IVGCVSW-4485 Remove Boost assert

 * Change boost assert to armnn assert
 * Change include file to armnn assert
 * Fix ARMNN_ASSERT_MSG issue with multiple conditions
 * Change BOOST_ASSERT to BOOST_TEST where appropriate
 * Remove unused include statements

Signed-off-by: Narumol Prangnawarat <narumol.prangnawarat@arm.com>
Change-Id: I5d0fa3a37b7c1c921216de68f0073aa34702c9ff

49 files changed, 175 insertions, 177 deletions

diff --git a/src/armnn/layers/AbsLayer.cpp b/src/armnn/layers/AbsLayer.cpp
index f67d965086..490b03ed79 100644
--- a/src/armnn/layers/AbsLayer.cpp
+++ b/src/armnn/layers/AbsLayer.cpp
@@ -36,7 +36,7 @@ void AbsLayer::ValidateTensorShapesFromInputs()
 
     auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
             "AbsLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/ActivationLayer.cpp b/src/armnn/layers/ActivationLayer.cpp
index 263fb72c20..d310b7efbc 100644
--- a/src/armnn/layers/ActivationLayer.cpp
+++ b/src/armnn/layers/ActivationLayer.cpp
@@ -34,7 +34,7 @@ void ActivationLayer::ValidateTensorShapesFromInputs()
 
     auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "ActivationLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/ArgMinMaxLayer.cpp b/src/armnn/layers/ArgMinMaxLayer.cpp
index b67c42b2e4..a9907871be 100644
--- a/src/armnn/layers/ArgMinMaxLayer.cpp
+++ b/src/armnn/layers/ArgMinMaxLayer.cpp
@@ -34,7 +34,7 @@ ArgMinMaxLayer* ArgMinMaxLayer::Clone(Graph& graph) const
 
 std::vector<TensorShape> ArgMinMaxLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
 {
-    BOOST_ASSERT(inputShapes.size() == 1);
+    ARMNN_ASSERT(inputShapes.size() == 1);
 
     TensorShape inputShape = inputShapes[0];
     auto inputNumDimensions = inputShape.GetNumDimensions();
@@ -42,7 +42,7 @@ std::vector<TensorShape> ArgMinMaxLayer::InferOutputShapes(const std::vector<Ten
     auto axis = m_Param.m_Axis;
     auto unsignedAxis = armnnUtils::GetUnsignedAxis(inputNumDimensions, axis);
 
-    BOOST_ASSERT(unsignedAxis <= inputNumDimensions);
+    ARMNN_ASSERT(unsignedAxis <= inputNumDimensions);
 
     // 1D input shape results in scalar output
     if (inputShape.GetNumDimensions() == 1)
@@ -75,7 +75,7 @@ void ArgMinMaxLayer::ValidateTensorShapesFromInputs()
 
     auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
             "ArgMinMaxLayer: TensorShape set on OutputSlot does not match the inferred shape.",
diff --git a/src/armnn/layers/BatchNormalizationLayer.cpp b/src/armnn/layers/BatchNormalizationLayer.cpp
index aed744714b..7f61cad40f 100644
--- a/src/armnn/layers/BatchNormalizationLayer.cpp
+++ b/src/armnn/layers/BatchNormalizationLayer.cpp
@@ -21,10 +21,10 @@ BatchNormalizationLayer::BatchNormalizationLayer(const armnn::BatchNormalization
 std::unique_ptr<IWorkload> BatchNormalizationLayer::CreateWorkload(const IWorkloadFactory& factory) const
 {
     // on this level constant data should not be released..
-    BOOST_ASSERT_MSG(m_Mean != nullptr, "BatchNormalizationLayer: Mean data should not be null.");
-    BOOST_ASSERT_MSG(m_Variance != nullptr, "BatchNormalizationLayer: Variance data should not be null.");
-    BOOST_ASSERT_MSG(m_Beta != nullptr, "BatchNormalizationLayer: Beta data should not be null.");
-    BOOST_ASSERT_MSG(m_Gamma != nullptr, "BatchNormalizationLayer: Gamma data should not be null.");
+    ARMNN_ASSERT_MSG(m_Mean != nullptr, "BatchNormalizationLayer: Mean data should not be null.");
+    ARMNN_ASSERT_MSG(m_Variance != nullptr, "BatchNormalizationLayer: Variance data should not be null.");
+    ARMNN_ASSERT_MSG(m_Beta != nullptr, "BatchNormalizationLayer: Beta data should not be null.");
+    ARMNN_ASSERT_MSG(m_Gamma != nullptr, "BatchNormalizationLayer: Gamma data should not be null.");
 
     BatchNormalizationQueueDescriptor descriptor;
 
@@ -54,7 +54,7 @@ void BatchNormalizationLayer::ValidateTensorShapesFromInputs()
 
     auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "BatchNormalizationLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/BatchToSpaceNdLayer.cpp b/src/armnn/layers/BatchToSpaceNdLayer.cpp
index 7e7045291c..1da88c63ac 100644
--- a/src/armnn/layers/BatchToSpaceNdLayer.cpp
+++ b/src/armnn/layers/BatchToSpaceNdLayer.cpp
@@ -47,7 +47,7 @@ void BatchToSpaceNdLayer::ValidateTensorShapesFromInputs()
 
     auto inferredShapes = InferOutputShapes({GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape()});
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "BatchToSpaceLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
@@ -56,7 +56,7 @@ void BatchToSpaceNdLayer::ValidateTensorShapesFromInputs()
 
 std::vector<TensorShape> BatchToSpaceNdLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
 {
-    BOOST_ASSERT(inputShapes.size() == 1);
+    ARMNN_ASSERT(inputShapes.size() == 1);
 
     const TensorShape& inputShape = inputShapes[0];
     TensorShape outputShape(inputShape);
@@ -66,7 +66,7 @@ std::vector<TensorShape> BatchToSpaceNdLayer::InferOutputShapes(const std::vecto
                                                          1U,
                                                          std::multiplies<>());
 
-    BOOST_ASSERT(inputShape[0] % accumulatedBlockShape == 0);
+    ARMNN_ASSERT(inputShape[0] % accumulatedBlockShape == 0);
 
     outputShape[0] = inputShape[0] / accumulatedBlockShape;
 
@@ -80,10 +80,10 @@ std::vector<TensorShape> BatchToSpaceNdLayer::InferOutputShapes(const std::vecto
     unsigned int outputHeight = inputShape[heightIndex] * m_Param.m_BlockShape[0];
     unsigned int outputWidth = inputShape[widthIndex] * m_Param.m_BlockShape[1];
 
-    BOOST_ASSERT_MSG(heightCrop <= outputHeight,
+    ARMNN_ASSERT_MSG(heightCrop <= outputHeight,
         "BatchToSpaceLayer: Overall height crop should be less than or equal to the uncropped output height.");
 
-    BOOST_ASSERT_MSG(widthCrop <= outputWidth,
+    ARMNN_ASSERT_MSG(widthCrop <= outputWidth,
         "BatchToSpaceLayer: Overall width crop should be less than or equal to the uncropped output width.");
 
     outputShape[heightIndex] = outputHeight - heightCrop;
diff --git a/src/armnn/layers/ComparisonLayer.cpp b/src/armnn/layers/ComparisonLayer.cpp
index 1f6e35fa85..91080457bf 100644
--- a/src/armnn/layers/ComparisonLayer.cpp
+++ b/src/armnn/layers/ComparisonLayer.cpp
@@ -33,11 +33,11 @@ ComparisonLayer* ComparisonLayer::Clone(Graph& graph) const
 
 std::vector<TensorShape> ComparisonLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
 {
-    BOOST_ASSERT(inputShapes.size() == 2);
+    ARMNN_ASSERT(inputShapes.size() == 2);
     const TensorShape& input0 = inputShapes[0];
     const TensorShape& input1 = inputShapes[1];
 
-    BOOST_ASSERT(input0.GetNumDimensions() == input1.GetNumDimensions());
+    ARMNN_ASSERT(input0.GetNumDimensions() == input1.GetNumDimensions());
     unsigned int numDims = input0.GetNumDimensions();
 
     std::vector<unsigned int> dims(numDims);
@@ -46,7 +46,7 @@ std::vector<TensorShape> ComparisonLayer::InferOutputShapes(const std::vector<Te
         unsigned int dim0 = input0[i];
         unsigned int dim1 = input1[i];
 
-        BOOST_ASSERT_MSG(dim0 == dim1 || dim0 == 1 || dim1 == 1,
+        ARMNN_ASSERT_MSG(dim0 == dim1 || dim0 == 1 || dim1 == 1,
                          "Dimensions should either match or one should be of size 1.");
 
         dims[i] = std::max(dim0, dim1);
@@ -63,7 +63,7 @@ void ComparisonLayer::ValidateTensorShapesFromInputs()
         GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(),
         GetInputSlot(1).GetConnection()->GetTensorInfo().GetShape()
     });
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "ComparisonLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/ConcatLayer.cpp b/src/armnn/layers/ConcatLayer.cpp
index f4024af65a..5df5ec8de5 100644
--- a/src/armnn/layers/ConcatLayer.cpp
+++ b/src/armnn/layers/ConcatLayer.cpp
@@ -111,7 +111,7 @@ void ConcatLayer::CreateTensors(const FactoryType& factory)
                 OutputSlot* slot = currentLayer->GetInputSlot(i).GetConnectedOutputSlot();
                 OutputHandler& outputHandler = slot->GetOutputHandler();
 
-                BOOST_ASSERT_MSG(subTensor, "ConcatLayer: Expected a valid sub-tensor for substitution.");
+                ARMNN_ASSERT_MSG(subTensor, "ConcatLayer: Expected a valid sub-tensor for substitution.");
                 outputHandler.SetData(std::move(subTensor));
 
