantizationScale(bias.GetInfo(), weights.GetInfo(), reshapedInfo);
+
+    ActivationFn activationFunction;
+    if (!GetInputActivationFunction(operation, 3, activationFunction, model, data))
+    {
+        return Fail("%s: Operation has invalid inputs", __func__);
+    }
+
+    armnn::FullyConnectedDescriptor desc;
+    desc.m_TransposeWeightMatrix = true;
+    desc.m_BiasEnabled           = true;
+
+    if (!IsLayerSupported(__func__,
+                          armnn::IsFullyConnectedSupported,
+                          data.m_Compute,
+                          inputInfo,
+                          outputInfo,
+                          weights.GetInfo(),
+                          bias.GetInfo(),
+                          desc))
+    {
+        return false;
+    }
+
+    armnn::IConnectableLayer* startLayer = data.m_Network->AddFullyConnectedLayer(desc, weights, bias);
+    armnn::IConnectableLayer* endLayer = ProcessActivation(outputInfo, activationFunction, startLayer, data);
+
+    if (endLayer != nullptr)
+    {
+        if (inputInfo.GetNumDimensions() > 2U)
+        {
+            armnn::ReshapeDescriptor reshapeDescriptor;
+            reshapeDescriptor.m_TargetShape = reshapedInfo.GetShape();
+
+            armnn::IConnectableLayer* reshapeLayer = data.m_Network->AddReshapeLayer(reshapeDescriptor);
+            assert(reshapeLayer != nullptr);
+            input.Connect(reshapeLayer->GetInputSlot(0));
+            reshapeLayer->GetOutputSlot(0).SetTensorInfo(reshapedInfo);
+            reshapeLayer->GetOutputSlot(0).Connect(startLayer->GetInputSlot(0));
+        }
+        else
+        {
+            input.Connect(startLayer->GetInputSlot(0));
+        }
+
+        return SetupAndTrackLayerOutputSlot(operation, 0, *endLayer, model, data);
+    }
+    else
+    {
+        return Fail("%s: ProcessActivation failed", __func__);
+    }
+}
+
+bool HalPolicy::ConvertLocalResponseNormalization(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);
+
+    const armnn::TensorInfo swizzledInputInfo  = armnnUtils::Permuted(inputInfo, NHWCToArmNN);
+    const armnn::TensorInfo swizzledOutputInfo = armnnUtils::Permuted(outputInfo, NHWCToArmNN);
+
+    armnn::NormalizationDescriptor descriptor;
+
+    descriptor.m_NormChannelType = armnn::NormalizationAlgorithmChannel::Across;
+    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))
+    {
+        return Fail("%s: Operation has invalid inputs", __func__);
+    }
+
+    // ArmNN expects normSize to be the full size of the normalization
+    // window rather than the radius as in AndroidNN.
