diff --git a/CMakeLists.txt b/CMakeLists.txt
index a6d88e0..c2e394d 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -645,6 +645,7 @@
         src/armnn/test/optimizations/ConvertConstantsHalfToFloatTests.cpp
         src/armnn/test/optimizations/Fp32NetworkToBf16ConverterTests.cpp
         src/armnn/test/optimizations/Fp32NetworkToFp16ConverterTests.cpp
+        src/armnn/test/optimizations/FuseBatchNormTests.cpp
         src/armnn/test/optimizations/InsertDebugLayerTests.cpp
         src/armnn/test/optimizations/MovePermuteUpTests.cpp
         src/armnn/test/optimizations/MoveTransposeUpTests.cpp
diff --git a/src/armnn/test/OptimizerTests.cpp b/src/armnn/test/OptimizerTests.cpp
index 879905b..8845dae 100644
--- a/src/armnn/test/OptimizerTests.cpp
+++ b/src/armnn/test/OptimizerTests.cpp
@@ -798,6 +798,7 @@
     }
 }
 
+// Tests that OptimizeForExclusiveConnections works, fusing when needed, using BatchNorm fusing as example
 BOOST_AUTO_TEST_CASE(OptimizeForExclusiveConnections_fuse_Test)
 {
     using namespace armnn;
@@ -882,6 +883,7 @@
                              &IsLayerOfType<OutputLayer>));
 }
 
+// Tests that OptimizeForExclusiveConnections works, not fusing when not needed, using BatchNorm fusing as example
 BOOST_AUTO_TEST_CASE(OptimizeForExclusiveConnections_notFuse_Test)
 {
     // Define the network
@@ -921,171 +923,4 @@
                              &IsLayerOfType<armnn::OutputLayer>,
                              &IsLayerOfType<armnn::OutputLayer>));
 }
-
-BOOST_AUTO_TEST_CASE(Fuse_batchNorm_into_Conv2D_Float32_Test)
-{
-    using namespace armnn;
-
-    // Define layers information
-    Convolution2dDescriptor convolution2dDescriptor;
-    convolution2dDescriptor.m_BiasEnabled = false;
-    convolution2dDescriptor.m_DataLayout = DataLayout::NHWC;
-    convolution2dDescriptor.m_StrideX = 1;
-    convolution2dDescriptor.m_StrideY = 1;
-    BatchNormalizationDescriptor batchNormDescriptor;
-    batchNormDescriptor.m_DataLayout = DataLayout::NHWC;
-
-    const unsigned int inputDimensionSizes[]   = {1, 4, 4, 3};  // NHWCin
-    const unsigned int weightsDimensionSizes[] = {4, 2, 2, 3};  // CoutHWCin
-    const unsigned int outputDimensionSizes[]  = {1, 3, 3, 4};  // NHWCout
-    const unsigned int outputChannelSize[]     = {outputDimensionSizes[3]};  // Cout
-
-    TensorInfo inputInfo (4, inputDimensionSizes, DataType::Float32);
-    TensorInfo outputInfo(4, outputDimensionSizes, DataType::Float32);
-
-    std::vector<float> weightsVector = { 1,  2,  3,  4,    5,  6,  7, 8,    9,  10,  11,  12,
-                                        11, 12, 13, 14,   15, 16, 17, 18,  19, 110, 111, 112,
-                                        21, 22, 23, 24,   25, 26, 27, 28,  29, 210, 211, 212,
-                                        31, 32, 33, 34,   35, 36, 37, 38,  39, 310, 311, 312};
-    TensorInfo weightsInfo(4, weightsDimensionSizes, DataType::Float32);
-    ConstTensor weights (weightsInfo, weightsVector);
-    std::vector<float> biasVector     = {3.3f, 3.2f, 3.1f, 3.0f};
-    TensorInfo biasInfo(1, outputChannelSize, DataType::Float32);
-    ConstTensor bias (biasInfo, biasVector);
-    Optional<ConstTensor> optionalBias = Optional<ConstTensor>(bias);
-
-    std::vector<float> betaVector     = {0.0f, 0.2f, 0.3f, 0.4f};
-    std::vector<float> gammaVector    = {0.5f, 0.6f, 0.7f, 0.8f};
-    std::vector<float> meanVector     = {0.1f, 0.2f, 0.3f, 0.4f};
-    std::vector<float> varianceVector = {1.0f, 1.1f, 1.2f, 1.3f};
-    ConstTensor beta    (TensorInfo(1, outputChannelSize, DataType::Float32), betaVector);
-    ConstTensor gamma   (TensorInfo(1, outputChannelSize, DataType::Float32), gammaVector);
-    ConstTensor mean    (TensorInfo(1, outputChannelSize, DataType::Float32), meanVector);
-    ConstTensor variance(TensorInfo(1, outputChannelSize, DataType::Float32), varianceVector);
-
-    auto inputSize = inputDimensionSizes[0]*inputDimensionSizes[1]*inputDimensionSizes[2]*inputDimensionSizes[3];
-    auto outputSize = outputDimensionSizes[0]*outputDimensionSizes[1]*outputDimensionSizes[2]*outputDimensionSizes[3];
-
-    // FIRST NETWORK: Fused
-
-    // Construct ArmNN network
-    NetworkId networkIdentifier;
-    INetworkPtr network = INetwork::Create();
-    IConnectableLayer *inputLayer = network->AddInputLayer(0);
-    IConnectableLayer *convLayer = network->AddConvolution2dLayer(convolution2dDescriptor,
