COMPMID-1740: Fuse batch normalization with Convolution Layer at graph level

Change-Id: I77ca51c2c72783cc26a099a6a9c3210cdbbe822d
Signed-off-by: giuros01 <giuseppe.rossini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/797
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>

18 files changed, 715 insertions, 98 deletions

diff --git a/arm_compute/graph/INodeVisitor.h b/arm_compute/graph/INodeVisitor.h
index 573d642892..842ca4bfb3 100644
--- a/arm_compute/graph/INodeVisitor.h
+++ b/arm_compute/graph/INodeVisitor.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -91,6 +91,11 @@ public:
      * @param[in] n Node to visit.
      */
     virtual void visit(FullyConnectedLayerNode &n) = 0;
+    /** Visit FusedConvolutionBatchNormalizationNode.
+     *
+     * @param[in] n Node to visit.
+     */
+    virtual void visit(FusedConvolutionBatchNormalizationNode &n) = 0;
     /** Visit InputNode.
      *
      * @param[in] n Node to visit.
@@ -195,6 +200,10 @@ public:
     {
         default_visit();
     }
+    virtual void visit(FusedConvolutionBatchNormalizationNode &n) override
+    {
+        default_visit();
+    }
     virtual void visit(InputNode &n) override
     {
         default_visit();
diff --git a/arm_compute/graph/TypePrinter.h b/arm_compute/graph/TypePrinter.h
index ca62d4ec17..b1cfbcf658 100644
--- a/arm_compute/graph/TypePrinter.h
+++ b/arm_compute/graph/TypePrinter.h
@@ -98,6 +98,9 @@ inline ::std::ostream &operator<<(::std::ostream &os, const NodeType &node_type)
         case NodeType::FullyConnectedLayer:
             os << "FullyConnectedLayer";
             break;
+        case NodeType::FusedConvolutionBatchNormalizationLayer:
+            os << "FusedConvolutionBatchNormalizationLayer";
+            break;
         case NodeType::GenerateProposalsLayer:
             os << "GenerateProposalsLayer";
             break;
diff --git a/arm_compute/graph/Types.h b/arm_compute/graph/Types.h
index 8377253338..2905dfcbf6 100644
--- a/arm_compute/graph/Types.h
+++ b/arm_compute/graph/Types.h
@@ -138,6 +138,7 @@ enum class NodeType
     EltwiseLayer,
     FlattenLayer,
     FullyConnectedLayer,
+    FusedConvolutionBatchNormalizationLayer,
     GenerateProposalsLayer,
     NormalizationLayer,
     NormalizePlanarYUVLayer,
diff --git a/arm_compute/graph/backends/FunctionHelpers.h b/arm_compute/graph/backends/FunctionHelpers.h
index 7242bc6ede..d0035d9a84 100644
--- a/arm_compute/graph/backends/FunctionHelpers.h
+++ b/arm_compute/graph/backends/FunctionHelpers.h
@@ -28,6 +28,7 @@
 #include "arm_compute/graph/Tensor.h"
 #include "arm_compute/graph/TypePrinter.h"
 #include "arm_compute/graph/Types.h"
+#include "arm_compute/graph/backends/FusedConvolutionBatchNormalizationFunction.h"
 #include "arm_compute/graph/backends/Utils.h"
 #include "arm_compute/graph/nodes/Nodes.h"
 
@@ -135,11 +136,12 @@ std::unique_ptr<IFunction> create_batch_normalization_layer(BatchNormalizationLa
     validate_node<TargetInfo>(node, 5 /* expected inputs */, 1 /* expected outputs */);
 
     // Extract IO and info
-    typename TargetInfo::TensorType *input     = get_backing_tensor<TargetInfo>(node.input(0));
-    typename TargetInfo::TensorType *mean      = get_backing_tensor<TargetInfo>(node.input(1));
-    typename TargetInfo::TensorType *var       = get_backing_tensor<TargetInfo>(node.input(2));
-    typename TargetInfo::TensorType *beta      = get_backing_tensor<TargetInfo>(node.input(3));
-    typename TargetInfo::TensorType *gamma     = get_backing_tensor<TargetInfo>(node.input(4));
+    typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));
+    typename TargetInfo::TensorType *mean  = get_backing_tensor<TargetInfo>(node.input(1));
+    typename TargetInfo::TensorType *var   = get_backing_tensor<TargetInfo>(node.input(2));
+    typename TargetInfo::TensorType *beta  = get_backing_tensor<TargetInfo>(node.input(3));
+    typename TargetInfo::TensorType *gamma = get_backing_tensor<TargetInfo>(node.input(4));
+
     typename TargetInfo::TensorType *output    = get_backing_tensor<TargetInfo>(node.output(0));
     const float                      epsilon   = node.epsilon();
     const ActivationLayerInfo        fused_act = node.fused_activation();
@@ -163,6 +165,61 @@ std::unique_ptr<IFunction> create_batch_normalization_layer(BatchNormalizationLa
     return std::move(func);
 }
 