                 Layer& inputLayer = slot->GetOwningLayer();
@@ -141,7 +141,7 @@ void ConcatLayer::CreateTensorHandles(const TensorHandleFactoryRegistry& registr
     else
     {
         ITensorHandleFactory* handleFactory = registry.GetFactory(factoryId);
-        BOOST_ASSERT(handleFactory);
+        ARMNN_ASSERT(handleFactory);
         CreateTensors(*handleFactory);
     }
 }
@@ -153,7 +153,7 @@ ConcatLayer* ConcatLayer::Clone(Graph& graph) const
 
 std::vector<TensorShape> ConcatLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
 {
-    BOOST_ASSERT(inputShapes.size() == m_Param.GetNumViews());
+    ARMNN_ASSERT(inputShapes.size() == m_Param.GetNumViews());
 
     unsigned int numDims = m_Param.GetNumDimensions();
     for (unsigned int i=0; i< inputShapes.size(); i++)
@@ -259,7 +259,7 @@ void ConcatLayer::ValidateTensorShapesFromInputs()
 
     auto inferredShapes = InferOutputShapes(inputShapes);
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "ConcatLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/ConvertBf16ToFp32Layer.cpp b/src/armnn/layers/ConvertBf16ToFp32Layer.cpp
index 147aa8f46a..30d20b87d6 100644
--- a/src/armnn/layers/ConvertBf16ToFp32Layer.cpp
+++ b/src/armnn/layers/ConvertBf16ToFp32Layer.cpp
@@ -36,7 +36,7 @@ void ConvertBf16ToFp32Layer::ValidateTensorShapesFromInputs()
 
     auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "ConvertBf16ToFp32Layer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/ConvertFp16ToFp32Layer.cpp b/src/armnn/layers/ConvertFp16ToFp32Layer.cpp
index 7873c94563..08f0e4a8c1 100644
--- a/src/armnn/layers/ConvertFp16ToFp32Layer.cpp
+++ b/src/armnn/layers/ConvertFp16ToFp32Layer.cpp
@@ -36,7 +36,7 @@ void ConvertFp16ToFp32Layer::ValidateTensorShapesFromInputs()
 
     auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "ConvertFp16ToFp32Layer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/ConvertFp32ToBf16Layer.cpp b/src/armnn/layers/ConvertFp32ToBf16Layer.cpp
index 936acf61ab..c9e0962dd5 100644
--- a/src/armnn/layers/ConvertFp32ToBf16Layer.cpp
+++ b/src/armnn/layers/ConvertFp32ToBf16Layer.cpp
@@ -36,7 +36,7 @@ void ConvertFp32ToBf16Layer::ValidateTensorShapesFromInputs()
 
     auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "ConvertFp32ToBf16Layer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/ConvertFp32ToFp16Layer.cpp b/src/armnn/layers/ConvertFp32ToFp16Layer.cpp
index bbf4dbffd8..95403e9e75 100644
--- a/src/armnn/layers/ConvertFp32ToFp16Layer.cpp
+++ b/src/armnn/layers/ConvertFp32ToFp16Layer.cpp
@@ -35,7 +35,7 @@ void ConvertFp32ToFp16Layer::ValidateTensorShapesFromInputs()
 
     auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "ConvertFp32ToFp16Layer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/Convolution2dLayer.cpp b/src/armnn/layers/Convolution2dLayer.cpp
index 55a243aa0b..d82908a128 100644
--- a/src/armnn/layers/Convolution2dLayer.cpp
+++ b/src/armnn/layers/Convolution2dLayer.cpp
@@ -49,7 +49,7 @@ void Convolution2dLayer::SerializeLayerParameters(ParameterStringifyFunction& fn
 std::unique_ptr<IWorkload> Convolution2dLayer::CreateWorkload(const IWorkloadFactory& factory) const
 {
     // on this level constant data should not be released..
-    BOOST_ASSERT_MSG(m_Weight != nullptr, "Convolution2dLayer: Weights data should not be null.");
+    ARMNN_ASSERT_MSG(m_Weight != nullptr, "Convolution2dLayer: Weights data should not be null.");
 
     Convolution2dQueueDescriptor descriptor;
 
@@ -57,7 +57,7 @@ std::unique_ptr<IWorkload> Convolution2dLayer::CreateWorkload(const IWorkloadFac
 
     if (m_Param.m_BiasEnabled)
     {
-        BOOST_ASSERT_MSG(m_Bias != nullptr, "Convolution2dLayer: Bias data should not be null.");
+        ARMNN_ASSERT_MSG(m_Bias != nullptr, "Convolution2dLayer: Bias data should not be null.");
         descriptor.m_Bias = m_Bias.get();
     }
     return factory.CreateConvolution2d(descriptor, PrepInfoAndDesc(descriptor));
@@ -79,12 +79,12 @@ Convolution2dLayer* Convolution2dLayer::Clone(Graph& graph) const
 
 std::vector<TensorShape> Convolution2dLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
 {
-    BOOST_ASSERT(inputShapes.size() == 2);
+    ARMNN_ASSERT(inputShapes.size() == 2);
     const TensorShape& inputShape = inputShapes[0];
     const TensorShape filterShape = inputShapes[1];
 
     // If we support multiple batch dimensions in the future, then this assert will need to change.
-    BOOST_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Convolutions will always have 4D input.");
+    ARMNN_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Convolutions will always have 4D input.");
 
     DataLayoutIndexed dataLayoutIndex(m_Param.m_DataLayout);
 
@@ -117,13 +117,13 @@ void Convolution2dLayer::ValidateTensorShapesFromInputs()
     VerifyLayerConnections(1, CHECK_LOCATION());
 
     // check if we m_Weight data is not nullptr
-    BOOST_ASSERT_MSG(m_Weight != nullptr, "Convolution2dLayer: Weights data should not be null.");
+    ARMNN_ASSERT_MSG(m_Weight != nullptr, "Convolution2dLayer: Weights data should not be null.");
 
     auto inferredShapes = InferOutputShapes({
         GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(),
         m_Weight->GetTensorInfo().GetShape() });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "Convolution2dLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/DebugLayer.cpp b/src/armnn/layers/DebugLayer.cpp
index 76d33f27e9..6aaf945878 100644
--- a/src/armnn/layers/DebugLayer.cpp
+++ b/src/armnn/layers/DebugLayer.cpp
@@ -41,7 +41,7 @@ void DebugLayer::ValidateTensorShapesFromInputs()
     std::vector<TensorShape> inferredShapes = InferOutputShapes({
         GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "DebugLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/DepthToSpaceLayer.cpp b/src/armnn/layers/DepthToSpaceLayer.cpp
index bb74232690..2d13271c77 100644
--- a/src/armnn/layers/DepthToSpaceLayer.cpp
+++ b/src/armnn/layers/DepthToSpaceLayer.cpp
@@ -38,7 +38,7 @@ DepthToSpaceLayer* DepthToSpaceLayer::Clone(Graph& graph) const
 
 std::vector<TensorShape> DepthToSpaceLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
 {
-    BOOST_ASSERT(inputShapes.size() == 1);
+    ARMNN_ASSERT(inputShapes.size() == 1);
 
     TensorShape inputShape = inputShapes[0];
     TensorShape outputShape(inputShape);
@@ -64,7 +64,7 @@ void DepthToSpaceLayer::ValidateTensorShapesFromInputs()
     std::vector<TensorShape> inferredShapes = InferOutputShapes({
         GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "DepthToSpaceLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/DepthwiseConvolution2dLayer.cpp b/src/armnn/layers/DepthwiseConvolution2dLayer.cpp
index f37096ac18..dc6b2c2fe7 100644
--- a/src/armnn/layers/DepthwiseConvolution2dLayer.cpp
+++ b/src/armnn/layers/DepthwiseConvolution2dLayer.cpp
@@ -51,7 +51,7 @@ void DepthwiseConvolution2dLayer::SerializeLayerParameters(ParameterStringifyFun
 std::unique_ptr<IWorkload> DepthwiseConvolution2dLayer::CreateWorkload(const IWorkloadFactory& factory) const
 {
     // on this level constant data should not be released..
-    BOOST_ASSERT_MSG(m_Weight != nullptr, "DepthwiseConvolution2dLayer: Weights data should not be null.");
+    ARMNN_ASSERT_MSG(m_Weight != nullptr, "DepthwiseConvolution2dLayer: Weights data should not be null.");
 
     DepthwiseConvolution2dQueueDescriptor descriptor;
 
@@ -59,7 +59,7 @@ std::unique_ptr<IWorkload> DepthwiseConvolution2dLayer::CreateWorkload(const IWo
 
     if (m_Param.m_BiasEnabled)
     {
-        BOOST_ASSERT_MSG(m_Bias != nullptr, "DepthwiseConvolution2dLayer: Bias data should not be null.");
+        ARMNN_ASSERT_MSG(m_Bias != nullptr, "DepthwiseConvolution2dLayer: Bias data should not be null.");
         descriptor.m_Bias = m_Bias.get();
     }
     return factory.CreateDepthwiseConvolution2d(descriptor, PrepInfoAndDesc(descriptor));
@@ -81,11 +81,11 @@ DepthwiseConvolution2dLayer* DepthwiseConvolution2dLayer::Clone(Graph& graph) co
 std::vector<TensorShape>
 DepthwiseConvolution2dLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
 {
-    BOOST_ASSERT(inputShapes.size() == 2);
+    ARMNN_ASSERT(inputShapes.size() == 2);
     const TensorShape& inputShape  = inputShapes[0];
     const TensorShape& filterShape = inputShapes[1];
 
-    BOOST_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Convolutions will always have 4D input.");
+    ARMNN_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Convolutions will always have 4D input.");
 
