COMPMID-782 Port examples to the new format

Change-Id: Ib178a97c080ff650094d02ee49e2a0aa22376dd0
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/115717
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>

19 files changed, 1886 insertions, 1808 deletions

diff --git a/examples/cl_events.cpp b/examples/cl_events.cpp
index c9c3e5d039..ec02fc30f9 100644
--- a/examples/cl_events.cpp
+++ b/examples/cl_events.cpp
@@ -33,78 +33,88 @@
 using namespace arm_compute;
 using namespace utils;
 
-void main_cl_events(int argc, char **argv)
+class CLEventsExample : public Example
 {
-    /** [OpenCL events] **/
-    PPMLoader     ppm;
-    CLImage       src, tmp_scale_median, tmp_median_gauss, dst;
-    constexpr int scale_factor = 2;
-
-    CLScheduler::get().default_init();
-
-    if(argc < 2)
-    {
-        // Print help
-        std::cout << "Usage: ./build/cl_events [input_image.ppm]\n\n";
-        std::cout << "No input_image provided, creating a dummy 640x480 image\n";
-        // Create an empty grayscale 640x480 image
-        src.allocator()->init(TensorInfo(640, 480, Format::U8));
-    }
-    else
+public:
+    void do_setup(int argc, char **argv) override
     {
-        ppm.open(argv[1]);
-        ppm.init_image(src, Format::U8);
-    }
+        /** [OpenCL events] **/
+        PPMLoader     ppm;
+        constexpr int scale_factor = 2;
 
-    // Declare and configure the functions to create the following pipeline: scale -> median -> gauss
-    CLScale       scale;
-    CLMedian3x3   median;
-    CLGaussian5x5 gauss;
+        CLScheduler::get().default_init();
 
-    TensorInfo dst_info(src.info()->dimension(0) / scale_factor, src.info()->dimension(1) / scale_factor, Format::U8);
+        if(argc < 2)
+        {
+            // Print help
+            std::cout << "Usage: ./build/cl_events [input_image.ppm]\n\n";
+            std::cout << "No input_image provided, creating a dummy 640x480 image\n";
+            // Create an empty grayscale 640x480 image
+            src.allocator()->init(TensorInfo(640, 480, Format::U8));
+        }
+        else
+        {
+            ppm.open(argv[1]);
+            ppm.init_image(src, Format::U8);
+        }
 
-    // Configure the temporary and destination images
-    dst.allocator()->init(dst_info);
-    tmp_scale_median.allocator()->init(dst_info);
-    tmp_median_gauss.allocator()->init(dst_info);
+        TensorInfo dst_info(src.info()->dimension(0) / scale_factor, src.info()->dimension(1) / scale_factor, Format::U8);
 
-    //Configure the functions:
-    scale.configure(&src, &tmp_scale_median, InterpolationPolicy::NEAREST_NEIGHBOR, BorderMode::REPLICATE);
-    median.configure(&tmp_scale_median, &tmp_median_gauss, BorderMode::REPLICATE);
-    gauss.configure(&tmp_median_gauss, &dst, BorderMode::REPLICATE);
+        // Configure the temporary and destination images
+        dst.allocator()->init(dst_info);
+        tmp_scale_median.allocator()->init(dst_info);
+        tmp_median_gauss.allocator()->init(dst_info);
 
-    // Allocate all the images
-    src.allocator()->allocate();
-    dst.allocator()->allocate();
-    tmp_scale_median.allocator()->allocate();
-    tmp_median_gauss.allocator()->allocate();
-    // Fill the input image with the content of the PPM image if a filename was provided:
-    if(ppm.is_open())
-    {
-        ppm.fill_image(src);
-    }
+        //Configure the functions:
+        scale.configure(&src, &tmp_scale_median, InterpolationPolicy::NEAREST_NEIGHBOR, BorderMode::REPLICATE);
+        median.configure(&tmp_scale_median, &tmp_median_gauss, BorderMode::REPLICATE);
+        gauss.configure(&tmp_median_gauss, &dst, BorderMode::REPLICATE);
 
-    // Enqueue and flush the scale OpenCL kernel:
-    scale.run();
-    // Create a synchronisation event between scale and median:
-    cl::Event scale_event = CLScheduler::get().enqueue_sync_event();
-    // Enqueue and flush the median OpenCL kernel:
-    median.run();
-    // Enqueue and flush the Gaussian OpenCL kernel:
-    gauss.run();
+        // Allocate all the images
+        src.allocator()->allocate();
+        dst.allocator()->allocate();
+        tmp_scale_median.allocator()->allocate();
+        tmp_median_gauss.allocator()->allocate();
 
-    //Make sure all the OpenCL jobs are done executing:
-    scale_event.wait();        // Block until Scale is done executing (Median3x3 and Gaussian5x5 might still be running)
-    CLScheduler::get().sync(); // Block until Gaussian5x5 is done executing
+        // Fill the input image with the content of the PPM image if a filename was provided:
+        if(ppm.is_open())
+        {
+            ppm.fill_image(src);
+            output_filename = std::string(argv[1]) + "_out.ppm";
+        }
+        /** [OpenCL events] **/
+    }
+    void do_run() override
+    {
+        // Enqueue and flush the scale OpenCL kernel:
+        scale.run();
+        // Create a synchronisation event between scale and median:
+        cl::Event scale_event = CLScheduler::get().enqueue_sync_event();
+        // Enqueue and flush the median OpenCL kernel:
+        median.run();
+        // Enqueue and flush the Gaussian OpenCL kernel:
+        gauss.run();
 
-    // Save the result to file:
-    if(ppm.is_open())
+        //Make sure all the OpenCL jobs are done executing:
+        scale_event.wait();        // Block until Scale is done executing (Median3x3 and Gaussian5x5 might still be running)
+        CLScheduler::get().sync(); // Block until Gaussian5x5 is done executing
+    }
+    void do_teardown() override
     {
-        const std::string output_filename = std::string(argv[1]) + "_out.ppm";
-        save_to_ppm(dst, output_filename); // save_to_ppm maps and unmaps the image to store as PPM
+        // Save the result to file:
+        if(!output_filename.empty())
+        {
+            save_to_ppm(dst, output_filename); // save_to_ppm maps and unmaps the image to store as PPM
+        }
     }
-    /** [OpenCL events] **/
-}
+
+private:
+    CLImage       src{}, tmp_scale_median{}, tmp_median_gauss{}, dst{};
+    CLScale       scale{};
+    CLMedian3x3   median{};
+    CLGaussian5x5 gauss{};
+    std::string   output_filename{};
+};
 
 /** Main program for convolution test
  *
@@ -113,5 +123,5 @@ void main_cl_events(int argc, char **argv)
  */
 int main(int argc, char **argv)
 {
-    return utils::run_example(argc, argv, main_cl_events);
+    return utils::run_example<CLEventsExample>(argc, argv);
 }
diff --git a/examples/cl_sgemm.cpp b/examples/cl_sgemm.cpp
index 939870fabf..f2c63985f6 100644
--- a/examples/cl_sgemm.cpp
+++ b/examples/cl_sgemm.cpp
@@ -36,174 +36,184 @@
 using namespace arm_compute;
 using namespace utils;
 
-void main_cl_sgemm(int argc, char **argv)
+class CLSGEMMExample : public Example
 {
-    NPYLoader npy0, npy1, npy2;
-    CLTensor  src0, src1, src2, dst;
-    float     alpha = 1.0f, beta = 0.0f;
-
-    CLTuner tuner;
-    CLScheduler::get().default_init(&tuner);
-
-    std::ifstream stream;
-    if(argc > 1)
+public:
+    void do_setup(int argc, char **argv) override
     {
-        stream.open(argv[1], std::fstream::in);
-    }
+        NPYLoader npy0, npy1, npy2;
+        alpha = 1.0f;
+        beta  = 0.0f;
 
-    if(argc < 3 || (argc < 4 && stream.bad()))
-    {
-        // Print help
-        std::cout << "Usage: 1) ./build/cl_sgemm input_matrix_1.npy input_matrix_2.npy [input_matrix_3.npy] [alpha = 1] [beta = 0]\n";
-        std::cout << "       2) ./build/cl_sgemm M N K [alpha = 1.0f] [beta = 0.0f]\n\n";
-        std::cout << "Too few or no input_matrices provided. Using M=7, N=3, K=5, alpha=1.0f and beta=0.0f\n\n";
-
-        src0.allocator()->init(TensorInfo(TensorShape(5U, 7U), 1, DataType::F32));
-        src1.allocator()->init(TensorInfo(TensorShape(3U, 5U), 1, DataType::F32));
-        src2.allocator()->init(TensorInfo(TensorShape(3U, 7U), 1, DataType::F32));
-    }
-    else
-    {
-        if(stream.good()) /* case file1.npy file2.npy [file3.npy] [alpha = 1.0f] [beta = 0.0f] */
+        CLScheduler::get().default_init(&tuner);
+
+        std::ifstream stream;
+        if(argc > 1)
         {
-            npy0.open(argv[1]);
-            npy0.init_tensor(src0, DataType::F32);
-            npy1.open(argv[2]);
-            npy1.init_tensor(src1, DataType::F32);
+            stream.open(argv[1], std::fstream::in);
+        }
 
-            if(argc > 3)
+        if(argc < 3 || (argc < 4 && stream.bad()))
+        {
+            // Print help
+            std::cout << "Usage: 1) ./build/cl_sgemm input_matrix_1.npy input_matrix_2.npy [input_matrix_3.npy] [alpha = 1] [beta = 0]\n";
+            std::cout << "       2) ./build/cl_sgemm M N K [alpha = 1.0f] [beta = 0.0f]\n\n";
+            std::cout << "Too few or no input_matrices provided. Using M=7, N=3, K=5, alpha=1.0f and beta=0.0f\n\n";
+
+            src0.allocator()->init(TensorInfo(TensorShape(5U, 7U), 1, DataType::F32));
+            src1.allocator()->init(TensorInfo(TensorShape(3U, 5U), 1, DataType::F32));
+            src2.allocator()->init(TensorInfo(TensorShape(3U, 7U), 1, DataType::F32));
+        }
+        else
+        {
+            if(stream.good()) /* case file1.npy file2.npy [file3.npy] [alpha = 1.0f] [beta = 0.0f] */
             {
-                stream.close();
-                stream.clear();
-                stream.open(argv[3], std::fstream::in);
-                if(stream.good()) /* case with third file */
-                {
-                    npy2.open(argv[3]);
-                    npy2.init_tensor(src2, DataType::F32);
+                npy0.open(argv[1]);
+                npy0.init_tensor(src0, DataType::F32);
+                npy1.open(argv[2]);
+                npy1.init_tensor(src1, DataType::F32);
 
-                    if(argc > 4)
+                if(argc > 3)
+                {
+                    stream.close();
+                    stream.clear();
+                    stream.open(argv[3], std::fstream::in);
+                    if(stream.good()) /* case with third file */
                     {
-                        // Convert string to float
-                        alpha = strtof(argv[4], nullptr);
+                        npy2.open(argv[3]);
+                        npy2.init_tensor(src2, DataType::F32);
 
-                        if(argc > 5)
+                        if(argc > 4)
                         {
                             // Convert string to float
-                            beta = strtof(argv[5], nullptr);
+                            alpha = strtof(argv[4], nullptr);
+
+                            if(argc > 5)
+                            {
+                                // Convert string to float
+                                beta = strtof(argv[5], nullptr);
+                            }
                         }
                     }
-                }
-                else /* case without third file */
-                {
-                    alpha = strtof(argv[3], nullptr);
-
-                    if(argc > 4)
+                    else /* case without third file */
                     {
-                        beta = strtof(argv[4], nullptr);
+                        alpha = strtof(argv[3], nullptr);
+
+                        if(argc > 4)
+                        {
+                            beta = strtof(argv[4], nullptr);
+                        }
                     }
                 }
             }
-        }
-        else /* case M N K [alpha = 1.0f] [beta = 0.0f] */
-        {
-            size_t M = strtol(argv[1], nullptr, 10);
-            size_t N = strtol(argv[2], nullptr, 10);
-            size_t K = strtol(argv[3], nullptr, 10);
-
-            src0.allocator()->init(TensorInfo(TensorShape(K, M), 1, DataType::F32));
-            src1.allocator()->init(TensorInfo(TensorShape(N, K), 1, DataType::F32));
-            src2.allocator()->init(TensorInfo(TensorShape(N, M), 1, DataType::F32));
-
-            if(argc > 4)
+            else /* case M N K [alpha = 1.0f] [beta = 0.0f] */
             {
-                alpha = strtof(argv[4], nullptr);
+                size_t M = strtol(argv[1], nullptr, 10);
+                size_t N = strtol(argv[2], nullptr, 10);
+                size_t K = strtol(argv[3], nullptr, 10);
+
+                src0.allocator()->init(TensorInfo(TensorShape(K, M), 1, DataType::F32));
+                src1.allocator()->init(TensorInfo(TensorShape(N, K), 1, DataType::F32));
+                src2.allocator()->init(TensorInfo(TensorShape(N, M), 1, DataType::F32));
 
-                if(argc > 5)
+                if(argc > 4)
                 {
-                    beta = strtof(argv[5], nullptr);
+                    alpha = strtof(argv[4], nullptr);
+
+                    if(argc > 5)
+                    {
+                        beta = strtof(argv[5], nullptr);
+                    }
                 }
             }
         }
-    }
 
-    init_sgemm_output(dst, src0, src1, DataType::F32);
+        init_sgemm_output(dst, src0, src1, DataType::F32);
 
-    // Configure function
-    CLGEMM sgemm;
-    sgemm.configure(&src0, &src1, (src2.info()->total_size() > 0) ? &src2 : nullptr, &dst, alpha, beta);
+        // Configure function
+        sgemm.configure(&src0, &src1, (src2.info()->total_size() > 0) ? &src2 : nullptr, &dst, alpha, beta);
 
-    // Allocate all the images
-    src0.allocator()->allocate();
-    src1.allocator()->allocate();
-    dst.allocator()->allocate();
+        // Allocate all the images
+        src0.allocator()->allocate();
+        src1.allocator()->allocate();
+        dst.allocator()->allocate();
 
-    // Fill the input images with either the data provided or random data
-    if(npy0.is_open())
-    {
-        npy0.fill_tensor(src0);
-        npy1.fill_tensor(src1);
+        // Fill the input images with either the data provided or random data
+        if(npy0.is_open())
+        {
+            npy0.fill_tensor(src0);
+            npy1.fill_tensor(src1);
+
+            output_filename = "sgemm_out.npy";
+            is_fortran      = npy0.is_fortran();
 
