diff --git a/arm_compute/graph/frontend/Layers.h b/arm_compute/graph/frontend/Layers.h
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+++ b/arm_compute/graph/frontend/Layers.h
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+/*
+ * Copyright (c) 2018 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_LAYERS_H__
+#define __ARM_COMPUTE_GRAPH_LAYERS_H__
+
+#include "arm_compute/graph/GraphBuilder.h"
+#include "arm_compute/graph/Types.h"
+#include "arm_compute/graph/frontend/ILayer.h"
+#include "arm_compute/graph/frontend/IStream.h"
+#include "arm_compute/graph/frontend/SubStream.h"
+
+#include "arm_compute/core/utils/misc/Utility.h"
+
+#include <memory>
+#include <string>
+
+namespace arm_compute
+{
+namespace graph
+{
+namespace frontend
+{
+/** Input Layer */
+class InputLayer final : public ILayer
+{
+public:
+    /** Construct an input layer.
+     *
+     * @param[in] desc     Description of input tensor.
+     * @param[in] accessor Accessor to get input tensor data from.
+     */
+    InputLayer(TensorDescriptor desc, ITensorAccessorUPtr accessor)
+        : _desc(desc), _accessor(std::move(accessor))
+    {
+    }
+
+    NodeID create_layer(IStream &s) override
+    {
+        NodeParams common_params = { "", s.hints().target_hint };
+        return GraphBuilder::add_input_node(s.graph(), common_params, _desc, std::move(_accessor));
+    }
+
+private:
+    TensorDescriptor    _desc;
+    ITensorAccessorUPtr _accessor;
+};
+
+/** Output Layer */
+class OutputLayer final : public ILayer
+{
+public:
+    /** Construct an output layer.
+     *
+     * @param[in] accessor Accessor to give output tensor data to.
+     */
+    OutputLayer(ITensorAccessorUPtr accessor)
+        : _accessor(std::move(accessor))
+    {
+    }
+
+    NodeID create_layer(IStream &s) override
+    {
+        NodeParams  common_params = { "", s.hints().target_hint };
+        NodeIdxPair input         = { s.tail_node(), 0 };
+        return GraphBuilder::add_output_node(s.graph(), common_params, input, std::move(_accessor));
+    }
+
+private:
+    ITensorAccessorUPtr _accessor;
+};
+
+/** Activation Layer */
+class ActivationLayer final : public ILayer
+{
+public:
+    /** Construct an activation layer.
+     *
+     * @param[in] act_info Activation information
+     */
+    ActivationLayer(ActivationLayerInfo act_info)
+        : _act_info(act_info)
+    {
+    }
+
+    NodeID create_layer(IStream &s) override
+    {
+        NodeParams  common_params = { "", s.hints().target_hint };
+        NodeIdxPair input         = { s.tail_node(), 0 };
+        return GraphBuilder::add_activation_node(s.graph(), common_params, input, _act_info);
+    }
+
+private:
+    ActivationLayerInfo _act_info;
+};
+
+/** Batchnormalization Layer */
+class BatchNormalizationLayer final : public ILayer
+{
+public:
+    /** Construct a batch normalization layer.
+     *
+     * @param[in] mean    Accessor to get mean tensor data from.
+     * @param[in] var     Accessor to get var tensor data from.
+     * @param[in] gamma   (Optional) Accessor to get gamma tensor data from. Default: nullptr.
+     * @param[in] beta    (Optional) Accessor to get beta tensor data from. Default: nullptr.
+     * @param[in] epsilon (Optional) Epsilon value. Default: 0.001.