+    descriptor.m_NormSize = 1 + (2 * descriptor.m_NormSize);
+
+    if (!IsLayerSupported(__func__,
+                        armnn::IsNormalizationSupported,
+                        data.m_Compute,
+                        swizzledInputInfo,
+                        swizzledOutputInfo,
+                        descriptor))
+    {
+        return false;
+    }
+
+
+    armnn::IConnectableLayer* layer = data.m_Network->AddNormalizationLayer(descriptor);
+    assert(layer != nullptr);
+    layer->GetOutputSlot(0).SetTensorInfo(swizzledOutputInfo);
+
+    armnn::IConnectableLayer& outSwizzleLayer = SwizzleInDeswizzleOut(*data.m_Network, input, *layer);
+
+    return SetupAndTrackLayerOutputSlot(operation, 0, outSwizzleLayer, model, data);
+}
+
+bool HalPolicy::ConvertLogistic(const Operation& operation, const Model& model, ConversionData& data)
+{
+    armnn::ActivationDescriptor desc;
+    desc.m_Function = armnn::ActivationFunction::Sigmoid;
+
+    return ConvertToActivation(operation, __func__, desc, model, data);
+}
+
+bool HalPolicy::ConvertLstm(const Operation& operation, const Model& model, ConversionData& data)
+{
+    // 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);
+    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);
+    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);
+    if (!cellStateIn.IsValid())
+    {
+        return Fail("%s: Could not read input 19: cellStateIn", __func__);
+    }
+
+    // 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);
+    // 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);
+    // 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);
+    // 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);
+    // 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);
+    // 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);
+    // 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);
+    // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
+    const ConstTensorPin cellBiasPin = ConvertOperationInputToConstTensorPin(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);
+
+    if (!inputToForgetWeightsPin.IsValid() ||
+        !inputToCellWeightsPin.IsValid() ||
+        !inputToOutputWeightsPin.IsValid() ||
+        !recurrentToForgetWeightsPin.IsValid() ||
+        !recurrentToCellWeightsPin.IsValid() ||
+        !recurrentToOutputWeightsPin.IsValid() ||
+        !forgetGateBiasPin.IsValid() ||
+        !cellBiasPin.IsValid() ||
+        !outputGateBiasPin.IsValid())
+    {
+        return Fail("%s: Operation has invalid tensor inputs", __func__);
+    }
+
+    // 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);
+    // 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);
+    // 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);
+    // 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);
+    // 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);
+    // 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);
+    // 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);
+    // 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);
+
+    if ((!inputToInputWeightsPin.IsValid() && !inputToInputWeightsPin.IsOptional()) ||
+        (!recurrentToInputWeightsPin.IsValid() && !recurrentToInputWeightsPin.IsOptional()) ||
+        (!cellToInputWeightsPin.IsValid() && !cellToInputWeightsPin.IsOptional()) ||
+        (!cellToForgetWeightsPin.IsValid() && !cellToForgetWeightsPin.IsOptional()) ||
+        (!cellToOutputWeightsPin.IsValid() && !cellToOutputWeightsPin.IsOptional()) ||
+        (!inputGateBiasPin.IsValid() && !inputGateBiasPin.IsOptional()) ||
+        (!projectionWeightsPin.IsValid() && !projectionWeightsPin.IsOptional()) ||
+        (!projectionBiasPin.IsValid() && !projectionBiasPin.IsOptional()))
+    {
+        return Fail("%s: Operation has invalid tensor inputs", __func__);
+    }
+
+    // Get the mandatory input scalars (actually 1-D tensors of size 1):
+    // 20: The activation function: A value indicating the activation function:
+    //     0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid.