     DataLayoutIndexed dataLayoutIndex(m_Param.m_DataLayout);
 
@@ -124,14 +124,14 @@ void DepthwiseConvolution2dLayer::ValidateTensorShapesFromInputs()
     VerifyLayerConnections(1, CHECK_LOCATION());
 
     // on this level constant data should not be released..
-    BOOST_ASSERT_MSG(m_Weight != nullptr, "DepthwiseConvolution2dLayer: Weights data should not be null.");
+    ARMNN_ASSERT_MSG(m_Weight != nullptr, "DepthwiseConvolution2dLayer: Weights data should not be null.");
 
     auto inferredShapes = InferOutputShapes({
         GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(),
         m_Weight->GetTensorInfo().GetShape()
      });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "DepthwiseConvolution2dLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/DequantizeLayer.cpp b/src/armnn/layers/DequantizeLayer.cpp
index 00a1d697b6..5b57279c43 100644
--- a/src/armnn/layers/DequantizeLayer.cpp
+++ b/src/armnn/layers/DequantizeLayer.cpp
@@ -36,7 +36,7 @@ void DequantizeLayer::ValidateTensorShapesFromInputs()
     std::vector<TensorShape> inferredShapes = InferOutputShapes({
         GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "DequantizeLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/DetectionPostProcessLayer.cpp b/src/armnn/layers/DetectionPostProcessLayer.cpp
index 8749b33ba2..e8d14d928c 100644
--- a/src/armnn/layers/DetectionPostProcessLayer.cpp
+++ b/src/armnn/layers/DetectionPostProcessLayer.cpp
@@ -39,9 +39,9 @@ void DetectionPostProcessLayer::ValidateTensorShapesFromInputs()
     VerifyLayerConnections(2, CHECK_LOCATION());
 
     // on this level constant data should not be released.
-    BOOST_ASSERT_MSG(m_Anchors != nullptr, "DetectionPostProcessLayer: Anchors data should not be null.");
+    ARMNN_ASSERT_MSG(m_Anchors != nullptr, "DetectionPostProcessLayer: Anchors data should not be null.");
 
-    BOOST_ASSERT_MSG(GetNumOutputSlots() == 4, "DetectionPostProcessLayer: The layer should return 4 outputs.");
+    ARMNN_ASSERT_MSG(GetNumOutputSlots() == 4, "DetectionPostProcessLayer: The layer should return 4 outputs.");
 
     unsigned int detectedBoxes = m_Param.m_MaxDetections * m_Param.m_MaxClassesPerDetection;
 
diff --git a/src/armnn/layers/ElementwiseBaseLayer.cpp b/src/armnn/layers/ElementwiseBaseLayer.cpp
index 761814176d..2c1e8717f4 100644
--- a/src/armnn/layers/ElementwiseBaseLayer.cpp
+++ b/src/armnn/layers/ElementwiseBaseLayer.cpp
@@ -8,8 +8,7 @@
 #include "InternalTypes.hpp"
 #include "armnn/Exceptions.hpp"
 #include <armnn/TypesUtils.hpp>
-
-#include <boost/assert.hpp>
+#include <armnn/utility/Assert.hpp>
 
 namespace armnn
 {
@@ -22,12 +21,12 @@ ElementwiseBaseLayer::ElementwiseBaseLayer(unsigned int numInputSlots, unsigned
 
 std::vector<TensorShape> ElementwiseBaseLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
 {
-    BOOST_ASSERT(inputShapes.size() == 2);
+    ARMNN_ASSERT(inputShapes.size() == 2);
     auto& input0 = inputShapes[0];
     auto& input1 = inputShapes[1];
 
     // Get the max of the inputs.
-    BOOST_ASSERT(input0.GetNumDimensions() == input1.GetNumDimensions());
+    ARMNN_ASSERT(input0.GetNumDimensions() == input1.GetNumDimensions());
     unsigned int numDims = input0.GetNumDimensions();
     std::vector<unsigned int> dims(numDims);
 
@@ -38,7 +37,7 @@ std::vector<TensorShape> ElementwiseBaseLayer::InferOutputShapes(const std::vect
 
 #if !NDEBUG
         // Validate inputs are broadcast compatible.
-        BOOST_ASSERT_MSG(dim0 == dim1 || dim0 == 1 || dim1 == 1,
+        ARMNN_ASSERT_MSG(dim0 == dim1 || dim0 == 1 || dim1 == 1,
                          "Dimensions should either match or one should be of size 1.");
 #endif
 
@@ -57,7 +56,7 @@ void ElementwiseBaseLayer::ValidateTensorShapesFromInputs()
         GetInputSlot(1).GetConnection()->GetTensorInfo().GetShape()
     });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     std::string msg = GetLayerTypeAsCString(GetType());
     msg += "Layer: TensorShape set on OutputSlot[0] does not match the inferred shape.";
diff --git a/src/armnn/layers/ElementwiseUnaryLayer.cpp b/src/armnn/layers/ElementwiseUnaryLayer.cpp
index d3843da060..c91057cc9f 100644
--- a/src/armnn/layers/ElementwiseUnaryLayer.cpp
+++ b/src/armnn/layers/ElementwiseUnaryLayer.cpp
@@ -34,7 +34,7 @@ ElementwiseUnaryLayer* ElementwiseUnaryLayer::Clone(Graph& graph) const
 std::vector<TensorShape> ElementwiseUnaryLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
 {
     // Should return the shape of the input tensor
-    BOOST_ASSERT(inputShapes.size() == 1);
+    ARMNN_ASSERT(inputShapes.size() == 1);
     const TensorShape& input = inputShapes[0];
 
     return std::vector<TensorShape>({ input });
@@ -46,7 +46,7 @@ void ElementwiseUnaryLayer::ValidateTensorShapesFromInputs()
 
     std::vector<TensorShape> inferredShapes = InferOutputShapes({
         GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape()});
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "ElementwiseUnaryLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/FakeQuantizationLayer.cpp b/src/armnn/layers/FakeQuantizationLayer.cpp
index 8611b9b73c..2b4ad8605f 100644
--- a/src/armnn/layers/FakeQuantizationLayer.cpp
+++ b/src/armnn/layers/FakeQuantizationLayer.cpp
@@ -35,7 +35,7 @@ void FakeQuantizationLayer::ValidateTensorShapesFromInputs()
 
     auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "FakeQuantizationLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/FloorLayer.cpp b/src/armnn/layers/FloorLayer.cpp
index 148543cf62..fb918f6e7a 100644
--- a/src/armnn/layers/FloorLayer.cpp
+++ b/src/armnn/layers/FloorLayer.cpp
@@ -35,7 +35,7 @@ void FloorLayer::ValidateTensorShapesFromInputs()
 
     auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "FloorLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/FullyConnectedLayer.cpp b/src/armnn/layers/FullyConnectedLayer.cpp
index 6b36bad713..4bbc9ba890 100644
--- a/src/armnn/layers/FullyConnectedLayer.cpp
+++ b/src/armnn/layers/FullyConnectedLayer.cpp
@@ -22,14 +22,14 @@ FullyConnectedLayer::FullyConnectedLayer(const FullyConnectedDescriptor& param,
 std::unique_ptr<IWorkload> FullyConnectedLayer::CreateWorkload(const IWorkloadFactory& factory) const
 {
     // on this level constant data should not be released..
-    BOOST_ASSERT_MSG(m_Weight != nullptr, "FullyConnectedLayer: Weights data should not be null.");
+    ARMNN_ASSERT_MSG(m_Weight != nullptr, "FullyConnectedLayer: Weights data should not be null.");
 
     FullyConnectedQueueDescriptor descriptor;
 
     descriptor.m_Weight = m_Weight.get();
     if (m_Param.m_BiasEnabled)
     {
-        BOOST_ASSERT_MSG(m_Bias != nullptr, "FullyConnectedLayer: Bias data should not be null.");
+        ARMNN_ASSERT_MSG(m_Bias != nullptr, "FullyConnectedLayer: Bias data should not be null.");
         descriptor.m_Bias = m_Bias.get();
     }
     return factory.CreateFullyConnected(descriptor, PrepInfoAndDesc(descriptor));
@@ -50,7 +50,7 @@ FullyConnectedLayer* FullyConnectedLayer::Clone(Graph& graph) const
 
 std::vector<TensorShape> FullyConnectedLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
 {
-    BOOST_ASSERT(inputShapes.size() == 2);
+    ARMNN_ASSERT(inputShapes.size() == 2);
     const TensorShape& inputShape = inputShapes[0];
     const TensorShape weightShape = inputShapes[1];
 
@@ -66,13 +66,13 @@ void FullyConnectedLayer::ValidateTensorShapesFromInputs()
     VerifyLayerConnections(1, CHECK_LOCATION());
 