+/** Create a backend batch normalization layer function
+ *
+ * @tparam BatchNormalizationLayerFunction Backend batch normalization function
+ * @tparam TargetInfo                      Target-specific information
+ *
+ * @param[in] node Node to create the backend function for
+ *
+ * @return Backend batch normalization layer function
+ */
+template <typename FusedLayerTypes, typename TargetInfo>
+std::unique_ptr<IFunction> create_fused_convolution_batch_normalization_layer(FusedConvolutionBatchNormalizationNode &node)
+{
+    validate_node<TargetInfo>(node, 7 /* expected inputs */, 1 /* expected outputs */);
+
+    // Extract IO and info
+    typename TargetInfo::TensorType *input   = get_backing_tensor<TargetInfo>(node.input(0));
+    typename TargetInfo::TensorType *weights = get_backing_tensor<TargetInfo>(node.input(1));
+    typename TargetInfo::TensorType *biases  = get_backing_tensor<TargetInfo>(node.input(2));
+    typename TargetInfo::TensorType *mean    = get_backing_tensor<TargetInfo>(node.input(3));
+    typename TargetInfo::TensorType *var     = get_backing_tensor<TargetInfo>(node.input(4));
+    typename TargetInfo::TensorType *beta    = get_backing_tensor<TargetInfo>(node.input(5));
+    typename TargetInfo::TensorType *gamma   = get_backing_tensor<TargetInfo>(node.input(6));
+
+    typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));
+
+    const PadStrideInfo       conv_info  = node.convolution_info();
+    const unsigned int        num_groups = node.num_groups();
+    const bool                fast_math  = node.fast_math_hint() == FastMathHint::Enabled;
+    const ActivationLayerInfo fused_act  = node.fused_activation();
+    const float               epsilon    = node.epsilon();
+
+    const bool is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type());
+    if(is_quantized && biases != nullptr)
+    {
+        biases->info()->set_data_type(DataType::S32);
+    }
+
+    // Create and configure function
+    auto func = support::cpp14::make_unique<FusedConvolutionBatchNormalizationFunction<TargetInfo, FusedLayerTypes>>();
+    func->configure(input, weights, biases, output, mean, var, beta, gamma, epsilon, conv_info, num_groups, fast_math, fused_act);
+
+    // Log info
+    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
+                               << node.name()
+                               << " Type: " << node.name()
+                               << " Target: " << TargetInfo::TargetType
+                               << " Data Type: " << input->info()->data_type()
+                               << " Input shape: " << input->info()->tensor_shape()
+                               << " Weights shape: " << weights->info()->tensor_shape()
+                               << " Output shape: " << output->info()->tensor_shape()
+                               << (fused_act.enabled() ? " " + to_string(fused_act.activation()) : "")
+                               << std::endl);
+    return std::move(func);
+}
+
 /** Create a backend bounding box transform layer function
  *
  * @tparam BoundingBoxTransformLayerFunction    Backend bounding box transform function
diff --git a/arm_compute/graph/backends/FusedConvolutionBatchNormalizationFunction.h b/arm_compute/graph/backends/FusedConvolutionBatchNormalizationFunction.h
new file mode 100644
index 0000000000..92af17b227
--- /dev/null
+++ b/arm_compute/graph/backends/FusedConvolutionBatchNormalizationFunction.h
@@ -0,0 +1,133 @@
+/*
+ * Copyright (c) 2019 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#ifndef __ARM_COMPUTE_GRAPH_BACKENDS_FUSED_CONVOLUTION_BATCH_NORMAZLIZATION_FUNCTION_H__
+#define __ARM_COMPUTE_GRAPH_BACKENDS_FUSED_CONVOLUTION_BATCH_NORMAZLIZATION_FUNCTION_H__
+
+#include "arm_compute/core/Types.h"
+#include "arm_compute/runtime/IFunction.h"
+
+namespace arm_compute
+{
+namespace graph
+{
+namespace backends
+{
+/** Wrapper function to first apply {NE, CL}BatchNormalizationLayer on the weights and then run {NE, CL}ConvolutionLayer with the modified weights */
+template <typename TargetInfo, typename FusedLayerTypes>
+class FusedConvolutionBatchNormalizationFunction : public IFunction
+{
+public:
+    using TensorType         = typename TargetInfo::TensorType;
+    using TensorConcreteType = typename TargetInfo::TensorConcreteType;
+
+    FusedConvolutionBatchNormalizationFunction()
+        : _conv_layer(), _fused_batch_norm_layer(), _fused_bias(), _is_prepared(false)
+    {
+    }
+
+    /** Set the input and output tensors.
+     *
+     * @param[in]  input      Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
+     *                        while every optional dimension from 4 and above represent a batch of inputs.
+     *                        Data types supported: QASYMM8/F16/F32.
+     * @param[in]  weights    Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as @p input.
+     * @param[in]  bias       Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
+     *                        Data type supported: Should match @p input data type, except for input of QASYMM8 type where biases should be of S32 type.
+     * @param[out] output     Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
+     *                        Data types supported: Same as @p input.
+     * @param[in]  mean       Mean values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
+     * @param[in]  var        Variance values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
+     * @param[in]  beta       Beta values tensor info. 1 dimension with size equal to the feature maps [FM]. If not provided, default value for beta is 0. Data types supported: Same as @p input
+     * @param[in]  gamma      Gamma values tensor info. 1 dimension with size equal to the feature maps [FM]. If not provided, default value for gamma is 1. Data types supported: Same as @p input
+     * @param[in]  epsilon    Small value to avoid division with zero. Default value is 0.001f.
+     * @param[in]  conv_info  Contains padding and stride information described in @ref PadStrideInfo.
+     * @param[in]  num_groups Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout
+     * @param[in]  fast_math  Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation
+     *                        available which may introduce a drop of accuracy as well. Default is false
+     * @param[in]  fused_act  Activation layer information in case of a fused activation.
+     *
+     */
+    void configure(TensorType       *input,
+                   TensorType       *weights,
+                   TensorType       *bias,
+                   TensorType       *output,
+                   const TensorType *mean,
+                   const TensorType *var,
+                   const TensorType *beta,
+                   const TensorType *gamma,
+                   float epsilon, const PadStrideInfo &conv_info, unsigned int num_groups, bool fast_math, ActivationLayerInfo const &fused_act)
+    {
+        // We don't run any validate, as we assume that the layers have been already validated
+        const bool        has_bias = (bias != nullptr);
+        const TensorType *bias_to_use;
+
+        // We check if the layer has a bias. If yes, use it in-place. If not, we need to create one
+        // as batch normalization might end up with a bias != 0
+        if(has_bias)
+        {
+            _fused_batch_norm_layer.configure(weights, mean, var, nullptr, nullptr, bias, beta, gamma, epsilon);
+            bias_to_use = bias;
+        }
+        else
+        {
+            _fused_batch_norm_layer.configure(weights, mean, var, nullptr, &_fused_bias, nullptr, beta, gamma, epsilon);
+            bias_to_use = &_fused_bias;
+        }
+
+        _conv_layer.configure(input, weights, bias_to_use, output, conv_info, WeightsInfo(), Size2D(1U, 1U), fused_act, fast_math, num_groups);
+
+        if(!has_bias)
+        {
+            _fused_bias.allocator()->allocate();
+        }
+    }
+
+    // Inherited methods overridden:
+    void run()
+    {
+        prepare();
+        _conv_layer.run();
+    }
+
+    void prepare()
+    {
+        if(!_is_prepared)
+        {
+            _fused_batch_norm_layer.run();
+            _is_prepared = true;
+        }
+    }
+
+private:
+    typename FusedLayerTypes::ConvolutionLayer       _conv_layer;
+    typename FusedLayerTypes::FuseBatchNormalization _fused_batch_norm_layer;
+    TensorConcreteType                               _fused_bias;
+    bool                                             _is_prepared;
+};
+} // namespace backends
+} // namespace graph
+} // namespace arm_compute
+
+#endif /* __ARM_COMPUTE_GRAPH_BACKENDS_FUSED_CONVOLUTION_BATCH_NORMAZLIZATION_FUNCTION_H__ */
diff --git a/arm_compute/graph/mutators/NodeFusionMutator.h b/arm_compute/graph/mutators/NodeFusionMutator.h
index 8f16c65dfa..b9ca464822 100644
--- a/arm_compute/graph/mutators/NodeFusionMutator.h
+++ b/arm_compute/graph/mutators/NodeFusionMutator.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -24,21 +24,13 @@
 #ifndef __ARM_COMPUTE_GRAPH_NODE_FUSION_MUTATOR_H__
 #define __ARM_COMPUTE_GRAPH_NODE_FUSION_MUTATOR_H__
 