-        if(npy2.is_open())
+            if(npy2.is_open())
+            {
+                src2.allocator()->allocate();
+                npy2.fill_tensor(src2);
+            }
+        }
+        else
         {
             src2.allocator()->allocate();
-            npy2.fill_tensor(src2);
+
+            fill_random_tensor(src0, -1.f, 1.f);
+            fill_random_tensor(src1, -1.f, 1.f);
+            fill_random_tensor(src2, -1.f, 1.f);
         }
-    }
-    else
-    {
-        src2.allocator()->allocate();
 
-        fill_random_tensor(src0, -1.f, 1.f);
-        fill_random_tensor(src1, -1.f, 1.f);
-        fill_random_tensor(src2, -1.f, 1.f);
+        // Dummy run for CLTuner
+        sgemm.run();
     }
-
-    // Dummy run for CLTuner
-    sgemm.run();
-
-    auto start = std::chrono::high_resolution_clock::now();
-
-    // Execute the function
-    sgemm.run();
-
-    // Make sure all the OpenCL jobs are done executing:
-    CLScheduler::get().sync();
-
-    auto stop = std::chrono::high_resolution_clock::now();
-
-    if(!npy0.is_open()) /* If the inputs were not files, print the results */
+    void do_run() override
     {
-        std::cout << "\nMatrix 1:" << std::endl;
-        src0.map(true);
-        src0.print(std::cout, IOFormatInfo());
-        src0.unmap();
-
-        std::cout << "Matrix 2:" << std::endl;
-        src1.map(true);
-        src1.print(std::cout, IOFormatInfo());
-        src1.unmap();
-
-        std::cout << "Matrix 3:" << std::endl;
-        src2.map(true);
-        src2.print(std::cout, IOFormatInfo());
-        src2.unmap();
-
-        std::cout << "Alpha:" << alpha << "\n\n";
-        std::cout << "Beta:" << beta << "\n\n";
-
-        std::cout << "Output Matrix:" << std::endl;
-        dst.map(true);
-        dst.print(std::cout, IOFormatInfo());
-        dst.unmap();
+        // Execute the function
+        sgemm.run();
+
+        // Make sure all the OpenCL jobs are done executing:
+        CLScheduler::get().sync();
     }
-    else /* Save to .npy file */
+    void do_teardown() override
     {
-        save_to_npy(dst, "sgemm_out.npy", npy0.is_fortran());
+        if(output_filename.empty()) /* If the inputs were not files, print the results */
+        {
+            std::cout << "\nMatrix 1:" << std::endl;
+            src0.map(true);
+            src0.print(std::cout, IOFormatInfo());
+            src0.unmap();
+
+            std::cout << "Matrix 2:" << std::endl;
+            src1.map(true);
+            src1.print(std::cout, IOFormatInfo());
+            src1.unmap();
+
+            std::cout << "Matrix 3:" << std::endl;
+            src2.map(true);
+            src2.print(std::cout, IOFormatInfo());
+            src2.unmap();
+
+            std::cout << "Alpha:" << alpha << "\n\n";
+            std::cout << "Beta:" << beta << "\n\n";
+
+            std::cout << "Output Matrix:" << std::endl;
+            dst.map(true);
+            dst.print(std::cout, IOFormatInfo());
+            dst.unmap();
+        }
+        else /* Save to .npy file */
+        {
+            save_to_npy(dst, output_filename, is_fortran);
+        }
     }
 
-    auto delta = std::chrono::duration_cast<std::chrono::microseconds>(stop - start);
-    std::cout << "Time elapsed: " << delta.count() << "us." << std::endl;
-}
+private:
+    CLTensor    src0{}, src1{}, src2{}, dst{};
+    CLGEMM      sgemm{};
+    CLTuner     tuner{};
+    float       alpha{}, beta{};
+    bool        is_fortran{};
+    std::string output_filename{};
+};
 
 /** Main program for sgemm test
  *
@@ -212,5 +222,5 @@ void main_cl_sgemm(int argc, char **argv)
  */
 int main(int argc, char **argv)
 {
-    return utils::run_example(argc, argv, main_cl_sgemm);
+    return utils::run_example<CLSGEMMExample>(argc, argv);
 }
diff --git a/examples/gc_absdiff.cpp b/examples/gc_absdiff.cpp
index 997feed8e8..32f946d421 100644
--- a/examples/gc_absdiff.cpp
+++ b/examples/gc_absdiff.cpp
@@ -34,71 +34,83 @@
 using namespace arm_compute;
 using namespace utils;
 
-void main_gc_absdiff(int argc, char **argv)
+class GCAbsDiffExample : public Example
 {
-    PPMLoader ppm1, ppm2;
-    GCImage   src1, src2, dst;
-    GCScheduler::get().default_init();
-    if(argc < 2)
+public:
+    void do_setup(int argc, char **argv) override
     {
-        // Print help
-        std::cout << "Usage: " << argv[0] << " [input0_image.ppm] [input1_image.ppm] \n\n";
-        std::cout << "No input_image provided, creating two dummy 640x480 images\n";
-        // Create two empty grayscale 640x480 images
-        src1.allocator()->init(TensorInfo(640, 480, Format::U8));
-        src2.allocator()->init(TensorInfo(640, 480, Format::U8));
-    }
-    else if(argc < 3)
-    {
-        // Print help
-        std::cout << "Usage: " << argv[0] << " [input0_image.ppm] [input1_image.ppm] \n\n";
-        std::cout << "Only one input_image provided, creating a dummy 640x480 image\n";
-        ppm1.open(argv[1]);
-        ppm1.init_image(src1, Format::U8);
-        // Create an empty grayscale 640x480 image
-        src2.allocator()->init(TensorInfo(640, 480, Format::U8));
-    }
-    else
-    {
-        ppm1.open(argv[1]);
-        ppm1.init_image(src1, Format::U8);
-        ppm2.open(argv[2]);
-        ppm2.init_image(src2, Format::U8);
-    }
+        PPMLoader ppm1, ppm2;
 
-    // Configure the temporary and destination images
-    dst.allocator()->init(*src1.info());
+        GCScheduler::get().default_init();
+        if(argc < 2)
+        {
+            // Print help
+            std::cout << "Usage: " << argv[0] << " [input0_image.ppm] [input1_image.ppm] \n\n";
+            std::cout << "No input_image provided, creating two dummy 640x480 images\n";
+            // Create two empty grayscale 640x480 images
+            src1.allocator()->init(TensorInfo(640, 480, Format::U8));
+            src2.allocator()->init(TensorInfo(640, 480, Format::U8));
+        }
+        else if(argc < 3)
+        {
+            // Print help
+            std::cout << "Usage: " << argv[0] << " [input0_image.ppm] [input1_image.ppm] \n\n";
+            std::cout << "Only one input_image provided, creating a dummy 640x480 image\n";
+            ppm1.open(argv[1]);
+            ppm1.init_image(src1, Format::U8);
+            // Create an empty grayscale 640x480 image
+            src2.allocator()->init(TensorInfo(640, 480, Format::U8));
+        }
+        else
+        {
+            ppm1.open(argv[1]);
+            ppm1.init_image(src1, Format::U8);
+            ppm2.open(argv[2]);
+            ppm2.init_image(src2, Format::U8);
+        }
 
-    GCAbsoluteDifference absdiff;
-    absdiff.configure(&src1, &src2, &dst);
+        // Configure the temporary and destination images
+        dst.allocator()->init(*src1.info());
 
-    // Allocate all the images
-    src1.allocator()->allocate();
-    src2.allocator()->allocate();
-    dst.allocator()->allocate();
+        absdiff.configure(&src1, &src2, &dst);
 
-    // Fill the input image with the content of the PPM image if a filename was provided:
-    if(ppm1.is_open())
-    {
-        ppm1.fill_image(src1);
+        // Allocate all the images
+        src1.allocator()->allocate();
+        src2.allocator()->allocate();
+        dst.allocator()->allocate();
+
+        // Fill the input image with the content of the PPM image if a filename was provided:
+        if(ppm1.is_open())
+        {
+            ppm1.fill_image(src1);
+            output_filename = std::string(argv[1]) + "_out.ppm";
+        }
+        if(ppm2.is_open())
+        {
+            ppm2.fill_image(src2);
+        }
     }
-    if(ppm2.is_open())
+    void do_run() override
     {
-        ppm2.fill_image(src2);
+        // Execute the functions:
+        absdiff.run();
     }
-
-    // Execute the functions:
-    absdiff.run();
-
-    // Save the result to file:
-    if(ppm1.is_open())
+    void do_teardown() override
     {
-        const std::string output_filename = std::string(argv[1]) + "_out.ppm";
-        // save_to_ppm maps and unmaps the image to store as PPM
-        // The GCTensor::map call inside the save_to_ppm will block until all pending operations on that image have completed
-        save_to_ppm(dst, output_filename);
+        // Save the result to file:
+        if(!output_filename.empty())
+        {
+            // save_to_ppm maps and unmaps the image to store as PPM
+            // The GCTensor::map call inside the save_to_ppm will block until all pending operations on that image have completed
+            save_to_ppm(dst, output_filename);
+        }
     }
-}
+
+private:
+    GCImage              src1{}, src2{}, dst{};
+    GCAbsoluteDifference absdiff{};
+    std::string          output_filename{};
+};
 
 /** Main program for absdiff test
  *
@@ -107,5 +119,5 @@ void main_gc_absdiff(int argc, char **argv)
  */
 int main(int argc, char **argv)
 {
-    return utils::run_example(argc, argv, main_gc_absdiff);
+    return utils::run_example<GCAbsDiffExample>(argc, argv);
 }
diff --git a/examples/gc_dc.cpp b/examples/gc_dc.cpp
index 174c88458d..8b6f4414e0 100644
--- a/examples/gc_dc.cpp
+++ b/examples/gc_dc.cpp
@@ -33,84 +33,91 @@
 using namespace arm_compute;
 using namespace utils;
 
-namespace
+class GCDCExample : public Example
 {
-TensorShape get_output_shape(TensorShape in_shape, TensorShape kernel_shape, const PadStrideInfo &info)
-{
-    TensorShape out_shape(in_shape);
-    const std::pair<unsigned int, unsigned int> scaled_dims = scaled_dimensions(in_shape.x(),
-                                                                                in_shape.y(),
-                                                                                kernel_shape.x(),
-                                                                                kernel_shape.y(),
-                                                                                info);
-    out_shape.set(0, scaled_dims.first);
-    out_shape.set(1, scaled_dims.second);
-    out_shape.set(2, kernel_shape[3]);
-    return out_shape;
-}
-} // namespace
-
-void main_gc_dc(int argc, char **argv)
-{
-    ARM_COMPUTE_UNUSED(argc);
-    ARM_COMPUTE_UNUSED(argv);
+public:
+    void do_setup(int argc, char **argv) override
+    {
+        ARM_COMPUTE_UNUSED(argc);
+        ARM_COMPUTE_UNUSED(argv);
 
-    // init instance
-    GCScheduler::get().default_init();
+        // init instance
+        GCScheduler::get().default_init();
 
-    const TensorShape  src_shape   = TensorShape{ 11U /* W */, 13U /* H */, 4U /* C */, 3U /* N */ };
-    const unsigned int kernel_size = 3;
-    const int          stride_x    = 1;
-    const int          stride_y    = 1;
-    const int          pad_x       = 0;
-    const int          pad_y       = 0;
-    const unsigned int num_kernels = 256;
-    const DataType     data_type   = DataType::F16;
+        const TensorShape  src_shape   = TensorShape{ 11U /* W */, 13U /* H */, 4U /* C */, 3U /* N */ };
+        const unsigned int kernel_size = 3;
+        const int          stride_x    = 1;
+        const int          stride_y    = 1;
+        const int          pad_x       = 0;
+        const int          pad_y       = 0;
+        const unsigned int num_kernels = 256;
+        const DataType     data_type   = DataType::F16;
 
-    // generate shape
-    const TensorShape   weights_shape(kernel_size, kernel_size, src_shape.z(), num_kernels);
-    const TensorShape   bias_shape(num_kernels);
-    const PadStrideInfo pad_info(stride_x, stride_y, pad_x, pad_y, DimensionRoundingType::FLOOR);
+        // generate shape
+        const TensorShape   weights_shape(kernel_size, kernel_size, src_shape.z(), num_kernels);
+        const TensorShape   bias_shape(num_kernels);
+        const PadStrideInfo pad_info(stride_x, stride_y, pad_x, pad_y, DimensionRoundingType::FLOOR);
 
-    // output shape should be 9*11*256*3 (W*H*C*N)
-    const TensorShape dst_shape = get_output_shape(src_shape, weights_shape, pad_info);
+        // output shape should be 9*11*256*3 (W*H*C*N)
+        const TensorShape dst_shape = get_output_shape(src_shape, weights_shape, pad_info);
 
-    // create tensors
-    GCTensor src, weights, bias, dst;
-    src.allocator()->init(TensorInfo(src_shape, 1, data_type));
-    weights.allocator()->init(TensorInfo(weights_shape, 1, data_type));
-    bias.allocator()->init(TensorInfo(bias_shape, 1, data_type));
-    dst.allocator()->init(TensorInfo(dst_shape, 1, data_type));
+        // create tensors
+        src.allocator()->init(TensorInfo(src_shape, 1, data_type));
+        weights.allocator()->init(TensorInfo(weights_shape, 1, data_type));
+        bias.allocator()->init(TensorInfo(bias_shape, 1, data_type));
+        dst.allocator()->init(TensorInfo(dst_shape, 1, data_type));
 
-    // configure layer
-    GCDirectConvolutionLayer conv;
-    conv.configure(&src, &weights, &bias, &dst, pad_info);
+        // configure layer
+        conv.configure(&src, &weights, &bias, &dst, pad_info);
 
-    // allocate tensors
-    src.allocator()->allocate();
-    weights.allocator()->allocate();
-    bias.allocator()->allocate();
-    dst.allocator()->allocate();
+        // allocate tensors
+        src.allocator()->allocate();
+        weights.allocator()->allocate();
+        bias.allocator()->allocate();
+        dst.allocator()->allocate();
 
-    // To demonstrate how to fill tensor with some values...
-    src.map();
-    Window window;
-    window.use_tensor_dimensions(src_shape);
-    Iterator it(&src, window);
-    execute_window_loop(window, [&](const Coordinates & id)
+        // To demonstrate how to fill tensor with some values...
+        src.map();
+        Window window;
+        window.use_tensor_dimensions(src_shape);
+        Iterator it(&src, window);
+        execute_window_loop(window, [&](const Coordinates & id)
+        {
+            *reinterpret_cast<half_float::half *>(it.ptr()) = half_float::half(1.f);
+        });
+        src.unmap();
+    }
+    void do_run() override
+    {
+        // run the layer
+        conv.run();
+    }
+    void do_teardown() override
     {
-        *reinterpret_cast<half_float::half *>(it.ptr()) = half_float::half(1.f);
-    });
-    src.unmap();
+        // check result
+        dst.map();
+        // do something
+        dst.unmap();
+    }
 