+    // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip].
+    //     If set to 0.0 then clipping is disabled.
+    // 22: The clipping threshold: for the output from the projection layer, such that values are bound within
+    //     [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
+    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))
+    {
+        return Fail("%s: Operation has invalid scalar inputs", __func__);
+    }
+
+    // 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);
+    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);
+    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);
+    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);
+    if (!output)
+    {
+        return Fail("%s: Could not read output 3: output", __func__);
+    }
+
+    // set the params structure for the AddLstmLayer call
+    armnn::LstmInputParams params;
+    params.m_InputToInputWeights = inputToInputWeightsPin.GetConstTensorPtr();
+    params.m_InputToForgetWeights = inputToForgetWeightsPin.GetConstTensorPtr();
+    params.m_InputToCellWeights = inputToCellWeightsPin.GetConstTensorPtr();
+    params.m_InputToOutputWeights = inputToOutputWeightsPin.GetConstTensorPtr();
+    params.m_RecurrentToInputWeights = recurrentToInputWeightsPin.GetConstTensorPtr();
+    params.m_RecurrentToForgetWeights = recurrentToForgetWeightsPin.GetConstTensorPtr();
+    params.m_RecurrentToCellWeights = recurrentToCellWeightsPin.GetConstTensorPtr();
+    params.m_RecurrentToOutputWeights = recurrentToOutputWeightsPin.GetConstTensorPtr();
+    params.m_CellToInputWeights = cellToInputWeightsPin.GetConstTensorPtr();
+    params.m_CellToForgetWeights = cellToForgetWeightsPin.GetConstTensorPtr();
+    params.m_CellToOutputWeights = cellToOutputWeightsPin.GetConstTensorPtr();
+    params.m_InputGateBias = inputGateBiasPin.GetConstTensorPtr();
+    params.m_ForgetGateBias = forgetGateBiasPin.GetConstTensorPtr();
+    params.m_CellBias = cellBiasPin.GetConstTensorPtr();
+    params.m_OutputGateBias = outputGateBiasPin.GetConstTensorPtr();
+    params.m_ProjectionWeights = projectionWeightsPin.GetConstTensorPtr();
+    params.m_ProjectionBias = projectionBiasPin.GetConstTensorPtr();
+
+    // set the layer descriptor
+    armnn::LstmDescriptor desc;
+    desc.m_ActivationFunc = activation;
+    desc.m_ClippingThresCell = cellClip;
+    desc.m_ClippingThresProj = projClip;
+    desc.m_CifgEnabled = (params.m_InputToInputWeights == nullptr ||
+                          params.m_RecurrentToInputWeights == nullptr ||
+                          params.m_InputGateBias == nullptr);
+    desc.m_PeepholeEnabled = (params.m_CellToForgetWeights != nullptr ||
+                              params.m_CellToOutputWeights != nullptr);
+    desc.m_ProjectionEnabled = (params.m_ProjectionWeights != nullptr);
+
+    // validate the optional input groups
+    if (desc.m_CifgEnabled &&
+        (params.m_InputToInputWeights != nullptr ||
+         params.m_RecurrentToInputWeights != nullptr ||
+         params.m_InputGateBias != nullptr))
+    {
+        return Fail("%s: All, or none, of input-to-input weights, recurrent-to-input weights,"
+                    " and input gate bias must be provided", __func__);
+    }
+
+    if (!desc.m_ProjectionEnabled && params.m_ProjectionBias != nullptr)
+    {
+        return Fail("%s: projection bias should not be provided without projection weights", __func__);
+    }
+
+    if (desc.m_PeepholeEnabled &&
+        (params.m_CellToForgetWeights == nullptr ||
+         params.m_CellToOutputWeights == nullptr ||
+         (!desc.m_CifgEnabled && params.m_CellToInputWeights == nullptr)))
+    {
+        return Fail("%s: All, or none, of cell-to-forget weights and cell-to-output weights must be provided"
+                    " and, if CIFG is not enabled, cell-to-input weights must also be provided", __func__);
+    }
+
+    // Check if the layer is supported
+    // Inputs
+    const armnn::TensorInfo& inputInfo         = input.GetTensorInfo();
