COMPMID-1310: Create graph validation executables.

Change-Id: I9e0b57b1b83fe5a95777cdaeddba6ecef650bafc
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/138697
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>

25 files changed, 717 insertions, 1055 deletions

diff --git a/examples/SConscript b/examples/SConscript
index c3576fb1a0..bada734659 100644
--- a/examples/SConscript
+++ b/examples/SConscript
@@ -47,6 +47,7 @@ else:
 
 # Build graph examples
 graph_utils = examples_env.Object("../utils/GraphUtils.cpp")
+graph_utils += examples_env.Object("../utils/CommonGraphOptions.cpp")
 examples_libs = examples_env.get("LIBS",[])
 for file in Glob("./graph_*.cpp"):
     example = os.path.basename(os.path.splitext(str(file))[0])
diff --git a/examples/cl_convolution.cpp b/examples/cl_convolution.cpp
index 8f3d7e3a30..b15bbb6cb4 100644
--- a/examples/cl_convolution.cpp
+++ b/examples/cl_convolution.cpp
@@ -57,7 +57,7 @@ const int16_t gaussian5x5[] =
 class CLConvolutionExample : public Example
 {
 public:
-    void do_setup(int argc, char **argv) override
+    bool do_setup(int argc, char **argv) override
     {
         PPMLoader ppm;
 
@@ -95,6 +95,8 @@ public:
             ppm.fill_image(src);
             output_filename = std::string(argv[1]) + "_out.ppm";
         }
+
+        return true;
     }
     void do_run() override
     {
diff --git a/examples/cl_events.cpp b/examples/cl_events.cpp
index 4a46df961c..a9c508ac58 100644
--- a/examples/cl_events.cpp
+++ b/examples/cl_events.cpp
@@ -37,7 +37,7 @@ using namespace utils;
 class CLEventsExample : public Example
 {
 public:
-    void do_setup(int argc, char **argv) override
+    bool do_setup(int argc, char **argv) override
     {
         /** [OpenCL events] **/
         PPMLoader     ppm;
@@ -84,6 +84,8 @@ public:
             output_filename = std::string(argv[1]) + "_out.ppm";
         }
         /** [OpenCL events] **/
+
+        return true;
     }
     void do_run() override
     {
diff --git a/examples/cl_sgemm.cpp b/examples/cl_sgemm.cpp
index fa57885450..805aec1cf3 100644
--- a/examples/cl_sgemm.cpp
+++ b/examples/cl_sgemm.cpp
@@ -39,7 +39,7 @@ using namespace utils;
 class CLSGEMMExample : public Example
 {
 public:
-    void do_setup(int argc, char **argv) override
+    bool do_setup(int argc, char **argv) override
     {
         NPYLoader npy0, npy1, npy2;
         alpha = 1.0f;
@@ -164,6 +164,8 @@ public:
 
         // Dummy run for CLTuner
         sgemm.run();
+
+        return true;
     }
     void do_run() override
     {
diff --git a/examples/gc_absdiff.cpp b/examples/gc_absdiff.cpp
index 1024dace26..f53459231f 100644
--- a/examples/gc_absdiff.cpp
+++ b/examples/gc_absdiff.cpp
@@ -38,7 +38,7 @@ using namespace utils;
 class GCAbsDiffExample : public Example
 {
 public:
-    void do_setup(int argc, char **argv) override
+    bool do_setup(int argc, char **argv) override
     {
         PPMLoader ppm1, ppm2;
 
@@ -90,6 +90,8 @@ public:
         {
             ppm2.fill_image(src2);
         }
+
+        return true;
     }
     void do_run() override
     {
diff --git a/examples/gc_dc.cpp b/examples/gc_dc.cpp
index 8b6f4414e0..f3f194252e 100644
--- a/examples/gc_dc.cpp
+++ b/examples/gc_dc.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017, 2018 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -36,7 +36,7 @@ using namespace utils;
 class GCDCExample : public Example
 {
 public:
-    void do_setup(int argc, char **argv) override
+    bool do_setup(int argc, char **argv) override
     {
         ARM_COMPUTE_UNUSED(argc);
         ARM_COMPUTE_UNUSED(argv);
@@ -86,6 +86,8 @@ public:
             *reinterpret_cast<half_float::half *>(it.ptr()) = half_float::half(1.f);
         });
         src.unmap();
+
+        return true;
     }
     void do_run() override
     {
diff --git a/examples/graph_alexnet.cpp b/examples/graph_alexnet.cpp
index 5328662b6d..95d36342f9 100644
--- a/examples/graph_alexnet.cpp
+++ b/examples/graph_alexnet.cpp
@@ -23,13 +23,10 @@
  */
 #include "arm_compute/graph.h"
 #include "support/ToolchainSupport.h"
+#include "utils/CommonGraphOptions.h"
 #include "utils/GraphUtils.h"
 #include "utils/Utils.h"
 
-#include <cstdlib>
-#include <iostream>
-#include <memory>
-
 using namespace arm_compute::utils;
 using namespace arm_compute::graph::frontend;
 using namespace arm_compute::graph_utils;
@@ -37,71 +34,47 @@ using namespace arm_compute::graph_utils;
 /** Example demonstrating how to implement AlexNet'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] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
+ * @param[in] argv Arguments
  */
 class GraphAlexnetExample : public Example
 {
 public:
-    void do_setup(int argc, char **argv) override
+    GraphAlexnetExample()
+        : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "AlexNet")
     {
-        std::string data_path; /* Path to the trainable data */
-        std::string image;     /* Image data */
-        std::string label;     /* Label data */
-
-        // Create a preprocessor object
-        const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } };
-        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
+    }
+    bool do_setup(int argc, char **argv) override
+    {
+        // Parse arguments
+        cmd_parser.parse(argc, argv);
 
-        // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
-        const int    target         = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
-        Target       target_hint    = set_target_hint(target);
-        FastMathHint fast_math_hint = FastMathHint::DISABLED;
+        // Consume common parameters
+        common_params = consume_common_graph_parameters(common_opts);
 
-        // Parse arguments
-        if(argc < 2)
-        {
-            // Print help
-            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\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] [image] [labels] [fast_math_hint]\n\n";
-            std::cout << "No data folder provided: using random values\n\n";
-        }
-        else if(argc == 3)
-        {
-            data_path = argv[2];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\n\n";
-            std::cout << "No image provided: using random values\n\n";
-        }
-        else if(argc == 4)
-        {
-            data_path = argv[2];
-            image     = argv[3];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n";
-            std::cout << "No text file with labels provided: skipping output accessor\n\n";
-        }
-        else if(argc == 5)
+        // Return when help menu is requested
+        if(common_params.help)
         {
-            data_path = argv[2];
-            image     = argv[3];
-            label     = argv[4];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n";
-            std::cout << "No fast math info provided: disabling fast math\n\n";
-        }
-        else
-        {
-            data_path      = argv[2];
-            image          = argv[3];
-            label          = argv[4];
-            fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
+            cmd_parser.print_help(argv[0]);
+            return false;
         }
 
-        graph << target_hint
-              << fast_math_hint
-              << InputLayer(TensorDescriptor(TensorShape(227U, 227U, 3U, 1U), DataType::F32),
-                            get_input_accessor(image, std::move(preprocessor)))
+        // Checks
+        ARM_COMPUTE_ERROR_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "Unsupported data type!");
+
+        // Print parameter values
+        std::cout << common_params << std::endl;
+
+        // Get trainable parameters data path
+        std::string data_path = common_params.data_path;
+
+        // Create a preprocessor object
+        const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } };
+        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
+
+        graph << common_params.target
+              << common_params.fast_math_hint
+              << InputLayer(TensorDescriptor(TensorShape(227U, 227U, 3U, 1U), common_params.data_type),
+                            get_input_accessor(common_params, std::move(preprocessor)))
               // Layer 1
               << ConvolutionLayer(
                   11U, 11U, 96U,
@@ -169,12 +142,15 @@ public:
               .set_name("fc8")
               // Softmax
               << SoftmaxLayer().set_name("prob")
-              << OutputLayer(get_output_accessor(label, 5));
+              << OutputLayer(get_output_accessor(common_params, 5));
 
         // Finalize graph
         GraphConfig config;
-        config.use_tuner = (target == 2);
-        graph.finalize(target_hint, config);
+        config.num_threads = common_params.threads;
+        config.use_tuner   = common_params.enable_tuner;
+        graph.finalize(common_params.target, config);
+
+        return true;
     }
     void do_run() override
     {
@@ -183,13 +159,18 @@ public:
     }
 
 private:
-    Stream graph{ 0, "AlexNet" };
+    CommandLineParser  cmd_parser;
+    CommonGraphOptions common_opts;
+    CommonGraphParams  common_params;
+    Stream             graph;
 };
 
 /** Main program for AlexNet
  *
  * @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] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
+ * @param[in] argv Arguments
+ *
+ * @return Return code
  */
 int main(int argc, char **argv)
 {
diff --git a/examples/graph_googlenet.cpp b/examples/graph_googlenet.cpp
index 2dba67f5eb..e23107f081 100644
--- a/examples/graph_googlenet.cpp
+++ b/examples/graph_googlenet.cpp
@@ -23,12 +23,10 @@
  */
 #include "arm_compute/graph.h"
 #include "support/ToolchainSupport.h"
+#include "utils/CommonGraphOptions.h"
 #include "utils/GraphUtils.h"
 #include "utils/Utils.h"
 