     // check if we m_Weight data is not nullptr
-    BOOST_ASSERT_MSG(m_Weight != nullptr, "FullyConnectedLayer: Weights data should not be null.");
+    ARMNN_ASSERT_MSG(m_Weight != nullptr, "FullyConnectedLayer: Weights data should not be null.");
 
     auto inferredShapes = InferOutputShapes({
         GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(),
         m_Weight->GetTensorInfo().GetShape() });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "FullyConnectedLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/InstanceNormalizationLayer.cpp b/src/armnn/layers/InstanceNormalizationLayer.cpp
index 9e0212f226..25b133acda 100644
--- a/src/armnn/layers/InstanceNormalizationLayer.cpp
+++ b/src/armnn/layers/InstanceNormalizationLayer.cpp
@@ -35,7 +35,7 @@ void InstanceNormalizationLayer::ValidateTensorShapesFromInputs()
 
     auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "InstanceNormalizationLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/L2NormalizationLayer.cpp b/src/armnn/layers/L2NormalizationLayer.cpp
index 3d9dc538f5..e6d5f064f3 100644
--- a/src/armnn/layers/L2NormalizationLayer.cpp
+++ b/src/armnn/layers/L2NormalizationLayer.cpp
@@ -35,7 +35,7 @@ void L2NormalizationLayer::ValidateTensorShapesFromInputs()
 
     auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "L2NormalizationLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/LogSoftmaxLayer.cpp b/src/armnn/layers/LogSoftmaxLayer.cpp
index 24b6fde339..627aa4cdd3 100644
--- a/src/armnn/layers/LogSoftmaxLayer.cpp
+++ b/src/armnn/layers/LogSoftmaxLayer.cpp
@@ -34,7 +34,7 @@ void LogSoftmaxLayer::ValidateTensorShapesFromInputs()
     VerifyLayerConnections(1, CHECK_LOCATION());
 
     auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "LogSoftmaxLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/LstmLayer.cpp b/src/armnn/layers/LstmLayer.cpp
index 1d945690d5..653b18a1c9 100644
--- a/src/armnn/layers/LstmLayer.cpp
+++ b/src/armnn/layers/LstmLayer.cpp
@@ -147,7 +147,7 @@ LstmLayer* LstmLayer::Clone(Graph& graph) const
 
 std::vector<TensorShape> LstmLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
 {
-    BOOST_ASSERT(inputShapes.size() == 3);
+    ARMNN_ASSERT(inputShapes.size() == 3);
 
     // Get input values for validation
     unsigned int batchSize = inputShapes[0][0];
@@ -173,35 +173,35 @@ void LstmLayer::ValidateTensorShapesFromInputs()
         GetInputSlot(2).GetConnection()->GetTensorInfo().GetShape()}
     );
 
-    BOOST_ASSERT(inferredShapes.size() == 4);
+    ARMNN_ASSERT(inferredShapes.size() == 4);
 
     // Check if the weights are nullptr
-    BOOST_ASSERT_MSG(m_BasicParameters.m_InputToForgetWeights != nullptr,
+    ARMNN_ASSERT_MSG(m_BasicParameters.m_InputToForgetWeights != nullptr,
                      "LstmLayer: m_BasicParameters.m_InputToForgetWeights should not be null.");
-    BOOST_ASSERT_MSG(m_BasicParameters.m_InputToCellWeights != nullptr,
+    ARMNN_ASSERT_MSG(m_BasicParameters.m_InputToCellWeights != nullptr,
                      "LstmLayer: m_BasicParameters.m_InputToCellWeights should not be null.");
-    BOOST_ASSERT_MSG(m_BasicParameters.m_InputToOutputWeights != nullptr,
+    ARMNN_ASSERT_MSG(m_BasicParameters.m_InputToOutputWeights != nullptr,
                      "LstmLayer: m_BasicParameters.m_InputToOutputWeights should not be null.");
-    BOOST_ASSERT_MSG(m_BasicParameters.m_RecurrentToForgetWeights != nullptr,
+    ARMNN_ASSERT_MSG(m_BasicParameters.m_RecurrentToForgetWeights != nullptr,
                      "LstmLayer: m_BasicParameters.m_RecurrentToForgetWeights should not be null.");
-    BOOST_ASSERT_MSG(m_BasicParameters.m_RecurrentToCellWeights != nullptr,
+    ARMNN_ASSERT_MSG(m_BasicParameters.m_RecurrentToCellWeights != nullptr,
                      "LstmLayer: m_BasicParameters.m_RecurrentToCellWeights should not be null.");
-    BOOST_ASSERT_MSG(m_BasicParameters.m_RecurrentToOutputWeights != nullptr,
+    ARMNN_ASSERT_MSG(m_BasicParameters.m_RecurrentToOutputWeights != nullptr,
                      "LstmLayer: m_BasicParameters.m_RecurrentToOutputWeights should not be null.");
-    BOOST_ASSERT_MSG(m_BasicParameters.m_ForgetGateBias != nullptr,
+    ARMNN_ASSERT_MSG(m_BasicParameters.m_ForgetGateBias != nullptr,
                      "LstmLayer: m_BasicParameters.m_ForgetGateBias should not be null.");
-    BOOST_ASSERT_MSG(m_BasicParameters.m_CellBias != nullptr,
+    ARMNN_ASSERT_MSG(m_BasicParameters.m_CellBias != nullptr,
                      "LstmLayer: m_BasicParameters.m_CellBias should not be null.");
-    BOOST_ASSERT_MSG(m_BasicParameters.m_OutputGateBias != nullptr,
+    ARMNN_ASSERT_MSG(m_BasicParameters.m_OutputGateBias != nullptr,
                      "LstmLayer: m_BasicParameters.m_OutputGateBias should not be null.");
 
     if (!m_Param.m_CifgEnabled)
     {
-        BOOST_ASSERT_MSG(m_CifgParameters.m_InputToInputWeights != nullptr,
+        ARMNN_ASSERT_MSG(m_CifgParameters.m_InputToInputWeights != nullptr,
                          "LstmLayer: m_CifgParameters.m_InputToInputWeights should not be null.");
-        BOOST_ASSERT_MSG(m_CifgParameters.m_RecurrentToInputWeights != nullptr,
+        ARMNN_ASSERT_MSG(m_CifgParameters.m_RecurrentToInputWeights != nullptr,
                          "LstmLayer: m_CifgParameters.m_RecurrentToInputWeights should not be null.");
-        BOOST_ASSERT_MSG(m_CifgParameters.m_InputGateBias != nullptr,
+        ARMNN_ASSERT_MSG(m_CifgParameters.m_InputGateBias != nullptr,
                          "LstmLayer: m_CifgParameters.m_InputGateBias should not be null.");
 
         ConditionalThrowIfNotEqual<LayerValidationException>(
@@ -211,11 +211,11 @@ void LstmLayer::ValidateTensorShapesFromInputs()
     }
     else
     {
-        BOOST_ASSERT_MSG(m_CifgParameters.m_InputToInputWeights == nullptr,
+        ARMNN_ASSERT_MSG(m_CifgParameters.m_InputToInputWeights == nullptr,
             "LstmLayer: m_CifgParameters.m_InputToInputWeights should not have a value when CIFG is enabled.");
-        BOOST_ASSERT_MSG(m_CifgParameters.m_RecurrentToInputWeights == nullptr,
+        ARMNN_ASSERT_MSG(m_CifgParameters.m_RecurrentToInputWeights == nullptr,
             "LstmLayer: m_CifgParameters.m_RecurrentToInputWeights should not have a value when CIFG is enabled.");
-        BOOST_ASSERT_MSG(m_CifgParameters.m_InputGateBias == nullptr,
+        ARMNN_ASSERT_MSG(m_CifgParameters.m_InputGateBias == nullptr,
             "LstmLayer: m_CifgParameters.m_InputGateBias should not have a value when CIFG is enabled.");
 
         ConditionalThrowIfNotEqual<LayerValidationException>(
@@ -226,7 +226,7 @@ void LstmLayer::ValidateTensorShapesFromInputs()
 
     if (m_Param.m_ProjectionEnabled)
     {
-        BOOST_ASSERT_MSG(m_ProjectionParameters.m_ProjectionWeights != nullptr,
+        ARMNN_ASSERT_MSG(m_ProjectionParameters.m_ProjectionWeights != nullptr,
                          "LstmLayer: m_ProjectionParameters.m_ProjectionWeights should not be null.");
     }
 
@@ -234,13 +234,13 @@ void LstmLayer::ValidateTensorShapesFromInputs()
     {
         if (!m_Param.m_CifgEnabled)
         {
-            BOOST_ASSERT_MSG(m_PeepholeParameters.m_CellToInputWeights != nullptr,
+            ARMNN_ASSERT_MSG(m_PeepholeParameters.m_CellToInputWeights != nullptr,
                              "LstmLayer: m_PeepholeParameters.m_CellToInputWeights should not be null "
                              "when Peephole is enabled and CIFG is disabled.");
         }
-        BOOST_ASSERT_MSG(m_PeepholeParameters.m_CellToForgetWeights != nullptr,
+        ARMNN_ASSERT_MSG(m_PeepholeParameters.m_CellToForgetWeights != nullptr,
                          "LstmLayer: m_PeepholeParameters.m_CellToForgetWeights should not be null.");
-        BOOST_ASSERT_MSG(m_PeepholeParameters.m_CellToOutputWeights != nullptr,
+        ARMNN_ASSERT_MSG(m_PeepholeParameters.m_CellToOutputWeights != nullptr,
                          "LstmLayer: m_PeepholeParameters.m_CellToOutputWeights should not be null.");
     }
 
@@ -261,14 +261,14 @@ void LstmLayer::ValidateTensorShapesFromInputs()
     {
         if(!m_Param.m_CifgEnabled)
         {
-            BOOST_ASSERT_MSG(m_LayerNormParameters.m_InputLayerNormWeights != nullptr,
+            ARMNN_ASSERT_MSG(m_LayerNormParameters.m_InputLayerNormWeights != nullptr,
                              "LstmLayer: m_LayerNormParameters.m_inputLayerNormWeights should not be null.");
         }
-        BOOST_ASSERT_MSG(m_LayerNormParameters.m_ForgetLayerNormWeights != nullptr,
+        ARMNN_ASSERT_MSG(m_LayerNormParameters.m_ForgetLayerNormWeights != nullptr,
                          "LstmLayer: m_LayerNormParameters.m_forgetLayerNormWeights should not be null.");
-        BOOST_ASSERT_MSG(m_LayerNormParameters.m_CellLayerNormWeights != nullptr,
+        ARMNN_ASSERT_MSG(m_LayerNormParameters.m_CellLayerNormWeights != nullptr,
                          "LstmLayer: m_LayerNormParameters.m_cellLayerNormWeights should not be null.");
-        BOOST_ASSERT_MSG(m_LayerNormParameters.m_OutputLayerNormWeights != nullptr,
+        ARMNN_ASSERT_MSG(m_LayerNormParameters.m_OutputLayerNormWeights != nullptr,
                          "LstmLayer: m_LayerNormParameters.m_outputLayerNormWeights should not be null.");
     }
 }
diff --git a/src/armnn/layers/MeanLayer.cpp b/src/armnn/layers/MeanLayer.cpp
index 30b88fa1b9..5fa88f9398 100644
--- a/src/armnn/layers/MeanLayer.cpp
+++ b/src/armnn/layers/MeanLayer.cpp
@@ -44,7 +44,7 @@ void MeanLayer::ValidateTensorShapesFromInputs()
 
     const TensorInfo& input = GetInputSlot(0).GetConnection()->GetTensorInfo();
 