+#include "arm_compute/graph/Graph.h"
 #include "arm_compute/graph/IGraphMutator.h"
 
 namespace arm_compute
 {
 namespace graph
 {
-namespace detail
-{
-/** Fused batch normalization with activation
- *
- * @param[in] g Graph to perform operation fusion on
- */
-void fuse_batch_norm_with_activation(Graph &g);
-} // namespace detail
-
 /** Mutation pass to fuss nodes */
 class NodeFusionMutator final : public IGraphMutator
 {
diff --git a/arm_compute/graph/nodes/ActivationLayerNode.h b/arm_compute/graph/nodes/ActivationLayerNode.h
index 570351bb94..723120655b 100644
--- a/arm_compute/graph/nodes/ActivationLayerNode.h
+++ b/arm_compute/graph/nodes/ActivationLayerNode.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -51,6 +51,9 @@ public:
     TensorDescriptor configure_output(size_t idx) const override;
     void accept(INodeVisitor &v) override;
 
+public:
+    static constexpr NodeType node_type = NodeType::ActivationLayer;
+
 private:
     ActivationLayerInfo _info;
 };
diff --git a/arm_compute/graph/nodes/FusedConvolutionBatchNormalizationNode.h b/arm_compute/graph/nodes/FusedConvolutionBatchNormalizationNode.h
new file mode 100644
index 0000000000..9b0f5b7ade
--- /dev/null
+++ b/arm_compute/graph/nodes/FusedConvolutionBatchNormalizationNode.h
@@ -0,0 +1,144 @@
+/*
+ * Copyright (c) 2019 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef __ARM_COMPUTE_GRAPH_FUSED_CONVOLUTION_BATCH_NORMALIZATION_NODE_H__
+#define __ARM_COMPUTE_GRAPH_FUSED_CONVOLUTION_BATCH_NORMALIZATION_NODE_H__
+
+#include "arm_compute/graph/INode.h"
+
+namespace arm_compute
+{
+namespace graph
+{
+/** Batch Normalization node */
+class FusedConvolutionBatchNormalizationNode final : public INode
+{
+public:
+    /** Constructor
+     *
+     * @param[in] epsilon          Epsilon parameter.
+     * @param[in] info             Convolution layer attributes.
+     * @param[in] num_groups       (Optional) Number of groups (Defaults to 1)
+     * @param[in] method           (Optional) Convolution method to use
+     * @param[in] fast_math_hint   (Optional) Fast math hint
+     * @param[in] out_quant_info   (Optional) Output quantization info
+     * @param[in] fused_activation (Optional) Fused activation layer. Disabled if not specified
+     */
+    FusedConvolutionBatchNormalizationNode(float epsilon, PadStrideInfo info,
+                                           unsigned int      num_groups     = 1,
+                                           ConvolutionMethod method         = ConvolutionMethod::Default,
+                                           FastMathHint      fast_math_hint = FastMathHint::Disabled,
+                                           QuantizationInfo out_quant_info = QuantizationInfo(), ActivationLayerInfo fused_activation = ActivationLayerInfo());
+
+    /** Epsilon parameter accessor
+     *
+     * @return Epsilon parameter
+     */
+    float epsilon() const;
+
+    /** Returns fused activation
+     *
+     * @return Fused activation
+     */
+    ActivationLayerInfo fused_activation() const;
+
+    /** Sets fused activation
+     *
+     * @param[in] fused_activation Fused activation to set
+     */
+    void set_fused_activation(ActivationLayerInfo fused_activation);
+
+    /** Computes convolution output descriptor
+     *
+     * @param[in] input_descriptor   Input descriptor
+     * @param[in] weights_descriptor Weights descriptor
+     * @param[in] info               Convolution operation attributes
+     *
+     * @return Output descriptor
+     */
+    static TensorDescriptor compute_output_descriptor(const TensorDescriptor &input_descriptor,
+                                                      const TensorDescriptor &weights_descriptor,
+                                                      const PadStrideInfo    &info);
+
+    /** Sets the convolution layer method to use
+     *
+     * @param[in] method Method to use for convolution
+     */
+    void set_convolution_method(ConvolutionMethod method);
+
+    /** Number of groups in convolution accessor
+     *
+     * @return Number of groups in convolution
+     */
+    unsigned int num_groups() const;
+
+    /** Convolution layer method accessor
+     *
+     * @note This is an indication on which convolution layer implementation to use,
+     *       if it fails to be created the library's heuristic approach will be used
+     *
+     * @return Convolution layer method to be used by the node
+     */
+    ConvolutionMethod convolution_method() const;