-#include <cstdlib>
-#include <tuple>
-
 using namespace arm_compute::utils;
 using namespace arm_compute::graph::frontend;
 using namespace arm_compute::graph_utils;
@@ -36,71 +34,47 @@ using namespace arm_compute::graph_utils;
 /** Example demonstrating how to implement Googlenet'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] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
+ * @param[in] argv Arguments
  */
 class GraphGooglenetExample : public Example
 {
 public:
-    void do_setup(int argc, char **argv) override
+    GraphGooglenetExample()
+        : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "GoogleNet")
     {
-        std::string data_path; /* Path to the trainable data */
-        std::string image;     /* Image data */
-        std::string label;     /* Label data */
-
-        // Create a preprocessor object
-        const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } };
-        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
+    }
+    bool do_setup(int argc, char **argv) override
+    {
+        // Parse arguments
+        cmd_parser.parse(argc, argv);
 
-        // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
-        const int    target         = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
-        Target       target_hint    = set_target_hint(target);
-        FastMathHint fast_math_hint = FastMathHint::DISABLED;
+        // Consume common parameters
+        common_params = consume_common_graph_parameters(common_opts);
 
-        // Parse arguments
-        if(argc < 2)
-        {
-            // Print help
-            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
-            std::cout << "No data folder provided: using random values\n\n";
-        }
-        else if(argc == 2)
+        // Return when help menu is requested
+        if(common_params.help)
         {
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\n\n";
-            std::cout << "No data folder provided: using random values\n\n";
-        }
-        else if(argc == 3)
-        {
-            data_path = argv[2];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\n\n";
-            std::cout << "No image provided: using random values\n\n";
-        }
-        else if(argc == 4)
-        {
-            data_path = argv[2];
-            image     = argv[3];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n";
-            std::cout << "No text file with labels provided: skipping output accessor\n\n";
-        }
-        else if(argc == 5)
-        {
-            data_path = argv[2];
-            image     = argv[3];
-            label     = argv[4];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n";
-            std::cout << "No fast math info provided: disabling fast math\n\n";
-        }
-        else
-        {
-            data_path      = argv[2];
-            image          = argv[3];
-            label          = argv[4];
-            fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
+            cmd_parser.print_help(argv[0]);
+            return false;
         }
 
-        graph << target_hint
-              << fast_math_hint
-              << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32),
-                            get_input_accessor(image, std::move(preprocessor)))
+        // Checks
+        ARM_COMPUTE_ERROR_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "Unsupported data type!");
+
+        // Print parameter values
+        std::cout << common_params << std::endl;
+
+        // Get trainable parameters data path
+        std::string data_path = common_params.data_path;
+
+        // Create a preprocessor object
+        const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } };
+        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
+
+        graph << common_params.target
+              << common_params.fast_math_hint
+              << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), common_params.data_type),
+                            get_input_accessor(common_params, std::move(preprocessor)))
               << ConvolutionLayer(
                   7U, 7U, 64U,
                   get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_w.npy"),
@@ -140,12 +114,15 @@ public:
                   get_weights_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_b.npy"))
               << SoftmaxLayer()
-              << OutputLayer(get_output_accessor(label, 5));
+              << OutputLayer(get_output_accessor(common_params, 5));
 
         // Finalize graph
         GraphConfig config;
-        config.use_tuner = (target == 2);
-        graph.finalize(target_hint, config);
+        config.num_threads = common_params.threads;
+        config.use_tuner   = common_params.enable_tuner;
+        graph.finalize(common_params.target, config);
+
+        return true;
     }
     void do_run() override
     {
@@ -154,7 +131,10 @@ public:
     }
 
 private:
-    Stream graph{ 0, "GoogleNet" };
+    CommandLineParser  cmd_parser;
+    CommonGraphOptions common_opts;
+    CommonGraphParams  common_params;
+    Stream             graph;
 
     BranchLayer get_inception_node(const std::string &data_path, std::string &&param_path,
                                    unsigned int a_filt,
@@ -215,7 +195,7 @@ private:
 /** Main program for Googlenet
  *
  * @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] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
+ * @param[in] argv Arguments
  */
 int main(int argc, char **argv)
 {
diff --git a/examples/graph_inception_v3.cpp b/examples/graph_inception_v3.cpp
index d1d6ab4e05..30b1b7d7ce 100644
--- a/examples/graph_inception_v3.cpp
+++ b/examples/graph_inception_v3.cpp
@@ -23,12 +23,10 @@
  */
 #include "arm_compute/graph.h"
 #include "support/ToolchainSupport.h"
+#include "utils/CommonGraphOptions.h"
 #include "utils/GraphUtils.h"
 #include "utils/Utils.h"
 
-#include <cstdlib>
-#include <tuple>
-
 using namespace arm_compute::utils;
 using namespace arm_compute::graph::frontend;
 using namespace arm_compute::graph_utils;
@@ -36,70 +34,46 @@ using namespace arm_compute::graph_utils;
 /** Example demonstrating how to implement InceptionV3'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] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
+ * @param[in] argv Arguments
  */
 class InceptionV3Example : public Example
 {
 public:
-    void do_setup(int argc, char **argv) override
+    InceptionV3Example()
+        : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "InceptionV3")
     {
-        std::string data_path; /* Path to the trainable data */
-        std::string image;     /* Image data */
-        std::string label;     /* Label data */
-
-        // Create a preprocessor object
-        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
+    }
+    bool do_setup(int argc, char **argv) override
+    {
+        // Parse arguments
+        cmd_parser.parse(argc, argv);
 
-        // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
-        const int    target         = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
-        Target       target_hint    = set_target_hint(target);
-        FastMathHint fast_math_hint = FastMathHint::DISABLED;
+        // Consume common parameters
+        common_params = consume_common_graph_parameters(common_opts);
 
-        // Parse arguments
-        if(argc < 2)
-        {
-            // Print help
-            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
-            std::cout << "No data folder provided: using random values\n\n";
-        }
-        else if(argc == 2)
+        // Return when help menu is requested
+        if(common_params.help)
         {
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\n\n";
-            std::cout << "No data folder provided: using random values\n\n";
-        }
-        else if(argc == 3)
-        {
-            data_path = argv[2];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\n\n";
-            std::cout << "No image provided: using random values\n\n";
-        }
-        else if(argc == 4)
-        {
-            data_path = argv[2];
-            image     = argv[3];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n";
-            std::cout << "No text file with labels provided: skipping output accessor\n\n";
-        }
-        else if(argc == 5)
-        {
-            data_path = argv[2];
-            image     = argv[3];
-            label     = argv[4];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n";
-            std::cout << "No fast math info provided: disabling fast math\n\n";
-        }
-        else
-        {
-            data_path      = argv[2];
-            image          = argv[3];
-            label          = argv[4];
-            fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
+            cmd_parser.print_help(argv[0]);
+            return false;
         }
 
-        graph << target_hint
-              << fast_math_hint
-              << InputLayer(TensorDescriptor(TensorShape(299U, 299U, 3U, 1U), DataType::F32),
-                            get_input_accessor(image, std::move(preprocessor), false))
+        // Checks
+        ARM_COMPUTE_ERROR_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "Unsupported data type!");
+
+        // Print parameter values
+        std::cout << common_params << std::endl;
+
+        // Get trainable parameters data path
+        std::string data_path = common_params.data_path;
+
+        // Create a preprocessor object
+        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
+
+        graph << common_params.target
+              << common_params.fast_math_hint
+              << InputLayer(TensorDescriptor(TensorShape(299U, 299U, 3U, 1U), common_params.data_type),
+                            get_input_accessor(common_params, std::move(preprocessor), false))
               << ConvolutionLayer(3U, 3U, 32U,
                                   get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_weights.npy"),
                                   std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0))
@@ -218,12 +192,15 @@ public:
               .set_name("Logits/Conv2d_1c_1x1/convolution")
               << ReshapeLayer(TensorShape(1001U)).set_name("Predictions/Reshape")
               << SoftmaxLayer().set_name("Predictions/Softmax")
-              << OutputLayer(get_output_accessor(label, 5));
+              << OutputLayer(get_output_accessor(common_params, 5));
 
         // Finalize graph
         GraphConfig config;
-        config.use_tuner = (target == 2);
-        graph.finalize(target_hint, config);
+        config.num_threads = common_params.threads;
+        config.use_tuner   = common_params.enable_tuner;
+        graph.finalize(common_params.target, config);
+
+        return true;
     }
 
     void do_run() override
@@ -232,7 +209,10 @@ public:
     }
 
 private:
-    Stream graph{ 0, "InceptionV3" };
+    CommandLineParser  cmd_parser;
+    CommonGraphOptions common_opts;
+    CommonGraphParams  common_params;
+    Stream             graph;
 
 private:
     BranchLayer get_inception_node_A(const std::string &data_path, std::string &&param_path,
@@ -863,7 +843,7 @@ private:
 /** Main program for Inception V3
  *
  * @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] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
+ * @param[in] argv Arguments
  */
 int main(int argc, char **argv)
 {
diff --git a/examples/graph_inception_v4.cpp b/examples/graph_inception_v4.cpp
index ed95baa99e..e7c1bc69e2 100644
--- a/examples/graph_inception_v4.cpp
+++ b/examples/graph_inception_v4.cpp
@@ -23,12 +23,10 @@
  */
 #include "arm_compute/graph.h"
 #include "support/ToolchainSupport.h"
+#include "utils/CommonGraphOptions.h"
 #include "utils/GraphUtils.h"
 #include "utils/Utils.h"
 
-#include <cstdlib>
-#include <tuple>
-
 using namespace arm_compute::utils;
 using namespace arm_compute::graph::frontend;
 using namespace arm_compute::graph_utils;
@@ -36,73 +34,46 @@ using namespace arm_compute::graph_utils;
 /** Example demonstrating how to implement InceptionV4'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] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
+ * @param[in] argv Arguments
  */
 class InceptionV4Example final : public Example
 {
 public:
-    void do_setup(int argc, char **argv) override
+    InceptionV4Example()
+        : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "InceptionV4")
     {
-        // Disabled the test for now because the process gets killed on Linux Firefly 32 bit even when using ConvolutionMethodHint::DIRECT.
-        // Needs to review/rework to run the code below.
-#if __aarch64__
-        std::string data_path; /* Path to the trainable data */
-        std::string image;     /* Image data */
-        std::string label;     /* Label data */
-
-        // Create a preprocessor object
-        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
+    }
+    bool do_setup(int argc, char **argv) override
+    {
+        // Parse arguments
+        cmd_parser.parse(argc, argv);
 