-    // run the layer
-    conv.run();
+private:
+    GCTensor                 src{}, weights{}, bias{}, dst{};
+    GCDirectConvolutionLayer conv{};
 
-    // check result
-    dst.map();
-    // do something
-    dst.unmap();
-}
+    TensorShape get_output_shape(TensorShape in_shape, TensorShape kernel_shape, const PadStrideInfo &info)
+    {
+        TensorShape out_shape(in_shape);
+        const std::pair<unsigned int, unsigned int> scaled_dims = scaled_dimensions(in_shape.x(),
+                                                                                    in_shape.y(),
+                                                                                    kernel_shape.x(),
+                                                                                    kernel_shape.y(),
+                                                                                    info);
+        out_shape.set(0, scaled_dims.first);
+        out_shape.set(1, scaled_dims.second);
+        out_shape.set(2, kernel_shape[3]);
+        return out_shape;
+    }
+};
 
 /** Main program for directconvolution test
  *
@@ -119,5 +126,5 @@ void main_gc_dc(int argc, char **argv)
  */
 int main(int argc, char **argv)
 {
-    return utils::run_example(argc, argv, main_gc_dc);
+    return utils::run_example<GCDCExample>(argc, argv);
 }
diff --git a/examples/graph_alexnet.cpp b/examples/graph_alexnet.cpp
index 6423fe48d3..8705c8ed1e 100644
--- a/examples/graph_alexnet.cpp
+++ b/examples/graph_alexnet.cpp
@@ -31,6 +31,7 @@
 #include <iostream>
 #include <memory>
 
+using namespace arm_compute::utils;
 using namespace arm_compute::graph;
 using namespace arm_compute::graph_utils;
 
@@ -39,122 +40,129 @@ using namespace arm_compute::graph_utils;
  * @param[in] argc Number of arguments
  * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels )
  */
-void main_graph_alexnet(int argc, char **argv)
+class GraphAlexnetExample : public Example
 {
-    std::string data_path; /* Path to the trainable data */
-    std::string image;     /* Image data */
-    std::string label;     /* Label data */
+public:
+    void do_setup(int argc, char **argv) override
+    {
+        std::string data_path; /* Path to the trainable data */
+        std::string image;     /* Image data */
+        std::string label;     /* Label data */
 
-    constexpr float mean_r = 122.68f; /* Mean value to subtract from red channel */
-    constexpr float mean_g = 116.67f; /* Mean value to subtract from green channel */
-    constexpr float mean_b = 104.01f; /* Mean value to subtract from blue channel */
+        constexpr float mean_r = 122.68f; /* Mean value to subtract from red channel */
+        constexpr float mean_g = 116.67f; /* Mean value to subtract from green channel */
+        constexpr float mean_b = 104.01f; /* Mean value to subtract from blue channel */
 
-    // Set target. 0 (NEON), 1 (OpenCL). By default it is NEON
-    TargetHint            target_hint      = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0);
-    ConvolutionMethodHint convolution_hint = target_hint == TargetHint::NEON ? ConvolutionMethodHint::GEMM : ConvolutionMethodHint::DIRECT;
+        // Set target. 0 (NEON), 1 (OpenCL). By default it is NEON
+        TargetHint            target_hint      = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0);
+        ConvolutionMethodHint convolution_hint = target_hint == TargetHint::NEON ? ConvolutionMethodHint::GEMM : ConvolutionMethodHint::DIRECT;
 
-    // Parse arguments
-    if(argc < 2)
-    {
-        // Print help
-        std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n";
-        std::cout << "No data folder provided: using random values\n\n";
-    }
-    else if(argc == 2)
-    {
-        std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\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]\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]\n\n";
-        std::cout << "No text file with labels provided: skipping output accessor\n\n";
+        // Parse arguments
+        if(argc < 2)
+        {
+            // Print help
+            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n";
+            std::cout << "No data folder provided: using random values\n\n";
+        }
+        else if(argc == 2)
+        {
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\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]\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]\n\n";
+            std::cout << "No text file with labels provided: skipping output accessor\n\n";
+        }
+        else
+        {
+            data_path = argv[2];
+            image     = argv[3];
+            label     = argv[4];
+        }
+
+        graph << target_hint
+              << Tensor(TensorInfo(TensorShape(227U, 227U, 3U, 1U), 1, DataType::F32),
+                        get_input_accessor(image, mean_r, mean_g, mean_b))
+              // Layer 1
+              << ConvolutionLayer(
+                  11U, 11U, 96U,
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv1_b.npy"),
+                  PadStrideInfo(4, 4, 0, 0))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)))
+              // Layer 2
+              << convolution_hint
+              << ConvolutionLayer(
+                  5U, 5U, 256U,
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv2_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv2_b.npy"),
+                  PadStrideInfo(1, 1, 2, 2), 2)
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)))
+              // Layer 3
+              << ConvolutionLayer(
+                  3U, 3U, 384U,
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv3_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv3_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              // Layer 4
+              << ConvolutionLayer(
+                  3U, 3U, 384U,
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv4_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv4_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1), 2)
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              // Layer 5
+              << ConvolutionLayer(
+                  3U, 3U, 256U,
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv5_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv5_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1), 2)
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)))
+              // Layer 6
+              << FullyConnectedLayer(
+                  4096U,
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc6_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc6_b.npy"))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              // Layer 7
+              << FullyConnectedLayer(
+                  4096U,
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc7_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc7_b.npy"))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              // Layer 8
+              << FullyConnectedLayer(
+                  1000U,
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc8_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc8_b.npy"))
+              // Softmax
+              << SoftmaxLayer()
+              << Tensor(get_output_accessor(label, 5));
     }
-    else
+    void do_run() override
     {
-        data_path = argv[2];
-        image     = argv[3];
-        label     = argv[4];
+        // Run graph
+        graph.run();
     }
 
-    Graph graph;
-
-    graph << target_hint
-          << Tensor(TensorInfo(TensorShape(227U, 227U, 3U, 1U), 1, DataType::F32),
-                    get_input_accessor(image, mean_r, mean_g, mean_b))
-          // Layer 1
-          << ConvolutionLayer(
-              11U, 11U, 96U,
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv1_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv1_b.npy"),
-              PadStrideInfo(4, 4, 0, 0))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
-          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)))
-          // Layer 2
-          << convolution_hint
-          << ConvolutionLayer(
-              5U, 5U, 256U,
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv2_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv2_b.npy"),
-              PadStrideInfo(1, 1, 2, 2), 2)
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
-          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)))
-          // Layer 3
-          << ConvolutionLayer(
-              3U, 3U, 384U,
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv3_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv3_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          // Layer 4
-          << ConvolutionLayer(
-              3U, 3U, 384U,
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv4_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv4_b.npy"),
-              PadStrideInfo(1, 1, 1, 1), 2)
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          // Layer 5
-          << ConvolutionLayer(
-              3U, 3U, 256U,
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv5_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv5_b.npy"),
-              PadStrideInfo(1, 1, 1, 1), 2)
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)))
-          // Layer 6
-          << FullyConnectedLayer(
-              4096U,
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc6_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc6_b.npy"))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          // Layer 7
-          << FullyConnectedLayer(
-              4096U,
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc7_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc7_b.npy"))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          // Layer 8
-          << FullyConnectedLayer(
-              1000U,
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc8_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc8_b.npy"))
-          // Softmax
-          << SoftmaxLayer()
-          << Tensor(get_output_accessor(label, 5));
-
-    // Run graph
-    graph.run();
-}
+private:
+    Graph graph{};
+};
 
 /** Main program for AlexNet
  *
@@ -163,5 +171,5 @@ void main_graph_alexnet(int argc, char **argv)
  */
 int main(int argc, char **argv)
 {
-    return arm_compute::utils::run_example(argc, argv, main_graph_alexnet);
+    return arm_compute::utils::run_example<GraphAlexnetExample>(argc, argv);
 }
diff --git a/examples/graph_googlenet.cpp b/examples/graph_googlenet.cpp
index 746d558389..1e9601b492 100644
--- a/examples/graph_googlenet.cpp
+++ b/examples/graph_googlenet.cpp
@@ -31,167 +31,173 @@
 #include <cstdlib>
 #include <tuple>
 
+using namespace arm_compute::utils;
 using namespace arm_compute::graph;
 using namespace arm_compute::graph_utils;
 
-namespace
-{
-BranchLayer get_inception_node(const std::string &data_path, std::string &&param_path,
-                               unsigned int a_filt,
-                               std::tuple<unsigned int, unsigned int> b_filters,
-                               std::tuple<unsigned int, unsigned int> c_filters,
-                               unsigned int d_filt)
-{
-    std::string total_path = "/cnn_data/googlenet_model/" + param_path + "/" + param_path + "_";
-    SubGraph    i_a;
-    i_a << ConvolutionLayer(
-            1U, 1U, a_filt,
-            get_weights_accessor(data_path, total_path + "1x1_w.npy"),
-            get_weights_accessor(data_path, total_path + "1x1_b.npy"),
-            PadStrideInfo(1, 1, 0, 0))
-        << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
-
-    SubGraph i_b;
-    i_b << ConvolutionLayer(
-            1U, 1U, std::get<0>(b_filters),
-            get_weights_accessor(data_path, total_path + "3x3_reduce_w.npy"),
-            get_weights_accessor(data_path, total_path + "3x3_reduce_b.npy"),
-            PadStrideInfo(1, 1, 0, 0))
-        << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-        << ConvolutionLayer(
-            3U, 3U, std::get<1>(b_filters),
-            get_weights_accessor(data_path, total_path + "3x3_w.npy"),
-            get_weights_accessor(data_path, total_path + "3x3_b.npy"),
-            PadStrideInfo(1, 1, 1, 1))
-        << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
-
-    SubGraph i_c;
-    i_c << ConvolutionLayer(
-            1U, 1U, std::get<0>(c_filters),
-            get_weights_accessor(data_path, total_path + "5x5_reduce_w.npy"),
-            get_weights_accessor(data_path, total_path + "5x5_reduce_b.npy"),
-            PadStrideInfo(1, 1, 0, 0))
-        << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-        << ConvolutionLayer(
-            5U, 5U, std::get<1>(c_filters),
-            get_weights_accessor(data_path, total_path + "5x5_w.npy"),
-            get_weights_accessor(data_path, total_path + "5x5_b.npy"),
-            PadStrideInfo(1, 1, 2, 2))
-        << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
-
-    SubGraph i_d;
-    i_d << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL)))
-        << ConvolutionLayer(
-            1U, 1U, d_filt,
-            get_weights_accessor(data_path, total_path + "pool_proj_w.npy"),
-            get_weights_accessor(data_path, total_path + "pool_proj_b.npy"),
-            PadStrideInfo(1, 1, 0, 0))
-        << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
-
-    return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));
-}
-} // namespace
-
 /** 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), [optional] Path to the weights folder, [optional] image, [optional] labels )
  */
-void main_graph_googlenet(int argc, char **argv)
+class GraphGooglenetExample : public Example
 {
-    std::string data_path; /* Path to the trainable data */
-    std::string image;     /* Image data */
-    std::string label;     /* Label data */
+public:
+    void do_setup(int argc, char **argv) override
+    {
+        std::string data_path; /* Path to the trainable data */
+        std::string image;     /* Image data */
+        std::string label;     /* Label data */
 
-    constexpr float mean_r = 122.68f; /* Mean value to subtract from red channel */
-    constexpr float mean_g = 116.67f; /* Mean value to subtract from green channel */
-    constexpr float mean_b = 104.01f; /* Mean value to subtract from blue channel */
+        constexpr float mean_r = 122.68f; /* Mean value to subtract from red channel */
+        constexpr float mean_g = 116.67f; /* Mean value to subtract from green channel */
+        constexpr float mean_b = 104.01f; /* Mean value to subtract from blue channel */
 
-    // Set target. 0 (NEON), 1 (OpenCL). By default it is NEON
-    TargetHint            target_hint      = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0);
-    ConvolutionMethodHint convolution_hint = target_hint == TargetHint::NEON ? ConvolutionMethodHint::GEMM : ConvolutionMethodHint::DIRECT;
+        // Set target. 0 (NEON), 1 (OpenCL). By default it is NEON
+        TargetHint            target_hint      = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0);
+        ConvolutionMethodHint convolution_hint = target_hint == TargetHint::NEON ? ConvolutionMethodHint::GEMM : ConvolutionMethodHint::DIRECT;
 
-    // Parse arguments
-    if(argc < 2)
-    {
-        // Print help
-        std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n";
-        std::cout << "No data folder provided: using random values\n\n";
-    }
-    else if(argc == 2)
-    {
-        std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\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]\n\n";
-        std::cout << "No image provided: using random values\n\n";
+        // Parse arguments
+        if(argc < 2)
+        {
+            // Print help
+            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n";
+            std::cout << "No data folder provided: using random values\n\n";
+        }
+        else if(argc == 2)
+        {
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\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]\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]\n\n";
+            std::cout << "No text file with labels provided: skipping output accessor\n\n";
+        }
+        else
+        {
+            data_path = argv[2];
+            image     = argv[3];
+            label     = argv[4];
+        }
+
+        graph << target_hint
+              << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32),
+                        get_input_accessor(image, mean_r, mean_g, mean_b))
+              << ConvolutionLayer(
+                  7U, 7U, 64U,
+                  get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_b.npy"),
+                  PadStrideInfo(2, 2, 3, 3))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+              << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
+              << convolution_hint
+              << ConvolutionLayer(
+                  1U, 1U, 64U,
+                  get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_b.npy"),
+                  PadStrideInfo(1, 1, 0, 0))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << ConvolutionLayer(
+                  3U, 3U, 192U,
+                  get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+              << get_inception_node(data_path, "inception_3a", 64, std::make_tuple(96U, 128U), std::make_tuple(16U, 32U), 32U)
+              << get_inception_node(data_path, "inception_3b", 128, std::make_tuple(128U, 192U), std::make_tuple(32U, 96U), 64U)
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+              << get_inception_node(data_path, "inception_4a", 192, std::make_tuple(96U, 208U), std::make_tuple(16U, 48U), 64U)
+              << get_inception_node(data_path, "inception_4b", 160, std::make_tuple(112U, 224U), std::make_tuple(24U, 64U), 64U)
+              << get_inception_node(data_path, "inception_4c", 128, std::make_tuple(128U, 256U), std::make_tuple(24U, 64U), 64U)
+              << get_inception_node(data_path, "inception_4d", 112, std::make_tuple(144U, 288U), std::make_tuple(32U, 64U), 64U)
+              << get_inception_node(data_path, "inception_4e", 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U)
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+              << get_inception_node(data_path, "inception_5a", 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U)
+              << get_inception_node(data_path, "inception_5b", 384, std::make_tuple(192U, 384U), std::make_tuple(48U, 128U), 128U)
+              << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 7, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL)))
+              << FullyConnectedLayer(
+                  1000U,
+                  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()
+              << Tensor(get_output_accessor(label, 5));
     }
-    else if(argc == 4)
+    void do_run() override
     {
-        data_path = argv[2];
-        image     = argv[3];
-        std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels]\n\n";
-        std::cout << "No text file with labels provided: skipping output accessor\n\n";
+        // Run graph
+        graph.run();
     }
-    else
+
+private:
+    Graph graph{};
+
+    BranchLayer get_inception_node(const std::string &data_path, std::string &&param_path,
+                                   unsigned int a_filt,
+                                   std::tuple<unsigned int, unsigned int> b_filters,
+                                   std::tuple<unsigned int, unsigned int> c_filters,
+                                   unsigned int d_filt)
     {
-        data_path = argv[2];
-        image     = argv[3];
-        label     = argv[4];
-    }
+        std::string total_path = "/cnn_data/googlenet_model/" + param_path + "/" + param_path + "_";
+        SubGraph    i_a;
+        i_a << ConvolutionLayer(
+                1U, 1U, a_filt,
+                get_weights_accessor(data_path, total_path + "1x1_w.npy"),
+                get_weights_accessor(data_path, total_path + "1x1_b.npy"),
+                PadStrideInfo(1, 1, 0, 0))
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
 