+     */
+    BatchNormalizationLayer(ITensorAccessorUPtr mean,
+                            ITensorAccessorUPtr var,
+                            ITensorAccessorUPtr gamma   = nullptr,
+                            ITensorAccessorUPtr beta    = nullptr,
+                            float               epsilon = 0.001f)
+        : _mean(std::move(mean)), _var(std::move(var)), _gamma(std::move(gamma)), _beta(std::move(beta)), _epsilon(epsilon)
+    {
+    }
+
+    NodeID create_layer(IStream &s) override
+    {
+        ARM_COMPUTE_ERROR_ON(_mean == nullptr);
+        ARM_COMPUTE_ERROR_ON(_var == nullptr);
+
+        NodeParams  common_params = { "", s.hints().target_hint };
+        NodeIdxPair input         = { s.tail_node(), 0 };
+        return GraphBuilder::add_batch_normalization_node(s.graph(), common_params, input, _epsilon,
+                                                          std::move(_mean), std::move(_var), std::move(_beta), std::move(_gamma));
+    }
+
+private:
+    ITensorAccessorUPtr _mean;
+    ITensorAccessorUPtr _var;
+    ITensorAccessorUPtr _gamma;
+    ITensorAccessorUPtr _beta;
+    float               _epsilon;
+};
+
+/** Convolution Layer */
+class ConvolutionLayer final : public ILayer
+{
+public:
+    /** Construct a convolution layer.
+     *
+     * @param[in] conv_width  Convolution width.
+     * @param[in] conv_height Convolution height.
+     * @param[in] ofm         Output feature map.
+     * @param[in] weights     Accessor to get kernel weights from.
+     * @param[in] bias        Accessor to get kernel bias from.
+     * @param[in] conv_info   Padding and stride information.
+     * @param[in] num_groups  (Optional) Number of groups. Default: 1.
+     */
+    ConvolutionLayer(unsigned int        conv_width,
+                     unsigned int        conv_height,
+                     unsigned int        ofm,
+                     ITensorAccessorUPtr weights,
+                     ITensorAccessorUPtr bias,
+                     PadStrideInfo       conv_info,
+                     unsigned int        num_groups = 1)
+        : _conv_width(conv_width),
+          _conv_height(conv_height),
+          _ofm(ofm),
+          _conv_info(std::move(conv_info)),
+          _num_groups(num_groups),
+          _weights(std::move(weights)),
+          _bias(std::move(bias))
+    {
+    }
+
+    NodeID create_layer(IStream &s) override
+    {
+        NodeIdxPair input         = { s.tail_node(), 0 };
+        NodeParams  common_params = { "", s.hints().target_hint };
+        return GraphBuilder::add_convolution_node(s.graph(), common_params, input,
+                                                  Size2D(_conv_width, _conv_height), _ofm, _conv_info, _num_groups,
+                                                  s.hints().convolution_method_hint,
+                                                  std::move(_weights), std::move(_bias));
+    }
+
+private:
+    unsigned int        _conv_width;
+    unsigned int        _conv_height;
+    unsigned int        _ofm;
+    const PadStrideInfo _conv_info;
+    unsigned int        _num_groups;
+    ITensorAccessorUPtr _weights;
+    ITensorAccessorUPtr _bias;
+};
+
+/** Depthwise Convolution Layer */
+class DepthwiseConvolutionLayer final : public ILayer
+{
+public:
+    /** Construct a depthwise convolution layer.
+     *
+     * @param[in] conv_width  Convolution width.
+     * @param[in] conv_height Convolution height.
+     * @param[in] weights     Accessor to get kernel weights from.
+     * @param[in] bias        Accessor to get kernel bias from.
+     * @param[in] conv_info   Padding and stride information.