-        // Set target. 0 (NEON), 1 (OpenCL). By default it is NEON
-        const int    target         = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
-        Target       target_hint    = set_target_hint(target);
-        FastMathHint fast_math_hint = FastMathHint::DISABLED;
+        // Consume common parameters
+        common_params = consume_common_graph_parameters(common_opts);
 
-        // Parse arguments
-        if(argc < 2)
-        {
-            // Print help
-            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
-            std::cout << "No data folder provided: using random values\n\n";
-        }
-        else if(argc == 2)
+        // Return when help menu is requested
+        if(common_params.help)
         {
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\n\n";
-            std::cout << "No data folder provided: using random values\n\n";
-        }
-        else if(argc == 3)
-        {
-            data_path = argv[2];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\n\n";
-            std::cout << "No image provided: using random values\n\n";
-        }
-        else if(argc == 4)
-        {
-            data_path = argv[2];
-            image     = argv[3];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n";
-            std::cout << "No text file with labels provided: skipping output accessor\n\n";
-        }
-        else if(argc == 5)
-        {
-            data_path = argv[2];
-            image     = argv[3];
-            label     = argv[4];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n";
-            std::cout << "No fast math info provided: disabling fast math\n\n";
-        }
-        else
-        {
-            data_path      = argv[2];
-            image          = argv[3];
-            label          = argv[4];
-            fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
+            cmd_parser.print_help(argv[0]);
+            return false;
         }
 
-        graph << target_hint
-              << fast_math_hint
-              << InputLayer(TensorDescriptor(TensorShape(299U, 299U, 3U, 1U), DataType::F32),
-                            get_input_accessor(image, std::move(preprocessor), false))
+        // Checks
+        ARM_COMPUTE_ERROR_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "Unsupported data type!");
+
+        // Print parameter values
+        std::cout << common_params << std::endl;
+
+        // Get trainable parameters data path
+        std::string data_path = common_params.data_path;
+
+        // Create a preprocessor object
+        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
+
+        graph << common_params.target
+              << common_params.fast_math_hint
+              << InputLayer(TensorDescriptor(TensorShape(299U, 299U, 3U, 1U), common_params.data_type),
+                            get_input_accessor(common_params, std::move(preprocessor), false))
               // Conv2d_1a_3x3
               << ConvolutionLayer(3U, 3U, 32U,
                                   get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_1a_3x3_weights.npy"),
@@ -165,28 +136,27 @@ public:
                   get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Logits_Logits_weights.npy"),
                   get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Logits_Logits_biases.npy"))
               << SoftmaxLayer()
-              << OutputLayer(get_output_accessor(label, 5));
+              << OutputLayer(get_output_accessor(common_params, 5));
 
         // Finalize graph
         GraphConfig config;
-        config.use_tuner = (target == 2);
-        graph.finalize(target_hint, config);
-#else  /* __aarch64__ */
-        using namespace arm_compute;
-        ARM_COMPUTE_UNUSED(argc);
-        ARM_COMPUTE_UNUSED(argv);
-#endif /* __aarch64__ */
+        config.num_threads = common_params.threads;
+        config.use_tuner   = common_params.enable_tuner;
+        graph.finalize(common_params.target, config);
+
+        return true;
     }
 
     void do_run() override
     {
-#if __aarch64__
         graph.run();
-#endif /* __aarch64__ */
     }
 
 private:
-    Stream graph{ 0, "InceptionV4" };
+    CommandLineParser  cmd_parser;
+    CommonGraphOptions common_opts;
+    CommonGraphParams  common_params;
+    Stream             graph;
 
 private:
     BranchLayer get_mixed_3a(const std::string &data_path)
@@ -747,7 +717,7 @@ private:
 /** Main program for Inception V4
  *
  * @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] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
+ * @param[in] argv Arguments
  */
 int main(int argc, char **argv)
 {
diff --git a/examples/graph_lenet.cpp b/examples/graph_lenet.cpp
index 32c75827d3..f90892aeee 100644
--- a/examples/graph_lenet.cpp
+++ b/examples/graph_lenet.cpp
@@ -22,13 +22,11 @@
  * SOFTWARE.
  */
 #include "arm_compute/graph.h"
-
 #include "support/ToolchainSupport.h"
+#include "utils/CommonGraphOptions.h"
 #include "utils/GraphUtils.h"
 #include "utils/Utils.h"
 
-#include <cstdlib>
-
 using namespace arm_compute::utils;
 using namespace arm_compute::graph::frontend;
 using namespace arm_compute::graph_utils;
@@ -41,55 +39,39 @@ using namespace arm_compute::graph_utils;
 class GraphLenetExample : public Example
 {
 public:
-    void do_setup(int argc, char **argv) override
+    GraphLenetExample()
+        : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "LeNet")
     {
-        std::string  data_path;   /** Path to the trainable data */
-        unsigned int batches = 4; /** Number of batches */
-
-        // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
-        const int target      = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
-        Target    target_hint = set_target_hint(target);
+    }
+    bool do_setup(int argc, char **argv) override
+    {
+        // Parse arguments
+        cmd_parser.parse(argc, argv);
 
-        FastMathHint fast_math_hint = FastMathHint::DISABLED;
+        // Consume common parameters
+        common_params = consume_common_graph_parameters(common_opts);
 
-        // Parse arguments
-        if(argc < 2)
+        // Return when help menu is requested
+        if(common_params.help)
         {
-            // Print help
-            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [batches] [fast_math_hint]\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] [batches] [fast_math_hint]\n\n";
-            std::cout << "No data folder provided: using random values\n\n";
-        }
-        else if(argc == 3)
-        {
-            //Do something with argv[1]
-            data_path = argv[2];
-            std::cout << "Usage: " << argv[0] << " [path_to_data] [batches] [fast_math_hint]\n\n";
-            std::cout << "No number of batches where specified, thus will use the default : " << batches << "\n\n";
-        }
-        else if(argc == 4)
-        {
-            data_path = argv[2];
-            batches   = std::strtol(argv[3], nullptr, 0);
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [fast_math_hint]\n\n";
-            std::cout << "No fast math info provided: disabling fast math\n\n";
-        }
-        else
-        {
-            //Do something with argv[1] and argv[2]
-            data_path      = argv[2];
-            batches        = std::strtol(argv[3], nullptr, 0);
-            fast_math_hint = (std::strtol(argv[4], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
+            cmd_parser.print_help(argv[0]);
+            return false;
         }
 
+        // Checks
+        ARM_COMPUTE_ERROR_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "Unsupported data type!");
+
+        // Print parameter values
+        std::cout << common_params << std::endl;
+
+        // Get trainable parameters data path
+        std::string  data_path = common_params.data_path;
+        unsigned int batches   = 4; /** Number of batches */
+
         //conv1 << pool1 << conv2 << pool2 << fc1 << act1 << fc2 << smx
-        graph << target_hint
-              << fast_math_hint
-              << InputLayer(TensorDescriptor(TensorShape(28U, 28U, 1U, batches), DataType::F32), get_input_accessor(""))
+        graph << common_params.target
+              << common_params.fast_math_hint
+              << InputLayer(TensorDescriptor(TensorShape(28U, 28U, 1U, batches), common_params.data_type), get_input_accessor(common_params))
               << ConvolutionLayer(
                   5U, 5U, 20U,
                   get_weights_accessor(data_path, "/cnn_data/lenet_model/conv1_w.npy"),
@@ -116,12 +98,15 @@ public:
                   get_weights_accessor(data_path, "/cnn_data/lenet_model/ip2_b.npy"))
               .set_name("ip2")
               << SoftmaxLayer().set_name("prob")
-              << OutputLayer(get_output_accessor(""));
+              << OutputLayer(get_output_accessor(common_params));
 
         // Finalize graph
         GraphConfig config;
-        config.use_tuner = (target == 2);
-        graph.finalize(target_hint, config);
+        config.num_threads = common_params.threads;
+        config.use_tuner   = common_params.enable_tuner;
+        graph.finalize(common_params.target, config);
+
+        return true;
     }
     void do_run() override
     {
@@ -130,7 +115,10 @@ public:
     }
 
 private:
-    Stream graph{ 0, "LeNet" };
+    CommandLineParser  cmd_parser;
+    CommonGraphOptions common_opts;
+    CommonGraphParams  common_params;
+    Stream             graph;
 };
 
 /** Main program for LeNet
diff --git a/examples/graph_mobilenet.cpp b/examples/graph_mobilenet.cpp
index 40243bb111..9304b2b380 100644
--- a/examples/graph_mobilenet.cpp
+++ b/examples/graph_mobilenet.cpp
@@ -23,11 +23,11 @@
  */
 #include "arm_compute/graph.h"
 #include "support/ToolchainSupport.h"
+#include "utils/CommonGraphOptions.h"
 #include "utils/GraphUtils.h"
 #include "utils/Utils.h"
 
