diff --git a/examples/graph_mnist.cpp b/examples/graph_mnist.cpp
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+++ b/examples/graph_mnist.cpp
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+/*
+ * 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.h"
+#include "support/ToolchainSupport.h"
+#include "utils/CommonGraphOptions.h"
+#include "utils/GraphUtils.h"
+#include "utils/Utils.h"
+
+using namespace arm_compute;
+using namespace arm_compute::utils;
+using namespace arm_compute::graph::frontend;
+using namespace arm_compute::graph_utils;
+
+/** Example demonstrating how to implement Mnist's network using the Compute Library's graph API */
+class GraphMnistExample : public Example
+{
+public:
+    GraphMnistExample()
+        : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "LeNet")
+    {
+    }
+    bool do_setup(int argc, char **argv) override
+    {
+        // Parse arguments
+        cmd_parser.parse(argc, argv);
+        cmd_parser.validate();
+
+        // Consume common parameters
+        common_params = consume_common_graph_parameters(common_opts);
+
+        // Return when help menu is requested
+        if(common_params.help)
+        {
+            cmd_parser.print_help(argv[0]);
+            return false;
+        }
+
+        // Print parameter values
+        std::cout << common_params << std::endl;
+
+        // Get trainable parameters data path
+        std::string data_path = common_params.data_path;
+
+        // Add model path to data path
+        if(!data_path.empty() && arm_compute::is_data_type_quantized_asymmetric(common_params.data_type))
+        {
+            data_path += "/cnn_data/mnist_qasymm8_model/";
+        }
+
+        // Create input descriptor
+        const TensorShape tensor_shape     = permute_shape(TensorShape(28U, 28U, 1U), DataLayout::NCHW, common_params.data_layout);
+        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
+
+        const QuantizationInfo in_quant_info = QuantizationInfo(0.003921568859368563f, 0);
+
+        const std::vector<std::pair<QuantizationInfo, QuantizationInfo>> conv_quant_info =
+        {
+            { QuantizationInfo(0.004083447158336639f, 138), QuantizationInfo(0.0046257381327450275f, 0) }, // conv0
+            { QuantizationInfo(0.0048590428195893764f, 149), QuantizationInfo(0.03558270260691643f, 0) },  // conv1
+            { QuantizationInfo(0.004008443560451269f, 146), QuantizationInfo(0.09117382764816284f, 0) },   // conv2
+            { QuantizationInfo(0.004344311077147722f, 160), QuantizationInfo(0.5494495034217834f, 167) },  // fc
+        };
+
+        // Set weights trained layout
+        const DataLayout        weights_layout = DataLayout::NHWC;
+        FullyConnectedLayerInfo fc_info        = FullyConnectedLayerInfo();
+        fc_info.set_weights_trained_layout(weights_layout);
+
+        graph << common_params.target
+              << common_params.fast_math_hint
+              << InputLayer(input_descriptor.set_quantization_info(in_quant_info),
+                            get_input_accessor(common_params))
+              << ConvolutionLayer(
+                  3U, 3U, 32U,
+                  get_weights_accessor(data_path, "conv2d_weights_quant_FakeQuantWithMinMaxVars.npy", weights_layout),
+                  get_weights_accessor(data_path, "conv2d_Conv2D_bias.npy"),
+                  PadStrideInfo(1U, 1U, 1U, 1U), 1, conv_quant_info.at(0).first, conv_quant_info.at(0).second)
+              .set_name("Conv0")
+
+              << ConvolutionLayer(
+                  3U, 3U, 32U,
+                  get_weights_accessor(data_path, "conv2d_1_weights_quant_FakeQuantWithMinMaxVars.npy", weights_layout),
+                  get_weights_accessor(data_path, "conv2d_1_Conv2D_bias.npy"),
+                  PadStrideInfo(1U, 1U, 1U, 1U), 1, conv_quant_info.at(1).first, conv_quant_info.at(1).second)
+              .set_name("conv1")
+
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("maxpool1")
+
+              << ConvolutionLayer(
+                  3U, 3U, 32U,
+                  get_weights_accessor(data_path, "conv2d_2_weights_quant_FakeQuantWithMinMaxVars.npy", weights_layout),
+                  get_weights_accessor(data_path, "conv2d_2_Conv2D_bias.npy"),
+                  PadStrideInfo(1U, 1U, 1U, 1U), 1, conv_quant_info.at(2).first, conv_quant_info.at(2).second)
+              .set_name("conv2")
+
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("maxpool2")
+
+              << FullyConnectedLayer(
+                  10U,
+                  get_weights_accessor(data_path, "dense_weights_quant_FakeQuantWithMinMaxVars_transpose.npy", weights_layout),
+                  get_weights_accessor(data_path, "dense_MatMul_bias.npy"),
+                  fc_info, conv_quant_info.at(3).first, conv_quant_info.at(3).second)
+              .set_name("fc")
+
+              << SoftmaxLayer().set_name("prob");
+
+        if(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type))
+        {
+            graph << DequantizationLayer().set_name("dequantize");
+        }
+
+        graph << OutputLayer(get_output_accessor(common_params, 5));
+
+        // Finalize graph
+        GraphConfig config;
+        config.num_threads = common_params.threads;
+        config.use_tuner   = common_params.enable_tuner;
+        config.tuner_mode  = common_params.tuner_mode;
+        config.tuner_file  = common_params.tuner_file;
+
+        graph.finalize(common_params.target, config);
+
+        return true;
+    }
+    void do_run() override
+    {
+        // Run graph
+        graph.run();
+    }
+
+private:
+    CommandLineParser  cmd_parser;
+    CommonGraphOptions common_opts;
+    CommonGraphParams  common_params;
+    Stream             graph;
+};
+
+/** Main program for Mnist Example
+ *
+ * @note To list all the possible arguments execute the binary appended with the --help option
+ *
+ * @param[in] argc Number of arguments
+ * @param[in] argv Arguments
+ */
+int main(int argc, char **argv)
+{
+    return arm_compute::utils::run_example<GraphMnistExample>(argc, argv);
+}