-                                                                  weights,
-                                                                  optionalBias,
-                                                                  "convolution");
-    IConnectableLayer *batchNormLayer = network->AddBatchNormalizationLayer(batchNormDescriptor,
-                                                                            mean,
-                                                                            variance,
-                                                                            beta,
-                                                                            gamma,
-                                                                            "batchNorm");
-    IConnectableLayer *outputLayer = network->AddOutputLayer(0);
-
-    inputLayer     ->GetOutputSlot(0).Connect(convLayer     ->GetInputSlot(0));
-    convLayer      ->GetOutputSlot(0).Connect(batchNormLayer->GetInputSlot(0));
-    batchNormLayer ->GetOutputSlot(0).Connect(outputLayer   ->GetInputSlot(0));
-
-    // Create ArmNN runtime
-    IRuntime::CreationOptions options; // default options
-    IRuntimePtr run = IRuntime::Create(options);
-
-    //Set the tensors in the network.
-    inputLayer     ->GetOutputSlot(0).SetTensorInfo(inputInfo);
-    convLayer      ->GetOutputSlot(0).SetTensorInfo(outputInfo);
-    batchNormLayer ->GetOutputSlot(0).SetTensorInfo(outputInfo);
-
-    // Optimise ArmNN network
-    IOptimizedNetworkPtr optNet = Optimize(*network, {Compute::CpuRef}, run->GetDeviceSpec());
-    if (!optNet)
-    {
-        // This shouldn't happen for this simple sample, with reference backend.
-        // But in general usage Optimize could fail if the hardware at runtime cannot
-        // support the model that has been provided.
-        std::cerr << "Error: Failed to optimise the input network." << std::endl;
-    }
-
-    // Load graph into runtime
-    run->LoadNetwork(networkIdentifier, std::move(optNet));
-
-    //Creates structures for inputs and outputs.
-    std::vector<float> inputData(inputSize, 128);
-    std::vector<float> outputData(outputSize);
-
-    InputTensors inputTensors  {{0, ConstTensor(run->GetInputTensorInfo (networkIdentifier, 0), inputData.data())}};
-    OutputTensors outputTensors{{0,      Tensor(run->GetOutputTensorInfo(networkIdentifier, 0), outputData.data())}};
-
-
-    // Execute network
-    run->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors);
-
-    // SECOND NETWORK: NotFused
-
-    // Construct ArmNN network
-    NetworkId networkIdentifierNotFused;
-    INetworkPtr networkNotFused = INetwork::Create();
-    IConnectableLayer *inputLayerNotFused = networkNotFused->AddInputLayer(0);
-    IConnectableLayer *convLayerNotFused  = networkNotFused->AddConvolution2dLayer(convolution2dDescriptor,
-                                                                                   weights,
-                                                                                   optionalBias,
-                                                                                   "convolution");
-    IConnectableLayer *batchNormLayerNotFused = networkNotFused->AddBatchNormalizationLayer(batchNormDescriptor,
-                                                                                            mean,
-                                                                                            variance,
-                                                                                            beta,
-                                                                                            gamma,
-                                                                                            "batchNorm");
-    IConnectableLayer *outputLayerNotFused = networkNotFused->AddOutputLayer(0);
-    IConnectableLayer *output2LayerNotFused = networkNotFused->AddOutputLayer(1);
-
-
-    inputLayerNotFused     ->GetOutputSlot(0).Connect(convLayerNotFused     ->GetInputSlot(0));
-    convLayerNotFused      ->GetOutputSlot(0).Connect(batchNormLayerNotFused->GetInputSlot(0));
-    batchNormLayerNotFused ->GetOutputSlot(0).Connect(outputLayerNotFused   ->GetInputSlot(0));
-    convLayerNotFused      ->GetOutputSlot(0).Connect(output2LayerNotFused  ->GetInputSlot(0));
-
-    // Create ArmNN runtime
-    IRuntimePtr runNotFused = IRuntime::Create(options);
-
-    //Set the tensors in the network.