-#include <cstdlib>
-
+using namespace arm_compute;
 using namespace arm_compute::utils;
 using namespace arm_compute::graph::frontend;
 using namespace arm_compute::graph_utils;
@@ -35,107 +35,112 @@ using namespace arm_compute::graph_utils;
 /** Example demonstrating how to implement 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] Model ID (0 = MobileNetV1_1.0_224, 1 = MobileNetV1_0.75_160), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] data layout, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
+ * @param[in] argv Arguments
  */
-class GraphMobilenetExample : public Example
+class GraphMobilenetExample : public Example // NOLINT
 {
 public:
-    void do_setup(int argc, char **argv) override
+    GraphMobilenetExample()
+        : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "MobileNetV1")
     {
-        std::string data_path; /* Path to the trainable data */
-        std::string image;     /* Image data */
-        std::string label;     /* Label data */
-
-        // Create a preprocessor object
-        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
+        // Add model id option
+        model_id_opt = cmd_parser.add_option<SimpleOption<int>>("model-id", 0);
+        model_id_opt->set_help("Mobilenet model id (0: 1.0_224, else: 0.75_160");
+    }
+    /** Prevent instances of this class from being copy constructed */
+    GraphMobilenetExample(const GraphMobilenetExample &) = delete;
+    /** Prevent instances of this class from being copied */
+    GraphMobilenetExample &operator=(const GraphMobilenetExample &) = delete;
+    bool do_setup(int argc, char **argv) override
+    {
+        // Parse arguments
+        cmd_parser.parse(argc, argv);
 
-        // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
-        const int                  target                     = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
-        Target                     target_hint                = set_target_hint(target);
-        DepthwiseConvolutionMethod depthwise_convolution_hint = DepthwiseConvolutionMethod::OPTIMIZED_3x3;
-        FastMathHint               fast_math_hint             = FastMathHint::DISABLED;
-
-        // Set model to execute. 0 (MobileNetV1_1.0_224), 1 (MobileNetV1_0.75_160)
-        int model_id = (argc > 2) ? std::strtol(argv[2], nullptr, 10) : 0;
-        ARM_COMPUTE_ERROR_ON_MSG(model_id > 1, "Invalid model ID. Model must be 0 (MobileNetV1_1.0_224) or 1 (MobileNetV1_0.75_160)");
-        int layout_id = (argc > 3) ? std::strtol(argv[3], nullptr, 10) : 0;
-        ARM_COMPUTE_ERROR_ON_MSG(layout_id > 1, "Invalid layout ID. Layout must be 0 (NCHW) or 1 (NHWC)");
-
-        float            depth_scale           = (model_id == 0) ? 1.f : 0.75;
-        unsigned int     spatial_size          = (model_id == 0) ? 224 : 160;
-        std::string      model_path            = (model_id == 0) ? "/cnn_data/mobilenet_v1_1_224_model/" : "/cnn_data/mobilenet_v1_075_160_model/";
-        TensorDescriptor input_descriptor_nchw = TensorDescriptor(TensorShape(spatial_size, spatial_size, 3U, 1U), DataType::F32);
-        TensorDescriptor input_descriptor_nhwc = TensorDescriptor(TensorShape(3U, spatial_size, spatial_size, 1U), DataType::F32).set_layout(DataLayout::NHWC);
-        TensorDescriptor input_descriptor      = (layout_id == 0) ? input_descriptor_nchw : input_descriptor_nhwc;
+        // Consume common parameters
+        common_params = consume_common_graph_parameters(common_opts);
 
-        // Parse arguments
-        if(argc < 2)
-        {
-            // Print help
-            std::cout << "Usage: " << argv[0] << " [target] [model] [layout] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
-            std::cout << "No model ID provided: using MobileNetV1_1.0_224\n\n";
-            std::cout << "No data layout provided: using NCHW\n\n";
-            std::cout << "No data folder provided: using random values\n\n";
-        }
-        else if(argc == 2)
+        // Return when help menu is requested
+        if(common_params.help)
         {
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [model] [layout] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
-            std::cout << "No model ID provided: using MobileNetV1_1.0_224\n\n";
-            std::cout << "No data layout provided: using NCHW\n\n";
-            std::cout << "No data folder provided: using random values\n\n";
+            cmd_parser.print_help(argv[0]);
+            return false;
         }
-        else if(argc == 3)
-        {
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [layout] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
-            std::cout << "No data layout provided: using NCHW\n\n";
-            std::cout << "No data folder provided: using random values\n\n";
-        }
-        else if(argc == 4)
-        {
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [path_to_data] [image] [labels] [fast_math_hint]\n\n";
-            std::cout << "No data folder provided: using random values\n\n";
-        }
-        else if(argc == 5)
-        {
-            data_path = argv[4];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [image] [labels] [fast_math_hint]\n\n";
-            std::cout << "No image provided: using random values\n\n";
-            std::cout << "No text file with labels provided: skipping output accessor\n\n";
-        }
-        else if(argc == 6)
+
+        // Print parameter values
+        std::cout << common_params << std::endl;
+
+        // Get model parameters
+        int model_id = model_id_opt->value();
+
+        // Create input descriptor
+        unsigned int spatial_size = (model_id == 0 || common_params.data_type == DataType::QASYMM8) ? 224 : 160;
+        TensorShape  tensor_shape = TensorShape(spatial_size, spatial_size, 3U, 1U);
+        if(common_params.data_layout == DataLayout::NHWC)
         {
-            data_path = argv[4];
-            image     = argv[5];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n";
-            std::cout << "No text file with labels provided: skipping output accessor\n\n";
+            arm_compute::permute(tensor_shape, arm_compute::PermutationVector(2U, 0U, 1U));
         }
-        else if(argc == 7)
+        TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
+
+        // Set graph hints
+        graph << common_params.target
+              << DepthwiseConvolutionMethod::OPTIMIZED_3x3 // FIXME(COMPMID-1073): Add heuristics to automatically call the optimized 3x3 method
+              << common_params.fast_math_hint;
+
+        // Create core graph
+        if(arm_compute::is_data_type_float(common_params.data_type))
         {
-            data_path = argv[4];
-            image     = argv[5];
-            label     = argv[6];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n";
-            std::cout << "No fast math info provided: disabling fast math\n\n";
+            create_graph_float(input_descriptor, model_id);
         }
         else
         {
-            data_path      = argv[4];
-            image          = argv[5];
-            label          = argv[6];
-            fast_math_hint = (std::strtol(argv[7], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
+            create_graph_qasymm(input_descriptor);
         }
 
+        // Create common tail
+        graph << ReshapeLayer(TensorShape(1001U)).set_name("Reshape")
+              << SoftmaxLayer().set_name("Softmax")
+              << OutputLayer(get_output_accessor(common_params, 5));
+
+        // Finalize graph
+        GraphConfig config;
+        config.num_threads = common_params.threads;
+        config.use_tuner   = common_params.enable_tuner;
+        graph.finalize(common_params.target, config);
+
+        return true;
+    }
+    void do_run() override
+    {
+        // Run graph
+        graph.run();
+    }
+
+private:
+    CommandLineParser  cmd_parser;
+    CommonGraphOptions common_opts;
+    SimpleOption<int> *model_id_opt{ nullptr };
+    CommonGraphParams  common_params;
+    Stream             graph;
+
+    void create_graph_float(TensorDescriptor &input_descriptor, int model_id)
+    {
+        float       depth_scale = (model_id == 0) ? 1.f : 0.75;
+        std::string model_path  = (model_id == 0) ? "/cnn_data/mobilenet_v1_1_224_model/" : "/cnn_data/mobilenet_v1_075_160_model/";
+
+        // Create a preprocessor object
+        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
+
+        // Get trainable parameters data path
+        std::string data_path = common_params.data_path;
+
         // Add model path to data path
         if(!data_path.empty())
         {
             data_path += model_path;
         }
 
-        graph << target_hint
-              << depthwise_convolution_hint
-              << fast_math_hint
-              << InputLayer(input_descriptor,
-                            get_input_accessor(image, std::move(preprocessor), false))
+        graph << InputLayer(input_descriptor,
+                            get_input_accessor(common_params, std::move(preprocessor), false))
               << ConvolutionLayer(
                   3U, 3U, 32U * depth_scale,
                   get_weights_accessor(data_path, "Conv2d_0_weights.npy", DataLayout::NCHW),
@@ -150,47 +155,122 @@ public:
                   0.001f)
               .set_name("Conv2d_0/BatchNorm")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name("Conv2d_0/Relu6");
-        graph << get_dwsc_node(data_path, "Conv2d_1", 64 * depth_scale, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0));
-        graph << get_dwsc_node(data_path, "Conv2d_2", 128 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
-        graph << get_dwsc_node(data_path, "Conv2d_3", 128 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
-        graph << get_dwsc_node(data_path, "Conv2d_4", 256 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
-        graph << get_dwsc_node(data_path, "Conv2d_5", 256 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
-        graph << get_dwsc_node(data_path, "Conv2d_6", 512 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
-        graph << get_dwsc_node(data_path, "Conv2d_7", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
-        graph << get_dwsc_node(data_path, "Conv2d_8", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
-        graph << get_dwsc_node(data_path, "Conv2d_9", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
-        graph << get_dwsc_node(data_path, "Conv2d_10", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
-        graph << get_dwsc_node(data_path, "Conv2d_11", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
-        graph << get_dwsc_node(data_path, "Conv2d_12", 1024 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
-        graph << get_dwsc_node(data_path, "Conv2d_13", 1024 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
+        graph << get_dwsc_node_float(data_path, "Conv2d_1", 64 * depth_scale, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0));
+        graph << get_dwsc_node_float(data_path, "Conv2d_2", 128 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
+        graph << get_dwsc_node_float(data_path, "Conv2d_3", 128 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
+        graph << get_dwsc_node_float(data_path, "Conv2d_4", 256 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
+        graph << get_dwsc_node_float(data_path, "Conv2d_5", 256 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
+        graph << get_dwsc_node_float(data_path, "Conv2d_6", 512 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
+        graph << get_dwsc_node_float(data_path, "Conv2d_7", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
+        graph << get_dwsc_node_float(data_path, "Conv2d_8", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
+        graph << get_dwsc_node_float(data_path, "Conv2d_9", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
+        graph << get_dwsc_node_float(data_path, "Conv2d_10", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
+        graph << get_dwsc_node_float(data_path, "Conv2d_11", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
+        graph << get_dwsc_node_float(data_path, "Conv2d_12", 1024 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
+        graph << get_dwsc_node_float(data_path, "Conv2d_13", 1024 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
         graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool_1a")
               << ConvolutionLayer(
                   1U, 1U, 1001U,
                   get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy", DataLayout::NCHW),
                   get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"),
                   PadStrideInfo(1, 1, 0, 0))
-              .set_name("Logits/Conv2d_1c_1x1")
-              << ReshapeLayer(TensorShape(1001U)).set_name("Reshape")
-              << SoftmaxLayer().set_name("Softmax")
-              << OutputLayer(get_output_accessor(label, 5));
-
-        // Finalize graph
-        GraphConfig config;
-        config.use_tuner = (target == 2);
-        graph.finalize(target_hint, config);
+              .set_name("Logits/Conv2d_1c_1x1");
     }
-    void do_run() override
+
+    void create_graph_qasymm(TensorDescriptor &input_descriptor)
     {
-        // Run graph
-        graph.run();
-    }
+        // Get trainable parameters data path
+        std::string data_path = common_params.data_path;
 