-    BOOST_ASSERT_MSG(input.GetNumDimensions() > 0 && input.GetNumDimensions() <= 4,
+    ARMNN_ASSERT_MSG(input.GetNumDimensions() > 0 && input.GetNumDimensions() <= 4,
                      "MeanLayer: Mean supports up to 4D input.");
 
     unsigned int rank = input.GetNumDimensions();
diff --git a/src/armnn/layers/MemCopyLayer.cpp b/src/armnn/layers/MemCopyLayer.cpp
index cf69c17cf5..e4009de022 100644
--- a/src/armnn/layers/MemCopyLayer.cpp
+++ b/src/armnn/layers/MemCopyLayer.cpp
@@ -39,7 +39,7 @@ void MemCopyLayer::ValidateTensorShapesFromInputs()
 
     auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "MemCopyLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/MemImportLayer.cpp b/src/armnn/layers/MemImportLayer.cpp
index 80f9fda803..bcccba1f4a 100644
--- a/src/armnn/layers/MemImportLayer.cpp
+++ b/src/armnn/layers/MemImportLayer.cpp
@@ -39,7 +39,7 @@ void MemImportLayer::ValidateTensorShapesFromInputs()
 
     auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "MemImportLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/MergeLayer.cpp b/src/armnn/layers/MergeLayer.cpp
index f2fd29fe9e..ad7d8b1416 100644
--- a/src/armnn/layers/MergeLayer.cpp
+++ b/src/armnn/layers/MergeLayer.cpp
@@ -36,7 +36,7 @@ void MergeLayer::ValidateTensorShapesFromInputs()
         GetInputSlot(1).GetConnection()->GetTensorInfo().GetShape(),
     });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "MergeLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
@@ -46,7 +46,7 @@ void MergeLayer::ValidateTensorShapesFromInputs()
 
 std::vector<TensorShape> MergeLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
 {
-    BOOST_ASSERT(inputShapes.size() == 2);
+    ARMNN_ASSERT(inputShapes.size() == 2);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "MergeLayer: TensorShapes set on inputs do not match",
diff --git a/src/armnn/layers/NormalizationLayer.cpp b/src/armnn/layers/NormalizationLayer.cpp
index 09f8a0d00e..44179fd534 100644
--- a/src/armnn/layers/NormalizationLayer.cpp
+++ b/src/armnn/layers/NormalizationLayer.cpp
@@ -35,7 +35,7 @@ void NormalizationLayer::ValidateTensorShapesFromInputs()
 
     auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "NormalizationLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/PermuteLayer.cpp b/src/armnn/layers/PermuteLayer.cpp
index 0fc3ce4bf6..e565b48b57 100644
--- a/src/armnn/layers/PermuteLayer.cpp
+++ b/src/armnn/layers/PermuteLayer.cpp
@@ -35,7 +35,7 @@ PermuteLayer* PermuteLayer::Clone(Graph& graph) const
 
 std::vector<TensorShape> PermuteLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
 {
-    BOOST_ASSERT(inputShapes.size() == 1);
+    ARMNN_ASSERT(inputShapes.size() == 1);
     const TensorShape& inShape = inputShapes[0];
     return std::vector<TensorShape> ({armnnUtils::Permuted(inShape, m_Param.m_DimMappings)});
 }
@@ -46,7 +46,7 @@ void PermuteLayer::ValidateTensorShapesFromInputs()
 
     auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "PermuteLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/Pooling2dLayer.cpp b/src/armnn/layers/Pooling2dLayer.cpp
index a3c2425097..ad2c82f761 100644
--- a/src/armnn/layers/Pooling2dLayer.cpp
+++ b/src/armnn/layers/Pooling2dLayer.cpp
@@ -37,12 +37,12 @@ Pooling2dLayer* Pooling2dLayer::Clone(Graph& graph) const
 
 std::vector<TensorShape> Pooling2dLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
 {
-    BOOST_ASSERT(inputShapes.size() == 1);
+    ARMNN_ASSERT(inputShapes.size() == 1);
     const TensorShape& inputShape = inputShapes[0];
     const DataLayoutIndexed dimensionIndices = m_Param.m_DataLayout;
 
     // If we support multiple batch dimensions in the future, then this assert will need to change.
-    BOOST_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Pooling2dLayer will always have 4D input.");
+    ARMNN_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Pooling2dLayer will always have 4D input.");
 
     unsigned int inWidth = inputShape[dimensionIndices.GetWidthIndex()];
     unsigned int inHeight = inputShape[dimensionIndices.GetHeightIndex()];
@@ -54,7 +54,7 @@ std::vector<TensorShape> Pooling2dLayer::InferOutputShapes(const std::vector<Ten
     unsigned int outHeight = 1;
     if (!isGlobalPooling)
     {
-        BOOST_ASSERT_MSG(m_Param.m_StrideX!=0 && m_Param.m_StrideY!=0,
+        ARMNN_ASSERT_MSG(m_Param.m_StrideX!=0 && m_Param.m_StrideY!=0,
                          "Stride can only be zero when performing global pooling");
 
         auto CalcSize = [](auto inSize, auto lowPad, auto highPad, auto poolSize, auto stride, auto outputShapeRounding)
@@ -72,7 +72,7 @@ std::vector<TensorShape> Pooling2dLayer::InferOutputShapes(const std::vector<Ten
                         size = static_cast<unsigned int>(floor(div)) + 1;
                         break;
                     default:
-                        BOOST_ASSERT_MSG(false, "Unsupported Output Shape Rounding");
+                        ARMNN_ASSERT_MSG(false, "Unsupported Output Shape Rounding");
                 }
 
                 // MakeS sure that border operations will start from inside the input and not the padded area.
@@ -106,7 +106,7 @@ void Pooling2dLayer::ValidateTensorShapesFromInputs()
 
     auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "Pooling2dLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/PreluLayer.cpp b/src/armnn/layers/PreluLayer.cpp
index d9e59224a0..609480673b 100644
--- a/src/armnn/layers/PreluLayer.cpp
+++ b/src/armnn/layers/PreluLayer.cpp
@@ -34,7 +34,7 @@ PreluLayer* PreluLayer::Clone(Graph& graph) const
 
 std::vector<TensorShape> PreluLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
 {
-    BOOST_ASSERT(inputShapes.size() == 2);
+    ARMNN_ASSERT(inputShapes.size() == 2);
 
     const TensorShape& inputShape = inputShapes[0];
     const TensorShape& alphaShape = inputShapes[1];
@@ -42,8 +42,8 @@ std::vector<TensorShape> PreluLayer::InferOutputShapes(const std::vector<TensorS
     const unsigned int inputShapeDimensions = inputShape.GetNumDimensions();
     const unsigned int alphaShapeDimensions = alphaShape.GetNumDimensions();
 
-    BOOST_ASSERT(inputShapeDimensions > 0);
-    BOOST_ASSERT(alphaShapeDimensions > 0);
+    ARMNN_ASSERT(inputShapeDimensions > 0);
+    ARMNN_ASSERT(alphaShapeDimensions > 0);
 
     // The size of the output is the maximum size along each dimension of the input operands,
     // it starts with the trailing dimensions, and works its way forward
@@ -63,7 +63,7 @@ std::vector<TensorShape> PreluLayer::InferOutputShapes(const std::vector<TensorS
         unsigned int alphaDimension = alphaShape[boost::numeric_cast<unsigned int>(alphaShapeIndex)];
 
         // Check that the inputs are broadcast compatible
-        BOOST_ASSERT_MSG(inputDimension == alphaDimension || inputDimension == 1 || alphaDimension == 1,
+        ARMNN_ASSERT_MSG(inputDimension == alphaDimension || inputDimension == 1 || alphaDimension == 1,
                          "PreluLayer: Dimensions should either match or one should be of size 1");
 
         outputShape[outputShapeIndex] = std::max(inputDimension, alphaDimension);
@@ -104,7 +104,7 @@ void PreluLayer::ValidateTensorShapesFromInputs()
         GetInputSlot(1).GetConnection()->GetTensorInfo().GetShape()
     });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "PreluLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/QLstmLayer.cpp b/src/armnn/layers/QLstmLayer.cpp
index 393a7029aa..9b940c1823 100644
--- a/src/armnn/layers/QLstmLayer.cpp
+++ b/src/armnn/layers/QLstmLayer.cpp
@@ -150,7 +150,7 @@ QLstmLayer* QLstmLayer::Clone(Graph& graph) const
 
 std::vector<TensorShape> QLstmLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
 {
-    BOOST_ASSERT(inputShapes.size() == 3);
+    ARMNN_ASSERT(inputShapes.size() == 3);
 