-    Graph graph;
+        SubGraph i_b;
+        i_b << ConvolutionLayer(
+                1U, 1U, std::get<0>(b_filters),
+                get_weights_accessor(data_path, total_path + "3x3_reduce_w.npy"),
+                get_weights_accessor(data_path, total_path + "3x3_reduce_b.npy"),
+                PadStrideInfo(1, 1, 0, 0))
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+            << ConvolutionLayer(
+                3U, 3U, std::get<1>(b_filters),
+                get_weights_accessor(data_path, total_path + "3x3_w.npy"),
+                get_weights_accessor(data_path, total_path + "3x3_b.npy"),
+                PadStrideInfo(1, 1, 1, 1))
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
 
-    graph << target_hint
-          << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32),
-                    get_input_accessor(image, mean_r, mean_g, mean_b))
-          << ConvolutionLayer(
-              7U, 7U, 64U,
-              get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_b.npy"),
-              PadStrideInfo(2, 2, 3, 3))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
-          << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
-          << convolution_hint
-          << ConvolutionLayer(
-              1U, 1U, 64U,
-              get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_b.npy"),
-              PadStrideInfo(1, 1, 0, 0))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << ConvolutionLayer(
-              3U, 3U, 192U,
-              get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
-          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
-          << get_inception_node(data_path, "inception_3a", 64, std::make_tuple(96U, 128U), std::make_tuple(16U, 32U), 32U)
-          << get_inception_node(data_path, "inception_3b", 128, std::make_tuple(128U, 192U), std::make_tuple(32U, 96U), 64U)
-          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
-          << get_inception_node(data_path, "inception_4a", 192, std::make_tuple(96U, 208U), std::make_tuple(16U, 48U), 64U)
-          << get_inception_node(data_path, "inception_4b", 160, std::make_tuple(112U, 224U), std::make_tuple(24U, 64U), 64U)
-          << get_inception_node(data_path, "inception_4c", 128, std::make_tuple(128U, 256U), std::make_tuple(24U, 64U), 64U)
-          << get_inception_node(data_path, "inception_4d", 112, std::make_tuple(144U, 288U), std::make_tuple(32U, 64U), 64U)
-          << get_inception_node(data_path, "inception_4e", 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U)
-          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
-          << get_inception_node(data_path, "inception_5a", 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U)
-          << get_inception_node(data_path, "inception_5b", 384, std::make_tuple(192U, 384U), std::make_tuple(48U, 128U), 128U)
-          << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 7, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL)))
-          << FullyConnectedLayer(
-              1000U,
-              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()
-          << Tensor(get_output_accessor(label, 5));
+        SubGraph i_c;
+        i_c << ConvolutionLayer(
+                1U, 1U, std::get<0>(c_filters),
+                get_weights_accessor(data_path, total_path + "5x5_reduce_w.npy"),
+                get_weights_accessor(data_path, total_path + "5x5_reduce_b.npy"),
+                PadStrideInfo(1, 1, 0, 0))
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+            << ConvolutionLayer(
+                5U, 5U, std::get<1>(c_filters),
+                get_weights_accessor(data_path, total_path + "5x5_w.npy"),
+                get_weights_accessor(data_path, total_path + "5x5_b.npy"),
+                PadStrideInfo(1, 1, 2, 2))
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
 
-    graph.run();
-}
+        SubGraph i_d;
+        i_d << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL)))
+            << ConvolutionLayer(
+                1U, 1U, d_filt,
+                get_weights_accessor(data_path, total_path + "pool_proj_w.npy"),
+                get_weights_accessor(data_path, total_path + "pool_proj_b.npy"),
+                PadStrideInfo(1, 1, 0, 0))
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+
+        return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));
+    }
+};
 
 /** Main program for Googlenet
  *
@@ -200,5 +206,5 @@ void main_graph_googlenet(int argc, char **argv)
  */
 int main(int argc, char **argv)
 {
-    return arm_compute::utils::run_example(argc, argv, main_graph_googlenet);
+    return arm_compute::utils::run_example<GraphGooglenetExample>(argc, argv);
 }
diff --git a/examples/graph_lenet.cpp b/examples/graph_lenet.cpp
index 3e4727f189..2442cffe08 100644
--- a/examples/graph_lenet.cpp
+++ b/examples/graph_lenet.cpp
@@ -29,103 +29,88 @@
 
 #include <cstdlib>
 
+using namespace arm_compute::utils;
 using namespace arm_compute::graph;
 using namespace arm_compute::graph_utils;
 
-namespace
-{
-/** Generates appropriate accessor according to the specified path
- *
- * @note If path is empty will generate a DummyAccessor else will generate a NumPyBinLoader
- *
- * @param path       Path to the data files
- * @param data_file  Relative path to the data files from path
- *
- * @return An appropriate tensor accessor
- */
-std::unique_ptr<ITensorAccessor> get_accessor(const std::string &path, const std::string &data_file)
-{
-    if(path.empty())
-    {
-        return arm_compute::support::cpp14::make_unique<DummyAccessor>();
-    }
-    else
-    {
-        return arm_compute::support::cpp14::make_unique<NumPyBinLoader>(path + data_file);
-    }
-}
-} // namespace
-
 /** Example demonstrating how to implement LeNet'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), [optional] Path to the weights folder, [optional] batches )
  */
-void main_graph_lenet(int argc, char **argv)
+class GraphLenetExample : public Example
 {
-    std::string  data_path;   /** Path to the trainable data */
-    unsigned int batches = 4; /** Number of batches */
+public:
+    void do_setup(int argc, char **argv) override
+    {
+        std::string  data_path;   /** Path to the trainable data */
+        unsigned int batches = 4; /** Number of batches */
 
-    // Set target. 0 (NEON), 1 (OpenCL). By default it is NEON
-    TargetHint target_hint = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0);
+        // Set target. 0 (NEON), 1 (OpenCL). By default it is NEON
+        TargetHint target_hint = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0);
 
-    // Parse arguments
-    if(argc < 2)
-    {
-        // Print help
-        std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [batches]\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]\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]\n\n";
-        std::cout << "No number of batches where specified, thus will use the default : " << batches << "\n\n";
+        // Parse arguments
+        if(argc < 2)
+        {
+            // Print help
+            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [batches]\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]\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]\n\n";
+            std::cout << "No number of batches where specified, thus will use the default : " << batches << "\n\n";
+        }
+        else
+        {
+            //Do something with argv[1] and argv[2]
+            data_path = argv[2];
+            batches   = std::strtol(argv[3], nullptr, 0);
+        }
+
+        //conv1 << pool1 << conv2 << pool2 << fc1 << act1 << fc2 << smx
+        graph << target_hint
+              << Tensor(TensorInfo(TensorShape(28U, 28U, 1U, batches), 1, DataType::F32), DummyAccessor())
+              << ConvolutionLayer(
+                  5U, 5U, 20U,
+                  get_weights_accessor(data_path, "/cnn_data/lenet_model/conv1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/lenet_model/conv1_b.npy"),
+                  PadStrideInfo(1, 1, 0, 0))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+              << ConvolutionLayer(
+                  5U, 5U, 50U,
+                  get_weights_accessor(data_path, "/cnn_data/lenet_model/conv2_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/lenet_model/conv2_b.npy"),
+                  PadStrideInfo(1, 1, 0, 0))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+              << FullyConnectedLayer(
+                  500U,
+                  get_weights_accessor(data_path, "/cnn_data/lenet_model/ip1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/lenet_model/ip1_b.npy"))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << FullyConnectedLayer(
+                  10U,
+                  get_weights_accessor(data_path, "/cnn_data/lenet_model/ip2_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/lenet_model/ip2_b.npy"))
+              << SoftmaxLayer()
+              << Tensor(DummyAccessor());
     }
-    else
+    void do_run() override
     {
-        //Do something with argv[1] and argv[2]
-        data_path = argv[2];
-        batches   = std::strtol(argv[3], nullptr, 0);
+        // Run graph
+        graph.run();
     }
 
-    Graph graph;
-
-    //conv1 << pool1 << conv2 << pool2 << fc1 << act1 << fc2 << smx
-    graph << target_hint
-          << Tensor(TensorInfo(TensorShape(28U, 28U, 1U, batches), 1, DataType::F32), DummyAccessor())
-          << ConvolutionLayer(
-              5U, 5U, 20U,
-              get_accessor(data_path, "/cnn_data/lenet_model/conv1_w.npy"),
-              get_accessor(data_path, "/cnn_data/lenet_model/conv1_b.npy"),
-              PadStrideInfo(1, 1, 0, 0))
-          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
-          << ConvolutionLayer(
-              5U, 5U, 50U,
-              get_accessor(data_path, "/cnn_data/lenet_model/conv2_w.npy"),
-              get_accessor(data_path, "/cnn_data/lenet_model/conv2_b.npy"),
-              PadStrideInfo(1, 1, 0, 0))
-          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
-          << FullyConnectedLayer(
-              500U,
-              get_accessor(data_path, "/cnn_data/lenet_model/ip1_w.npy"),
-              get_accessor(data_path, "/cnn_data/lenet_model/ip1_b.npy"))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << FullyConnectedLayer(
-              10U,
-              get_accessor(data_path, "/cnn_data/lenet_model/ip2_w.npy"),
-              get_accessor(data_path, "/cnn_data/lenet_model/ip2_b.npy"))
-          << SoftmaxLayer()
-          << Tensor(DummyAccessor());
-
-    graph.run();
-}
+private:
+    Graph graph{};
+};
 
 /** Main program for LeNet
  *
@@ -134,5 +119,5 @@ void main_graph_lenet(int argc, char **argv)
  */
 int main(int argc, char **argv)
 {
-    return arm_compute::utils::run_example(argc, argv, main_graph_lenet);
+    return arm_compute::utils::run_example<GraphLenetExample>(argc, argv);
 }
diff --git a/examples/graph_mobilenet.cpp b/examples/graph_mobilenet.cpp
index c468093e56..193e5c336e 100644
--- a/examples/graph_mobilenet.cpp
+++ b/examples/graph_mobilenet.cpp
@@ -29,142 +29,148 @@
 
 #include <cstdlib>
 
+using namespace arm_compute::utils;
 using namespace arm_compute::graph;
 using namespace arm_compute::graph_utils;
 
-namespace
-{
-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)
-{
-    std::string total_path = "/cnn_data/mobilenet_v1_model/" + param_path + "_";
-    SubGraph    sg;
-    sg << DepthwiseConvolutionLayer(
-           3U, 3U,
-           get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy"),
-           std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-           dwc_pad_stride_info,
-           true)
-       << BatchNormalizationLayer(
-           get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_mean.npy"),
-           get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_variance.npy"),
-           get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_beta.npy"),
-           get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_gamma.npy"),
-           0.001f)
-       << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
-       << ConvolutionLayer(
-           1U, 1U, conv_filt,
-           get_weights_accessor(data_path, total_path + "pointwise_weights.npy"),
-           std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-           conv_pad_stride_info)
-       << BatchNormalizationLayer(
-           get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_mean.npy"),
-           get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_variance.npy"),
-           get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_beta.npy"),
-           get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_gamma.npy"),
-           0.001f)
-       << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f));
-
-    return BranchLayer(std::move(sg));
-}
-} // namespace
-
 /** 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), [optional] Path to the weights folder, [optional] image, [optional] labels )
  */
-void main_graph_mobilenet(int argc, char **argv)
+class GraphMobilenetExample : public Example
 {
-    std::string data_path; /* Path to the trainable data */
-    std::string image;     /* Image data */
-    std::string label;     /* Label data */
+public:
+    void do_setup(int argc, char **argv) override
+    {
+        std::string data_path; /* Path to the trainable data */
+        std::string image;     /* Image data */
+        std::string label;     /* Label data */
 
-    constexpr float mean_r = 122.68f; /* Mean value to subtract from red channel */
-    constexpr float mean_g = 116.67f; /* Mean value to subtract from green channel */
-    constexpr float mean_b = 104.01f; /* Mean value to subtract from blue channel */
+        constexpr float mean_r = 122.68f; /* Mean value to subtract from red channel */
+        constexpr float mean_g = 116.67f; /* Mean value to subtract from green channel */
+        constexpr float mean_b = 104.01f; /* Mean value to subtract from blue channel */
 
-    // Set target. 0 (NEON), 1 (OpenCL). By default it is NEON
-    TargetHint            target_hint      = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0);
-    ConvolutionMethodHint convolution_hint = target_hint == TargetHint::NEON ? ConvolutionMethodHint::GEMM : ConvolutionMethodHint::DIRECT;
+        // Set target. 0 (NEON), 1 (OpenCL). By default it is NEON
+        TargetHint            target_hint      = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0);
+        ConvolutionMethodHint convolution_hint = target_hint == TargetHint::NEON ? ConvolutionMethodHint::GEMM : ConvolutionMethodHint::DIRECT;
 