+     */
+    DepthwiseConvolutionLayer(unsigned int        conv_width,
+                              unsigned int        conv_height,
+                              ITensorAccessorUPtr weights,
+                              ITensorAccessorUPtr bias,
+                              PadStrideInfo       conv_info)
+        : _conv_width(conv_width),
+          _conv_height(conv_height),
+          _conv_info(std::move(conv_info)),
+          _weights(std::move(weights)),
+          _bias(std::move(bias))
+    {
+    }
+
+    NodeID create_layer(IStream &s) override
+    {
+        NodeIdxPair input         = { s.tail_node(), 0 };
+        NodeParams  common_params = { "", s.hints().target_hint };
+        return GraphBuilder::add_depthwise_convolution_node(s.graph(), common_params,
+                                                            input, Size2D(_conv_width, _conv_height), _conv_info,
+                                                            s.hints().depthwise_convolution_method_hint,
+                                                            std::move(_weights), std::move(_bias));
+    }
+
+private:
+    unsigned int        _conv_width;
+    unsigned int        _conv_height;
+    const PadStrideInfo _conv_info;
+    ITensorAccessorUPtr _weights;
+    ITensorAccessorUPtr _bias;
+};
+
+/** Flatten Layer */
+class FlattenLayer final : public ILayer
+{
+public:
+    /** Construct a flatten layer. */
+    FlattenLayer()
+    {
+    }
+
+    NodeID create_layer(IStream &s) override
+    {
+        NodeParams  common_params = { "", s.hints().target_hint };
+        NodeIdxPair input         = { s.tail_node(), 0 };
+        return GraphBuilder::add_flatten_node(s.graph(), common_params, input);
+    }
+};
+
+/** Fully Connected Layer */
+class FullyConnectedLayer final : public ILayer
+{
+public:
+    /** Construct a fully connected layer.
+     *
+     * @param[in] num_outputs Number of outputs.
+     * @param[in] weights     Accessor to get weights from.
+     * @param[in] bias        Accessor to get bias from.
+     */
+    FullyConnectedLayer(unsigned int        num_outputs,
+                        ITensorAccessorUPtr weights,
+                        ITensorAccessorUPtr bias)
+        : _num_outputs(num_outputs), _weights(std::move(weights)), _bias(std::move(bias))
+    {
+    }
+
+    NodeID create_layer(IStream &s) override
+    {
+        NodeParams  common_params = { "", s.hints().target_hint };
+        NodeIdxPair input         = { s.tail_node(), 0 };
+        return GraphBuilder::add_fully_connected_layer(s.graph(), common_params, input, _num_outputs,
+                                                       std::move(_weights), std::move(_bias));
+    }
+
+private:
+    unsigned int        _num_outputs;
+    ITensorAccessorUPtr _weights;
+    ITensorAccessorUPtr _bias;
+};
+
+/** Normalization Layer */
+class NormalizationLayer final : public ILayer
+{
+public:
+    /** Construct a normalization layer.
+     *
+     * @param[in] norm_info Normalization information.
+     */
+    NormalizationLayer(NormalizationLayerInfo norm_info)
+        : _norm_info(norm_info)
+    {
+    }
+
+    NodeID create_layer(IStream &s) override
+    {
+        NodeParams  common_params = { "", s.hints().target_hint };
+        NodeIdxPair input         = { s.tail_node(), 0 };
+        return GraphBuilder::add_normalization_node(s.graph(), common_params, input, _norm_info);
+    }
+
+private:
+    NormalizationLayerInfo _norm_info;
+};
+
+/** Pooling Layer */
+class PoolingLayer final : public ILayer
+{
+public:
+    /** Construct a pooling layer.
+     *
+     * @param[in] pool_info Pooling information.
+     */
+    PoolingLayer(PoolingLayerInfo pool_info)
+        : _pool_info(pool_info)
+    {
+    }
+
+    NodeID create_layer(IStream &s) override
+    {
+        NodeParams  common_params = { "", s.hints().target_hint };
+        NodeIdxPair input         = { s.tail_node(), 0 };
+        return GraphBuilder::add_pooling_node(s.graph(), common_params, input, _pool_info);
+    }
+
+private:
+    PoolingLayerInfo _pool_info;
+};
+
+/** Reshape Layer */
+class ReshapeLayer final : public ILayer
+{
+public:
+    /** Construct a reshape layer.
+     *
+     * @param[in] shape Target shape.
+     */
+    ReshapeLayer(TensorShape shape)
+        : _shape(shape)
+    {
+    }
+
+    NodeID create_layer(IStream &s) override
+    {
+        NodeParams  common_params = { "", s.hints().target_hint };
+        NodeIdxPair input         = { s.tail_node(), 0 };
+        return GraphBuilder::add_reshape_node(s.graph(), common_params, input, _shape);
+    }
+
+private:
+    TensorShape _shape;
+};
+
+/** Softmax Layer */
+class SoftmaxLayer final : public ILayer
+{
+public:
+    /** Construct a softmax layer.
