diff --git a/src/backends/backendsCommon/test/layerTests/BatchNormalizationTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/BatchNormalizationTestImpl.cpp
new file mode 100644
index 0000000..d8f87e1
--- /dev/null
+++ b/src/backends/backendsCommon/test/layerTests/BatchNormalizationTestImpl.cpp
@@ -0,0 +1,566 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "BatchNormalizationTestImpl.hpp"
+
+#include <DataLayoutIndexed.hpp>
+#include <ResolveType.hpp>
+
+#include <armnn/ArmNN.hpp>
+
+#include <backendsCommon/CpuTensorHandle.hpp>
+#include <backendsCommon/IBackendInternal.hpp>
+#include <backendsCommon/WorkloadFactory.hpp>
+
+#include <backendsCommon/test/QuantizeHelper.hpp>
+#include <backendsCommon/test/TensorCopyUtils.hpp>
+#include <backendsCommon/test/WorkloadTestUtils.hpp>
+
+#include <test/TensorHelpers.hpp>
+
+namespace
+{
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 4> BatchNormTestImpl(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    const armnn::TensorShape& inputOutputTensorShape,
+    const std::vector<float>& inputValues,
+    const std::vector<float>& expectedOutputValues,
+    float qScale,
+    int32_t qOffset,
+    armnn::DataLayout dataLayout)
+{
+    armnn::TensorInfo inputTensorInfo(inputOutputTensorShape, ArmnnType);
+    armnn::TensorInfo outputTensorInfo(inputOutputTensorShape, ArmnnType);
+
+    armnnUtils::DataLayoutIndexed dataLayoutIndexed(dataLayout);
+
+    armnn::TensorInfo tensorInfo({ inputOutputTensorShape[dataLayoutIndexed.GetChannelsIndex()] },
+                                 ArmnnType);
+
+    // Set quantization parameters if the requested type is a quantized type.
+    if (armnn::IsQuantizedType<T>())
+    {
+        inputTensorInfo.SetQuantizationScale(qScale);
+        inputTensorInfo.SetQuantizationOffset(qOffset);
+        outputTensorInfo.SetQuantizationScale(qScale);
+        outputTensorInfo.SetQuantizationOffset(qOffset);
+        tensorInfo.SetQuantizationScale(qScale);
+        tensorInfo.SetQuantizationOffset(qOffset);
+    }
+
+    auto inputTensor = MakeTensor<T, 4>(inputTensorInfo,
+                                        QuantizedVector<T>(qScale, qOffset, inputValues));
+
+    // These values are per-channel of the input.
+    auto mean     = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {3, -2}));
+    auto variance = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {4,  9}));
+    auto beta     = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {3,  2}));
+    auto gamma    = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {2,  1}));
+
+    LayerTestResult<T, 4> result(outputTensorInfo);
+
+    result.outputExpected = MakeTensor<T, 4>(inputTensorInfo,
+                                             QuantizedVector<T>(qScale, qOffset, expectedOutputValues));
+
+    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+    armnn::ScopedCpuTensorHandle meanTensor(tensorInfo);
+    armnn::ScopedCpuTensorHandle varianceTensor(tensorInfo);
+    armnn::ScopedCpuTensorHandle betaTensor(tensorInfo);
+    armnn::ScopedCpuTensorHandle gammaTensor(tensorInfo);
+
+    armnn::BatchNormalizationQueueDescriptor descriptor;
+    descriptor.m_Mean                    = &meanTensor;
+    descriptor.m_Variance                = &varianceTensor;
+    descriptor.m_Beta                    = &betaTensor;
+    descriptor.m_Gamma                   = &gammaTensor;
+    descriptor.m_Parameters.m_Eps        = 0.0f;
+    descriptor.m_Parameters.m_DataLayout = dataLayout;
+    armnn::WorkloadInfo info;
+
+    AllocateAndCopyDataToITensorHandle(&meanTensor, &mean[0]);
+    AllocateAndCopyDataToITensorHandle(&varianceTensor, &variance[0]);
+    AllocateAndCopyDataToITensorHandle(&betaTensor, &beta[0]);
+    AllocateAndCopyDataToITensorHandle(&gammaTensor, &gamma[0]);
+
+    AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
+    AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
+
+    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateBatchNormalization(descriptor, info);
+
+    inputHandle->Allocate();
+    outputHandle->Allocate();
+
+    CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0][0][0]);
+
+    workload->Execute();
+
+    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
+
+    return result;
+}
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T,4> BatchNormTestNhwcImpl(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    float qScale,
+    int32_t qOffset)
+{
+    const unsigned int width    = 2;
+    const unsigned int height   = 3;
+    const unsigned int channels = 2;
+    const unsigned int num      = 1;
+
+    armnn::TensorInfo inputTensorInfo({num, height, width, channels}, ArmnnType);
+    armnn::TensorInfo outputTensorInfo({num, height, width, channels}, ArmnnType);
+    armnn::TensorInfo tensorInfo({channels}, ArmnnType);
+
+    // Set quantization parameters if the requested type is a quantized type.