     // Get input values for validation
     unsigned int batchSize = inputShapes[0][0];
@@ -176,35 +176,35 @@ void QLstmLayer::ValidateTensorShapesFromInputs()
         GetInputSlot(2).GetConnection()->GetTensorInfo().GetShape() //  previousCellStateIn
     });
 
-    BOOST_ASSERT(inferredShapes.size() == 3);
+    ARMNN_ASSERT(inferredShapes.size() == 3);
 
     // Check if the weights are nullptr for basic params
-    BOOST_ASSERT_MSG(m_BasicParameters.m_InputToForgetWeights != nullptr,
+    ARMNN_ASSERT_MSG(m_BasicParameters.m_InputToForgetWeights != nullptr,
             "QLstmLayer: m_BasicParameters.m_InputToForgetWeights should not be null.");
-    BOOST_ASSERT_MSG(m_BasicParameters.m_InputToCellWeights != nullptr,
+    ARMNN_ASSERT_MSG(m_BasicParameters.m_InputToCellWeights != nullptr,
             "QLstmLayer: m_BasicParameters.m_InputToCellWeights should not be null.");
-    BOOST_ASSERT_MSG(m_BasicParameters.m_InputToOutputWeights != nullptr,
+    ARMNN_ASSERT_MSG(m_BasicParameters.m_InputToOutputWeights != nullptr,
             "QLstmLayer: m_BasicParameters.m_InputToOutputWeights should not be null.");
-    BOOST_ASSERT_MSG(m_BasicParameters.m_RecurrentToForgetWeights != nullptr,
+    ARMNN_ASSERT_MSG(m_BasicParameters.m_RecurrentToForgetWeights != nullptr,
             "QLstmLayer: m_BasicParameters.m_RecurrentToForgetWeights should not be null.");
-    BOOST_ASSERT_MSG(m_BasicParameters.m_RecurrentToCellWeights != nullptr,
+    ARMNN_ASSERT_MSG(m_BasicParameters.m_RecurrentToCellWeights != nullptr,
             "QLstmLayer: m_BasicParameters.m_RecurrentToCellWeights should not be null.");
-    BOOST_ASSERT_MSG(m_BasicParameters.m_RecurrentToOutputWeights != nullptr,
+    ARMNN_ASSERT_MSG(m_BasicParameters.m_RecurrentToOutputWeights != nullptr,
             "QLstmLayer: m_BasicParameters.m_RecurrentToOutputWeights should not be null.");
-    BOOST_ASSERT_MSG(m_BasicParameters.m_ForgetGateBias != nullptr,
+    ARMNN_ASSERT_MSG(m_BasicParameters.m_ForgetGateBias != nullptr,
             "QLstmLayer: m_BasicParameters.m_ForgetGateBias should not be null.");
-    BOOST_ASSERT_MSG(m_BasicParameters.m_CellBias != nullptr,
+    ARMNN_ASSERT_MSG(m_BasicParameters.m_CellBias != nullptr,
             "QLstmLayer: m_BasicParameters.m_CellBias should not be null.");
-    BOOST_ASSERT_MSG(m_BasicParameters.m_OutputGateBias != nullptr,
+    ARMNN_ASSERT_MSG(m_BasicParameters.m_OutputGateBias != nullptr,
             "QLstmLayer: m_BasicParameters.m_OutputGateBias should not be null.");
 
     if (!m_Param.m_CifgEnabled)
     {
-        BOOST_ASSERT_MSG(m_CifgParameters.m_InputToInputWeights != nullptr,
+        ARMNN_ASSERT_MSG(m_CifgParameters.m_InputToInputWeights != nullptr,
                 "QLstmLayer: m_CifgParameters.m_InputToInputWeights should not be null.");
-        BOOST_ASSERT_MSG(m_CifgParameters.m_RecurrentToInputWeights != nullptr,
+        ARMNN_ASSERT_MSG(m_CifgParameters.m_RecurrentToInputWeights != nullptr,
                 "QLstmLayer: m_CifgParameters.m_RecurrentToInputWeights should not be null.");
-        BOOST_ASSERT_MSG(m_CifgParameters.m_InputGateBias != nullptr,
+        ARMNN_ASSERT_MSG(m_CifgParameters.m_InputGateBias != nullptr,
                 "QLstmLayer: m_CifgParameters.m_InputGateBias should not be null.");
 
         ConditionalThrowIfNotEqual<LayerValidationException>(
@@ -214,12 +214,12 @@ void QLstmLayer::ValidateTensorShapesFromInputs()
     }
     else
     {
-        BOOST_ASSERT_MSG(m_CifgParameters.m_InputToInputWeights == nullptr,
+        ARMNN_ASSERT_MSG(m_CifgParameters.m_InputToInputWeights == nullptr,
                 "QLstmLayer: m_CifgParameters.m_InputToInputWeights should not have a value when CIFG is enabled.");
-        BOOST_ASSERT_MSG(m_CifgParameters.m_RecurrentToInputWeights == nullptr,
+        ARMNN_ASSERT_MSG(m_CifgParameters.m_RecurrentToInputWeights == nullptr,
                 "QLstmLayer: m_CifgParameters.m_RecurrentToInputWeights should "
                              "not have a value when CIFG is enabled.");
-        BOOST_ASSERT_MSG(m_CifgParameters.m_InputGateBias == nullptr,
+        ARMNN_ASSERT_MSG(m_CifgParameters.m_InputGateBias == nullptr,
                 "QLstmLayer: m_CifgParameters.m_InputGateBias should not have a value when CIFG is enabled.");
 
         ConditionalThrowIfNotEqual<LayerValidationException>(
@@ -230,23 +230,23 @@ void QLstmLayer::ValidateTensorShapesFromInputs()
 
     if (m_Param.m_ProjectionEnabled)
     {
-        BOOST_ASSERT_MSG(m_ProjectionParameters.m_ProjectionWeights != nullptr,
+        ARMNN_ASSERT_MSG(m_ProjectionParameters.m_ProjectionWeights != nullptr,
                          "QLstmLayer: m_ProjectionParameters.m_ProjectionWeights should not be null.");
-        BOOST_ASSERT_MSG(m_ProjectionParameters.m_ProjectionBias != nullptr,
+        ARMNN_ASSERT_MSG(m_ProjectionParameters.m_ProjectionBias != nullptr,
                          "QLstmLayer: m_ProjectionParameters.m_ProjectionBias should not be null.");
     }
 
     if (m_Param.m_PeepholeEnabled)
     {
         if (!m_Param.m_CifgEnabled) {
-            BOOST_ASSERT_MSG(m_PeepholeParameters.m_CellToInputWeights != nullptr,
+            ARMNN_ASSERT_MSG(m_PeepholeParameters.m_CellToInputWeights != nullptr,
                     "QLstmLayer: m_PeepholeParameters.m_CellToInputWeights should not be null "
                     "when Peephole is enabled and CIFG is disabled.");
         }
 
-        BOOST_ASSERT_MSG(m_PeepholeParameters.m_CellToForgetWeights != nullptr,
+        ARMNN_ASSERT_MSG(m_PeepholeParameters.m_CellToForgetWeights != nullptr,
                          "QLstmLayer: m_PeepholeParameters.m_CellToForgetWeights should not be null.");
-        BOOST_ASSERT_MSG(m_PeepholeParameters.m_CellToOutputWeights != nullptr,
+        ARMNN_ASSERT_MSG(m_PeepholeParameters.m_CellToOutputWeights != nullptr,
                          "QLstmLayer: m_PeepholeParameters.m_CellToOutputWeights should not be null.");
     }
 
@@ -263,14 +263,14 @@ void QLstmLayer::ValidateTensorShapesFromInputs()
     {
         if(!m_Param.m_CifgEnabled)
         {
-            BOOST_ASSERT_MSG(m_LayerNormParameters.m_InputLayerNormWeights != nullptr,
+            ARMNN_ASSERT_MSG(m_LayerNormParameters.m_InputLayerNormWeights != nullptr,
                              "QLstmLayer: m_LayerNormParameters.m_InputLayerNormWeights should not be null.");
         }
-        BOOST_ASSERT_MSG(m_LayerNormParameters.m_ForgetLayerNormWeights != nullptr,
+        ARMNN_ASSERT_MSG(m_LayerNormParameters.m_ForgetLayerNormWeights != nullptr,
                          "QLstmLayer: m_LayerNormParameters.m_ForgetLayerNormWeights should not be null.");
-        BOOST_ASSERT_MSG(m_LayerNormParameters.m_CellLayerNormWeights != nullptr,
+        ARMNN_ASSERT_MSG(m_LayerNormParameters.m_CellLayerNormWeights != nullptr,
                          "QLstmLayer: m_LayerNormParameters.m_CellLayerNormWeights should not be null.");
-        BOOST_ASSERT_MSG(m_LayerNormParameters.m_OutputLayerNormWeights != nullptr,
+        ARMNN_ASSERT_MSG(m_LayerNormParameters.m_OutputLayerNormWeights != nullptr,
                          "QLstmLayer: m_LayerNormParameters.m_UutputLayerNormWeights should not be null.");
     }
 }
diff --git a/src/armnn/layers/QuantizedLstmLayer.cpp b/src/armnn/layers/QuantizedLstmLayer.cpp
index 8717041a53..b56ae3ff52 100644
--- a/src/armnn/layers/QuantizedLstmLayer.cpp
+++ b/src/armnn/layers/QuantizedLstmLayer.cpp
@@ -78,7 +78,7 @@ QuantizedLstmLayer* QuantizedLstmLayer::Clone(Graph& graph) const
 
 std::vector<TensorShape> QuantizedLstmLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
 {
-    BOOST_ASSERT(inputShapes.size() == 3);
+    ARMNN_ASSERT(inputShapes.size() == 3);
 