+
+    /** Sets the fast math fast hint
+     *
+     * @param[in] hint Hint to use for convolution
+     */
+    void set_fast_math_hint(FastMathHint hint);
+
+    /** Fast math hint accessor
+     *
+     * @return Fast math hint to be used by the node
+     */
+    FastMathHint fast_math_hint() const;
+
+    /** Convolution metadata accessor
+     *
+     * @return Convolution information
+     */
+    PadStrideInfo convolution_info() const;
+
+    // Inherited overridden methods:
+    NodeType         type() const override;
+    bool             forward_descriptors() override;
+    TensorDescriptor configure_output(size_t idx) const override;
+    void accept(INodeVisitor &v) override;
+
+public:
+    static constexpr NodeType node_type = NodeType::FusedConvolutionBatchNormalizationLayer;
+
+private:
+    float _epsilon;
+
+    PadStrideInfo       _info;
+    unsigned int        _num_groups;
+    ConvolutionMethod   _method;
+    FastMathHint        _fast_math_hint;
+    QuantizationInfo    _out_quant_info;
+    ActivationLayerInfo _fused_activation;
+};
+
+} // namespace graph
+} // namespace arm_compute
+#endif /* __ARM_COMPUTE_GRAPH_BATCH_NORMALIZATION_LAYER_NODE_H__ */
diff --git a/arm_compute/graph/nodes/Nodes.h b/arm_compute/graph/nodes/Nodes.h
index 24064855e8..e23b2b9897 100644
--- a/arm_compute/graph/nodes/Nodes.h
+++ b/arm_compute/graph/nodes/Nodes.h
@@ -38,6 +38,7 @@
 #include "arm_compute/graph/nodes/EltwiseLayerNode.h"
 #include "arm_compute/graph/nodes/FlattenLayerNode.h"
 #include "arm_compute/graph/nodes/FullyConnectedLayerNode.h"
+#include "arm_compute/graph/nodes/FusedConvolutionBatchNormalizationNode.h"
 #include "arm_compute/graph/nodes/GenerateProposalsLayerNode.h"
 #include "arm_compute/graph/nodes/InputNode.h"
 #include "arm_compute/graph/nodes/NormalizationLayerNode.h"
diff --git a/arm_compute/graph/nodes/NodesFwd.h b/arm_compute/graph/nodes/NodesFwd.h
index cbda3092fd..80576d4608 100644
--- a/arm_compute/graph/nodes/NodesFwd.h
+++ b/arm_compute/graph/nodes/NodesFwd.h
@@ -44,6 +44,7 @@ class DummyNode;
 class EltwiseLayerNode;
 class FlattenLayerNode;
 class FullyConnectedLayerNode;
+class FusedConvolutionBatchNormalizationNode;
 class GenerateProposalsLayerNode;
 class InputNode;
 class NormalizationLayerNode;
diff --git a/arm_compute/graph/printers/DotGraphPrinter.h b/arm_compute/graph/printers/DotGraphPrinter.h
index d4cf6928e5..9d2ea46fde 100644
--- a/arm_compute/graph/printers/DotGraphPrinter.h
+++ b/arm_compute/graph/printers/DotGraphPrinter.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -56,6 +56,7 @@ public:
     void visit(ConvolutionLayerNode &n) override;
     void visit(DepthwiseConvolutionLayerNode &n) override;
     void visit(EltwiseLayerNode &n) override;
+    void visit(FusedConvolutionBatchNormalizationNode &n) override;
     void visit(NormalizationLayerNode &n) override;
     void visit(PoolingLayerNode &n) override;
     void default_visit() override;
diff --git a/src/core/CL/cl_kernels/batchnormalization_layer.cl b/src/core/CL/cl_kernels/batchnormalization_layer.cl
index dfd16e0da3..60307bc9a7 100644
--- a/src/core/CL/cl_kernels/batchnormalization_layer.cl
+++ b/src/core/CL/cl_kernels/batchnormalization_layer.cl
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2018 ARM Limited.
+ * Copyright (c) 2017-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -341,22 +341,10 @@ __kernel void fuse_batchnormalization_layer(TENSOR4D_DECLARATION(conv_w),
     Vector   bn_mean = CONVERT_TO_VECTOR_STRUCT_NO_STEP(bn_mean);
     Vector   bn_var  = CONVERT_TO_VECTOR_STRUCT_NO_STEP(bn_var);
 
-    // In-place ops
-#ifdef IN_PLACE_W
-    Tensor4D fused_w = conv_w;
-#else  /* IN_PLACE_W */
-    Tensor4D  fused_w                      = CONVERT_TO_TENSOR4D_STRUCT(fused_w, NUM_CHANNELS);
-#endif /* IN_PLACE */
-#ifdef IN_PLACE_B
-    Vector fused_b = conv_b;
-#else  /* IN_PLACE_W */
-    Vector    fused_b                      = CONVERT_TO_VECTOR_STRUCT_NO_STEP(fused_b);
-#endif /* IN_PLACE */
-
     // Conditional ops
 #ifdef HAS_BIAS
     Vector conv_b = CONVERT_TO_VECTOR_STRUCT_NO_STEP(conv_b);
-#endif /* USE_DEFAULT_BETA */
+#endif /* HAS_BIAS */
 #ifndef USE_DEFAULT_BETA
     Vector bn_beta = CONVERT_TO_VECTOR_STRUCT_NO_STEP(bn_beta);
 #endif /* USE_DEFAULT_BETA */
@@ -364,6 +352,19 @@ __kernel void fuse_batchnormalization_layer(TENSOR4D_DECLARATION(conv_w),
     Vector bn_gamma = CONVERT_TO_VECTOR_STRUCT_NO_STEP(bn_gamma);
 #endif /* USE_DEFAULT_GAMMA */
 