-    inputLayerNotFused     ->GetOutputSlot(0).SetTensorInfo(inputInfo);
-    convLayerNotFused      ->GetOutputSlot(0).SetTensorInfo(outputInfo);
-    batchNormLayerNotFused ->GetOutputSlot(0).SetTensorInfo(outputInfo);
-
-    // Optimise ArmNN network
-    IOptimizedNetworkPtr optNetNotFused = Optimize(*networkNotFused, {Compute::CpuRef}, runNotFused->GetDeviceSpec());
-    if (!optNetNotFused)
-    {
-        // This shouldn't happen for this simple sample, with reference backend.
-        // But in general usage Optimize could fail if the hardware at runtime cannot
-        // support the model that has been provided.
-        std::cerr << "Error: Failed to optimise the input network." << std::endl;
-    }
-
-    // Load graph into runtime
-    runNotFused->LoadNetwork(networkIdentifierNotFused, std::move(optNetNotFused));
-
-    //Creates structures for inputs and outputs.
-    std::vector<float> inputDataNotFused(inputSize, 128);
-    std::vector<float> outputDataNotFused(outputSize);
-    std::vector<float> outputData2NotFused(outputSize);
-
-    InputTensors inputTensorsNotFused{
-        {0, ConstTensor(runNotFused->GetInputTensorInfo(networkIdentifierNotFused, 0), inputDataNotFused.data())}};
-    OutputTensors outputTensorsNotFused{
-        {0, Tensor(runNotFused->GetOutputTensorInfo(networkIdentifierNotFused, 0), outputDataNotFused.data())},
-        {1, Tensor(runNotFused->GetOutputTensorInfo(networkIdentifierNotFused, 1), outputData2NotFused.data())}};
-
-    // Execute network
-    runNotFused->EnqueueWorkload(networkIdentifierNotFused, inputTensorsNotFused, outputTensorsNotFused);
-
-    // Check the output of the fused-convolution matches with the output of the batchNormm in the "NotFused" network
-    for (unsigned int n = 0; n < outputData.size(); ++n)
-    {
-        BOOST_CHECK_CLOSE(outputData[n], outputDataNotFused[n], 0.001);
-    }
-}
-
 BOOST_AUTO_TEST_SUITE_END()
diff --git a/src/armnn/test/optimizations/FuseBatchNormTests.cpp b/src/armnn/test/optimizations/FuseBatchNormTests.cpp
new file mode 100644
index 0000000..74cb8f9
--- /dev/null
+++ b/src/armnn/test/optimizations/FuseBatchNormTests.cpp
@@ -0,0 +1,163 @@
+//
+// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "LayersFwd.hpp"
+#include <boost/test/unit_test.hpp>
+
+BOOST_AUTO_TEST_SUITE(Optimizer)
+using namespace armnn;
+
+// This unit test needs the reference backend, it's not available if the reference backend is not built
+#if defined(ARMNNREF_ENABLED)
+BOOST_AUTO_TEST_CASE(Fuse_batchNorm_into_Conv2D_Float32_Test)
+{
+    // Define layers information
+    Convolution2dDescriptor convolution2dDescriptor;
+    convolution2dDescriptor.m_BiasEnabled = false;
+    convolution2dDescriptor.m_DataLayout = DataLayout::NHWC;
+    convolution2dDescriptor.m_StrideX = 1;
+    convolution2dDescriptor.m_StrideY = 1;
+    BatchNormalizationDescriptor batchNormDescriptor;
+    batchNormDescriptor.m_DataLayout = DataLayout::NHWC;
+
+    const unsigned int inputDimensionSizes[]   = {1, 4, 4, 3};  // NHWCin
+    const unsigned int weightsDimensionSizes[] = {4, 2, 2, 3};  // CoutHWCin
+    const unsigned int outputDimensionSizes[]  = {1, 3, 3, 4};  // NHWCout
+    const unsigned int outputChannelSize[]     = {outputDimensionSizes[3]};  // Cout
+
+    TensorInfo inputInfo (4, inputDimensionSizes, DataType::Float32);
+    TensorInfo outputInfo(4, outputDimensionSizes, DataType::Float32);
+
+    std::vector<float> weightsVector = { 1,  2,  3,  4,    5,  6,  7, 8,    9,  10,  11,  12,
+                                         11, 12, 13, 14,   15, 16, 17, 18,  19, 110, 111, 112,