     // Get input values for validation
     unsigned int numBatches = inputShapes[0][0];
@@ -102,34 +102,34 @@ void QuantizedLstmLayer::ValidateTensorShapesFromInputs()
         GetInputSlot(2).GetConnection()->GetTensorInfo().GetShape()  // previousOutputIn
     });
 
-    BOOST_ASSERT(inferredShapes.size() == 2);
+    ARMNN_ASSERT(inferredShapes.size() == 2);
 
     // Check weights and bias for nullptr
-    BOOST_ASSERT_MSG(m_QuantizedLstmParameters.m_InputToInputWeights != nullptr,
+    ARMNN_ASSERT_MSG(m_QuantizedLstmParameters.m_InputToInputWeights != nullptr,
                      "QuantizedLstmLayer: m_QuantizedLstmParameters.m_InputToInputWeights should not be null.");
-    BOOST_ASSERT_MSG(m_QuantizedLstmParameters.m_InputToForgetWeights != nullptr,
+    ARMNN_ASSERT_MSG(m_QuantizedLstmParameters.m_InputToForgetWeights != nullptr,
                      "QuantizedLstmLayer: m_QuantizedLstmParameters.m_InputToForgetWeights should not be null.");
-    BOOST_ASSERT_MSG(m_QuantizedLstmParameters.m_InputToCellWeights != nullptr,
+    ARMNN_ASSERT_MSG(m_QuantizedLstmParameters.m_InputToCellWeights != nullptr,
                      "QuantizedLstmLayer: m_QuantizedLstmParameters.m_InputToCellWeights should not be null.");
-    BOOST_ASSERT_MSG(m_QuantizedLstmParameters.m_InputToOutputWeights != nullptr,
+    ARMNN_ASSERT_MSG(m_QuantizedLstmParameters.m_InputToOutputWeights != nullptr,
                      "QuantizedLstmLayer: m_QuantizedLstmParameters.m_InputToOutputWeights should not be null.");
 
-    BOOST_ASSERT_MSG(m_QuantizedLstmParameters.m_RecurrentToInputWeights != nullptr,
+    ARMNN_ASSERT_MSG(m_QuantizedLstmParameters.m_RecurrentToInputWeights != nullptr,
                      "QuantizedLstmLayer: m_QuantizedLstmParameters.m_RecurrentToInputWeights should not be null.");
-    BOOST_ASSERT_MSG(m_QuantizedLstmParameters.m_RecurrentToForgetWeights != nullptr,
+    ARMNN_ASSERT_MSG(m_QuantizedLstmParameters.m_RecurrentToForgetWeights != nullptr,
                      "QuantizedLstmLayer: m_QuantizedLstmParameters.m_RecurrentToForgetWeights should not be null.");
-    BOOST_ASSERT_MSG(m_QuantizedLstmParameters.m_RecurrentToCellWeights != nullptr,
+    ARMNN_ASSERT_MSG(m_QuantizedLstmParameters.m_RecurrentToCellWeights != nullptr,
                      "QuantizedLstmLayer: m_QuantizedLstmParameters.m_RecurrentToCellWeights should not be null.");
-    BOOST_ASSERT_MSG(m_QuantizedLstmParameters.m_RecurrentToOutputWeights != nullptr,
+    ARMNN_ASSERT_MSG(m_QuantizedLstmParameters.m_RecurrentToOutputWeights != nullptr,
                      "QuantizedLstmLayer: m_QuantizedLstmParameters.m_RecurrentToOutputWeights should not be null.");
 
-    BOOST_ASSERT_MSG(m_QuantizedLstmParameters.m_InputGateBias != nullptr,
+    ARMNN_ASSERT_MSG(m_QuantizedLstmParameters.m_InputGateBias != nullptr,
                      "QuantizedLstmLayer: m_QuantizedLstmParameters.m_InputGateBias should not be null.");
-    BOOST_ASSERT_MSG(m_QuantizedLstmParameters.m_ForgetGateBias != nullptr,
+    ARMNN_ASSERT_MSG(m_QuantizedLstmParameters.m_ForgetGateBias != nullptr,
                      "QuantizedLstmLayer: m_QuantizedLstmParameters.m_ForgetGateBias should not be null.");
-    BOOST_ASSERT_MSG(m_QuantizedLstmParameters.m_CellBias != nullptr,
+    ARMNN_ASSERT_MSG(m_QuantizedLstmParameters.m_CellBias != nullptr,
                      "QuantizedLstmLayer: m_QuantizedLstmParameters.m_CellBias should not be null.");
-    BOOST_ASSERT_MSG(m_QuantizedLstmParameters.m_OutputGateBias != nullptr,
+    ARMNN_ASSERT_MSG(m_QuantizedLstmParameters.m_OutputGateBias != nullptr,
                      "QuantizedLstmLayer: m_QuantizedLstmParameters.m_OutputGateBias should not be null.");
 
     // Check output TensorShape(s) match inferred shape
diff --git a/src/armnn/layers/ReshapeLayer.cpp b/src/armnn/layers/ReshapeLayer.cpp
index fbf3eaa80a..b496dbb642 100644
--- a/src/armnn/layers/ReshapeLayer.cpp
+++ b/src/armnn/layers/ReshapeLayer.cpp
@@ -42,7 +42,7 @@ void ReshapeLayer::ValidateTensorShapesFromInputs()
 
     auto inferredShapes = InferOutputShapes({  });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "ReshapeLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/ResizeLayer.cpp b/src/armnn/layers/ResizeLayer.cpp
index e341191de1..9654e58b43 100644
--- a/src/armnn/layers/ResizeLayer.cpp
+++ b/src/armnn/layers/ResizeLayer.cpp
@@ -36,7 +36,7 @@ ResizeLayer* ResizeLayer::Clone(Graph& graph) const
 
 std::vector<TensorShape> ResizeLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
 {
-    BOOST_ASSERT(inputShapes.size() == 1);
+    ARMNN_ASSERT(inputShapes.size() == 1);
 
     const TensorShape& inputShape = inputShapes[0];
     const DataLayoutIndexed dimensionIndices = m_Param.m_DataLayout;
@@ -59,7 +59,7 @@ void ResizeLayer::ValidateTensorShapesFromInputs()
 
     auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "ResizeLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/RsqrtLayer.cpp b/src/armnn/layers/RsqrtLayer.cpp
index 6ff7372aa7..dfd466dca3 100644
--- a/src/armnn/layers/RsqrtLayer.cpp
+++ b/src/armnn/layers/RsqrtLayer.cpp
@@ -36,7 +36,7 @@ void RsqrtLayer::ValidateTensorShapesFromInputs()
 
     auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
             "RsqrtLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/SliceLayer.cpp b/src/armnn/layers/SliceLayer.cpp
index ec82082c4a..d92ed6fc48 100644
--- a/src/armnn/layers/SliceLayer.cpp
+++ b/src/armnn/layers/SliceLayer.cpp
@@ -12,7 +12,6 @@
 #include <backendsCommon/WorkloadData.hpp>
 #include <backendsCommon/WorkloadFactory.hpp>
 
-#include <boost/assert.hpp>
 #include <boost/numeric/conversion/cast.hpp>
 
 namespace armnn
@@ -40,7 +39,7 @@ void SliceLayer::ValidateTensorShapesFromInputs()
 
     auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
             "SliceLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
@@ -51,7 +50,7 @@ void SliceLayer::ValidateTensorShapesFromInputs()
 std::vector<TensorShape> SliceLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
 {
     IgnoreUnused(inputShapes);
-    BOOST_ASSERT(inputShapes.size() == 1);
+    ARMNN_ASSERT(inputShapes.size() == 1);
 
     TensorShape outputShape(boost::numeric_cast<unsigned int>(m_Param.m_Size.size()), m_Param.m_Size.data());
 
diff --git a/src/armnn/layers/SoftmaxLayer.cpp b/src/armnn/layers/SoftmaxLayer.cpp
index cb70bbc20d..738347c1b3 100644
--- a/src/armnn/layers/SoftmaxLayer.cpp
+++ b/src/armnn/layers/SoftmaxLayer.cpp
@@ -35,7 +35,7 @@ void SoftmaxLayer::ValidateTensorShapesFromInputs()
 
     auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "SoftmaxLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/SpaceToBatchNdLayer.cpp b/src/armnn/layers/SpaceToBatchNdLayer.cpp
index ec724bafd0..ce48b5b5c2 100644
--- a/src/armnn/layers/SpaceToBatchNdLayer.cpp
+++ b/src/armnn/layers/SpaceToBatchNdLayer.cpp
@@ -41,7 +41,7 @@ SpaceToBatchNdLayer* SpaceToBatchNdLayer::Clone(Graph& graph) const
 
 std::vector<TensorShape> SpaceToBatchNdLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
 {
-    BOOST_ASSERT(inputShapes.size() == 1);
+    ARMNN_ASSERT(inputShapes.size() == 1);
 
     TensorShape inputShape = inputShapes[0];
     TensorShape outputShape(inputShape);
@@ -73,7 +73,7 @@ void SpaceToBatchNdLayer::ValidateTensorShapesFromInputs()
     std::vector<TensorShape> inferredShapes = InferOutputShapes({
         GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "SpaceToBatchNdLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/SpaceToDepthLayer.cpp b/src/armnn/layers/SpaceToDepthLayer.cpp
index 8aa0c9f8cd..bf65240e0c 100644
--- a/src/armnn/layers/SpaceToDepthLayer.cpp
+++ b/src/armnn/layers/SpaceToDepthLayer.cpp
@@ -41,7 +41,7 @@ SpaceToDepthLayer* SpaceToDepthLayer::Clone(Graph& graph) const
 
 std::vector<TensorShape> SpaceToDepthLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
 {
-    BOOST_ASSERT(inputShapes.size() == 1);
+    ARMNN_ASSERT(inputShapes.size() == 1);
 