+    const armnn::TensorInfo& outputStateInInfo = outputStateIn.GetTensorInfo();
+    const armnn::TensorInfo& cellStateInInfo   = cellStateIn.GetTensorInfo();
+
+    // Outputs
+    const armnn::TensorInfo& scratchBufferInfo  = GetTensorInfoForOperand(*scratchBuffer);
+    const armnn::TensorInfo& outputStateOutInfo = GetTensorInfoForOperand(*outputStateOut);
+    const armnn::TensorInfo& cellStateOutInfo   = GetTensorInfoForOperand(*cellStateOut);
+    const armnn::TensorInfo& outputInfo         = GetTensorInfoForOperand(*output);
+
+    // Basic parameters
+    const armnn::TensorInfo& inputToForgetWeights = params.m_InputToForgetWeights->GetInfo();
+    const armnn::TensorInfo& inputToCellWeights   = params.m_InputToCellWeights->GetInfo();
+    const armnn::TensorInfo& inputToOutputWeights = params.m_InputToOutputWeights->GetInfo();
+    const armnn::TensorInfo& recurrentToForgetWeights = params.m_RecurrentToForgetWeights->GetInfo();
+    const armnn::TensorInfo& recurrentToCellWeights = params.m_RecurrentToCellWeights->GetInfo();
+    const armnn::TensorInfo& recurrentToOutputWeights = params.m_RecurrentToOutputWeights->GetInfo();
+    const armnn::TensorInfo& forgetGateBias = params.m_ForgetGateBias->GetInfo();
+    const armnn::TensorInfo& cellBias = params.m_CellBias->GetInfo();
+    const armnn::TensorInfo& outputGateBias = params.m_OutputGateBias->GetInfo();
+
+    //Optional parameters
+    const armnn::TensorInfo* inputToInputWeights = nullptr;
+    const armnn::TensorInfo* recurrentToInputWeights = nullptr;
+    const armnn::TensorInfo* cellToInputWeights = nullptr;
+    const armnn::TensorInfo* inputGateBias = nullptr;
+    const armnn::TensorInfo* projectionWeights = nullptr;
+    const armnn::TensorInfo* projectionBias    = nullptr;
+    const armnn::TensorInfo* cellToForgetWeights = nullptr;
+    const armnn::TensorInfo* cellToOutputWeights = nullptr;
+
+    if(!desc.m_CifgEnabled)
+    {
+        inputToInputWeights = &(params.m_InputToInputWeights->GetInfo());
+        recurrentToInputWeights = &(params.m_RecurrentToInputWeights->GetInfo());
+        if (params.m_CellToInputWeights != nullptr)
+        {
+            cellToInputWeights = &(params.m_CellToInputWeights->GetInfo());
+        }
+        inputGateBias = &(params.m_InputGateBias->GetInfo());
+    }
+
+    if(desc.m_ProjectionEnabled)
+    {
+        projectionWeights = &(params.m_ProjectionWeights->GetInfo());
+        if (params.m_ProjectionBias != nullptr)
+        {
+            projectionBias = &(params.m_ProjectionBias->GetInfo());
+        }
+    }
+
+    if(desc.m_PeepholeEnabled)
+    {
+        cellToForgetWeights = &(params.m_CellToForgetWeights->GetInfo());
+        cellToOutputWeights = &(params.m_CellToOutputWeights->GetInfo());
+    }
+
+    if (!IsLayerSupported(__func__,
+                          armnn::IsLstmSupported,
+                          data.m_Compute,
+                          inputInfo,
+                          outputStateInInfo,
+                          cellStateInInfo,
+                          scratchBufferInfo,
+                          outputStateOutInfo,
+                          cellStateOutInfo,
+                          outputInfo,
+                          desc,
+                          inputToForgetWeights,
+                          inputToCellWeights,
+                          inputToOutputWeights,
+                          recurrentToForgetWeights,
+                          recurrentToCellWeights,
+                          recurrentToOutputWeights,
+                          forgetGateBias,
+                          cellBias,
+                          outputGateBias,
+                          inputToInputWeights,
+                          recurrentToInputWeights,
+                          cellToInputWeights,
+                          inputGateBias,
+                          projectionWeights,
+                          projectionBias,
+                          cellToForgetWeights,
+                          cellToOutputWeights))
+    {
+        return false;
+    }
+
+    // Add the layer
