diff --git a/examples/graph_mobilenet_qasymm8.cpp b/examples/graph_mobilenet_qasymm8.cpp
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+/*
+ * Copyright (c) 2017-2018 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.
+ */
+#include "arm_compute/graph/Graph.h"
+#include "arm_compute/graph/Nodes.h"
+#include "support/ToolchainSupport.h"
+#include "utils/GraphUtils.h"
+#include "utils/Utils.h"
+
+using namespace arm_compute;
+using namespace arm_compute::graph;
+using namespace arm_compute::graph_utils;
+
+/** Example demonstrating how to implement QASYMM8 MobileNet's network using the Compute Library's graph API
+ *
+ * @param[in] argc Number of arguments
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] npy_input, [optional] labels )
+ */
+class GraphMobileNetQASYMM8Example : public utils::Example
+{
+public:
+    void do_setup(int argc, char **argv) override
+    {
+        std::string data_path; /* Path to the trainable data */
+        std::string input;     /* Image data */
+        std::string label;     /* Label data */
+
+        // Quantization info taken from the AndroidNN QASYMM8 MobileNet example
+        const QuantizationInfo in_quant_info  = QuantizationInfo(0.0078125f, 128);
+        const QuantizationInfo mid_quant_info = QuantizationInfo(0.0784313753247f, 128);
+
+        const std::vector<QuantizationInfo> conv_weights_quant_info =
+        {
+            QuantizationInfo(0.031778190285f, 156), // conv0
+            QuantizationInfo(0.00604454148561f, 66) // conv14
+        };
+
+        const std::vector<QuantizationInfo> depth_weights_quant_info =
+        {
+            QuantizationInfo(0.254282623529f, 129),  // dwsc1
+            QuantizationInfo(0.12828284502f, 172),   // dwsc2
+            QuantizationInfo(0.265911251307f, 83),   // dwsc3
+            QuantizationInfo(0.0985597148538f, 30),  // dwsc4
+            QuantizationInfo(0.0631204470992f, 54),  // dwsc5
+            QuantizationInfo(0.0137207424268f, 141), // dwsc6
+            QuantizationInfo(0.0817828401923f, 125), // dwsc7
+            QuantizationInfo(0.0393880493939f, 164), // dwsc8
+            QuantizationInfo(0.211694166064f, 129),  // dwsc9
+            QuantizationInfo(0.158015936613f, 103),  // dwsc10
+            QuantizationInfo(0.0182712618262f, 137), // dwsc11
+            QuantizationInfo(0.0127998134121f, 134), // dwsc12
+            QuantizationInfo(0.299285322428f, 161)   // dwsc13
+        };
+
+        const std::vector<QuantizationInfo> point_weights_quant_info =
+        {
+            QuantizationInfo(0.0425766184926f, 129),  // dwsc1
+            QuantizationInfo(0.0250773020089f, 94),   // dwsc2
+            QuantizationInfo(0.015851572156f, 93),    // dwsc3
+            QuantizationInfo(0.0167811904103f, 98),   // dwsc4
+            QuantizationInfo(0.00951790809631f, 135), // dwsc5
+            QuantizationInfo(0.00999817531556f, 128), // dwsc6
+            QuantizationInfo(0.00590536883101f, 126), // dwsc7
+            QuantizationInfo(0.00576109671965f, 133), // dwsc8
+            QuantizationInfo(0.00830461271107f, 142), // dwsc9
+            QuantizationInfo(0.0152327232063f, 72),   // dwsc10
+            QuantizationInfo(0.00741417845711f, 125), // dwsc11
+            QuantizationInfo(0.0135628981516f, 142),  // dwsc12
+            QuantizationInfo(0.0338749065995f, 140)   // dwsc13
+        };
+
+        // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
+        const int  int_target_hint = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
+        TargetHint target_hint     = set_target_hint(int_target_hint);
+
+        // Parse arguments
+        if(argc < 2)
+        {
+            // Print help
+            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [npy_input] [labels]\n\n";
+            std::cout << "No data folder provided: using random values\n\n";
+        }
+        else if(argc == 2)
+        {
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [npy_input] [labels]\n\n";
+            std::cout << "No input provided: using random values\n\n";
+        }
+        else if(argc == 4)
+        {
+            data_path = argv[2];
+            input     = argv[3];
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels]\n\n";
+            std::cout << "No text file with labels provided: skipping output accessor\n\n";
+        }
+        else
+        {
+            data_path = argv[2];
+            input     = argv[3];
+            label     = argv[4];
+        }
+
+        // Initialize graph
+        graph.graph_init(int_target_hint == 2);
+
+        graph << target_hint
+              << arm_compute::graph::Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::QASYMM8, in_quant_info),
+                                            get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/" + input))
+              << ConvolutionLayer(
+                  3U, 3U, 32U,
+                  get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Conv2d_0_weights.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Conv2d_0_bias.npy"),
+                  PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR),
+                  1, WeightsInfo(),
+                  conv_weights_quant_info.at(0),
+                  mid_quant_info)
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f))