-    // Parse arguments
-    if(argc < 2)
-    {
-        // Print help
-        std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n";
-        std::cout << "No data folder provided: using random values\n\n";
-    }
-    else if(argc == 2)
-    {
-        std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\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]\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]\n\n";
-        std::cout << "No text file with labels provided: skipping output accessor\n\n";
+        // Parse arguments
+        if(argc < 2)
+        {
+            // Print help
+            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n";
+            std::cout << "No data folder provided: using random values\n\n";
+        }
+        else if(argc == 2)
+        {
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\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]\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]\n\n";
+            std::cout << "No text file with labels provided: skipping output accessor\n\n";
+        }
+        else
+        {
+            data_path = argv[2];
+            image     = argv[3];
+            label     = argv[4];
+        }
+
+        graph << target_hint
+              << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32),
+                        get_input_accessor(image, mean_r, mean_g, mean_b))
+              << convolution_hint
+              << ConvolutionLayer(
+                  3U, 3U, 32U,
+                  get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_weights.npy"),
+                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                  PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))
+              << BatchNormalizationLayer(
+                  get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_BatchNorm_moving_mean.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_BatchNorm_moving_variance.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_BatchNorm_beta.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_BatchNorm_gamma.npy"),
+                  0.001f)
+
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
+              << get_dwsc_node(data_path, "Conv2d_1", 64, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0))
+              << get_dwsc_node(data_path, "Conv2d_2", 128, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+              << get_dwsc_node(data_path, "Conv2d_3", 128, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+              << get_dwsc_node(data_path, "Conv2d_4", 256, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+              << get_dwsc_node(data_path, "Conv2d_5", 256, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+              << get_dwsc_node(data_path, "Conv2d_6", 512, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+              << get_dwsc_node(data_path, "Conv2d_7", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+              << get_dwsc_node(data_path, "Conv2d_8", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+              << get_dwsc_node(data_path, "Conv2d_9", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+              << get_dwsc_node(data_path, "Conv2d_10", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+              << get_dwsc_node(data_path, "Conv2d_11", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+              << get_dwsc_node(data_path, "Conv2d_12", 1024, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+              << get_dwsc_node(data_path, "Conv2d_13", 1024, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
+              << ConvolutionLayer(
+                  1U, 1U, 1001U,
+                  get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Logits_Conv2d_1c_1x1_weights.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Logits_Conv2d_1c_1x1_biases.npy"),
+                  PadStrideInfo(1, 1, 0, 0))
+              << ReshapeLayer(TensorShape(1001U))
+              << SoftmaxLayer()
+              << Tensor(get_output_accessor(label, 5));
     }
-    else
+    void do_run() override
     {
-        data_path = argv[2];
-        image     = argv[3];
-        label     = argv[4];
+        // Run graph
+        graph.run();
     }
 
-    Graph graph;
+private:
+    Graph graph{};
 
-    graph << target_hint
-          << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32),
-                    get_input_accessor(image, mean_r, mean_g, mean_b))
-          << convolution_hint
-          << ConvolutionLayer(
-              3U, 3U, 32U,
-              get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_weights.npy"),
-              std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-              PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))
-          << BatchNormalizationLayer(
-              get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_BatchNorm_moving_mean.npy"),
-              get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_BatchNorm_moving_variance.npy"),
-              get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_BatchNorm_beta.npy"),
-              get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_BatchNorm_gamma.npy"),
-              0.001f)
-
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
-          << get_dwsc_node(data_path, "Conv2d_1", 64, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0))
-          << get_dwsc_node(data_path, "Conv2d_2", 128, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
-          << get_dwsc_node(data_path, "Conv2d_3", 128, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
-          << get_dwsc_node(data_path, "Conv2d_4", 256, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
-          << get_dwsc_node(data_path, "Conv2d_5", 256, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
-          << get_dwsc_node(data_path, "Conv2d_6", 512, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
-          << get_dwsc_node(data_path, "Conv2d_7", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
-          << get_dwsc_node(data_path, "Conv2d_8", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
-          << get_dwsc_node(data_path, "Conv2d_9", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
-          << get_dwsc_node(data_path, "Conv2d_10", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
-          << get_dwsc_node(data_path, "Conv2d_11", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
-          << get_dwsc_node(data_path, "Conv2d_12", 1024, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
-          << get_dwsc_node(data_path, "Conv2d_13", 1024, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
-          << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
-          << ConvolutionLayer(
-              1U, 1U, 1001U,
-              get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Logits_Conv2d_1c_1x1_weights.npy"),
-              get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Logits_Conv2d_1c_1x1_biases.npy"),
-              PadStrideInfo(1, 1, 0, 0))
-          << ReshapeLayer(TensorShape(1001U))
-          << SoftmaxLayer()
-          << Tensor(get_output_accessor(label, 5));
+    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)
+    {
+        std::string total_path = "/cnn_data/mobilenet_v1_model/" + param_path + "_";
+        SubGraph    sg;
+        sg << DepthwiseConvolutionLayer(
+               3U, 3U,
+               get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy"),
+               std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+               dwc_pad_stride_info,
+               true)
+           << BatchNormalizationLayer(
+               get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_mean.npy"),
+               get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_variance.npy"),
+               get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_beta.npy"),
+               get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_gamma.npy"),
+               0.001f)
+           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
+           << ConvolutionLayer(
+               1U, 1U, conv_filt,
+               get_weights_accessor(data_path, total_path + "pointwise_weights.npy"),
+               std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+               conv_pad_stride_info)
+           << BatchNormalizationLayer(
+               get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_mean.npy"),
+               get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_variance.npy"),
+               get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_beta.npy"),
+               get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_gamma.npy"),
+               0.001f)
+           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f));
 
-    graph.run();
-}
+        return BranchLayer(std::move(sg));
+    }
+};
 
 /** Main program for MobileNetV1
  *
@@ -173,5 +179,5 @@ void main_graph_mobilenet(int argc, char **argv)
  */
 int main(int argc, char **argv)
 {
-    return arm_compute::utils::run_example(argc, argv, main_graph_mobilenet);
+    return arm_compute::utils::run_example<GraphMobilenetExample>(argc, argv);
 }
diff --git a/examples/graph_squeezenet.cpp b/examples/graph_squeezenet.cpp
index 11a80a7bd8..b21f2fe5c4 100644
--- a/examples/graph_squeezenet.cpp
+++ b/examples/graph_squeezenet.cpp
@@ -31,33 +31,13 @@
 #include <cstdlib>
 #include <tuple>
 
+using namespace arm_compute::utils;
 using namespace arm_compute::graph;
 using namespace arm_compute::graph_utils;
 using namespace arm_compute::logging;
 
 namespace
 {
-BranchLayer get_expand_fire_node(const std::string &data_path, std::string &&param_path, unsigned int expand1_filt, unsigned int expand3_filt)
-{
-    std::string total_path = "/cnn_data/squeezenet_v1.0_model/" + param_path + "_";
-    SubGraph    i_a;
-    i_a << ConvolutionLayer(
-            1U, 1U, expand1_filt,
-            get_weights_accessor(data_path, total_path + "expand1x1_w.npy"),
-            get_weights_accessor(data_path, total_path + "expand1x1_b.npy"),
-            PadStrideInfo(1, 1, 0, 0))
-        << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
-
-    SubGraph i_b;
-    i_b << ConvolutionLayer(
-            3U, 3U, expand3_filt,
-            get_weights_accessor(data_path, total_path + "expand3x3_w.npy"),
-            get_weights_accessor(data_path, total_path + "expand3x3_b.npy"),
-            PadStrideInfo(1, 1, 1, 1))
-        << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
-
-    return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b));
-}
 } // namespace
 
 /** Example demonstrating how to implement Squeezenet's network using the Compute Library's graph API
@@ -65,136 +45,166 @@ BranchLayer get_expand_fire_node(const std::string &data_path, std::string &&par
  * @param[in] argc Number of arguments
  * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels )
  */
-void main_graph_squeezenet(int argc, char **argv)
+class GraphSqueezenetExample : public Example
 {
-    std::string data_path; /* Path to the trainable data */
-    std::string image;     /* Image data */
-    std::string label;     /* Label data */
+public:
+    void do_setup(int argc, char **argv) override
+    {
+        std::string data_path; /* Path to the trainable data */
+        std::string image;     /* Image data */
+        std::string label;     /* Label data */
 
-    constexpr float mean_r = 122.68f; /* Mean value to subtract from red channel */
-    constexpr float mean_g = 116.67f; /* Mean value to subtract from green channel */
-    constexpr float mean_b = 104.01f; /* Mean value to subtract from blue channel */
+        constexpr float mean_r = 122.68f; /* Mean value to subtract from red channel */
+        constexpr float mean_g = 116.67f; /* Mean value to subtract from green channel */
+        constexpr float mean_b = 104.01f; /* Mean value to subtract from blue channel */
 
-    // Set target. 0 (NEON), 1 (OpenCL). By default it is NEON
-    TargetHint            target_hint      = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0);
-    ConvolutionMethodHint convolution_hint = target_hint == TargetHint::NEON ? ConvolutionMethodHint::GEMM : ConvolutionMethodHint::DIRECT;
+        // Set target. 0 (NEON), 1 (OpenCL). By default it is NEON
+        TargetHint            target_hint      = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0);
+        ConvolutionMethodHint convolution_hint = target_hint == TargetHint::NEON ? ConvolutionMethodHint::GEMM : ConvolutionMethodHint::DIRECT;
 
-    // Parse arguments
-    if(argc < 2)
-    {
-        // Print help
-        std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n";
-        std::cout << "No data folder provided: using random values\n\n";
-    }
-    else if(argc == 2)
-    {
-        std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\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]\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]\n\n";
-        std::cout << "No text file with labels provided: skipping output accessor\n\n";
+        // Parse arguments
+        if(argc < 2)
+        {
+            // Print help
+            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n";
+            std::cout << "No data folder provided: using random values\n\n";
+        }
+        else if(argc == 2)
+        {
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\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]\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]\n\n";
+            std::cout << "No text file with labels provided: skipping output accessor\n\n";
+        }
+        else
+        {
+            data_path = argv[2];
+            image     = argv[3];
+            label     = argv[4];
+        }
+
+        graph << target_hint
+              << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32),
+                        get_input_accessor(image, mean_r, mean_g, mean_b))
+              << ConvolutionLayer(
+                  7U, 7U, 96U,
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_b.npy"),
+                  PadStrideInfo(2, 2, 0, 0))
+              << convolution_hint
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+              << ConvolutionLayer(
+                  1U, 1U, 16U,
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_b.npy"),
+                  PadStrideInfo(1, 1, 0, 0))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << get_expand_fire_node(data_path, "fire2", 64U, 64U)
+              << ConvolutionLayer(
+                  1U, 1U, 16U,
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_b.npy"),
+                  PadStrideInfo(1, 1, 0, 0))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << get_expand_fire_node(data_path, "fire3", 64U, 64U)
+              << ConvolutionLayer(
+                  1U, 1U, 32U,
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_b.npy"),
+                  PadStrideInfo(1, 1, 0, 0))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << get_expand_fire_node(data_path, "fire4", 128U, 128U)
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+              << ConvolutionLayer(
+                  1U, 1U, 32U,
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_b.npy"),
+                  PadStrideInfo(1, 1, 0, 0))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << get_expand_fire_node(data_path, "fire5", 128U, 128U)
+              << ConvolutionLayer(
+                  1U, 1U, 48U,
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_b.npy"),
+                  PadStrideInfo(1, 1, 0, 0))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << get_expand_fire_node(data_path, "fire6", 192U, 192U)
+              << ConvolutionLayer(
+                  1U, 1U, 48U,
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_b.npy"),
+                  PadStrideInfo(1, 1, 0, 0))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << get_expand_fire_node(data_path, "fire7", 192U, 192U)
+              << ConvolutionLayer(
+                  1U, 1U, 64U,
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_b.npy"),
+                  PadStrideInfo(1, 1, 0, 0))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << get_expand_fire_node(data_path, "fire8", 256U, 256U)
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+              << ConvolutionLayer(
+                  1U, 1U, 64U,
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_b.npy"),
+                  PadStrideInfo(1, 1, 0, 0))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << get_expand_fire_node(data_path, "fire9", 256U, 256U)
+              << ConvolutionLayer(
+                  1U, 1U, 1000U,
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_b.npy"),
+                  PadStrideInfo(1, 1, 0, 0))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
+              << FlattenLayer()
+              << SoftmaxLayer()
+              << Tensor(get_output_accessor(label, 5));
     }
-    else
+    void do_run() override
     {
-        data_path = argv[2];
-        image     = argv[3];
-        label     = argv[4];
+        // Run graph
+        graph.run();
     }
 
-    Graph graph;
+private:
+    Graph graph{};
 
-    graph << target_hint
-          << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32),
-                    get_input_accessor(image, mean_r, mean_g, mean_b))
-          << ConvolutionLayer(
-              7U, 7U, 96U,
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_b.npy"),
-              PadStrideInfo(2, 2, 0, 0))
-          << convolution_hint
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
-          << ConvolutionLayer(
-              1U, 1U, 16U,
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_b.npy"),
-              PadStrideInfo(1, 1, 0, 0))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << get_expand_fire_node(data_path, "fire2", 64U, 64U)
-          << ConvolutionLayer(
-              1U, 1U, 16U,
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_b.npy"),
-              PadStrideInfo(1, 1, 0, 0))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << get_expand_fire_node(data_path, "fire3", 64U, 64U)
-          << ConvolutionLayer(
-              1U, 1U, 32U,
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_b.npy"),
-              PadStrideInfo(1, 1, 0, 0))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << get_expand_fire_node(data_path, "fire4", 128U, 128U)
-          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
-          << ConvolutionLayer(
-              1U, 1U, 32U,
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_b.npy"),
-              PadStrideInfo(1, 1, 0, 0))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << get_expand_fire_node(data_path, "fire5", 128U, 128U)
-          << ConvolutionLayer(
-              1U, 1U, 48U,
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_b.npy"),
-              PadStrideInfo(1, 1, 0, 0))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << get_expand_fire_node(data_path, "fire6", 192U, 192U)
-          << ConvolutionLayer(
-              1U, 1U, 48U,
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_b.npy"),
-              PadStrideInfo(1, 1, 0, 0))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << get_expand_fire_node(data_path, "fire7", 192U, 192U)
-          << ConvolutionLayer(
-              1U, 1U, 64U,
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_b.npy"),
-              PadStrideInfo(1, 1, 0, 0))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << get_expand_fire_node(data_path, "fire8", 256U, 256U)
-          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
-          << ConvolutionLayer(
-              1U, 1U, 64U,
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_b.npy"),
-              PadStrideInfo(1, 1, 0, 0))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << get_expand_fire_node(data_path, "fire9", 256U, 256U)
-          << ConvolutionLayer(
-              1U, 1U, 1000U,
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_b.npy"),
-              PadStrideInfo(1, 1, 0, 0))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
-          << FlattenLayer()
-          << SoftmaxLayer()
-          << Tensor(get_output_accessor(label, 5));
+    BranchLayer get_expand_fire_node(const std::string &data_path, std::string &&param_path, unsigned int expand1_filt, unsigned int expand3_filt)
+    {
+        std::string total_path = "/cnn_data/squeezenet_v1.0_model/" + param_path + "_";
+        SubGraph    i_a;
+        i_a << ConvolutionLayer(
+                1U, 1U, expand1_filt,
+                get_weights_accessor(data_path, total_path + "expand1x1_w.npy"),
+                get_weights_accessor(data_path, total_path + "expand1x1_b.npy"),
+                PadStrideInfo(1, 1, 0, 0))
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
 