+    if(armnn::IsQuantizedType<T>())
+    {
+        inputTensorInfo.SetQuantizationScale(qScale);
+        inputTensorInfo.SetQuantizationOffset(qOffset);
+        outputTensorInfo.SetQuantizationScale(qScale);
+        outputTensorInfo.SetQuantizationOffset(qOffset);
+        tensorInfo.SetQuantizationScale(qScale);
+        tensorInfo.SetQuantizationOffset(qOffset);
+    }
+
+    auto input = MakeTensor<T, 4>(inputTensorInfo,
+        QuantizedVector<T>(qScale, qOffset,
+        {
+            1.f, 1.f, 4.f, 1.f,
+            4.f, 4.f, 2.f, 1.f,
+            1.f, -2.f, 6.f, 4.f
+        }));
+    // These values are per-channel of the input.
+    auto mean     = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {3, -2}));
+    auto variance = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {4, 9}));
+    auto beta     = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {3, 2}));
+    auto gamma    = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {2, 1}));
+    LayerTestResult<T,4> ret(outputTensorInfo);
+
+    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+    armnn::BatchNormalizationQueueDescriptor data;
+    armnn::WorkloadInfo info;
+    armnn::ScopedCpuTensorHandle meanTensor(tensorInfo);
+    armnn::ScopedCpuTensorHandle varianceTensor(tensorInfo);
+    armnn::ScopedCpuTensorHandle betaTensor(tensorInfo);
+    armnn::ScopedCpuTensorHandle gammaTensor(tensorInfo);
+
+    AllocateAndCopyDataToITensorHandle(&meanTensor, &mean[0]);
+    AllocateAndCopyDataToITensorHandle(&varianceTensor, &variance[0]);
+    AllocateAndCopyDataToITensorHandle(&betaTensor, &beta[0]);
+    AllocateAndCopyDataToITensorHandle(&gammaTensor, &gamma[0]);
+
+    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
+    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
+    data.m_Mean             = &meanTensor;
+    data.m_Variance         = &varianceTensor;
+    data.m_Beta             = &betaTensor;
+    data.m_Gamma            = &gammaTensor;
+    data.m_Parameters.m_Eps = 0.0f;
+    data.m_Parameters.m_DataLayout = armnn::DataLayout::NHWC;
+
+    // For each channel:
+    // substract mean, divide by standard deviation (with an epsilon to avoid div by 0),
+    // multiply by gamma and add beta
+    ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo,
+        QuantizedVector<T>(qScale, qOffset,
+        {
+            1.f, 3.f, 4.f, 3.f,
+            4.f, 4.f, 2.f, 3.f,
+            1.f, 2.f, 6.f, 4.f
+        }));
+
+    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateBatchNormalization(data, info);
+
+    inputHandle->Allocate();
+    outputHandle->Allocate();
+
+    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
+
+    workload->Execute();
+
+    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
+
+    return ret;
+}
+
+} // anonymous namespace
+
+LayerTestResult<float, 4> BatchNormFloatTest(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    // BatchSize: 1
+    // Channels: 2
+    // Height: 3
+    // Width: 2
+
+    const armnn::TensorShape inputOutputShape{ 1, 2, 3, 2 };
+    std::vector<float> inputValues
+    {
+        // Batch 0, Channel 0, Height (3) x Width (2)
+         1.f, 4.f,
+         4.f, 2.f,
+         1.f, 6.f,
+