-private:
-    Stream graph{ 0, "MobileNetV1" };
+        // 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
+        };
 
-    BranchLayer get_dwsc_node(const std::string &data_path, std::string &&param_path,
-                              unsigned int  conv_filt,
-                              PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info)
+        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
+        };
+
+        graph << InputLayer(input_descriptor.set_quantization_info(in_quant_info),
+                            get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/" + common_params.image))
+              << 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, conv_weights_quant_info.at(0), mid_quant_info)
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f));
+        graph << get_dwsc_node_qasymm(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));
+        graph << get_dwsc_node_qasymm(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));
+        graph << get_dwsc_node_qasymm(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));
+        graph << get_dwsc_node_qasymm(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));
+        graph << get_dwsc_node_qasymm(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));
+        graph << get_dwsc_node_qasymm(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));
+        graph << get_dwsc_node_qasymm(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));
+        graph << get_dwsc_node_qasymm(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));
+        graph << get_dwsc_node_qasymm(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));
+        graph << get_dwsc_node_qasymm(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));
+        graph << get_dwsc_node_qasymm(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));
+        graph << get_dwsc_node_qasymm(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));
+        graph << get_dwsc_node_qasymm(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, conv_weights_quant_info.at(1));
+    }
+
+    BranchLayer get_dwsc_node_float(const std::string &data_path, std::string &&param_path,
+                                    unsigned int  conv_filt,
+                                    PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info)
     {
         std::string total_path = param_path + "_";
         SubStream   sg(graph);
@@ -225,18 +305,36 @@ private:
 
         return BranchLayer(std::move(sg));
     }
+
+    BranchLayer get_dwsc_node_qasymm(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 + "_";
+        SubStream   sg(graph);
+
+        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, 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, point_weights_quant_info)
+           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f));
+
+        return BranchLayer(std::move(sg));
+    }
 };
 
 /** Main program for MobileNetV1
  *
  * @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner),
- *                             [optional] Model ID (0 = MobileNetV1_1.0_224, 1 = MobileNetV1_0.75_160),
- *                             [optional] Path to the weights folder,
- *                             [optional] image,
- *                             [optional] labels,
- *                             [optional] data layout,
- *                             [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
+ * @param[in] argv Arguments
  */
 int main(int argc, char **argv)
 {
diff --git a/examples/graph_mobilenet_qasymm8.cpp b/examples/graph_mobilenet_qasymm8.cpp
deleted file mode 100644
index 2801209985..0000000000
--- a/examples/graph_mobilenet_qasymm8.cpp
+++ /dev/null
@@ -1,239 +0,0 @@
-/*
- * 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.h"
-#include "support/ToolchainSupport.h"
-#include "utils/GraphUtils.h"
-#include "utils/Utils.h"
-
-#include <cstdlib>
-
-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 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, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
- */
-class GraphMobileNetQASYMM8Example : public 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    target         = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
-        Target       target_hint    = set_target_hint(target);
-        FastMathHint fast_math_hint = FastMathHint::DISABLED;
-
-        // Parse arguments
-        if(argc < 2)
-        {
-            // Print help
-            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [npy_input] [labels] [fast_math_hint]\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] [fast_math_hint]\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] [fast_math_hint]\n\n";
-            std::cout << "No text file with labels provided: skipping output accessor\n\n";
-        }
-        else if(argc == 5)
-        {
-            data_path = argv[2];
-            input     = argv[3];
-            label     = argv[4];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n";
-            std::cout << "No fast math info provided: disabling fast math\n\n";
-        }
-        else
-        {
-            data_path      = argv[2];
-            input          = argv[3];
-            label          = argv[4];
-            fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
-        }
-
-        graph << target_hint
-              << DepthwiseConvolutionMethod::OPTIMIZED_3x3 // FIXME(COMPMID-1073): Add heuristics to automatically call the optimized 3x3 method
-              << fast_math_hint
-              << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), 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, conv_weights_quant_info.at(0), mid_quant_info)
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f));
-        graph << 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));
-        graph << 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));
-        graph << 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));
-        graph << 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));
-        graph << 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));
-        graph << 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));
-        graph << 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));
-        graph << 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));
-        graph << 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));
-        graph << 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));
-        graph << 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));
-        graph << 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));
-        graph << 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, conv_weights_quant_info.at(1))
-              << ReshapeLayer(TensorShape(1001U))
-              << SoftmaxLayer()
-              << OutputLayer(get_output_accessor(label, 5));
-
-        // Finalize graph
-        GraphConfig config;
-        config.use_tuner = (target == 2);
-        graph.finalize(target_hint, config);
-    }
-    void do_run() override
-    {
-        // Run graph
-        graph.run();
-    }
-
-private:
-    Stream graph{ 0, "MobileNetV1_QASYMM8" };
-
-    /** 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 + "_";
-        SubStream   sg(graph);
-
-        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, 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, 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, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
- */
-int main(int argc, char **argv)
-{
-    return arm_compute::utils::run_example<GraphMobileNetQASYMM8Example>(argc, argv);
-}
diff --git a/examples/graph_resnet50.cpp b/examples/graph_resnet50.cpp
index bafa9a5852..66fc6e869d 100644
--- a/examples/graph_resnet50.cpp
+++ b/examples/graph_resnet50.cpp
@@ -23,11 +23,10 @@
  */
 #include "arm_compute/graph.h"
 #include "support/ToolchainSupport.h"
+#include "utils/CommonGraphOptions.h"
 #include "utils/GraphUtils.h"
 #include "utils/Utils.h"
 
-#include <cstdlib>
-
 using namespace arm_compute::utils;
 using namespace arm_compute::graph::frontend;
 using namespace arm_compute::graph_utils;
@@ -35,72 +34,47 @@ using namespace arm_compute::graph_utils;
 /** Example demonstrating how to implement ResNet50 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] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
+ * @param[in] argv Arguments
  */
 class GraphResNet50Example : public Example
 {
 public:
-    void do_setup(int argc, char **argv) override
+    GraphResNet50Example()
+        : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "ResNet50")
     {
-        std::string data_path; /* Path to the trainable data */
-        std::string image;     /* Image data */
-        std::string label;     /* Label data */
-
-        // Create a preprocessor object
-        const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } };
-        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb,
-                                                                                                                   false /* Do not convert to BGR */);
+    }
+    bool do_setup(int argc, char **argv) override
+    {
+        // Parse arguments
+        cmd_parser.parse(argc, argv);
 
-        // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
-        const int    target         = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
-        Target       target_hint    = set_target_hint(target);
-        FastMathHint fast_math_hint = FastMathHint::DISABLED;
+        // Consume common parameters
+        common_params = consume_common_graph_parameters(common_opts);
 
-        // Parse arguments
-        if(argc < 2)
-        {
-            // Print help
-            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\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] [image] [labels] [fast_math_hint]\n\n";
-            std::cout << "No data folder provided: using random values\n\n";
-        }
-        else if(argc == 3)
+        // Return when help menu is requested
+        if(common_params.help)
         {
-            data_path = argv[2];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\n\n";
-            std::cout << "No image provided: using random values\n\n";
-        }
-        else if(argc == 4)
-        {
-            data_path = argv[2];
-            image     = argv[3];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n";
-            std::cout << "No text file with labels provided: skipping output accessor\n\n";
-        }
-        else if(argc == 5)
-        {
-            data_path = argv[2];
-            image     = argv[3];
-            label     = argv[4];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n";
-            std::cout << "No fast math info provided: disabling fast math\n\n";
-        }
-        else
-        {
-            data_path      = argv[2];
-            image          = argv[3];
-            label          = argv[4];
-            fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
+            cmd_parser.print_help(argv[0]);
+            return false;
         }
 
-        graph << target_hint
-              << fast_math_hint
-              << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32),
-                            get_input_accessor(image, std::move(preprocessor), false /* Do not convert to BGR */))
+        // Checks
+        ARM_COMPUTE_ERROR_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "Unsupported data type!");
+
+        // Print parameter values
+        std::cout << common_params << std::endl;
+
+        // Get trainable parameters data path
+        std::string data_path = common_params.data_path;
+
+        // Create a preprocessor object
+        const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } };
+        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb,
+                                                                                                                   false /* Do not convert to BGR */);
+        graph << common_params.target
+              << common_params.fast_math_hint
+              << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), common_params.data_type),
+                            get_input_accessor(common_params, std::move(preprocessor), false /* Do not convert to BGR */))
               << ConvolutionLayer(
                   7U, 7U, 64U,
                   get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_weights.npy"),
@@ -131,12 +105,15 @@ public:
               .set_name("logits/convolution")
               << FlattenLayer().set_name("predictions/Reshape")
               << SoftmaxLayer().set_name("predictions/Softmax")
-              << OutputLayer(get_output_accessor(label, 5));
+              << OutputLayer(get_output_accessor(common_params, 5));
 