+    // In-place ops
+#ifdef IN_PLACE_W
+    Tensor4D fused_w          = conv_w;
+    uint     fused_w_stride_x = conv_w_stride_x;
+#else  /* IN_PLACE_W */
+    Tensor4D  fused_w                      = CONVERT_TO_TENSOR4D_STRUCT(fused_w, NUM_CHANNELS);
+#endif /* IN_PLACE_W */
+#ifdef IN_PLACE_B
+    Vector fused_b = conv_b;
+#else  /* IN_PLACE_B */
+    Vector    fused_b                      = CONVERT_TO_VECTOR_STRUCT_NO_STEP(fused_b);
+#endif /* IN_PLACE_B */
+
     const int current_slice = get_global_id(2) / NUM_CHANNELS;
 
 #if defined(VEC_SIZE) && defined(LAST_ACCESSED_X)
diff --git a/src/graph/backends/CL/CLFunctionsFactory.cpp b/src/graph/backends/CL/CLFunctionsFactory.cpp
index b9e3ddc0a3..7473ff480f 100644
--- a/src/graph/backends/CL/CLFunctionsFactory.cpp
+++ b/src/graph/backends/CL/CLFunctionsFactory.cpp
@@ -40,7 +40,8 @@ namespace backends
 /** Target specific information structure used to pass information to the layer templates */
 struct CLTargetInfo
 {
-    using TensorType = arm_compute::ICLTensor;
+    using TensorType         = arm_compute::ICLTensor;
+    using TensorConcreteType = CLTensor;
     static Target TargetType;
 };
 
@@ -69,6 +70,14 @@ struct CLEltwiseFunctions
     using Subtraction    = CLArithmeticSubtraction;
     using Multiplication = CLPixelWiseMultiplication;
 };
+
+/** Function and tensor types to be used inside a CL fused convolution/batch normalization layer */
+struct CLFusedLayerTypes
+{
+    using ConvolutionLayer       = CLConvolutionLayer;
+    using FuseBatchNormalization = CLFuseBatchNormalization;
+};
+
 // TODO (isagot01): Remove once we support heterogeneous scheduling at function level
 /** Wrapper for the CPP Function in the OpenCL backend **/
 class CPPWrapperFunction : public IFunction
@@ -192,6 +201,8 @@ std::unique_ptr<IFunction> CLFunctionFactory::create(INode *node, GraphContext &
             return detail::create_flatten_layer<CLFlattenLayer, CLTargetInfo>(*polymorphic_downcast<FlattenLayerNode *>(node));
         case NodeType::FullyConnectedLayer:
             return detail::create_fully_connected_layer<CLFullyConnectedLayer, CLTargetInfo>(*polymorphic_downcast<FullyConnectedLayerNode *>(node), ctx);
+        case NodeType::FusedConvolutionBatchNormalizationLayer:
+            return detail::create_fused_convolution_batch_normalization_layer<CLFusedLayerTypes, CLTargetInfo>(*polymorphic_downcast<FusedConvolutionBatchNormalizationNode *>(node));
         case NodeType::GenerateProposalsLayer:
             return detail::create_generate_proposals_layer<CLGenerateProposalsLayer, CLTargetInfo>(*polymorphic_downcast<GenerateProposalsLayerNode *>(node), ctx);
         case NodeType::NormalizationLayer:
diff --git a/src/graph/backends/NEON/NEFunctionFactory.cpp b/src/graph/backends/NEON/NEFunctionFactory.cpp
index dc987dd86e..f23845c314 100644
--- a/src/graph/backends/NEON/NEFunctionFactory.cpp
+++ b/src/graph/backends/NEON/NEFunctionFactory.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -46,7 +46,8 @@ namespace backends
 /** Target specific information structure used to pass information to the layer templates */
 struct NETargetInfo
 {
-    using TensorType = arm_compute::ITensor;
+    using TensorType         = arm_compute::ITensor;
+    using TensorConcreteType = arm_compute::Tensor;
     static Target TargetType;
 };
 
@@ -76,6 +77,13 @@ struct NEEltwiseFunctions
     using Multiplication = NEPixelWiseMultiplication;
 };
 
+/** Function and tensor types to be used inside a NEON fused convolution/batch normalization layer */
+struct NEFusedLayerTypes
+{
+    using ConvolutionLayer       = NEConvolutionLayer;
+    using FuseBatchNormalization = NEFuseBatchNormalization;
+};
+
 namespace detail
 {
 // Specialized functions
@@ -210,6 +218,8 @@ std::unique_ptr<IFunction> NEFunctionFactory::create(INode *node, GraphContext &
             return detail::create_flatten_layer<NEFlattenLayer, NETargetInfo>(*polymorphic_downcast<FlattenLayerNode *>(node));
         case NodeType::FullyConnectedLayer:
             return detail::create_fully_connected_layer<NEFullyConnectedLayer, NETargetInfo>(*polymorphic_downcast<FullyConnectedLayerNode *>(node), ctx);
+        case NodeType::FusedConvolutionBatchNormalizationLayer:
+            return detail::create_fused_convolution_batch_normalization_layer<NEFusedLayerTypes, NETargetInfo>(*polymorphic_downcast<FusedConvolutionBatchNormalizationNode *>(node));
         case NodeType::NormalizationLayer:
             return detail::create_normalization_layer<NENormalizationLayer, NETargetInfo>(*polymorphic_downcast<NormalizationLayerNode *>(node), ctx);
         case NodeType::PermuteLayer:
diff --git a/src/graph/mutators/NodeFusionMutator.cpp b/src/graph/mutators/NodeFusionMutator.cpp
index 9dc02d1ad1..445748caf7 100644
--- a/src/graph/mutators/NodeFusionMutator.cpp
+++ b/src/graph/mutators/NodeFusionMutator.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -23,9 +23,11 @@
  */
 #include "arm_compute/graph/mutators/NodeFusionMutator.h"
 
-#include "arm_compute/graph/Graph.h"
+#include "arm_compute/graph/GraphBuilder.h"
 #include "arm_compute/graph/Logger.h"
 #include "arm_compute/graph/Utils.h"
+#include "arm_compute/graph/backends/BackendRegistry.h"
+#include "arm_compute/graph/nodes/FusedConvolutionBatchNormalizationNode.h"
 #include "arm_compute/graph/nodes/Nodes.h"
 