+        // Batch 0, Channel 1, Height (3) x Width (2)
+         1.f, 1.f,
+         4.f, 1.f,
+        -2.f, 4.f
+    };
+    std::vector<float> expectedOutputValues
+    {
+        // Batch 0, Channel 0, Height (3) x Width (2)
+        1.f, 4.f,
+        4.f, 2.f,
+        1.f, 6.f,
+
+        // Batch 0, Channel 1, Height (3) x Width (2)
+        3.f, 3.f,
+        4.f, 3.f,
+        2.f, 4.f
+    };
+
+    return BatchNormTestImpl<armnn::DataType::Float32>(
+        workloadFactory,
+        memoryManager,
+        inputOutputShape,
+        inputValues,
+        expectedOutputValues,
+        0.f,
+        0,
+        armnn::DataLayout::NCHW);
+}
+
+LayerTestResult<float, 4> BatchNormFloatNhwcTest(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    // BatchSize: 1
+    // Height: 3
+    // Width: 2
+    // Channels: 2
+
+    const armnn::TensorShape inputOutputShape{ 1, 3, 2, 2 };
+    std::vector<float> inputValues
+    {
+        // Batch 0, Height 0, Width (2) x Channel (2)
+        1.f,  1.f,
+        4.f,  1.f,
+
+        // Batch 0, Height 1, Width (2) x Channel (2)
+        4.f,  4.f,
+        2.f,  1.f,
+
+        // Batch 0, Height 2, Width (2) x Channel (2)
+        1.f, -2.f,
+        6.f,  4.f
+    };
+    std::vector<float> expectedOutputValues
+    {
+        // Batch 0, Height 0, Width (2) x Channel (2)
+        1.f, 3.f,
+        4.f, 3.f,
+
+        // Batch 0, Height 1, Width (2) x Channel (2)
+        4.f, 4.f,
+        2.f, 3.f,
+
+        // Batch 0, Height 2, Width (2) x Channel (2)
+        1.f, 2.f,
+        6.f, 4.f
+    };
+
+    return BatchNormTestImpl<armnn::DataType::Float32>(
+        workloadFactory,
+        memoryManager,
+        inputOutputShape,
+        inputValues,
+        expectedOutputValues,
+        0.f,
+        0,
+        armnn::DataLayout::NHWC);
+}
+
+LayerTestResult<uint8_t, 4> BatchNormUint8Test(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    // BatchSize: 1
+    // Channels: 2
+    // Height: 3
+    // Width: 2
+
+    const armnn::TensorShape inputOutputShape{ 1, 2, 3, 2 };
+    std::vector<float> inputValues
+    {
+        // Batch 0, Channel 0, Height (3) x Width (2)
+         1.f, 4.f,
+         4.f, 2.f,
+         1.f, 6.f,
+
+        // Batch 0, Channel 1, Height (3) x Width (2)
+         1.f, 1.f,
+         4.f, 1.f,
+        -2.f, 4.f
+    };
+    std::vector<float> expectedOutputValues
+    {
+        // Batch 0, Channel 0, Height (3) x Width (2)
+        1.f, 4.f,
+        4.f, 2.f,
+        1.f, 6.f,
+
+        // Batch 0, Channel 1, Height (3) x Width (2)
+        3.f, 3.f,
+        4.f, 3.f,
+        2.f, 4.f
+    };
+
+    return BatchNormTestImpl<armnn::DataType::QuantisedAsymm8>(
+        workloadFactory,
+        memoryManager,
+        inputOutputShape,
+        inputValues,
+        expectedOutputValues,
+        1.f / 20.f,
+        50,
+        armnn::DataLayout::NCHW);
+}
+
+LayerTestResult<uint8_t, 4> BatchNormUint8NhwcTest(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    // BatchSize: 1
+    // Height: 3
+    // Width: 2
+    // Channels: 2
+
+    const armnn::TensorShape inputOutputShape{ 1, 3, 2, 2 };
+    std::vector<float> inputValues
+    {
+        // Batch 0, Height 0, Width (2) x Channel (2)
+        1.f,  1.f,