         // Finalize graph
         GraphConfig config;
-        config.use_tuner = (target == 2);
-        graph.finalize(target_hint, config);
+        config.num_threads = common_params.threads;
+        config.use_tuner   = common_params.enable_tuner;
+        graph.finalize(common_params.target, config);
+
+        return true;
     }
 
     void do_run() override
@@ -146,7 +123,10 @@ public:
     }
 
 private:
-    Stream graph{ 0, "ResNet50" };
+    CommandLineParser  cmd_parser;
+    CommonGraphOptions common_opts;
+    CommonGraphParams  common_params;
+    Stream             graph;
 
     void add_residual_block(const std::string &data_path, const std::string &name, unsigned int base_depth, unsigned int num_units, unsigned int stride)
     {
@@ -252,7 +232,7 @@ private:
 /** Main program for ResNet50
  *
  * @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] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
+ * @param[in] argv Arguments
  */
 int main(int argc, char **argv)
 {
diff --git a/examples/graph_resnext50.cpp b/examples/graph_resnext50.cpp
index f96a02e6d6..c0a2308a1f 100644
--- a/examples/graph_resnext50.cpp
+++ b/examples/graph_resnext50.cpp
@@ -23,11 +23,10 @@
  */
 #include "arm_compute/graph.h"
 #include "support/ToolchainSupport.h"
+#include "utils/CommonGraphOptions.h"
 #include "utils/GraphUtils.h"
 #include "utils/Utils.h"
 
-#include <cstdlib>
-
 using namespace arm_compute::utils;
 using namespace arm_compute::graph::frontend;
 using namespace arm_compute::graph_utils;
@@ -35,67 +34,43 @@ using namespace arm_compute::graph_utils;
 /** Example demonstrating how to implement ResNeXt50 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_in, [optional] npy_out, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
+ * @param[in] argv Arguments
  */
 class GraphResNeXt50Example : public Example
 {
 public:
-    void do_setup(int argc, char **argv) override
+    GraphResNeXt50Example()
+        : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "ResNeXt50")
+    {
+    }
+    bool do_setup(int argc, char **argv) override
     {
-        std::string data_path; /* Path to the trainable data */
-        std::string npy_in;    /* Input npy data */
-        std::string npy_out;   /* Output npy data */
+        // Parse arguments
+        cmd_parser.parse(argc, argv);
 
-        // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
-        const int    target         = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
-        Target       target_hint    = set_target_hint(target);
-        FastMathHint fast_math_hint = FastMathHint::DISABLED;
+        // Consume common parameters
+        common_params = consume_common_graph_parameters(common_opts);
 
-        // Parse arguments
-        if(argc < 2)
+        // Return when help menu is requested
+        if(common_params.help)
         {
-            // Print help
-            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [npy_in] [npy_out] [fast_math_hint]\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_in] [npy_out] [fast_math_hint]\n\n";
-            std::cout << "No data folder provided: using random values\n\n";
-        }
-        else if(argc == 3)
-        {
-            data_path = argv[2];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [npy_in] [npy_out] [fast_math_hint]\n\n";
-            std::cout << "No input npy file provided: using random values\n\n";
-        }
-        else if(argc == 4)
-        {
-            data_path = argv[2];
-            npy_in    = argv[3];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [npy_out] [fast_math_hint]\n\n";
-            std::cout << "No output npy file provided: skipping output accessor\n\n";
-        }
-        else if(argc == 5)
-        {
-            data_path = argv[2];
-            npy_in    = argv[3];
-            npy_out   = argv[4];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n";
-            std::cout << "No fast math info provided: disabling fast math\n\n";
-        }
-        else
-        {
-            data_path      = argv[2];
-            npy_in         = argv[3];
-            npy_out        = argv[4];
-            fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
+            cmd_parser.print_help(argv[0]);
+            return false;
         }
 
-        graph << target_hint
-              << fast_math_hint
-              << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32),
-                            get_input_accessor(npy_in))
+        // Checks
+        ARM_COMPUTE_ERROR_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "Unsupported data type!");
+
+        // Print parameter values
+        std::cout << common_params << std::endl;
+
+        // Get trainable parameters data path
+        std::string data_path = common_params.data_path;
+
+        graph << common_params.target
+              << common_params.fast_math_hint
+              << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), common_params.data_type),
+                            get_input_accessor(common_params))
               << ScaleLayer(get_weights_accessor(data_path, "/cnn_data/resnext50_model/bn_data_mul.npy"),
                             get_weights_accessor(data_path, "/cnn_data/resnext50_model/bn_data_add.npy"))
               .set_name("bn_data/Scale")
@@ -115,12 +90,15 @@ public:
 
         graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("pool1")
               << FlattenLayer().set_name("predictions/Reshape")
-              << OutputLayer(get_npy_output_accessor(npy_out, TensorShape(2048U), DataType::F32));
+              << OutputLayer(get_npy_output_accessor(common_params.labels, TensorShape(2048U), DataType::F32));
 
         // Finalize graph
         GraphConfig config;
-        config.use_tuner = (target == 2);
-        graph.finalize(target_hint, config);
+        config.num_threads = common_params.threads;
+        config.use_tuner   = common_params.enable_tuner;
+        graph.finalize(common_params.target, config);
+
+        return true;
     }
 
     void do_run() override
@@ -130,7 +108,10 @@ public:
     }
 
 private:
-    Stream graph{ 0, "ResNeXt50" };
+    CommandLineParser  cmd_parser;
+    CommonGraphOptions common_opts;
+    CommonGraphParams  common_params;
+    Stream             graph;
 
     void add_residual_block(const std::string &data_path, unsigned int base_depth, unsigned int stage, unsigned int num_units, unsigned int stride_conv_unit1)
     {
@@ -200,7 +181,7 @@ private:
 /** Main program for ResNeXt50
  *
  * @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_in, [optional] npy_out )
+ * @param[in] argv Arguments
  */
 int main(int argc, char **argv)
 {
diff --git a/examples/graph_squeezenet.cpp b/examples/graph_squeezenet.cpp
index b632688839..a290b91148 100644
--- a/examples/graph_squeezenet.cpp
+++ b/examples/graph_squeezenet.cpp
@@ -23,85 +23,58 @@
  */
 #include "arm_compute/graph.h"
 #include "support/ToolchainSupport.h"
+#include "utils/CommonGraphOptions.h"
 #include "utils/GraphUtils.h"
 #include "utils/Utils.h"
 
-#include <cstdlib>
-#include <tuple>
-
 using namespace arm_compute::utils;
 using namespace arm_compute::graph::frontend;
 using namespace arm_compute::graph_utils;
-using namespace arm_compute::logging;
 
 /** Example demonstrating how to implement Squeezenet'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] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
+ * @param[in] argv Arguments
  */
 class GraphSqueezenetExample : public Example
 {
 public:
-    void do_setup(int argc, char **argv) override
+    GraphSqueezenetExample()
+        : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "SqueezeNetV1")
     {
-        std::string data_path; /* Path to the trainable data */
-        std::string image;     /* Image data */
-        std::string label;     /* Label data */
-
-        // Create a preprocessor object
-        const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } };
-        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
+    }
+    bool do_setup(int argc, char **argv) override
+    {
+        // Parse arguments
+        cmd_parser.parse(argc, argv);
 
-        // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
-        const int    target         = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
-        Target       target_hint    = set_target_hint(target);
-        FastMathHint fast_math_hint = FastMathHint::DISABLED;
+        // Consume common parameters
+        common_params = consume_common_graph_parameters(common_opts);
 
-        // Parse arguments
-        if(argc < 2)
-        {
-            // Print help
-            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
-            std::cout << "No data folder provided: using random values\n\n";
-        }
-        else if(argc == 2)
+        // Return when help menu is requested
+        if(common_params.help)
         {
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\n\n";
-            std::cout << "No data folder provided: using random values\n\n";
-        }
-        else if(argc == 3)
-        {
-            data_path = argv[2];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\n\n";
-            std::cout << "No image provided: using random values\n\n";
-        }
-        else if(argc == 4)
-        {
-            data_path = argv[2];
-            image     = argv[3];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n";
-            std::cout << "No text file with labels provided: skipping output accessor\n\n";
-        }
-        else if(argc == 5)
-        {
-            data_path = argv[2];
-            image     = argv[3];
-            label     = argv[4];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n";
-            std::cout << "No fast math info provided: disabling fast math\n\n";
-        }
-        else
-        {
-            data_path      = argv[2];
-            image          = argv[3];
-            label          = argv[4];
-            fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
+            cmd_parser.print_help(argv[0]);
+            return false;
         }
 
-        graph << target_hint
-              << fast_math_hint
-              << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32),
-                            get_input_accessor(image, std::move(preprocessor)))
+        // Checks
+        ARM_COMPUTE_ERROR_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "Unsupported data type!");
+
+        // Print parameter values
+        std::cout << common_params << std::endl;
+
+        // Get trainable parameters data path
+        std::string data_path = common_params.data_path;
+
+        // Create a preprocessor object
+        const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } };
+        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
+
+        graph << common_params.target
+              << common_params.fast_math_hint
+              << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), common_params.data_type),
+                            get_input_accessor(common_params, std::move(preprocessor)))
               << ConvolutionLayer(
                   7U, 7U, 96U,
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_w.npy"),
@@ -176,12 +149,15 @@ public:
               << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
               << FlattenLayer()
               << SoftmaxLayer()
-              << OutputLayer(get_output_accessor(label, 5));
+              << OutputLayer(get_output_accessor(common_params, 5));
 