-    graph.run();
-}
+        SubGraph i_b;
+        i_b << ConvolutionLayer(
+                3U, 3U, expand3_filt,
+                get_weights_accessor(data_path, total_path + "expand3x3_w.npy"),
+                get_weights_accessor(data_path, total_path + "expand3x3_b.npy"),
+                PadStrideInfo(1, 1, 1, 1))
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+
+        return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b));
+    }
+};
 
 /** Main program for Squeezenet v1.0
  *
@@ -203,5 +213,5 @@ void main_graph_squeezenet(int argc, char **argv)
  */
 int main(int argc, char **argv)
 {
-    return arm_compute::utils::run_example(argc, argv, main_graph_squeezenet);
+    return arm_compute::utils::run_example<GraphSqueezenetExample>(argc, argv);
 }
diff --git a/examples/graph_vgg16.cpp b/examples/graph_vgg16.cpp
index fcaa6182cb..1a804a4882 100644
--- a/examples/graph_vgg16.cpp
+++ b/examples/graph_vgg16.cpp
@@ -29,6 +29,7 @@
 
 #include <cstdlib>
 
+using namespace arm_compute::utils;
 using namespace arm_compute::graph;
 using namespace arm_compute::graph_utils;
 
@@ -37,179 +38,186 @@ using namespace arm_compute::graph_utils;
  * @param[in] argc Number of arguments
  * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels )
  */
-void main_graph_vgg16(int argc, char **argv)
+class GraphVGG16Example : public Example
 {
-    std::string data_path; /* Path to the trainable data */
-    std::string image;     /* Image data */
-    std::string label;     /* Label data */
+public:
+    void do_setup(int argc, char **argv) override
+    {
+        std::string data_path; /* Path to the trainable data */
+        std::string image;     /* Image data */
+        std::string label;     /* Label data */
 
-    constexpr float mean_r = 123.68f;  /* Mean value to subtract from red channel */
-    constexpr float mean_g = 116.779f; /* Mean value to subtract from green channel */
-    constexpr float mean_b = 103.939f; /* Mean value to subtract from blue channel */
+        constexpr float mean_r = 123.68f;  /* Mean value to subtract from red channel */
+        constexpr float mean_g = 116.779f; /* Mean value to subtract from green channel */
+        constexpr float mean_b = 103.939f; /* Mean value to subtract from blue channel */
 
-    // Set target. 0 (NEON), 1 (OpenCL). By default it is NEON
-    TargetHint            target_hint      = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0);
-    ConvolutionMethodHint convolution_hint = ConvolutionMethodHint::DIRECT;
+        // Set target. 0 (NEON), 1 (OpenCL). By default it is NEON
+        TargetHint            target_hint      = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0);
+        ConvolutionMethodHint convolution_hint = ConvolutionMethodHint::DIRECT;
 
-    // Parse arguments
-    if(argc < 2)
-    {
-        // Print help
-        std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n";
-        std::cout << "No data folder provided: using random values\n\n";
-    }
-    else if(argc == 2)
-    {
-        std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\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]\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]\n\n";
-        std::cout << "No text file with labels provided: skipping output accessor\n\n";
+        // Parse arguments
+        if(argc < 2)
+        {
+            // Print help
+            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n";
+            std::cout << "No data folder provided: using random values\n\n";
+        }
+        else if(argc == 2)
+        {
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\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]\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]\n\n";
+            std::cout << "No text file with labels provided: skipping output accessor\n\n";
+        }
+        else
+        {
+            data_path = argv[2];
+            image     = argv[3];
+            label     = argv[4];
+        }
+
+        graph << target_hint
+              << convolution_hint
+              << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32),
+                        get_input_accessor(image, mean_r, mean_g, mean_b))
+              << ConvolutionMethodHint::DIRECT
+              // Layer 1
+              << ConvolutionLayer(
+                  3U, 3U, 64U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_1_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              // Layer 2
+              << ConvolutionLayer(
+                  3U, 3U, 64U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_2_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_2_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+              // Layer 3
+              << ConvolutionLayer(
+                  3U, 3U, 128U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_1_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              // Layer 4
+              << ConvolutionLayer(
+                  3U, 3U, 128U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_2_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_2_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+              // Layer 5
+              << ConvolutionLayer(
+                  3U, 3U, 256U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_1_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              // Layer 6
+              << ConvolutionLayer(
+                  3U, 3U, 256U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_2_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_2_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              // Layer 7
+              << ConvolutionLayer(
+                  3U, 3U, 256U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_3_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_3_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+              // Layer 8
+              << ConvolutionLayer(
+                  3U, 3U, 512U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_1_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              // Layer 9
+              << ConvolutionLayer(
+                  3U, 3U, 512U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_2_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_2_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              // Layer 10
+              << ConvolutionLayer(
+                  3U, 3U, 512U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_3_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_3_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+              // Layer 11
+              << ConvolutionLayer(
+                  3U, 3U, 512U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_1_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              // Layer 12
+              << ConvolutionLayer(
+                  3U, 3U, 512U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_2_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_2_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              // Layer 13
+              << ConvolutionLayer(
+                  3U, 3U, 512U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_3_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_3_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+              // Layer 14
+              << FullyConnectedLayer(
+                  4096U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc6_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc6_b.npy"))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              // Layer 15
+              << FullyConnectedLayer(
+                  4096U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc7_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc7_b.npy"))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              // Layer 16
+              << FullyConnectedLayer(
+                  1000U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc8_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc8_b.npy"))
+              // Softmax
+              << SoftmaxLayer()
+              << Tensor(get_output_accessor(label, 5));
     }
-    else
+    void do_run() override
     {
-        data_path = argv[2];
-        image     = argv[3];
-        label     = argv[4];
+        // Run graph
+        graph.run();
     }
 
-    Graph graph;
-
-    graph << target_hint
-          << convolution_hint
-          << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32),
-                    get_input_accessor(image, mean_r, mean_g, mean_b))
-          << ConvolutionMethodHint::DIRECT
-          // Layer 1
-          << ConvolutionLayer(
-              3U, 3U, 64U,
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_1_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_1_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          // Layer 2
-          << ConvolutionLayer(
-              3U, 3U, 64U,
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_2_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_2_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
-          // Layer 3
-          << ConvolutionLayer(
-              3U, 3U, 128U,
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_1_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_1_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          // Layer 4
-          << ConvolutionLayer(
-              3U, 3U, 128U,
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_2_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_2_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
-          // Layer 5
-          << ConvolutionLayer(
-              3U, 3U, 256U,
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_1_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_1_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          // Layer 6
-          << ConvolutionLayer(
-              3U, 3U, 256U,
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_2_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_2_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          // Layer 7
-          << ConvolutionLayer(
-              3U, 3U, 256U,
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_3_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_3_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
-          // Layer 8
-          << ConvolutionLayer(
-              3U, 3U, 512U,
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_1_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_1_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          // Layer 9
-          << ConvolutionLayer(
-              3U, 3U, 512U,
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_2_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_2_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          // Layer 10
-          << ConvolutionLayer(
-              3U, 3U, 512U,
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_3_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_3_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
-          // Layer 11
-          << ConvolutionLayer(
-              3U, 3U, 512U,
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_1_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_1_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          // Layer 12
-          << ConvolutionLayer(
-              3U, 3U, 512U,
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_2_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_2_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          // Layer 13
-          << ConvolutionLayer(
-              3U, 3U, 512U,
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_3_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_3_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
-          // Layer 14
-          << FullyConnectedLayer(
-              4096U,
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc6_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc6_b.npy"))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          // Layer 15
-          << FullyConnectedLayer(
-              4096U,
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc7_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc7_b.npy"))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          // Layer 16
-          << FullyConnectedLayer(
-              1000U,
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc8_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc8_b.npy"))
-          // Softmax
-          << SoftmaxLayer()
-          << Tensor(get_output_accessor(label, 5));
-
-    // Run graph
-    graph.run();
-}
+private:
+    Graph graph{};
+};
 
 /** Main program for VGG16
  *
@@ -218,5 +226,5 @@ void main_graph_vgg16(int argc, char **argv)
  */
 int main(int argc, char **argv)
 {
-    return arm_compute::utils::run_example(argc, argv, main_graph_vgg16);
+    return arm_compute::utils::run_example<GraphVGG16Example>(argc, argv);
 }
diff --git a/examples/graph_vgg19.cpp b/examples/graph_vgg19.cpp
index 1f6cba4441..5214438d7f 100644
--- a/examples/graph_vgg19.cpp
+++ b/examples/graph_vgg19.cpp
@@ -29,6 +29,7 @@
 
 #include <cstdlib>
 
+using namespace arm_compute::utils;
 using namespace arm_compute::graph;
 using namespace arm_compute::graph_utils;
 
@@ -37,188 +38,195 @@ using namespace arm_compute::graph_utils;
  * @param[in] argc Number of arguments
  * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels )
  */
-void main_graph_vgg19(int argc, char **argv)
+class GraphVGG19Example : public Example
 {
-    std::string data_path; /* Path to the trainable data */
-    std::string image;     /* Image data */
-    std::string label;     /* Label data */
+public:
+    void do_setup(int argc, char **argv) override
+    {
+        std::string data_path; /* Path to the trainable data */
+        std::string image;     /* Image data */
+        std::string label;     /* Label data */
 
-    constexpr float mean_r = 123.68f;  /* Mean value to subtract from red channel */
-    constexpr float mean_g = 116.779f; /* Mean value to subtract from green channel */
-    constexpr float mean_b = 103.939f; /* Mean value to subtract from blue channel */
+        constexpr float mean_r = 123.68f;  /* Mean value to subtract from red channel */
+        constexpr float mean_g = 116.779f; /* Mean value to subtract from green channel */
+        constexpr float mean_b = 103.939f; /* Mean value to subtract from blue channel */
 
-    // Set target. 0 (NEON), 1 (OpenCL). By default it is NEON
-    TargetHint            target_hint      = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0);
-    ConvolutionMethodHint convolution_hint = ConvolutionMethodHint::DIRECT;
+        // Set target. 0 (NEON), 1 (OpenCL). By default it is NEON
+        TargetHint            target_hint      = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0);
+        ConvolutionMethodHint convolution_hint = ConvolutionMethodHint::DIRECT;
 
-    // Parse arguments
-    if(argc < 2)
-    {
-        // Print help
-        std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n";
-        std::cout << "No data folder provided: using random values\n\n";
-    }
-    else if(argc == 2)
-    {
-        std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\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]\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]\n\n";
-        std::cout << "No text file with labels provided: skipping output accessor\n\n";
+        // Parse arguments
+        if(argc < 2)
+        {
+            // Print help
+            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n";
+            std::cout << "No data folder provided: using random values\n\n";
+        }
+        else if(argc == 2)
+        {
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\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]\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]\n\n";
+            std::cout << "No text file with labels provided: skipping output accessor\n\n";
+        }
+        else
+        {
+            data_path = argv[2];
+            image     = argv[3];
+            label     = argv[4];
+        }
+
+        graph << target_hint
+              << convolution_hint
+              << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32),
+                        get_input_accessor(image, mean_r, mean_g, mean_b))
+              // Layer 1
+              << ConvolutionLayer(
+                  3U, 3U, 64U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_1_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << ConvolutionLayer(
+                  3U, 3U, 64U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_2_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_2_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+              // Layer 2
+              << ConvolutionLayer(
+                  3U, 3U, 128U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_1_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << ConvolutionLayer(
+                  3U, 3U, 128U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_2_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_2_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+              // Layer 3
+              << ConvolutionLayer(
+                  3U, 3U, 256U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_1_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << ConvolutionLayer(
+                  3U, 3U, 256U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_2_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_2_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << ConvolutionLayer(
+                  3U, 3U, 256U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_3_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_3_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << ConvolutionLayer(
+                  3U, 3U, 256U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_4_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_4_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+              // Layer 4
+              << ConvolutionLayer(
+                  3U, 3U, 512U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_1_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << ConvolutionLayer(
+                  3U, 3U, 512U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_2_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_2_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << ConvolutionLayer(
+                  3U, 3U, 512U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_3_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_3_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << ConvolutionLayer(
+                  3U, 3U, 512U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_4_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_4_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+              // Layer 5
+              << ConvolutionLayer(
+                  3U, 3U, 512U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_1_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << ConvolutionLayer(
+                  3U, 3U, 512U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_2_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_2_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << ConvolutionLayer(
+                  3U, 3U, 512U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_3_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_3_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << ConvolutionLayer(
+                  3U, 3U, 512U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_4_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_4_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+              // Layer 6
+              << FullyConnectedLayer(
+                  4096U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc6_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc6_b.npy"))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              // Layer 7
+              << FullyConnectedLayer(
+                  4096U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc7_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc7_b.npy"))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              // Layer 8
+              << FullyConnectedLayer(
+                  1000U,
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc8_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc8_b.npy"))
+              // Softmax
+              << SoftmaxLayer()
+              << Tensor(get_output_accessor(label, 5));
     }
-    else
+    void do_run() override
     {
-        data_path = argv[2];
-        image     = argv[3];
-        label     = argv[4];
+        // Run graph
+        graph.run();
     }
 