+    armnn::IConnectableLayer* layer = data.m_Network->AddLstmLayer(desc, params, "Lstm");
+
+    input.Connect(layer->GetInputSlot(0));
+    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));
+}
+
+bool HalPolicy::ConvertL2Normalization(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);
+
+    const armnn::TensorInfo swizzledInputInfo  = armnnUtils::Permuted(inputInfo, NHWCToArmNN);
+    const armnn::TensorInfo swizzledOutputInfo = armnnUtils::Permuted(outputInfo, NHWCToArmNN);
+
+    if (!IsLayerSupported(__func__,
+                          armnn::IsL2NormalizationSupported,
+                          data.m_Compute,
+                          swizzledInputInfo,
+                          swizzledOutputInfo))
+    {
+        return false;
+    }
+
+    armnn::IConnectableLayer* layer = data.m_Network->AddL2NormalizationLayer();
+    assert(layer != nullptr);
+    layer->GetOutputSlot(0).SetTensorInfo(swizzledOutputInfo);
+
+    armnn::IConnectableLayer& outSwizzleLayer = SwizzleInDeswizzleOut(*data.m_Network, input, *layer);
+
+    return SetupAndTrackLayerOutputSlot(operation, 0, outSwizzleLayer, model, data);
+}
+
+bool HalPolicy::ConvertL2Pool2d(const Operation& operation, const Model& model, ConversionData& data)
+{
+    return ConvertPooling2d(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);
+}
+
+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);
+
+    if (!input0.IsValid() || !input1.IsValid())
+    {
+        return Fail("%s: Operation has invalid inputs", __func__);
+    }
+
+    // The FuseActivation parameter is always the input index 2
+    // and it should be optional
+    ActivationFn activationFunction;
+    if (!GetOptionalInputActivation(operation, 2, activationFunction, model, data))
+    {
+        return Fail("%s: Operation has invalid inputs", __func__);
+    }
+
+    const Operand* outputOperand = GetOutputOperand(operation, 0, model);
+
+    if (outputOperand == nullptr)
+    {
+        return false;
+    }
+
+    const armnn::TensorInfo& outInfo = GetTensorInfoForOperand(*outputOperand);
+
+    if (!IsLayerSupported(__func__,
+                          armnn::IsMultiplicationSupported,
+                          data.m_Compute,
+                          input0.GetTensorInfo(),
+                          input1.GetTensorInfo(),
+                          outInfo))
+    {
+        return false;
+    }
+
+    armnn::IConnectableLayer* const startLayer = data.m_Network->AddMultiplicationLayer();
+    armnn::IConnectableLayer* const endLayer = ProcessActivation(outInfo, activationFunction, startLayer, data);
+
+    const armnn::TensorInfo& inputTensorInfo0 = input0.GetTensorInfo();
+    const armnn::TensorInfo& inputTensorInfo1 = input1.GetTensorInfo();
+
+    if (endLayer != nullptr)
+    {
+        BroadcastTensor(input0, input1, startLayer, *data.m_Network);
+        return SetupAndTrackLayerOutputSlot(operation, 0, *endLayer, model, data);
+    }
+    else
+    {
+        return Fail("%s: ProcessActivation failed", __func__);
+    }
+}
+
+bool HalPolicy::ConvertReLu(const Operation& operation, const Model& model, ConversionData& data)
+{
+    armnn::ActivationDescriptor desc;
+    desc.m_Function = armnn::ActivationFunction::ReLu;
+
+    return ConvertToActivation(operation, __func__, desc, model, data);
+}
+
+bool HalPolicy::ConvertReLu1(const Operation& operation, const Model& model, ConversionData& data)
+{
+    armnn::ActivationDescriptor desc;
+    desc.m_Function = armnn::ActivationFunction::BoundedReLu;
+    desc.m_A        = 1.0f;
+    desc.m_B        = -1.0f;
+
+    return ConvertToActivation(operation, __func__, desc, model, data);
+}
+
+bool HalPolicy::ConvertReLu6(const Operation& operation, const Model& model, ConversionData& data)
+{
+    armnn::ActivationDescriptor desc;
+    desc.m_Function = armnn::ActivationFunction::BoundedReLu;
+    desc.m_A        = 6.0f;
+
+    return ConvertToActivation(operation, __func__, desc, model, data);
+}
+
+bool HalPolicy::ConvertSoftmax(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* outputOperand = GetOutputOperand(operation, 0, model);