         // Finalize graph
         GraphConfig config;
-        config.use_tuner = (target == 2);
-        graph.finalize(target_hint, config);
+        config.num_threads = common_params.threads;
+        config.use_tuner   = common_params.enable_tuner;
+        graph.finalize(common_params.target, config);
+
+        return true;
     }
     void do_run() override
     {
@@ -190,7 +166,10 @@ public:
     }
 
 private:
-    Stream graph{ 0, "SqueezeNetV1" };
+    CommandLineParser  cmd_parser;
+    CommonGraphOptions common_opts;
+    CommonGraphParams  common_params;
+    Stream             graph;
 
     BranchLayer get_expand_fire_node(const std::string &data_path, std::string &&param_path, unsigned int expand1_filt, unsigned int expand3_filt)
     {
@@ -218,7 +197,7 @@ private:
 /** Main program for Squeezenet v1.0
  *
  * @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] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
+ * @param[in] argv Arguments
  */
 int main(int argc, char **argv)
 {
diff --git a/examples/graph_squeezenet_v1_1.cpp b/examples/graph_squeezenet_v1_1.cpp
index b2c5a442cd..8ce928c5b1 100644
--- a/examples/graph_squeezenet_v1_1.cpp
+++ b/examples/graph_squeezenet_v1_1.cpp
@@ -23,12 +23,10 @@
  */
 #include "arm_compute/graph.h"
 #include "support/ToolchainSupport.h"
+#include "utils/CommonGraphOptions.h"
 #include "utils/GraphUtils.h"
 #include "utils/Utils.h"
 
-#include <cstdlib>
-#include <tuple>
-
 using namespace arm_compute::utils;
 using namespace arm_compute::graph::frontend;
 using namespace arm_compute::graph_utils;
@@ -36,71 +34,47 @@ using namespace arm_compute::graph_utils;
 /** Example demonstrating how to implement Squeezenet's v1.1 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] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
+ * @param[in] argv Arguments
  */
 class GraphSqueezenet_v1_1Example : public Example
 {
 public:
-    void do_setup(int argc, char **argv) override
+    GraphSqueezenet_v1_1Example()
+        : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "SqueezeNetV1.1")
     {
-        std::string data_path; /* Path to the trainable data */
-        std::string image;     /* Image data */
-        std::string label;     /* Label data */
-
-        // Create a preprocessor object
-        const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } };
-        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
+    }
+    bool do_setup(int argc, char **argv) override
+    {
+        // Parse arguments
+        cmd_parser.parse(argc, argv);
 
-        // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
-        const int    target         = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
-        Target       target_hint    = set_target_hint(target);
-        FastMathHint fast_math_hint = FastMathHint::DISABLED;
+        // Consume common parameters
+        common_params = consume_common_graph_parameters(common_opts);
 
-        // Parse arguments
-        if(argc < 2)
-        {
-            // Print help
-            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
-            std::cout << "No data folder provided: using random values\n\n";
-        }
-        else if(argc == 2)
+        // Return when help menu is requested
+        if(common_params.help)
         {
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\n\n";
-            std::cout << "No data folder provided: using random values\n\n";
-        }
-        else if(argc == 3)
-        {
-            data_path = argv[2];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\n\n";
-            std::cout << "No image provided: using random values\n\n";
-        }
-        else if(argc == 4)
-        {
-            data_path = argv[2];
-            image     = argv[3];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n";
-            std::cout << "No text file with labels provided: skipping output accessor\n\n";
-        }
-        else if(argc == 5)
-        {
-            data_path = argv[2];
-            image     = argv[3];
-            label     = argv[4];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n";
-            std::cout << "No fast math info provided: disabling fast math\n\n";
-        }
-        else
-        {
-            data_path      = argv[2];
-            image          = argv[3];
-            label          = argv[4];
-            fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
+            cmd_parser.print_help(argv[0]);
+            return false;
         }
 
-        graph << target_hint
-              << fast_math_hint
-              << InputLayer(TensorDescriptor(TensorShape(227U, 227U, 3U, 1U), DataType::F32),
-                            get_input_accessor(image, std::move(preprocessor)))
+        // Checks
+        ARM_COMPUTE_ERROR_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "Unsupported data type!");
+
+        // Print parameter values
+        std::cout << common_params << std::endl;
+
+        // Get trainable parameters data path
+        std::string data_path = common_params.data_path;
+
+        // Create a preprocessor object
+        const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } };
+        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
+
+        graph << common_params.target
+              << common_params.fast_math_hint
+              << InputLayer(TensorDescriptor(TensorShape(227U, 227U, 3U, 1U), common_params.data_type),
+                            get_input_accessor(common_params, std::move(preprocessor)))
               << ConvolutionMethod::DIRECT
               << ConvolutionLayer(
                   3U, 3U, 64U,
@@ -177,12 +151,15 @@ public:
               << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
               << FlattenLayer()
               << SoftmaxLayer()
-              << OutputLayer(get_output_accessor(label, 5));
+              << OutputLayer(get_output_accessor(common_params, 5));
 
         // Finalize graph
         GraphConfig config;
-        config.use_tuner = (target == 2);
-        graph.finalize(target_hint, config);
+        config.num_threads = common_params.threads;
+        config.use_tuner   = common_params.enable_tuner;
+        graph.finalize(common_params.target, config);
+
+        return true;
     }
     void do_run() override
     {
@@ -191,7 +168,10 @@ public:
     }
 
 private:
-    Stream graph{ 0, "SqueezeNetV1.1" };
+    CommandLineParser  cmd_parser;
+    CommonGraphOptions common_opts;
+    CommonGraphParams  common_params;
+    Stream             graph;
 
     BranchLayer get_expand_fire_node(const std::string &data_path, std::string &&param_path, unsigned int expand1_filt, unsigned int expand3_filt)
     {
@@ -219,7 +199,7 @@ private:
 /** Main program for Squeezenet v1.1
  *
  * @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] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
+ * @param[in] argv Arguments
  */
 int main(int argc, char **argv)
 {
diff --git a/examples/graph_vgg16.cpp b/examples/graph_vgg16.cpp
index d70c56eadd..5ff306507f 100644
--- a/examples/graph_vgg16.cpp
+++ b/examples/graph_vgg16.cpp
@@ -23,11 +23,10 @@
  */
 #include "arm_compute/graph.h"
 #include "support/ToolchainSupport.h"
+#include "utils/CommonGraphOptions.h"
 #include "utils/GraphUtils.h"
 #include "utils/Utils.h"
 
-#include <cstdlib>
-
 using namespace arm_compute::utils;
 using namespace arm_compute::graph::frontend;
 using namespace arm_compute::graph_utils;
@@ -35,71 +34,47 @@ using namespace arm_compute::graph_utils;
 /** Example demonstrating how to implement VGG16'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] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
+ * @param[in] argv Arguments
  */
 class GraphVGG16Example : public Example
 {
 public:
-    void do_setup(int argc, char **argv) override
+    GraphVGG16Example()
+        : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "VGG16")
     {
-        std::string data_path; /* Path to the trainable data */
-        std::string image;     /* Image data */
-        std::string label;     /* Label data */
-
-        // Create a preprocessor object
-        const std::array<float, 3> mean_rgb{ { 123.68f, 116.779f, 103.939f } };
-        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
+    }
+    bool do_setup(int argc, char **argv) override
+    {
+        // Parse arguments
+        cmd_parser.parse(argc, argv);
 
-        // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
-        const int    target         = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
-        Target       target_hint    = set_target_hint(target);
-        FastMathHint fast_math_hint = FastMathHint::DISABLED;
+        // Consume common parameters
+        common_params = consume_common_graph_parameters(common_opts);
 
-        // Parse arguments
-        if(argc < 2)
-        {
-            // Print help
-            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
-            std::cout << "No data folder provided: using random values\n\n";
-        }
-        else if(argc == 2)
+        // Return when help menu is requested
+        if(common_params.help)
         {
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\n\n";
-            std::cout << "No data folder provided: using random values\n\n";
-        }
-        else if(argc == 3)
-        {
-            data_path = argv[2];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\n\n";
-            std::cout << "No image provided: using random values\n\n";
-        }
-        else if(argc == 4)
-        {
-            data_path = argv[2];
-            image     = argv[3];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n";
-            std::cout << "No text file with labels provided: skipping output accessor\n\n";
-        }
-        else if(argc == 5)
-        {
-            data_path = argv[2];
-            image     = argv[3];
-            label     = argv[4];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n";
-            std::cout << "No fast math info provided: disabling fast math\n\n";
-        }
-        else
-        {
-            data_path      = argv[2];
-            image          = argv[3];
-            label          = argv[4];
-            fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
+            cmd_parser.print_help(argv[0]);
+            return false;
         }
 
-        graph << target_hint
-              << fast_math_hint
-              << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32),
-                            get_input_accessor(image, std::move(preprocessor)))
+        // Checks
+        ARM_COMPUTE_ERROR_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "Unsupported data type!");
+
+        // Print parameter values
+        std::cout << common_params << std::endl;
+
+        // Get trainable parameters data path
+        std::string data_path = common_params.data_path;
+
+        // Create a preprocessor object
+        const std::array<float, 3> mean_rgb{ { 123.68f, 116.779f, 103.939f } };
+        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
+
+        graph << common_params.target
+              << common_params.fast_math_hint
+              << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), common_params.data_type),
+                            get_input_accessor(common_params, std::move(preprocessor)))
               // Layer 1
               << ConvolutionLayer(
                   3U, 3U, 64U,
@@ -231,12 +206,15 @@ public:
               .set_name("fc8")
               // Softmax
               << SoftmaxLayer().set_name("prob")
-              << OutputLayer(get_output_accessor(label, 5));
+              << OutputLayer(get_output_accessor(common_params, 5));
 