+     *
+     * @param[in] beta (Optional) Beta value. Default 1.0.
+     */
+    SoftmaxLayer(float beta = 1.0f)
+        : _beta(beta)
+    {
+    }
+
+    NodeID create_layer(IStream &s) override
+    {
+        NodeParams  common_params = { "", s.hints().target_hint };
+        NodeIdxPair input         = { s.tail_node(), 0 };
+        return GraphBuilder::add_softmax_node(s.graph(), common_params, input, _beta);
+    }
+
+private:
+    float _beta;
+};
+
+/** Branch Layer */
+class BranchLayer final : public ILayer
+{
+public:
+    /** Construct a branch layer
+     *
+     * @param[in] merge_method     Branch merging method
+     * @param[in] sub_stream1      First graph branch
+     * @param[in] sub_stream2      Second graph branch
+     * @param[in] rest_sub_streams Rest sub-graph branches
+     */
+    template <typename... Ts>
+    BranchLayer(BranchMergeMethod merge_method, SubStream &&sub_stream1, SubStream &&sub_stream2, Ts &&... rest_sub_streams)
+        : _branch_merge_method(merge_method), _sub_streams()
+    {
+        _sub_streams.push_back(arm_compute::support::cpp14::make_unique<SubStream>(std::move(sub_stream1)));
+        _sub_streams.push_back(arm_compute::support::cpp14::make_unique<SubStream>(std::move(sub_stream2)));
+
+        utility::for_each([&](SubStream && sub_stream)
+        {
+            _sub_streams.push_back(arm_compute::support::cpp14::make_unique<SubStream>(std::move(sub_stream)));
+        },
+        std::move(rest_sub_streams)...);
+    }
+    /** Construct a branch layer
+     *
+     * @param[in] sub_stream Sub-stream
+     */
+    template <typename... Ts>
+    BranchLayer(SubStream &&sub_stream)
+        : _branch_merge_method(BranchMergeMethod::DEPTH_CONCATENATE), _sub_streams()
+    {
+        _sub_streams.push_back(arm_compute::support::cpp14::make_unique<SubStream>(std::move(sub_stream)));
+    }
+    NodeID create_layer(IStream &s) override
+    {
+        NodeID     nid           = EmptyNodeID;
+        NodeParams common_params = { "", s.hints().target_hint };
+        if(_sub_streams.size() == 1 && _sub_streams.at(0) != nullptr)
+        {
+            nid = _sub_streams[0]->tail_node();
+        }
+        else if(_branch_merge_method == BranchMergeMethod::DEPTH_CONCATENATE)
+        {
+            // Collect tail nodes and perform DepthConcatenate
+            std::vector<NodeIdxPair> nodes;
+            for(auto &ss : _sub_streams)
+            {
+                if(ss && (ss->tail_node() != EmptyNodeID))
+                {
+                    const auto tail_node = s.graph().node(ss->tail_node());
+                    if(tail_node != nullptr && tail_node->type() != NodeType::Output)
+                    {
+                        nodes.push_back({ ss->tail_node(), 0 });
+                    }
+                }
+            }
+            nid = GraphBuilder::add_depth_concatenate_node(s.graph(), common_params, nodes);
+        }
+        else
+        {
+            ARM_COMPUTE_ERROR_ON(_sub_streams.size() != 2);
+            NodeIdxPair input0 = { _sub_streams[0]->tail_node(), 0 };
+            NodeIdxPair input1 = { _sub_streams[1]->tail_node(), 0 };
+            nid                = GraphBuilder::add_elementwise_node(s.graph(), common_params, input0, input1, EltwiseOperation::ADD);
+        }
+        return nid;
+    }
+
+private:
+    BranchMergeMethod                       _branch_merge_method;
+    std::vector<std::unique_ptr<SubStream>> _sub_streams;
+};
+} // namespace frontend
+} // namespace graph
+} // namespace arm_compute
+#endif /* __ARM_COMPUTE_GRAPH_LAYERS_H__ */