+                                         21, 22, 23, 24,   25, 26, 27, 28,  29, 210, 211, 212,
+                                         31, 32, 33, 34,   35, 36, 37, 38,  39, 310, 311, 312};
+    TensorInfo weightsInfo(4, weightsDimensionSizes, DataType::Float32);
+    ConstTensor weights (weightsInfo, weightsVector);
+    std::vector<float> biasVector     = {3.3f, 3.2f, 3.1f, 3.0f};
+    TensorInfo biasInfo(1, outputChannelSize, DataType::Float32);
+    ConstTensor bias (biasInfo, biasVector);
+    Optional<ConstTensor> optionalBias = Optional<ConstTensor>(bias);
+
+    std::vector<float> betaVector     = {0.0f, 0.2f, 0.3f, 0.4f};
+    std::vector<float> gammaVector    = {0.5f, 0.6f, 0.7f, 0.8f};
+    std::vector<float> meanVector     = {0.1f, 0.2f, 0.3f, 0.4f};
+    std::vector<float> varianceVector = {1.0f, 1.1f, 1.2f, 1.3f};
+    ConstTensor beta    (TensorInfo(1, outputChannelSize, DataType::Float32), betaVector);
+    ConstTensor gamma   (TensorInfo(1, outputChannelSize, DataType::Float32), gammaVector);
+    ConstTensor mean    (TensorInfo(1, outputChannelSize, DataType::Float32), meanVector);
+    ConstTensor variance(TensorInfo(1, outputChannelSize, DataType::Float32), varianceVector);
+
+    auto inputSize = inputDimensionSizes[0]*inputDimensionSizes[1]*inputDimensionSizes[2]*inputDimensionSizes[3];
+    auto outputSize = outputDimensionSizes[0]*outputDimensionSizes[1]*outputDimensionSizes[2]*outputDimensionSizes[3];
+
+    // FIRST NETWORK: Fused
+
+    // Construct ArmNN network
+    NetworkId networkIdentifier;
+    INetworkPtr network = INetwork::Create();
+    IConnectableLayer *inputLayer     = network->AddInputLayer(0);
+    IConnectableLayer *convLayer      = network->AddConvolution2dLayer(convolution2dDescriptor,
+                                                                       weights,
+                                                                       optionalBias,
+                                                                       "convolution");
+    IConnectableLayer *batchNormLayer = network->AddBatchNormalizationLayer(batchNormDescriptor,
+                                                                            mean,
+                                                                            variance,
+                                                                            beta,
+                                                                            gamma,
+                                                                            "batchNorm");
+    IConnectableLayer *outputLayer    = network->AddOutputLayer(0);
+
+    inputLayer     ->GetOutputSlot(0).Connect(convLayer     ->GetInputSlot(0));
+    convLayer      ->GetOutputSlot(0).Connect(batchNormLayer->GetInputSlot(0));
+    batchNormLayer ->GetOutputSlot(0).Connect(outputLayer   ->GetInputSlot(0));
+
+    //Set the tensors in the network.
+    inputLayer     ->GetOutputSlot(0).SetTensorInfo(inputInfo);
+    convLayer      ->GetOutputSlot(0).SetTensorInfo(outputInfo);
+    batchNormLayer ->GetOutputSlot(0).SetTensorInfo(outputInfo);
+
+    // Create ArmNN runtime
+    IRuntime::CreationOptions options; // default options
+    IRuntimePtr run = IRuntime::Create(options);
+
+    // Optimise ArmNN network
+    IOptimizedNetworkPtr optNet = Optimize(*network, {Compute::CpuRef}, run->GetDeviceSpec());
+
+    // Load graph into runtime
+    BOOST_TEST(run->LoadNetwork(networkIdentifier, std::move(optNet)) == Status::Success);
+
+    //Creates structures for inputs and outputs.