 #include "arm_compute/core/utils/misc/Cast.h"
@@ -38,69 +40,156 @@ namespace graph
 {
 namespace detail
 {
+void fuse_convolution_with_batch_normalization(Graph &g, const Edge *output_edge)
+{
+    ARM_COMPUTE_ERROR_ON(output_edge == nullptr);
+
+    auto *conv_node = arm_compute::utils::cast::polymorphic_downcast<ConvolutionLayerNode *>(output_edge->producer());
+    auto *bn_node   = arm_compute::utils::cast::polymorphic_downcast<BatchNormalizationLayerNode *>(output_edge->consumer());
+
+    // Not fusing if number of groups is greater than 1
+    if(conv_node->num_groups() > 1)
+    {
+        return;
+    }
+
+    ARM_COMPUTE_LOG_GRAPH_VERBOSE("Fusing convolution node with ID : " << output_edge->producer_id()
+                                  << " with BatchNormalization Layer node with ID : " << output_edge->consumer_id() << std::endl);
+
+    // Prevent fusion if fused node has an output accessor
+    if(conv_node->output(0)->accessor() == nullptr)
+    {
+        const Target assigned_target = conv_node->assigned_target();
+
+        // Extract conv inputs
+        const auto   conv_input_id   = conv_node->input_edge(0)->producer_id();
+        const auto   conv_weights_id = conv_node->input_edge(1)->producer_id();
+        const auto   out_quant_info  = conv_node->output(0)->desc().quant_info;
+        const auto   conv_info       = conv_node->convolution_info();
+        const auto   conv_method     = conv_node->convolution_method();
+        const auto   num_groups      = conv_node->num_groups();
+        const auto   act_info        = bn_node->fused_activation();
+        FastMathHint fast_math_hint  = conv_node->fast_math_hint();
+
+        // Extract bn inputs
+        const auto bn_mean_id  = bn_node->input_edge(1)->producer_id();
+        const auto bn_var_id   = bn_node->input_edge(2)->producer_id();
+        const auto bn_beta_id  = bn_node->input_edge(3)->producer_id();
+        const auto bn_gamma_id = bn_node->input_edge(4)->producer_id();
+        const auto epsilon     = bn_node->epsilon();
+
+        // Create the fused node
+        const NodeID fused_id = g.add_node<FusedConvolutionBatchNormalizationNode>(epsilon, conv_info, num_groups, conv_method, fast_math_hint, out_quant_info, act_info);
+
+        if(conv_node->input_edge(2) != nullptr)
+        {
+            auto conv_bias_id = conv_node->input_edge(2)->producer_id();
+            g.add_connection(conv_bias_id, 0, fused_id, 2);
+        }
+
+        // Add connections from the conv/batch_norm inputs to the fused node
+        g.add_connection(conv_input_id, 0, fused_id, 0);
+        g.add_connection(conv_weights_id, 0, fused_id, 1);
+        g.add_connection(bn_mean_id, 0, fused_id, 3);
+        g.add_connection(bn_var_id, 0, fused_id, 4);
+        g.add_connection(bn_beta_id, 0, fused_id, 5);
+        g.add_connection(bn_gamma_id, 0, fused_id, 6);
+
+        auto                     fused_node       = g.node(fused_id);
+        std::vector<NodeIdxPair> bn_driving_nodes = get_driving_nodes(*bn_node);
+
+        // Extract batch normalization node accessor if any
+        auto bn_node_accessor = bn_node->output(0)->extract_accessor();
+        auto bn_node_name     = bn_node->name();
+
+        // Remove batch normalization node
+        g.remove_node(bn_node->id());
+
+        // Get driving nodes of batch normalization node
+        for(auto &driving_node : bn_driving_nodes)
+        {
+            g.add_connection(fused_id, 0, driving_node.node_id, driving_node.index);
+            configure_tensor(fused_node->output(0));
+        }
+        // Update fused node outputs
+        fused_node->output(0)->set_accessor(std::move(bn_node_accessor));
+        fused_node->set_assigned_target(assigned_target);
+        fused_node->set_common_node_parameters(NodeParams{ conv_node->name() + "+" + bn_node_name, assigned_target });
+
+        // Remove convolution node
+        g.remove_node(conv_node->id());
+    }
+    else
+    {
+        ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented fusion of convolution with batch normalization due to the presence of an output accessor\n");
+    }
+}
+
 template <typename N>
-void fuse_node_with_activation(Graph                              &g,
-                               const std::set<Activation>         &supported_fused_activations,
-                               std::function<bool(INode &)> const &prec)
+void fuse_node_with_activation(Graph &g, const Edge *output_edge, const std::set<Activation> &supported_fused_activations)
+{
+    ARM_COMPUTE_ERROR_ON(output_edge == nullptr);
+
+    auto *n_node   = arm_compute::utils::cast::polymorphic_downcast<N *>(output_edge->producer());
+    auto *act_node = arm_compute::utils::cast::polymorphic_downcast<ActivationLayerNode *>(output_edge->consumer());
+
+    ARM_COMPUTE_ERROR_ON(act_node->output(0) == nullptr || n_node->output(0) == nullptr);
+
+    // Check if activation is supported for fusion
+    if(supported_fused_activations.count(act_node->activation_info().activation()) == 0)
+    {
+        return;
+    }
+
+    ARM_COMPUTE_LOG_GRAPH_VERBOSE("Fusing node with ID : " << output_edge->producer_id()
+                                  << " with Activation Layer node with ID : " << output_edge->consumer_id() << std::endl);
+
+    // Prevent fusion if fused node has an output accessor
+    if(n_node->output(0)->accessor() == nullptr)
+    {
+        // Get driving nodes of activation node
+        std::vector<NodeIdxPair> act_driving_nodes = get_driving_nodes(*act_node);
+
+        // Set activation info to fused node
+        n_node->set_fused_activation(act_node->activation_info());
+
+        // Extract activation node accessor if any
+        auto act_node_accessor = act_node->output(0)->extract_accessor();
+
+        // Remove activation node
+        g.remove_node(act_node->id());
+
+        // Update fused node outputs
+        for(auto &driving_node : act_driving_nodes)
+        {
+            g.add_connection(n_node->id(), 0, driving_node.node_id, driving_node.index);
+        }
+
+        // Update accessor to fused node
+        n_node->output(0)->set_accessor(std::move(act_node_accessor));
+    }
+    else
+    {
+        ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented fusion of node with activation due to the presence of an output accessor\n");
+    }
+}
+
+template <typename N1, typename N2, typename F, typename... Args>
+void fuse_layer(Graph &g, std::function<bool(INode &)> const &prec, const F fuse_fcn, Args &&... optional_arguments)
 {
     // Not interested in the order of nodes
     for(auto &node : g.nodes())
     {
         // Check if the node is of type N and not a branching node
-        if(node && node->type() == N::node_type && node->output_edges().size() == 1)
+        if(node && node->type() == N1::node_type && node->output_edges().size() == 1)
         {
-            auto output_edge_id = *node->output_edges().begin();
-            auto output_edge    = g.edge(output_edge_id);
+            const auto output_edge_id = *node->output_edges().begin();
+            const auto output_edge    = g.edge(output_edge_id);
+
             // Check if following node is an activation layer node
-            if((output_edge != nullptr) && (output_edge->consumer() != nullptr) && (output_edge->consumer()->type() == NodeType::ActivationLayer))
+            if((output_edge != nullptr) && (output_edge->consumer() != nullptr) && (output_edge->consumer()->type() == N2::node_type) && prec(*output_edge->producer()))
             {
-                auto *n_node   = arm_compute::utils::cast::polymorphic_downcast<N *>(output_edge->producer());
-                auto *act_node = arm_compute::utils::cast::polymorphic_downcast<ActivationLayerNode *>(output_edge->consumer());
-
-                ARM_COMPUTE_ERROR_ON(act_node->output(0) == nullptr || n_node->output(0) == nullptr);
-
-                // Check given precondition
-                if(!prec(*n_node))
-                {
-                    continue;
-                }
-                // Check if activation is supported for fusion
-                if(supported_fused_activations.count(act_node->activation_info().activation()) == 0)
-                {
-                    continue;
-                }
-
-                ARM_COMPUTE_LOG_GRAPH_VERBOSE("Fusing node with ID : " << output_edge->producer_id()
-                                              << " with Activation Layer node with ID : " << output_edge->consumer_id() << std::endl);
-
-                // Prevent fusion if fused node has an output accessor
-                if(n_node->output(0)->accessor() == nullptr)
-                {
-                    // Get driving nodes of activation node
-                    std::vector<NodeIdxPair> act_driving_nodes = get_driving_nodes(*act_node);
-
-                    // Set activation info to fused node
-                    n_node->set_fused_activation(act_node->activation_info());
-
-                    // Extract activation node accessor if any
-                    auto act_node_accessor = act_node->output(0)->extract_accessor();
-
-                    // Remove activation node
-                    g.remove_node(act_node->id());
-
-                    // Update fused node outputs
-                    for(auto &driving_node : act_driving_nodes)
-                    {
-                        g.add_connection(n_node->id(), 0, driving_node.node_id, driving_node.index);
-                    }
-
-                    // Update accessor to fused node
-                    n_node->output(0)->set_accessor(std::move(act_node_accessor));
-                }
-                else
-                {
-                    ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented fusion of node with activation due to the presence of an output accessor\n");
-                }
+                fuse_fcn(g, output_edge, optional_arguments...);
             }
         }
     }
@@ -129,9 +218,10 @@ void NodeFusionMutator::mutate(Graph &g)
     };
 