+    if (!outputOperand)
+    {
+        return Fail("%s: Operation has no outputs", __func__);
+    }
+
+    const armnn::TensorInfo outInfo = GetTensorInfoForOperand(*outputOperand);
+
+    armnn::SoftmaxDescriptor desc;
+    if (!GetInputFloat32(operation, 1, desc.m_Beta, model, data))
+    {
+        return Fail("%s: Operation has invalid inputs", __func__);
+    }
+
+    if (!IsLayerSupported(__func__,
+                          armnn::IsSoftmaxSupported,
+                          data.m_Compute,
+                          input.GetTensorInfo(),
+                          outInfo,
+                          desc))
+    {
+        return false;
+    }
+
+    armnn::IConnectableLayer* layer = data.m_Network->AddSoftmaxLayer(desc);
+    assert(layer != nullptr);
+    input.Connect(layer->GetInputSlot(0));
+
+    return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data);
+}
+
+bool HalPolicy::ConvertTanH(const Operation& operation, const Model& model, ConversionData& data)
+{
+    armnn::ActivationDescriptor desc;
+    desc.m_Function = armnn::ActivationFunction::TanH;
+    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);
+}
+
+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);
+
+    if (inputOperand == nullptr
+        || requestedShapeOperand == nullptr
+        || outputOperand == nullptr)
+    {
+        return Fail("%s: Operation has invalid inputs", __func__);
+    }
+
+
+    if (requestedShapeOperand->dimensions.size() != 1)
+    {
+        return Fail("%s: Input 1 expected to be one-dimensional (found %i dimensions)",
+            __func__, requestedShapeOperand->dimensions.size());
+    }
+
+    std::vector<int32_t> targetDimensions;
+    if (!GetTensorInt32Values(*requestedShapeOperand, targetDimensions, model, data))
+    {
+        return Fail("%s: Could not read values of input 1", __func__);
+    }
+
+    const Shape inputOperandShape = GetOperandShape(*inputOperand);
+
+    Shape requestedShape;
+    // targetDimensions may contain special values (e.g. -1). reshapePrepare() is an AndroidNN provided utility
+    // function that resolves these values into a fully specified tensor shape.
+    if (!reshapePrepare(inputOperandShape, targetDimensions.data(), targetDimensions.size(), &requestedShape))
+    {
+        return Fail("%s: Failed to resolve the requested shape", __func__);
+    }
+
+    const Shape outputOperandShape = GetOperandShape(*outputOperand);
+    if (!SameShape(requestedShape, outputOperandShape))
+    {
+        return Fail("%s: Shape of output operand does not match resolved requested shape", __func__);
+    }
+
+    LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data);
+    if (!input.IsValid())
+    {
+        return Fail("%s: Could not read input 0", __func__);
+    }
+
+    if (!IsLayerSupported(__func__,
+                          armnn::IsReshapeSupported,
+                          data.m_Compute,
+                          input.GetTensorInfo()))
+    {
+        return false;
+    }
+
+
+    armnn::ReshapeDescriptor reshapeDescriptor;
+    reshapeDescriptor.m_TargetShape = armnn::TensorShape(requestedShape.dimensions.size(),
+                                                         requestedShape.dimensions.data());
+
+    armnn::IConnectableLayer* layer = data.m_Network->AddReshapeLayer(reshapeDescriptor);
+    assert(layer != nullptr);
+    input.Connect(layer->GetInputSlot(0));
+
+    return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data);
+}
+
+bool HalPolicy::ConvertResizeBilinear(const Operation& operation, const Model& model, ConversionData& data)
+{
+    LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data);
+    if (!input.IsValid())
+    {
+        return Fail("%s: Could not read input 0", __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);
+
+    const armnn::TensorInfo swizzledInputInfo = armnnUtils::Permuted(inputInfo, NHWCToArmNN);
+    const armnn::TensorInfo swizzledOutputInfo = armnnUtils::Permuted(outputInfo, NHWCToArmNN);
+
+    if (!IsLayerSupported(__func__,