+        4.f,  1.f,
+
+        // Batch 0, Height 1, Width (2) x Channel (2)
+        4.f,  4.f,
+        2.f,  1.f,
+
+        // Batch 0, Height 2, Width (2) x Channel (2)
+        1.f, -2.f,
+        6.f,  4.f
+    };
+    std::vector<float> expectedOutputValues
+    {
+        // Batch 0, Height 0, Width (2) x Channel (2)
+        1.f, 3.f,
+        4.f, 3.f,
+
+        // Batch 0, Height 1, Width (2) x Channel (2)
+        4.f, 4.f,
+        2.f, 3.f,
+
+        // Batch 0, Height 2, Width (2) x Channel (2)
+        1.f, 2.f,
+        6.f, 4.f
+    };
+
+    return BatchNormTestImpl<armnn::DataType::QuantisedAsymm8>(
+        workloadFactory,
+        memoryManager,
+        inputOutputShape, inputValues, expectedOutputValues,
+         1.f/20.f, 50, armnn::DataLayout::NHWC);
+}
+
+LayerTestResult<int16_t, 4> BatchNormInt16Test(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    // BatchSize: 1
+    // Channels: 2
+    // Height: 3
+    // Width: 2
+
+    const armnn::TensorShape inputOutputShape{ 1, 2, 3, 2 };
+    std::vector<float> inputValues
+    {
+        // Batch 0, Channel 0, Height (3) x Width (2)
+         1.f, 4.f,
+         4.f, 2.f,
+         1.f, 6.f,
+
+        // Batch 0, Channel 1, Height (3) x Width (2)
+         1.f, 1.f,
+         4.f, 1.f,
+        -2.f, 4.f
+    };
+    std::vector<float> expectedOutputValues
+    {
+        // Batch 0, Channel 0, Height (3) x Width (2)
+        1.f, 4.f,
+        4.f, 2.f,
+        1.f, 6.f,
+
+        // Batch 0, Channel 1, Height (3) x Width (2)
+        3.f, 3.f,
+        4.f, 3.f,
+        2.f, 4.f
+    };
+
+    return BatchNormTestImpl<armnn::DataType::QuantisedSymm16>(
+        workloadFactory,
+        memoryManager,
+        inputOutputShape,
+        inputValues,
+        expectedOutputValues,
+        1.f / 20.f,
+        50,
+        armnn::DataLayout::NCHW);
+}
+
+LayerTestResult<int16_t, 4> BatchNormInt16NhwcTest(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    // BatchSize: 1
+    // Height: 3
+    // Width: 2
+    // Channels: 2
+
+    const armnn::TensorShape inputOutputShape{ 1, 3, 2, 2 };
+    std::vector<float> inputValues
+    {
+        // Batch 0, Height 0, Width (2) x Channel (2)
+        1.f,  1.f,
+        4.f,  1.f,
+
+        // Batch 0, Height 1, Width (2) x Channel (2)
+        4.f,  4.f,
+        2.f,  1.f,
+
+        // Batch 0, Height 2, Width (2) x Channel (2)
+        1.f, -2.f,
+        6.f,  4.f
+    };
+    std::vector<float> expectedOutputValues
+    {
+        // Batch 0, Height 0, Width (2) x Channel (2)
+        1.f, 3.f,
+        4.f, 3.f,
+
+        // Batch 0, Height 1, Width (2) x Channel (2)
+        4.f, 4.f,
+        2.f, 3.f,
+
+        // Batch 0, Height 2, Width (2) x Channel (2)
+        1.f, 2.f,
+        6.f, 4.f
+    };
+
+    return BatchNormTestImpl<armnn::DataType::QuantisedSymm16>(
+        workloadFactory,
+        memoryManager,
+        inputOutputShape,
+        inputValues,
+        expectedOutputValues,
+        1.f / 20.f,
+        50,
+        armnn::DataLayout::NHWC);
+}
+
+LayerTestResult<float,4> CompareBatchNormTest(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    armnn::IWorkloadFactory& refWorkloadFactory)
+{
+    const unsigned int width     = 2;