     TensorShape inputShape = inputShapes[0];
     TensorShape outputShape(inputShape);
@@ -66,7 +66,7 @@ void SpaceToDepthLayer::ValidateTensorShapesFromInputs()
     std::vector<TensorShape> inferredShapes = InferOutputShapes({
         GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "SpaceToDepthLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/SplitterLayer.cpp b/src/armnn/layers/SplitterLayer.cpp
index f655e712c8..8ec8121495 100644
--- a/src/armnn/layers/SplitterLayer.cpp
+++ b/src/armnn/layers/SplitterLayer.cpp
@@ -115,7 +115,7 @@ void SplitterLayer::CreateTensorHandles(const TensorHandleFactoryRegistry& regis
     else
     {
         ITensorHandleFactory* handleFactory = registry.GetFactory(factoryId);
-        BOOST_ASSERT(handleFactory);
+        ARMNN_ASSERT(handleFactory);
         CreateTensors(*handleFactory);
     }
 }
@@ -128,7 +128,7 @@ SplitterLayer* SplitterLayer::Clone(Graph& graph) const
 std::vector<TensorShape> SplitterLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
 {
     IgnoreUnused(inputShapes);
-    BOOST_ASSERT(inputShapes.size() ==  m_Param.GetNumViews());
+    ARMNN_ASSERT(inputShapes.size() ==  m_Param.GetNumViews());
     std::vector<TensorShape> outShapes;
     //Output shapes must match View shapes.
     for (unsigned int viewIdx = 0; viewIdx < m_Param.GetNumViews(); viewIdx++)
@@ -150,7 +150,7 @@ void SplitterLayer::ValidateTensorShapesFromInputs()
 
     auto inferredShapes = InferOutputShapes(views);
 
-    BOOST_ASSERT(inferredShapes.size() == m_Param.GetNumViews());
+    ARMNN_ASSERT(inferredShapes.size() == m_Param.GetNumViews());
 
     for (unsigned int viewIdx = 0; viewIdx < m_Param.GetNumViews(); viewIdx++)
     {
diff --git a/src/armnn/layers/StackLayer.cpp b/src/armnn/layers/StackLayer.cpp
index 6f793caecc..e034cb46a6 100644
--- a/src/armnn/layers/StackLayer.cpp
+++ b/src/armnn/layers/StackLayer.cpp
@@ -38,7 +38,7 @@ std::vector<TensorShape> StackLayer::InferOutputShapes(const std::vector<TensorS
     const unsigned int inputNumDimensions = inputShape.GetNumDimensions();
     const unsigned int axis = m_Param.m_Axis;
 
-    BOOST_ASSERT(axis <= inputNumDimensions);
+    ARMNN_ASSERT(axis <= inputNumDimensions);
 
     std::vector<unsigned int> dimensionSizes(inputNumDimensions + 1, 0);
     for (unsigned int i = 0; i < axis; ++i)
@@ -84,7 +84,7 @@ void StackLayer::ValidateTensorShapesFromInputs()
 
     auto inferredShapes = InferOutputShapes(inputShapes);
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "StackLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/StridedSliceLayer.cpp b/src/armnn/layers/StridedSliceLayer.cpp
index c31b9a4280..b100f7ab6b 100644
--- a/src/armnn/layers/StridedSliceLayer.cpp
+++ b/src/armnn/layers/StridedSliceLayer.cpp
@@ -45,7 +45,7 @@ StridedSliceLayer* StridedSliceLayer::Clone(Graph& graph) const
 std::vector<TensorShape> StridedSliceLayer::InferOutputShapes(
     const std::vector<TensorShape>& inputShapes) const
 {
-    BOOST_ASSERT(inputShapes.size() == 1);
+    ARMNN_ASSERT(inputShapes.size() == 1);
 
     TensorShape inputShape = inputShapes[0];
     std::vector<unsigned int> outputShape;
@@ -86,7 +86,7 @@ void StridedSliceLayer::ValidateTensorShapesFromInputs()
 
     auto inferredShapes = InferOutputShapes({GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape()});
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
                     "StridedSlice: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/SwitchLayer.cpp b/src/armnn/layers/SwitchLayer.cpp
index 4cacda6318..c4b065a735 100644
--- a/src/armnn/layers/SwitchLayer.cpp
+++ b/src/armnn/layers/SwitchLayer.cpp
@@ -31,14 +31,14 @@ void SwitchLayer::ValidateTensorShapesFromInputs()
 {
     VerifyLayerConnections(2, CHECK_LOCATION());
 
-    BOOST_ASSERT_MSG(GetNumOutputSlots() == 2, "SwitchLayer: The layer should return 2 outputs.");
+    ARMNN_ASSERT_MSG(GetNumOutputSlots() == 2, "SwitchLayer: The layer should return 2 outputs.");
 
     // Assuming first input is the Input and second input is the Constant
     std::vector<TensorShape> inferredShapes = InferOutputShapes({
         GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(),
         GetInputSlot(1).GetConnection()->GetTensorInfo().GetShape() });
 
-    BOOST_ASSERT(inferredShapes.size() == 2);
+    ARMNN_ASSERT(inferredShapes.size() == 2);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "SwitchLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/TransposeConvolution2dLayer.cpp b/src/armnn/layers/TransposeConvolution2dLayer.cpp
index dca77b4c09..05941f7d78 100644
--- a/src/armnn/layers/TransposeConvolution2dLayer.cpp
+++ b/src/armnn/layers/TransposeConvolution2dLayer.cpp
@@ -26,14 +26,14 @@ TransposeConvolution2dLayer::TransposeConvolution2dLayer(const TransposeConvolut
 
 std::unique_ptr<IWorkload> TransposeConvolution2dLayer::CreateWorkload(const IWorkloadFactory& factory) const
 {
-    BOOST_ASSERT_MSG(m_Weight != nullptr, "TransposeConvolution2dLayer: Weights data should not be null.");
+    ARMNN_ASSERT_MSG(m_Weight != nullptr, "TransposeConvolution2dLayer: Weights data should not be null.");
 
     TransposeConvolution2dQueueDescriptor descriptor;
     descriptor.m_Weight = m_Weight.get();
 
     if (m_Param.m_BiasEnabled)
     {
-        BOOST_ASSERT_MSG(m_Bias != nullptr, "TransposeConvolution2dLayer: Bias data should not be null.");
+        ARMNN_ASSERT_MSG(m_Bias != nullptr, "TransposeConvolution2dLayer: Bias data should not be null.");
         descriptor.m_Bias = m_Bias.get();
     }
 
@@ -57,11 +57,11 @@ TransposeConvolution2dLayer* TransposeConvolution2dLayer::Clone(Graph& graph) co
 std::vector<TensorShape> TransposeConvolution2dLayer::InferOutputShapes(
     const std::vector<TensorShape>& inputShapes) const
 {
-    BOOST_ASSERT(inputShapes.size() == 2);
+    ARMNN_ASSERT(inputShapes.size() == 2);
     const TensorShape& inputShape  = inputShapes[0];
     const TensorShape& kernelShape = inputShapes[1];
 
-    BOOST_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Transpose convolutions will always have 4D input");
+    ARMNN_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Transpose convolutions will always have 4D input");
 
     DataLayoutIndexed dataLayoutIndex(m_Param.m_DataLayout);
 
@@ -82,8 +82,8 @@ std::vector<TensorShape> TransposeConvolution2dLayer::InferOutputShapes(
     unsigned int kernelElements = kernelShape[0] * kernelShape[dataLayoutIndex.GetChannelsIndex()];
     unsigned int inputElements  = batches * inputShape[dataLayoutIndex.GetChannelsIndex()];
 
-    BOOST_ASSERT_MSG(inputElements != 0, "Invalid number of input elements");
-    BOOST_ASSERT_MSG(kernelElements % inputElements == 0, "Invalid number of elements");
+    ARMNN_ASSERT_MSG(inputElements != 0, "Invalid number of input elements");
+    ARMNN_ASSERT_MSG(kernelElements % inputElements == 0, "Invalid number of elements");
 
     unsigned int channels =  kernelElements / inputElements;
 
@@ -98,13 +98,13 @@ void TransposeConvolution2dLayer::ValidateTensorShapesFromInputs()
 {
     VerifyLayerConnections(1, CHECK_LOCATION());
 
-    BOOST_ASSERT_MSG(m_Weight != nullptr, "TransposeConvolution2dLayer: Weight data cannot be null.");
+    ARMNN_ASSERT_MSG(m_Weight != nullptr, "TransposeConvolution2dLayer: Weight data cannot be null.");
 
     auto inferredShapes = InferOutputShapes({
          GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(),
          m_Weight->GetTensorInfo().GetShape() });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "TransposeConvolution2dLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
diff --git a/src/armnn/layers/TransposeLayer.cpp b/src/armnn/layers/TransposeLayer.cpp
index 3c22b545b9..c058332c90 100644
--- a/src/armnn/layers/TransposeLayer.cpp
+++ b/src/armnn/layers/TransposeLayer.cpp
@@ -35,7 +35,7 @@ TransposeLayer* TransposeLayer::Clone(Graph& graph) const
 
 std::vector<TensorShape> TransposeLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
 {
-    BOOST_ASSERT(inputShapes.size() == 1);
+    ARMNN_ASSERT(inputShapes.size() == 1);
     const TensorShape& inShape = inputShapes[0];
     return std::vector<TensorShape> ({armnnUtils::TransposeTensorShape(inShape, m_Param.m_DimMappings)});
 }
@@ -46,7 +46,7 @@ void TransposeLayer::ValidateTensorShapesFromInputs()
 
     auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
 
-    BOOST_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "TransposeLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",