     // Fusion mutations
-    detail::fuse_node_with_activation<BatchNormalizationLayerNode>(g, supported_fused_activations, empty_prec);
-    detail::fuse_node_with_activation<ConvolutionLayerNode>(g, supported_fused_activations, empty_prec);
-    detail::fuse_node_with_activation<DepthwiseConvolutionLayerNode>(g, supported_fused_activations, qs8_prec);
+    detail::fuse_layer<BatchNormalizationLayerNode, ActivationLayerNode>(g, empty_prec, detail::fuse_node_with_activation<BatchNormalizationLayerNode>, supported_fused_activations);
+    detail::fuse_layer<ConvolutionLayerNode, ActivationLayerNode>(g, empty_prec, detail::fuse_node_with_activation<ConvolutionLayerNode>, supported_fused_activations);
+    detail::fuse_layer<DepthwiseConvolutionLayerNode, ActivationLayerNode>(g, qs8_prec, detail::fuse_node_with_activation<DepthwiseConvolutionLayerNode>, supported_fused_activations);
+    detail::fuse_layer<ConvolutionLayerNode, BatchNormalizationLayerNode>(g, empty_prec, detail::fuse_convolution_with_batch_normalization);
 }
 } // namespace graph
 } // namespace arm_compute
diff --git a/src/graph/nodes/ActivationLayerNode.cpp b/src/graph/nodes/ActivationLayerNode.cpp
index 414684cf30..85cb10bbdb 100644
--- a/src/graph/nodes/ActivationLayerNode.cpp
+++ b/src/graph/nodes/ActivationLayerNode.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -67,7 +67,7 @@ TensorDescriptor ActivationLayerNode::configure_output(size_t idx) const
 