+    const unsigned int height    = 3;
+    const unsigned int channels  = 5;
+    const unsigned int batchSize = 3;
+
+    armnn::TensorInfo inputTensorInfo;
+    armnn::TensorInfo outputTensorInfo;
+    armnn::TensorInfo tensorInfo;
+
+    constexpr unsigned int shape[]       = {batchSize, channels, height, width};
+    constexpr unsigned int tensorShape[] = {channels};
+
+    inputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
+    outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
+    tensorInfo = armnn::TensorInfo(1, tensorShape, armnn::DataType::Float32);
+
+    auto input = MakeRandomTensor<float, 4>(inputTensorInfo, 21312);
+
+    auto mean     = MakeRandomTensor<float, 1>(tensorInfo, 123);
+    auto variance = MakeRandomTensor<float, 1>(tensorInfo, 234, 0.0f);
+    auto beta     = MakeRandomTensor<float, 1>(tensorInfo, 123);
+    auto gamma    = MakeRandomTensor<float, 1>(tensorInfo, 345);
+
+    LayerTestResult<float,4> ret(outputTensorInfo);
+
+    std::unique_ptr<armnn::ITensorHandle> inputHandle  = workloadFactory.CreateTensorHandle(inputTensorInfo);
+    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+    std::unique_ptr<armnn::ITensorHandle> inputHandleRef  = refWorkloadFactory.CreateTensorHandle(inputTensorInfo);
+    std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo);
+
+    armnn::BatchNormalizationQueueDescriptor data;
+    armnn::WorkloadInfo info;
+    armnn::ScopedCpuTensorHandle meanTensor(tensorInfo);
+    armnn::ScopedCpuTensorHandle varianceTensor(tensorInfo);
+    armnn::ScopedCpuTensorHandle betaTensor(tensorInfo);
+    armnn::ScopedCpuTensorHandle gammaTensor(tensorInfo);
+
+    AllocateAndCopyDataToITensorHandle(&meanTensor, &mean[0]);
+    AllocateAndCopyDataToITensorHandle(&varianceTensor, &variance[0]);
+    AllocateAndCopyDataToITensorHandle(&betaTensor, &beta[0]);
+    AllocateAndCopyDataToITensorHandle(&gammaTensor, &gamma[0]);
+
+    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
+    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
+    data.m_Mean             = &meanTensor;
+    data.m_Variance         = &varianceTensor;
+    data.m_Beta             = &betaTensor;
+    data.m_Gamma            = &gammaTensor;
+    data.m_Parameters.m_Eps = 0.01f;
+
+    armnn::BatchNormalizationQueueDescriptor refData = data;
+    armnn::WorkloadInfo refInfo = info;
+    SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get());
+    SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());
+
+    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateBatchNormalization(data, info);
+    std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateBatchNormalization(refData, refInfo);
+
+    inputHandle->Allocate();
+    outputHandle->Allocate();
+    inputHandleRef->Allocate();
+    outputHandleRef->Allocate();
+
+    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
+    CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]);
+
+    workload->PostAllocationConfigure();
+    workload->Execute();
+    workloadRef->PostAllocationConfigure();
+    workloadRef->Execute();
+
+    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
+    CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get());
+
+    return ret;
+}