-    Graph graph;
-
-    graph << target_hint
-          << convolution_hint
-          << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32),
-                    get_input_accessor(image, mean_r, mean_g, mean_b))
-          // Layer 1
-          << ConvolutionLayer(
-              3U, 3U, 64U,
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_1_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_1_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << ConvolutionLayer(
-              3U, 3U, 64U,
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_2_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_2_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
-          // Layer 2
-          << ConvolutionLayer(
-              3U, 3U, 128U,
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_1_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_1_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << ConvolutionLayer(
-              3U, 3U, 128U,
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_2_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_2_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
-          // Layer 3
-          << ConvolutionLayer(
-              3U, 3U, 256U,
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_1_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_1_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << ConvolutionLayer(
-              3U, 3U, 256U,
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_2_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_2_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << ConvolutionLayer(
-              3U, 3U, 256U,
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_3_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_3_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << ConvolutionLayer(
-              3U, 3U, 256U,
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_4_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_4_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
-          // Layer 4
-          << ConvolutionLayer(
-              3U, 3U, 512U,
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_1_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_1_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << ConvolutionLayer(
-              3U, 3U, 512U,
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_2_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_2_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << ConvolutionLayer(
-              3U, 3U, 512U,
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_3_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_3_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << ConvolutionLayer(
-              3U, 3U, 512U,
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_4_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_4_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
-          // Layer 5
-          << ConvolutionLayer(
-              3U, 3U, 512U,
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_1_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_1_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << ConvolutionLayer(
-              3U, 3U, 512U,
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_2_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_2_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << ConvolutionLayer(
-              3U, 3U, 512U,
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_3_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_3_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << ConvolutionLayer(
-              3U, 3U, 512U,
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_4_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_4_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
-          // Layer 6
-          << FullyConnectedLayer(
-              4096U,
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc6_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc6_b.npy"))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          // Layer 7
-          << FullyConnectedLayer(
-              4096U,
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc7_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc7_b.npy"))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          // Layer 8
-          << FullyConnectedLayer(
-              1000U,
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc8_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc8_b.npy"))
-          // Softmax
-          << SoftmaxLayer()
-          << Tensor(get_output_accessor(label, 5));
-
-    // Run graph
-    graph.run();
-}
+private:
+    Graph graph{};
+};
 
 /** Main program for VGG19
  *
@@ -227,5 +235,5 @@ void main_graph_vgg19(int argc, char **argv)
  */
 int main(int argc, char **argv)
 {
-    return arm_compute::utils::run_example(argc, argv, main_graph_vgg19);
+    return arm_compute::utils::run_example<GraphVGG19Example>(argc, argv);
 }
diff --git a/examples/neon_cartoon_effect.cpp b/examples/neon_cartoon_effect.cpp
index 0ecd9410a1..da8ce3f84f 100644
--- a/examples/neon_cartoon_effect.cpp
+++ b/examples/neon_cartoon_effect.cpp
@@ -30,67 +30,76 @@
 using namespace arm_compute;
 using namespace utils;
 
-void main_neon_cartoon_effect(int argc, char **argv)
+class NEONCartoonEffectExample : public Example
 {
-    // Open PPM file
-    PPMLoader ppm;
-    Image     src_img;
-    Image     dst_img;
-    Image     gaus5x5_img;
-    Image     canny_edge_img;
-
-    if(argc < 2)
-    {
-        // Print help
-        std::cout << "Usage: ./build/neon_cartoon_effect [input_image.ppm]\n\n";
-        std::cout << "No input_image provided, creating a dummy 640x480 image\n";
-        // Create an empty grayscale 640x480 image
-        src_img.allocator()->init(TensorInfo(640, 480, Format::U8));
-    }
-    else
+public:
+    void do_setup(int argc, char **argv) override
     {
-        ppm.open(argv[1]);
-        ppm.init_image(src_img, Format::U8);
-    }
+        // Open PPM file
+        PPMLoader ppm;
 
-    // Initialize just the dimensions and format of the images:
-    gaus5x5_img.allocator()->init(*src_img.info());
-    canny_edge_img.allocator()->init(*src_img.info());
-    dst_img.allocator()->init(*src_img.info());
+        if(argc < 2)
+        {
+            // Print help
+            std::cout << "Usage: ./build/neon_cartoon_effect [input_image.ppm]\n\n";
+            std::cout << "No input_image provided, creating a dummy 640x480 image\n";
+            // Create an empty grayscale 640x480 image
+            src_img.allocator()->init(TensorInfo(640, 480, Format::U8));
+        }
+        else
+        {
+            ppm.open(argv[1]);
+            ppm.init_image(src_img, Format::U8);
+        }
 
-    NEGaussian5x5           gaus5x5;
-    NECannyEdge             canny_edge;
-    NEArithmeticSubtraction sub;
+        // Initialize just the dimensions and format of the images:
+        gaus5x5_img.allocator()->init(*src_img.info());
+        canny_edge_img.allocator()->init(*src_img.info());
+        dst_img.allocator()->init(*src_img.info());
 
-    // Configure the functions to call
-    gaus5x5.configure(&src_img, &gaus5x5_img, BorderMode::REPLICATE);
-    canny_edge.configure(&src_img, &canny_edge_img, 100, 80, 3, 1, BorderMode::REPLICATE);
-    sub.configure(&gaus5x5_img, &canny_edge_img, &dst_img, ConvertPolicy::SATURATE);
+        // Configure the functions to call
+        gaus5x5.configure(&src_img, &gaus5x5_img, BorderMode::REPLICATE);
+        canny_edge.configure(&src_img, &canny_edge_img, 100, 80, 3, 1, BorderMode::REPLICATE);
+        sub.configure(&gaus5x5_img, &canny_edge_img, &dst_img, ConvertPolicy::SATURATE);
 
-    // Now that the padding requirements are known we can allocate the images:
-    src_img.allocator()->allocate();
-    dst_img.allocator()->allocate();
-    gaus5x5_img.allocator()->allocate();
-    canny_edge_img.allocator()->allocate();
+        // Now that the padding requirements are known we can allocate the images:
+        src_img.allocator()->allocate();
+        dst_img.allocator()->allocate();
+        gaus5x5_img.allocator()->allocate();
+        canny_edge_img.allocator()->allocate();
 
-    // Fill the input image with the content of the PPM image if a filename was provided:
-    if(ppm.is_open())
-    {
-        ppm.fill_image(src_img);
+        // Fill the input image with the content of the PPM image if a filename was provided:
+        if(ppm.is_open())
+        {
+            ppm.fill_image(src_img);
+            output_filename = std::string(argv[1]) + "_out.ppm";
+        }
     }
 
-    // Execute the functions:
-    gaus5x5.run();
-    canny_edge.run();
-    sub.run();
+    void do_run() override
+    {
+        // Execute the functions:
+        gaus5x5.run();
+        canny_edge.run();
+        sub.run();
+    }
 
-    // Save the result to file:
-    if(ppm.is_open())
+    void do_teardown() override
     {
-        const std::string output_filename = std::string(argv[1]) + "_out.ppm";
-        save_to_ppm(dst_img, output_filename);
+        // Save the result to file:
+        if(!output_filename.empty())
+        {
+            save_to_ppm(dst_img, output_filename); // save_to_ppm maps and unmaps the image to store as PPM
+        }
     }
-}
+
+private:
+    Image                   src_img{}, dst_img{}, gaus5x5_img{}, canny_edge_img{};
+    NEGaussian5x5           gaus5x5{};
+    NECannyEdge             canny_edge{};
+    NEArithmeticSubtraction sub{};
+    std::string             output_filename{};
+};
 
 /** Main program for cartoon effect test
  *
@@ -99,5 +108,5 @@ void main_neon_cartoon_effect(int argc, char **argv)
  */
 int main(int argc, char **argv)
 {
-    return utils::run_example(argc, argv, main_neon_cartoon_effect);
+    return utils::run_example<NEONCartoonEffectExample>(argc, argv);
 }
diff --git a/examples/neon_cnn.cpp b/examples/neon_cnn.cpp
index 2be5acfbaf..05b6c832bc 100644
--- a/examples/neon_cnn.cpp
+++ b/examples/neon_cnn.cpp
@@ -33,254 +33,261 @@
 using namespace arm_compute;
 using namespace utils;
 
-void main_cnn(int argc, char **argv)
+class NEONCNNExample : public Example
 {
-    ARM_COMPUTE_UNUSED(argc);
-    ARM_COMPUTE_UNUSED(argv);
+public:
+    void do_setup(int argc, char **argv) override
+    {
+        ARM_COMPUTE_UNUSED(argc);
+        ARM_COMPUTE_UNUSED(argv);
 
-    // Create NEON allocator
-    Allocator allocator;
+        // Create memory manager components
+        // We need 2 memory managers: 1 for handling the tensors within the functions (mm_layers) and 1 for handling the input and output tensors of the functions (mm_transitions))
+        auto lifetime_mgr0  = std::make_shared<BlobLifetimeManager>();                           // Create lifetime manager
+        auto lifetime_mgr1  = std::make_shared<BlobLifetimeManager>();                           // Create lifetime manager
+        auto pool_mgr0      = std::make_shared<PoolManager>();                                   // Create pool manager
+        auto pool_mgr1      = std::make_shared<PoolManager>();                                   // Create pool manager
+        auto mm_layers      = std::make_shared<MemoryManagerOnDemand>(lifetime_mgr0, pool_mgr0); // Create the memory manager
+        auto mm_transitions = std::make_shared<MemoryManagerOnDemand>(lifetime_mgr1, pool_mgr1); // Create the memory manager
 
-    // Create memory manager components
-    // We need 2 memory managers: 1 for handling the tensors within the functions (mm_layers) and 1 for handling the input and output tensors of the functions (mm_transitions))
-    auto lifetime_mgr0  = std::make_shared<BlobLifetimeManager>();                           // Create lifetime manager
-    auto lifetime_mgr1  = std::make_shared<BlobLifetimeManager>();                           // Create lifetime manager
-    auto pool_mgr0      = std::make_shared<PoolManager>();                                   // Create pool manager
-    auto pool_mgr1      = std::make_shared<PoolManager>();                                   // Create pool manager
-    auto mm_layers      = std::make_shared<MemoryManagerOnDemand>(lifetime_mgr0, pool_mgr0); // Create the memory manager
-    auto mm_transitions = std::make_shared<MemoryManagerOnDemand>(lifetime_mgr1, pool_mgr1); // Create the memory manager
+        // The weights and biases tensors should be initialized with the values inferred with the training
 
-    // The src tensor should contain the input image
-    Tensor src;
-
-    // The weights and biases tensors should be initialized with the values inferred with the training
-    Tensor weights0;
-    Tensor weights1;
-    Tensor weights2;
-    Tensor biases0;
-    Tensor biases1;
-    Tensor biases2;
-
-    Tensor out_conv0;
-    Tensor out_conv1;
-    Tensor out_act0;
-    Tensor out_act1;
-    Tensor out_act2;
-    Tensor out_pool0;
-    Tensor out_pool1;
-    Tensor out_fc0;
-    Tensor out_softmax;
-
-    // Create layers and set memory manager where allowed to manage internal memory requirements
-    NEConvolutionLayer    conv0(mm_layers);
-    NEConvolutionLayer    conv1(mm_layers);
-    NEPoolingLayer        pool0;
-    NEPoolingLayer        pool1;
-    NEFullyConnectedLayer fc0(mm_layers);
-    NEActivationLayer     act0;
-    NEActivationLayer     act1;
-    NEActivationLayer     act2;
-    NESoftmaxLayer        softmax(mm_layers);
+        // Set memory manager where allowed to manage internal memory requirements
+        conv0   = arm_compute::support::cpp14::make_unique<NEConvolutionLayer>(mm_layers);
+        conv1   = arm_compute::support::cpp14::make_unique<NEConvolutionLayer>(mm_layers);
+        fc0     = arm_compute::support::cpp14::make_unique<NEFullyConnectedLayer>(mm_layers);
+        softmax = arm_compute::support::cpp14::make_unique<NESoftmaxLayer>(mm_layers);
 
-    /* [Initialize tensors] */
+        /* [Initialize tensors] */
 
-    // Initialize src tensor
-    constexpr unsigned int width_src_image  = 32;
-    constexpr unsigned int height_src_image = 32;
-    constexpr unsigned int ifm_src_img      = 1;
+        // Initialize src tensor
+        constexpr unsigned int width_src_image  = 32;
+        constexpr unsigned int height_src_image = 32;
+        constexpr unsigned int ifm_src_img      = 1;
 
-    const TensorShape src_shape(width_src_image, height_src_image, ifm_src_img);
-    src.allocator()->init(TensorInfo(src_shape, 1, DataType::F32));
+        const TensorShape src_shape(width_src_image, height_src_image, ifm_src_img);
+        src.allocator()->init(TensorInfo(src_shape, 1, DataType::F32));
 
-    // Initialize tensors of conv0
-    constexpr unsigned int kernel_x_conv0 = 5;
-    constexpr unsigned int kernel_y_conv0 = 5;
-    constexpr unsigned int ofm_conv0      = 8;
+        // Initialize tensors of conv0
+        constexpr unsigned int kernel_x_conv0 = 5;
+        constexpr unsigned int kernel_y_conv0 = 5;
+        constexpr unsigned int ofm_conv0      = 8;
 
-    const TensorShape weights_shape_conv0(kernel_x_conv0, kernel_y_conv0, src_shape.z(), ofm_conv0);
-    const TensorShape biases_shape_conv0(weights_shape_conv0[3]);
-    const TensorShape out_shape_conv0(src_shape.x(), src_shape.y(), weights_shape_conv0[3]);
+        const TensorShape weights_shape_conv0(kernel_x_conv0, kernel_y_conv0, src_shape.z(), ofm_conv0);
+        const TensorShape biases_shape_conv0(weights_shape_conv0[3]);
+        const TensorShape out_shape_conv0(src_shape.x(), src_shape.y(), weights_shape_conv0[3]);
 