         // Finalize graph
         GraphConfig config;
-        config.use_tuner = (target == 2);
-        graph.finalize(target_hint, config);
+        config.num_threads = common_params.threads;
+        config.use_tuner   = common_params.enable_tuner;
+        graph.finalize(common_params.target, config);
+
+        return true;
     }
     void do_run() override
     {
@@ -245,13 +223,16 @@ public:
     }
 
 private:
-    Stream graph{ 0, "VGG16" };
+    CommandLineParser  cmd_parser;
+    CommonGraphOptions common_opts;
+    CommonGraphParams  common_params;
+    Stream             graph;
 };
 
 /** Main program for VGG16
  *
  * @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] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
+ * @param[in] argv Arguments
  */
 int main(int argc, char **argv)
 {
diff --git a/examples/graph_vgg19.cpp b/examples/graph_vgg19.cpp
index 8a0ec6fdbd..8bf88b96ed 100644
--- a/examples/graph_vgg19.cpp
+++ b/examples/graph_vgg19.cpp
@@ -23,83 +23,57 @@
  */
 #include "arm_compute/graph.h"
 #include "support/ToolchainSupport.h"
+#include "utils/CommonGraphOptions.h"
 #include "utils/GraphUtils.h"
 #include "utils/Utils.h"
 
-#include <cstdlib>
-
 using namespace arm_compute::utils;
 using namespace arm_compute::graph::frontend;
 using namespace arm_compute::graph_utils;
-
 /** Example demonstrating how to implement VGG19'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] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
+ * @param[in] argv Arguments
  */
 class GraphVGG19Example : public Example
 {
 public:
-    void do_setup(int argc, char **argv) override
+    GraphVGG19Example()
+        : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "VGG19")
     {
-        std::string data_path; /* Path to the trainable data */
-        std::string image;     /* Image data */
-        std::string label;     /* Label data */
-
-        // Create a preprocessor object
-        const std::array<float, 3> mean_rgb{ { 123.68f, 116.779f, 103.939f } };
-        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
+    }
+    bool do_setup(int argc, char **argv) override
+    {
+        // Parse arguments
+        cmd_parser.parse(argc, argv);
 
-        // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
-        const int    target         = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
-        Target       target_hint    = set_target_hint(target);
-        FastMathHint fast_math_hint = FastMathHint::DISABLED;
+        // Consume common parameters
+        common_params = consume_common_graph_parameters(common_opts);
 
-        // Parse arguments
-        if(argc < 2)
-        {
-            // Print help
-            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\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] [image] [labels] [fast_math_hint]\n\n";
-            std::cout << "No data folder provided: using random values\n\n";
-        }
-        else if(argc == 3)
+        // Return when help menu is requested
+        if(common_params.help)
         {
-            data_path = argv[2];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\n\n";
-            std::cout << "No image provided: using random values\n\n";
-        }
-        else if(argc == 4)
-        {
-            data_path = argv[2];
-            image     = argv[3];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n";
-            std::cout << "No text file with labels provided: skipping output accessor\n\n";
-        }
-        else if(argc == 5)
-        {
-            data_path = argv[2];
-            image     = argv[3];
-            label     = argv[4];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n";
-            std::cout << "No fast math info provided: disabling fast math\n\n";
-        }
-        else
-        {
-            data_path      = argv[2];
-            image          = argv[3];
-            label          = argv[4];
-            fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
+            cmd_parser.print_help(argv[0]);
+            return false;
         }
 
-        graph << target_hint
-              << fast_math_hint
-              << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32),
-                            get_input_accessor(image, std::move(preprocessor)))
+        // Checks
+        ARM_COMPUTE_ERROR_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "Unsupported data type!");
+
+        // Print parameter values
+        std::cout << common_params << std::endl;
+
+        // Get trainable parameters data path
+        std::string data_path = common_params.data_path;
+
+        // Create a preprocessor object
+        const std::array<float, 3> mean_rgb{ { 123.68f, 116.779f, 103.939f } };
+        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
+
+        graph << common_params.target
+              << common_params.fast_math_hint
+              << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), common_params.data_type),
+                            get_input_accessor(common_params, std::move(preprocessor)))
               // Layer 1
               << ConvolutionLayer(
                   3U, 3U, 64U,
@@ -244,12 +218,15 @@ public:
               .set_name("fc8")
               // Softmax
               << SoftmaxLayer().set_name("prob")
-              << OutputLayer(get_output_accessor(label, 5));
+              << OutputLayer(get_output_accessor(common_params, 5));
 
         // Finalize graph
         GraphConfig config;
-        config.use_tuner = (target == 2);
-        graph.finalize(target_hint, config);
+        config.num_threads = common_params.threads;
+        config.use_tuner   = common_params.enable_tuner;
+        graph.finalize(common_params.target, config);
+
+        return true;
     }
     void do_run() override
     {
@@ -258,13 +235,16 @@ public:
     }
 
 private:
-    Stream graph{ 0, "VGG19" };
+    CommandLineParser  cmd_parser;
+    CommonGraphOptions common_opts;
+    CommonGraphParams  common_params;
+    Stream             graph;
 };
 
 /** Main program for VGG19
  *
  * @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] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
+ * @param[in] argv Arguments
  */
 int main(int argc, char **argv)
 {
diff --git a/examples/neon_cartoon_effect.cpp b/examples/neon_cartoon_effect.cpp
index e6e0f34154..4285aa41e3 100644
--- a/examples/neon_cartoon_effect.cpp
+++ b/examples/neon_cartoon_effect.cpp
@@ -34,7 +34,7 @@ using namespace utils;
 class NEONCartoonEffectExample : public Example
 {
 public:
-    void do_setup(int argc, char **argv) override
+    bool do_setup(int argc, char **argv) override
     {
         // Open PPM file
         PPMLoader ppm;
@@ -75,6 +75,8 @@ public:
             ppm.fill_image(src_img);
             output_filename = std::string(argv[1]) + "_out.ppm";
         }
+
+        return true;
     }
 
     void do_run() override
diff --git a/examples/neon_cnn.cpp b/examples/neon_cnn.cpp
index 05b6c832bc..1df81256b9 100644
--- a/examples/neon_cnn.cpp
+++ b/examples/neon_cnn.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2016, 2018 ARM Limited.
+ * Copyright (c) 2016-2018 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -36,7 +36,7 @@ using namespace utils;
 class NEONCNNExample : public Example
 {
 public:
-    void do_setup(int argc, char **argv) override
+    bool do_setup(int argc, char **argv) override
     {
         ARM_COMPUTE_UNUSED(argc);
         ARM_COMPUTE_UNUSED(argv);
@@ -227,6 +227,8 @@ public:
 
         // Finalize the manager. (Validity checks, memory allocations etc)
         mm_transitions->finalize();
+
+        return true;
     }
     void do_run() override
     {
diff --git a/examples/neon_convolution.cpp b/examples/neon_convolution.cpp
index 8efb932081..1a7e865908 100644
--- a/examples/neon_convolution.cpp
+++ b/examples/neon_convolution.cpp
@@ -53,7 +53,7 @@ const int16_t gaussian5x5[] =
 class NEONConvolutionExample : public Example
 {
 public:
-    void do_setup(int argc, char **argv) override
+    bool do_setup(int argc, char **argv) override
     {
         /** [Accurate padding] **/
         PPMLoader ppm;
@@ -94,6 +94,8 @@ public:
             output_filename = std::string(argv[1]) + "_out.ppm";
         }
         /** [Accurate padding] **/
+
+        return true;
     }
     void do_run() override
     {
diff --git a/examples/neon_copy_objects.cpp b/examples/neon_copy_objects.cpp
index 9409cf366c..84a2abd379 100644
--- a/examples/neon_copy_objects.cpp
+++ b/examples/neon_copy_objects.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2016, 2018 ARM Limited.
+ * Copyright (c) 2016-2018 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -36,7 +36,7 @@ using namespace utils;
 class NEONCopyObjectsExample : public Example
 {
 public:
-    void do_setup(int argc, char **argv) override
+    bool do_setup(int argc, char **argv) override
     {
         ARM_COMPUTE_UNUSED(argc);
         ARM_COMPUTE_UNUSED(argv);
@@ -135,6 +135,8 @@ public:
         output_it);
 
         /** [Copy objects example] */
+
+        return true;
     }
     void do_run() override
     {
diff --git a/examples/neon_scale.cpp b/examples/neon_scale.cpp
index 252bfc9ae7..b04d916aaf 100644
--- a/examples/neon_scale.cpp
+++ b/examples/neon_scale.cpp
@@ -33,7 +33,7 @@ using namespace utils;
 class NEONScaleExample : public Example
 {
 public:
-    void do_setup(int argc, char **argv) override
+    bool do_setup(int argc, char **argv) override
     {
         PPMLoader ppm;
 
@@ -72,6 +72,8 @@ public:
             ppm.fill_image(src);
             output_filename = std::string(argv[1]) + "_out.ppm";
         }
+
+        return true;
     }
     void do_run() override
     {
diff --git a/examples/neoncl_scale_median_gaussian.cpp b/examples/neoncl_scale_median_gaussian.cpp
index 173575c061..1b26517d9f 100644
--- a/examples/neoncl_scale_median_gaussian.cpp
+++ b/examples/neoncl_scale_median_gaussian.cpp
@@ -43,7 +43,7 @@ using namespace utils;
 class NEONCLScaleMedianGaussianExample : public Example
 {
 public:
-    void do_setup(int argc, char **argv) override
+    bool do_setup(int argc, char **argv) override
     {
         /** [NEON / OpenCL Interop] */
         PPMLoader ppm;
@@ -88,6 +88,8 @@ public:
             const std::string output_filename = std::string(argv[1]) + "_out.ppm";
         }
         /** [NEON / OpenCL Interop] */
+
+        return true;
     }
     void do_run() override
     {