+    std::vector<float> inputData(inputSize, 128);
+    std::vector<float> outputData(outputSize);
+
+    InputTensors inputTensors  {{0, ConstTensor(run->GetInputTensorInfo (networkIdentifier, 0), inputData.data())}};
+    OutputTensors outputTensors{{0,      Tensor(run->GetOutputTensorInfo(networkIdentifier, 0), outputData.data())}};
+
+    // Execute network
+    run->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors);
+
+    // SECOND NETWORK: NotFused
+
+    // Construct ArmNN network
+    NetworkId networkIdentifierNotFused;
+    INetworkPtr networkNotFused = INetwork::Create();
+    IConnectableLayer *inputLayerNotFused     = networkNotFused->AddInputLayer(0);
+    IConnectableLayer *convLayerNotFused      = networkNotFused->AddConvolution2dLayer(convolution2dDescriptor,
+                                                                                       weights,
+                                                                                       optionalBias,
+                                                                                       "convolution");
+    IConnectableLayer *batchNormLayerNotFused = networkNotFused->AddBatchNormalizationLayer(batchNormDescriptor,
+                                                                                            mean,
+                                                                                            variance,
+                                                                                            beta,
+                                                                                            gamma,
+                                                                                            "batchNorm");
+    IConnectableLayer *outputLayerNotFused    = networkNotFused->AddOutputLayer(0);
+    IConnectableLayer *output2LayerNotFused   = networkNotFused->AddOutputLayer(1);
+
+    inputLayerNotFused     ->GetOutputSlot(0).Connect(convLayerNotFused     ->GetInputSlot(0));
+    convLayerNotFused      ->GetOutputSlot(0).Connect(batchNormLayerNotFused->GetInputSlot(0));
+    batchNormLayerNotFused ->GetOutputSlot(0).Connect(outputLayerNotFused   ->GetInputSlot(0));
+    convLayerNotFused      ->GetOutputSlot(0).Connect(output2LayerNotFused  ->GetInputSlot(0));
+
+    //Set the tensors in the network.
+    inputLayerNotFused     ->GetOutputSlot(0).SetTensorInfo(inputInfo);
+    convLayerNotFused      ->GetOutputSlot(0).SetTensorInfo(outputInfo);
+    batchNormLayerNotFused ->GetOutputSlot(0).SetTensorInfo(outputInfo);
+
+    // Create ArmNN runtime
+    IRuntimePtr runNotFused = IRuntime::Create(options);
+
+    // Optimise ArmNN network
+    IOptimizedNetworkPtr optNetNotFused = Optimize(*networkNotFused, {Compute::CpuRef}, runNotFused->GetDeviceSpec());
+
+    // Load graph into runtime
+    BOOST_TEST(runNotFused->LoadNetwork(networkIdentifierNotFused, std::move(optNetNotFused)) == Status::Success);
+
+    //Creates structures for inputs and outputs.
+    std::vector<float> inputDataNotFused(inputSize, 128);
+    std::vector<float> outputDataNotFused(outputSize);
+    std::vector<float> outputData2NotFused(outputSize);
+
+    InputTensors inputTensorsNotFused{
+            {0, ConstTensor(runNotFused->GetInputTensorInfo(networkIdentifierNotFused, 0), inputDataNotFused.data())}};
+    OutputTensors outputTensorsNotFused{
+            {0, Tensor(runNotFused->GetOutputTensorInfo(networkIdentifierNotFused, 0), outputDataNotFused.data())},
+            {1, Tensor(runNotFused->GetOutputTensorInfo(networkIdentifierNotFused, 1), outputData2NotFused.data())}};
+
+    // Execute network
+    runNotFused->EnqueueWorkload(networkIdentifierNotFused, inputTensorsNotFused, outputTensorsNotFused);
+
+    // Check the output of the fused-convolution matches with the output of the batchNormm in the "NotFused" network
+    for (unsigned int n = 0; n < outputData.size(); ++n)
+    {
+        BOOST_CHECK_CLOSE(outputData[n], outputDataNotFused[n], 0.001);
+    }
+}
+#endif
+
+BOOST_AUTO_TEST_SUITE_END()