+                          armnn::IsResizeBilinearSupported,
+                          data.m_Compute,
+                          swizzledInputInfo))
+    {
+        return false;
+    }
+
+    armnn::ResizeBilinearDescriptor desc;
+
+    if (   !GetInputScalar(operation, 1, OperandType::INT32, desc.m_TargetHeight, model, data)
+        || !GetInputScalar(operation, 2, OperandType::INT32, desc.m_TargetWidth, model, data))
+    {
+        return Fail("%s: Operation has invalid inputs", __func__);
+    }
+
+    armnn::IConnectableLayer* layer = data.m_Network->AddResizeBilinearLayer(desc);
+    assert(layer != nullptr);
+    layer->GetOutputSlot(0).SetTensorInfo(swizzledOutputInfo);
+
+    armnn::IConnectableLayer& outSwizzleLayer = SwizzleInDeswizzleOut(*data.m_Network, input, *layer);
+
+    return SetupAndTrackLayerOutputSlot(operation, 0, outSwizzleLayer, model, data);
+
+}
+
+} // namespace hal_1_0
+} // namespace armnn_driver
\ No newline at end of file
diff --git a/1.0/HalPolicy.hpp b/1.0/HalPolicy.hpp
new file mode 100644
index 0000000..c596075
--- /dev/null
+++ b/1.0/HalPolicy.hpp
@@ -0,0 +1,75 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include "ConversionUtils.hpp"
+
+#include <HalInterfaces.h>
+
+namespace V1_0 = ::android::hardware::neuralnetworks::V1_0;
+
+namespace armnn_driver
+{
+namespace hal_1_0
+{
+
+class HalPolicy
+{
+public:
+    using Model                     = V1_0::Model;
+    using Operation                 = V1_0::Operation;
+    using getSupportedOperations_cb = V1_0::IDevice::getSupportedOperations_cb;
+
+    static bool ConvertOperation(const Operation& operation, const Model& model, ConversionData& data);
+
+private:
+    static bool ConvertAdd(const Operation& operation, const Model& model, ConversionData& data);
+
+    static bool ConvertAveragePool2d(const Operation& operation, const Model& model, ConversionData& data);
+
+    static bool ConvertConcatenation(const Operation& operation, const Model& model, ConversionData& data);
+
+    static bool ConvertConv2d(const Operation& operation, const Model& model, ConversionData& data);
+
+    static bool ConvertDepthwiseConv2d(const Operation& operation, const Model& model, ConversionData& data);
+
+    static bool ConvertFloor(const Operation& operation, const Model& model, ConversionData& data);
+
+    static bool ConvertFullyConnected(const Operation& operation, const Model& model, ConversionData& data);
+
+    static bool ConvertLocalResponseNormalization(const Operation& operation,
+                                                  const Model& model,
+                                                  ConversionData& data);
+
+    static bool ConvertLogistic(const Operation& operation, const Model& model, ConversionData& data);
+
+    static bool ConvertLstm(const Operation& operation, const Model& model, ConversionData& data);
+
+    static bool ConvertL2Normalization(const Operation& operation, const Model& model, ConversionData& data);
+
+    static bool ConvertL2Pool2d(const Operation& operation, const Model& model, ConversionData& data);
+
+    static bool ConvertMaxPool2d(const Operation& operation, const Model& model, ConversionData& data);
+
+    static bool ConvertMul(const Operation& operation, const Model& model, ConversionData& data);
+
+    static bool ConvertReLu(const Operation& operation, const Model& model, ConversionData& data);
+
+    static bool ConvertReLu1(const Operation& operation, const Model& model, ConversionData& data);
+
+    static bool ConvertReLu6(const Operation& operation, const Model& model, ConversionData& data);
+
+    static bool ConvertSoftmax(const Operation& operation, const Model& model, ConversionData& data);
+
+    static bool ConvertTanH(const Operation& operation, const Model& model, ConversionData& data);
+
+    static bool ConvertReshape(const Operation& operation, const Model& model, ConversionData& data);
+
+    static bool ConvertResizeBilinear(const Operation& operation, const Model& model, ConversionData& data);
+};
+
+} // namespace hal_1_0
+} // namespace armnn_driver