+              << get_dwsc_node(data_path, "Conv2d_1", 64U, PadStrideInfo(1U, 1U, 1U, 1U), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(0), point_weights_quant_info.at(0))
+              << get_dwsc_node(data_path, "Conv2d_2", 128U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(1),
+                               point_weights_quant_info.at(1))
+              << get_dwsc_node(data_path, "Conv2d_3", 128U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(2),
+                               point_weights_quant_info.at(2))
+              << get_dwsc_node(data_path, "Conv2d_4", 256U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(3),
+                               point_weights_quant_info.at(3))
+              << get_dwsc_node(data_path, "Conv2d_5", 256U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(4),
+                               point_weights_quant_info.at(4))
+              << get_dwsc_node(data_path, "Conv2d_6", 512U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(5),
+                               point_weights_quant_info.at(5))
+              << get_dwsc_node(data_path, "Conv2d_7", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(6),
+                               point_weights_quant_info.at(6))
+              << get_dwsc_node(data_path, "Conv2d_8", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(7),
+                               point_weights_quant_info.at(7))
+              << get_dwsc_node(data_path, "Conv2d_9", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(8),
+                               point_weights_quant_info.at(8))
+              << get_dwsc_node(data_path, "Conv2d_10", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(9),
+                               point_weights_quant_info.at(9))
+              << get_dwsc_node(data_path, "Conv2d_11", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(10),
+                               point_weights_quant_info.at(10))
+              << get_dwsc_node(data_path, "Conv2d_12", 1024U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(11),
+                               point_weights_quant_info.at(11))
+              << get_dwsc_node(data_path, "Conv2d_13", 1024U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(12),
+                               point_weights_quant_info.at(12))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
+              << ConvolutionLayer(
+                  1U, 1U, 1001U,
+                  get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Logits_Conv2d_1c_1x1_weights.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Logits_Conv2d_1c_1x1_bias.npy"),
+                  PadStrideInfo(1U, 1U, 0U, 0U), 1, WeightsInfo(), conv_weights_quant_info.at(1))
+              << ReshapeLayer(TensorShape(1001U))
+              << SoftmaxLayer()
+              << arm_compute::graph::Tensor(get_output_accessor(label, 5));
+    }
+    void do_run() override
+    {
+        // Run graph
+        graph.run();
+    }
+
+private:
+    Graph graph{};
+
+    /** This function produces a depthwise separable convolution node (i.e. depthwise + pointwise layers) with ReLU6 activation after each layer.
+     *
+     * @param[in] data_path                Path to trainable data folder
+     * @param[in] param_path               Prefix of specific set of weights/biases data
+     * @param[in] conv_filt                Filters depths for pointwise convolution
+     * @param[in] dwc_pad_stride_info      PadStrideInfo for depthwise convolution
+     * @param[in] conv_pad_stride_info     PadStrideInfo for pointwise convolution
+     * @param[in] depth_weights_quant_info QuantizationInfo for depthwise convolution's weights
+     * @param[in] point_weights_quant_info QuantizationInfo for pointwise convolution's weights
+     *
+     * @return The complete dwsc node
+     */
+    BranchLayer get_dwsc_node(const std::string &data_path, std::string &&param_path,
+                              const unsigned int conv_filt,
+                              PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info,
+                              QuantizationInfo depth_weights_quant_info, QuantizationInfo point_weights_quant_info)
+    {
+        std::string total_path = "/cnn_data/mobilenet_qasymm8_model/" + param_path + "_";
+        SubGraph    sg;
+
+        sg << DepthwiseConvolutionLayer(
+               3U, 3U,
+               get_weights_accessor(data_path, total_path + "depthwise_weights.npy"),
+               get_weights_accessor(data_path, total_path + "depthwise_bias.npy"),
+               dwc_pad_stride_info,
+               true,
+               depth_weights_quant_info)
+           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f))
+           << ConvolutionLayer(
+               1U, 1U, conv_filt,
+               get_weights_accessor(data_path, total_path + "pointwise_weights.npy"),
+               get_weights_accessor(data_path, total_path + "pointwise_bias.npy"),
+               conv_pad_stride_info,
+               1, WeightsInfo(),
+               point_weights_quant_info)
+           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f));
+
+        return BranchLayer(std::move(sg));
+    }
+};
+/** Main program for MobileNetQASYMM8
+ *
+ * @param[in] argc Number of arguments
+ * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] npy_input, [optional] labels )
+ */
+int main(int argc, char **argv)
+{
+    return utils::run_example<GraphMobileNetQASYMM8Example>(argc, argv);
+}