 NodeType ActivationLayerNode::type() const
 {
-    return NodeType::ActivationLayer;
+    return ActivationLayerNode::node_type;
 }
 
 void ActivationLayerNode::accept(INodeVisitor &v)
@@ -75,4 +75,4 @@ void ActivationLayerNode::accept(INodeVisitor &v)
     v.visit(*this);
 }
 } // namespace graph
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/graph/nodes/FusedConvolutionBatchNormalizationNode.cpp b/src/graph/nodes/FusedConvolutionBatchNormalizationNode.cpp
new file mode 100644
index 0000000000..27a348fa69
--- /dev/null
+++ b/src/graph/nodes/FusedConvolutionBatchNormalizationNode.cpp
@@ -0,0 +1,152 @@
+/*
+ * Copyright (c) 2019 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/graph/nodes/FusedConvolutionBatchNormalizationNode.h"
+
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/graph/Graph.h"
+#include "arm_compute/graph/INodeVisitor.h"
+#include "arm_compute/graph/Utils.h"
+
+namespace arm_compute
+{
+namespace graph
+{
+FusedConvolutionBatchNormalizationNode::FusedConvolutionBatchNormalizationNode(float epsilon, PadStrideInfo info,
+                                                                               unsigned int      num_groups,
+                                                                               ConvolutionMethod method,
+                                                                               FastMathHint      fast_math_hint,
+                                                                               QuantizationInfo out_quant_info, ActivationLayerInfo fused_activation)
+    : _epsilon(epsilon), _info(std::move(info)), _num_groups(num_groups), _method(method), _fast_math_hint(fast_math_hint), _out_quant_info(out_quant_info), _fused_activation(fused_activation)
+{
+    _input_edges.resize(7, EmptyEdgeID);
+    _outputs.resize(1, NullTensorID);
+}
+
+void FusedConvolutionBatchNormalizationNode::set_convolution_method(ConvolutionMethod method)
+{
+    _method = method;
+}
+
+float FusedConvolutionBatchNormalizationNode::epsilon() const
+{
+    return _epsilon;
+}
+
+ConvolutionMethod FusedConvolutionBatchNormalizationNode::convolution_method() const
+{
+    return _method;
+}
+
+void FusedConvolutionBatchNormalizationNode::set_fast_math_hint(FastMathHint hint)
+{
+    _fast_math_hint = hint;
+}
+
+FastMathHint FusedConvolutionBatchNormalizationNode::fast_math_hint() const
+{
+    return _fast_math_hint;
+}
+
+PadStrideInfo FusedConvolutionBatchNormalizationNode::convolution_info() const
+{
+    return _info;
+}
+
+unsigned int FusedConvolutionBatchNormalizationNode::num_groups() const
+{
+    return _num_groups;
+}
+
+ActivationLayerInfo FusedConvolutionBatchNormalizationNode::fused_activation() const
+{
+    return _fused_activation;
+}
+
+void FusedConvolutionBatchNormalizationNode::set_fused_activation(ActivationLayerInfo fused_activation)
+{
+    _fused_activation = fused_activation;
+}
+
+TensorDescriptor FusedConvolutionBatchNormalizationNode::compute_output_descriptor(const TensorDescriptor &input_descriptor,
+                                                                                   const TensorDescriptor &weights_descriptor,
+                                                                                   const PadStrideInfo    &info)
+{
+    unsigned int output_width  = 0;
+    unsigned int output_height = 0;
+
+    const unsigned int input_width   = get_dimension_size(input_descriptor, DataLayoutDimension::WIDTH);
+    const unsigned int input_height  = get_dimension_size(input_descriptor, DataLayoutDimension::HEIGHT);
+    const unsigned int kernel_width  = get_dimension_size(weights_descriptor, DataLayoutDimension::WIDTH);
+    const unsigned int kernel_height = get_dimension_size(weights_descriptor, DataLayoutDimension::HEIGHT);
+
+    std::tie(output_width, output_height) = scaled_dimensions(input_width, input_height, kernel_width, kernel_height, info);
+
+    TensorDescriptor output_descriptor = input_descriptor;
+    output_descriptor.shape.set(get_dimension_idx(output_descriptor, DataLayoutDimension::WIDTH), output_width);
+    output_descriptor.shape.set(get_dimension_idx(output_descriptor, DataLayoutDimension::HEIGHT), output_height);
+    output_descriptor.shape.set(get_dimension_idx(output_descriptor, DataLayoutDimension::CHANNEL), weights_descriptor.shape[3]);
+
+    return output_descriptor;
+}
+
+bool FusedConvolutionBatchNormalizationNode::forward_descriptors()
+{
+    if((input_id(0) != NullTensorID) && (input_id(1) != NullTensorID) && (output_id(0) != NullTensorID))
+    {
+        Tensor *dst = output(0);
+        ARM_COMPUTE_ERROR_ON(dst == nullptr);
+        dst->desc() = configure_output(0);
+        return true;
+    }
+    return false;
+}
+
+TensorDescriptor FusedConvolutionBatchNormalizationNode::configure_output(size_t idx) const
+{
+    ARM_COMPUTE_UNUSED(idx);
+    const Tensor *src     = input(0);
+    const Tensor *weights = input(1);
+
+    ARM_COMPUTE_ERROR_ON(src == nullptr || weights == nullptr);
+
+    TensorDescriptor output_info = compute_output_descriptor(src->desc(), weights->desc(), _info);
+    if(!_out_quant_info.empty())
+    {
+        output_info.quant_info = _out_quant_info;
+    }
+
+    return output_info;
+}
+
+NodeType FusedConvolutionBatchNormalizationNode::type() const
+{
+    return FusedConvolutionBatchNormalizationNode::node_type;
+}
+
+void FusedConvolutionBatchNormalizationNode::accept(INodeVisitor &v)
+{
+    v.visit(*this);
+}
+} // namespace graph
+} // namespace arm_compute
diff --git a/src/graph/printers/DotGraphPrinter.cpp b/src/graph/printers/DotGraphPrinter.cpp
index ef156ea252..c939de1b64 100644
--- a/src/graph/printers/DotGraphPrinter.cpp
+++ b/src/graph/printers/DotGraphPrinter.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -77,6 +77,14 @@ void DotGraphVisitor::visit(EltwiseLayerNode &n)
     _info = ss.str();
 }
 
+void DotGraphVisitor::visit(FusedConvolutionBatchNormalizationNode &n)
+{
+    ARM_COMPUTE_UNUSED(n);
+    std::stringstream ss;
+    ss << "FusedConvolutionBatchNormalizationNode";
+    _info = ss.str();
+}
+
 void DotGraphVisitor::visit(NormalizationLayerNode &n)
 {
     std::stringstream ss;