-    weights0.allocator()->init(TensorInfo(weights_shape_conv0, 1, DataType::F32));
-    biases0.allocator()->init(TensorInfo(biases_shape_conv0, 1, DataType::F32));
-    out_conv0.allocator()->init(TensorInfo(out_shape_conv0, 1, DataType::F32));
-
-    // Initialize tensor of act0
-    out_act0.allocator()->init(TensorInfo(out_shape_conv0, 1, DataType::F32));
-
-    // Initialize tensor of pool0
-    TensorShape out_shape_pool0 = out_shape_conv0;
-    out_shape_pool0.set(0, out_shape_pool0.x() / 2);
-    out_shape_pool0.set(1, out_shape_pool0.y() / 2);
-    out_pool0.allocator()->init(TensorInfo(out_shape_pool0, 1, DataType::F32));
-
-    // Initialize tensors of conv1
-    constexpr unsigned int kernel_x_conv1 = 3;
-    constexpr unsigned int kernel_y_conv1 = 3;
-    constexpr unsigned int ofm_conv1      = 16;
-
-    const TensorShape weights_shape_conv1(kernel_x_conv1, kernel_y_conv1, out_shape_pool0.z(), ofm_conv1);
-
-    const TensorShape biases_shape_conv1(weights_shape_conv1[3]);
-    const TensorShape out_shape_conv1(out_shape_pool0.x(), out_shape_pool0.y(), weights_shape_conv1[3]);
-
-    weights1.allocator()->init(TensorInfo(weights_shape_conv1, 1, DataType::F32));
-    biases1.allocator()->init(TensorInfo(biases_shape_conv1, 1, DataType::F32));
-    out_conv1.allocator()->init(TensorInfo(out_shape_conv1, 1, DataType::F32));
-
-    // Initialize tensor of act1
-    out_act1.allocator()->init(TensorInfo(out_shape_conv1, 1, DataType::F32));
-
-    // Initialize tensor of pool1
-    TensorShape out_shape_pool1 = out_shape_conv1;
-    out_shape_pool1.set(0, out_shape_pool1.x() / 2);
-    out_shape_pool1.set(1, out_shape_pool1.y() / 2);
-    out_pool1.allocator()->init(TensorInfo(out_shape_pool1, 1, DataType::F32));
-
-    // Initialize tensor of fc0
-    constexpr unsigned int num_labels = 128;
-
-    const TensorShape weights_shape_fc0(out_shape_pool1.x() * out_shape_pool1.y() * out_shape_pool1.z(), num_labels);
-    const TensorShape biases_shape_fc0(num_labels);
-    const TensorShape out_shape_fc0(num_labels);
-
-    weights2.allocator()->init(TensorInfo(weights_shape_fc0, 1, DataType::F32));
-    biases2.allocator()->init(TensorInfo(biases_shape_fc0, 1, DataType::F32));
-    out_fc0.allocator()->init(TensorInfo(out_shape_fc0, 1, DataType::F32));
-
-    // Initialize tensor of act2
-    out_act2.allocator()->init(TensorInfo(out_shape_fc0, 1, DataType::F32));
-
-    // Initialize tensor of softmax
-    const TensorShape out_shape_softmax(out_shape_fc0.x());
-    out_softmax.allocator()->init(TensorInfo(out_shape_softmax, 1, DataType::F32));
+        weights0.allocator()->init(TensorInfo(weights_shape_conv0, 1, DataType::F32));
+        biases0.allocator()->init(TensorInfo(biases_shape_conv0, 1, DataType::F32));
+        out_conv0.allocator()->init(TensorInfo(out_shape_conv0, 1, DataType::F32));
 
-    /* -----------------------End: [Initialize tensors] */
+        // Initialize tensor of act0
+        out_act0.allocator()->init(TensorInfo(out_shape_conv0, 1, DataType::F32));
 
-    /* [Configure functions] */
+        // Initialize tensor of pool0
+        TensorShape out_shape_pool0 = out_shape_conv0;
+        out_shape_pool0.set(0, out_shape_pool0.x() / 2);
+        out_shape_pool0.set(1, out_shape_pool0.y() / 2);
+        out_pool0.allocator()->init(TensorInfo(out_shape_pool0, 1, DataType::F32));
 
-    // in:32x32x1: 5x5 convolution, 8 output features maps (OFM)
-    conv0.configure(&src, &weights0, &biases0, &out_conv0, PadStrideInfo(1 /* stride_x */, 1 /* stride_y */, 2 /* pad_x */, 2 /* pad_y */));
+        // Initialize tensors of conv1
+        constexpr unsigned int kernel_x_conv1 = 3;
+        constexpr unsigned int kernel_y_conv1 = 3;
+        constexpr unsigned int ofm_conv1      = 16;
 
-    // in:32x32x8, out:32x32x8, Activation function: relu
-    act0.configure(&out_conv0, &out_act0, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+        const TensorShape weights_shape_conv1(kernel_x_conv1, kernel_y_conv1, out_shape_pool0.z(), ofm_conv1);
 
-    // in:32x32x8, out:16x16x8 (2x2 pooling), Pool type function: Max
-    pool0.configure(&out_act0, &out_pool0, PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2 /* stride_x */, 2 /* stride_y */)));
+        const TensorShape biases_shape_conv1(weights_shape_conv1[3]);
+        const TensorShape out_shape_conv1(out_shape_pool0.x(), out_shape_pool0.y(), weights_shape_conv1[3]);
+
+        weights1.allocator()->init(TensorInfo(weights_shape_conv1, 1, DataType::F32));
+        biases1.allocator()->init(TensorInfo(biases_shape_conv1, 1, DataType::F32));
+        out_conv1.allocator()->init(TensorInfo(out_shape_conv1, 1, DataType::F32));
+
+        // Initialize tensor of act1
+        out_act1.allocator()->init(TensorInfo(out_shape_conv1, 1, DataType::F32));
 
-    // in:16x16x8: 3x3 convolution, 16 output features maps (OFM)
-    conv1.configure(&out_pool0, &weights1, &biases1, &out_conv1, PadStrideInfo(1 /* stride_x */, 1 /* stride_y */, 1 /* pad_x */, 1 /* pad_y */));
+        // Initialize tensor of pool1
+        TensorShape out_shape_pool1 = out_shape_conv1;
+        out_shape_pool1.set(0, out_shape_pool1.x() / 2);
+        out_shape_pool1.set(1, out_shape_pool1.y() / 2);
+        out_pool1.allocator()->init(TensorInfo(out_shape_pool1, 1, DataType::F32));
 
-    // in:16x16x16, out:16x16x16, Activation function: relu
-    act1.configure(&out_conv1, &out_act1, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+        // Initialize tensor of fc0
+        constexpr unsigned int num_labels = 128;
 
-    // in:16x16x16, out:8x8x16 (2x2 pooling), Pool type function: Average
-    pool1.configure(&out_act1, &out_pool1, PoolingLayerInfo(PoolingType::AVG, 2, PadStrideInfo(2 /* stride_x */, 2 /* stride_y */)));
+        const TensorShape weights_shape_fc0(out_shape_pool1.x() * out_shape_pool1.y() * out_shape_pool1.z(), num_labels);
+        const TensorShape biases_shape_fc0(num_labels);
+        const TensorShape out_shape_fc0(num_labels);
 
-    // in:8x8x16, out:128
-    fc0.configure(&out_pool1, &weights2, &biases2, &out_fc0);
+        weights2.allocator()->init(TensorInfo(weights_shape_fc0, 1, DataType::F32));
+        biases2.allocator()->init(TensorInfo(biases_shape_fc0, 1, DataType::F32));
+        out_fc0.allocator()->init(TensorInfo(out_shape_fc0, 1, DataType::F32));
 
-    // in:128, out:128, Activation function: relu
-    act2.configure(&out_fc0, &out_act2, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+        // Initialize tensor of act2
+        out_act2.allocator()->init(TensorInfo(out_shape_fc0, 1, DataType::F32));
 
-    // in:128, out:128
-    softmax.configure(&out_act2, &out_softmax);
+        // Initialize tensor of softmax
+        const TensorShape out_shape_softmax(out_shape_fc0.x());
+        out_softmax.allocator()->init(TensorInfo(out_shape_softmax, 1, DataType::F32));
 
-    /* -----------------------End: [Configure functions] */
+        /* -----------------------End: [Initialize tensors] */
 
-    /*[ Add tensors to memory manager ]*/
+        /* [Configure functions] */
 
-    // We need 2 memory groups for handling the input and output
-    // We call explicitly allocate after manage() in order to avoid overlapping lifetimes
-    MemoryGroup memory_group0(mm_transitions);
-    MemoryGroup memory_group1(mm_transitions);
+        // in:32x32x1: 5x5 convolution, 8 output features maps (OFM)
+        conv0->configure(&src, &weights0, &biases0, &out_conv0, PadStrideInfo(1 /* stride_x */, 1 /* stride_y */, 2 /* pad_x */, 2 /* pad_y */));
 
-    memory_group0.manage(&out_conv0);
-    out_conv0.allocator()->allocate();
-    memory_group1.manage(&out_act0);
-    out_act0.allocator()->allocate();
-    memory_group0.manage(&out_pool0);
-    out_pool0.allocator()->allocate();
-    memory_group1.manage(&out_conv1);
-    out_conv1.allocator()->allocate();
-    memory_group0.manage(&out_act1);
-    out_act1.allocator()->allocate();
-    memory_group1.manage(&out_pool1);
-    out_pool1.allocator()->allocate();
-    memory_group0.manage(&out_fc0);
-    out_fc0.allocator()->allocate();
-    memory_group1.manage(&out_act2);
-    out_act2.allocator()->allocate();
-    memory_group0.manage(&out_softmax);
-    out_softmax.allocator()->allocate();
+        // in:32x32x8, out:32x32x8, Activation function: relu
+        act0.configure(&out_conv0, &out_act0, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
 
-    /* -----------------------End: [ Add tensors to memory manager ] */
+        // in:32x32x8, out:16x16x8 (2x2 pooling), Pool type function: Max
+        pool0.configure(&out_act0, &out_pool0, PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2 /* stride_x */, 2 /* stride_y */)));
 
-    /* [Allocate tensors] */
+        // in:16x16x8: 3x3 convolution, 16 output features maps (OFM)
+        conv1->configure(&out_pool0, &weights1, &biases1, &out_conv1, PadStrideInfo(1 /* stride_x */, 1 /* stride_y */, 1 /* pad_x */, 1 /* pad_y */));
 
-    // Now that the padding requirements are known we can allocate all tensors
-    src.allocator()->allocate();
-    weights0.allocator()->allocate();
-    weights1.allocator()->allocate();
-    weights2.allocator()->allocate();
-    biases0.allocator()->allocate();
-    biases1.allocator()->allocate();
-    biases2.allocator()->allocate();
+        // in:16x16x16, out:16x16x16, Activation function: relu
+        act1.configure(&out_conv1, &out_act1, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
 
-    /* -----------------------End: [Allocate tensors] */
+        // in:16x16x16, out:8x8x16 (2x2 pooling), Pool type function: Average
+        pool1.configure(&out_act1, &out_pool1, PoolingLayerInfo(PoolingType::AVG, 2, PadStrideInfo(2 /* stride_x */, 2 /* stride_y */)));
 
-    // Finalize layers memory manager
+        // in:8x8x16, out:128
+        fc0->configure(&out_pool1, &weights2, &biases2, &out_fc0);
 
-    // Set allocator that the memory manager will use
-    mm_layers->set_allocator(&allocator);
+        // in:128, out:128, Activation function: relu
+        act2.configure(&out_fc0, &out_act2, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
 
-    // Number of pools that the manager will create. This specifies how many layers you want to run in parallel
-    mm_layers->set_num_pools(1);
+        // in:128, out:128
+        softmax->configure(&out_act2, &out_softmax);
 
-    // Finalize the manager. (Validity checks, memory allocations etc)
-    mm_layers->finalize();
+        /* -----------------------End: [Configure functions] */
 
-    // Finalize transitions memory manager
+        /*[ Add tensors to memory manager ]*/
 
-    // Set allocator that the memory manager will use
-    mm_transitions->set_allocator(&allocator);
+        // We need 2 memory groups for handling the input and output
+        // We call explicitly allocate after manage() in order to avoid overlapping lifetimes
+        memory_group0 = arm_compute::support::cpp14::make_unique<MemoryGroup>(mm_transitions);
+        memory_group1 = arm_compute::support::cpp14::make_unique<MemoryGroup>(mm_transitions);
 
-    // Number of pools that the manager will create. This specifies how many models we can run in parallel.
-    // Setting to 2 as we need one for the input and one for the output at any given time
-    mm_transitions->set_num_pools(2);
+        memory_group0->manage(&out_conv0);
+        out_conv0.allocator()->allocate();
+        memory_group1->manage(&out_act0);
+        out_act0.allocator()->allocate();
+        memory_group0->manage(&out_pool0);
+        out_pool0.allocator()->allocate();
+        memory_group1->manage(&out_conv1);
+        out_conv1.allocator()->allocate();
+        memory_group0->manage(&out_act1);
+        out_act1.allocator()->allocate();
+        memory_group1->manage(&out_pool1);
+        out_pool1.allocator()->allocate();
+        memory_group0->manage(&out_fc0);
+        out_fc0.allocator()->allocate();
+        memory_group1->manage(&out_act2);
+        out_act2.allocator()->allocate();
+        memory_group0->manage(&out_softmax);
+        out_softmax.allocator()->allocate();
 
-    // Finalize the manager. (Validity checks, memory allocations etc)
-    mm_transitions->finalize();
+        /* -----------------------End: [ Add tensors to memory manager ] */
 
-    /* [Initialize weights and biases tensors] */
+        /* [Allocate tensors] */
 
-    // Once the tensors have been allocated, the src, weights and biases tensors can be initialized
-    // ...
+        // Now that the padding requirements are known we can allocate all tensors
+        src.allocator()->allocate();
+        weights0.allocator()->allocate();
+        weights1.allocator()->allocate();
+        weights2.allocator()->allocate();
+        biases0.allocator()->allocate();
+        biases1.allocator()->allocate();
+        biases2.allocator()->allocate();
 
-    /* -----------------------[Initialize weights and biases tensors] */
+        /* -----------------------End: [Allocate tensors] */
 
-    /* [Execute the functions] */
+        // Finalize layers memory manager
 
-    // Acquire memory for the memory groups
-    memory_group0.acquire();
-    memory_group1.acquire();
+        // Set allocator that the memory manager will use
+        mm_layers->set_allocator(&allocator);
 
-    conv0.run();
-    act0.run();
-    pool0.run();
-    conv1.run();
-    act1.run();
-    pool1.run();
-    fc0.run();
-    act2.run();
-    softmax.run();
+        // Number of pools that the manager will create. This specifies how many layers you want to run in parallel
+        mm_layers->set_num_pools(1);
 
-    // Release memory
-    memory_group0.release();
-    memory_group1.release();
+        // Finalize the manager. (Validity checks, memory allocations etc)
+        mm_layers->finalize();
 
-    /* -----------------------End: [Execute the functions] */
-}
+        // Finalize transitions memory manager
+
+        // Set allocator that the memory manager will use
+        mm_transitions->set_allocator(&allocator);
+
+        // Number of pools that the manager will create. This specifies how many models we can run in parallel.
+        // Setting to 2 as we need one for the input and one for the output at any given time
+        mm_transitions->set_num_pools(2);
+
+        // Finalize the manager. (Validity checks, memory allocations etc)
+        mm_transitions->finalize();
+    }
+    void do_run() override
+    {
+        // Acquire memory for the memory groups
+        memory_group0->acquire();
+        memory_group1->acquire();
+
+        conv0->run();
+        act0.run();
+        pool0.run();
+        conv1->run();
+        act1.run();
+        pool1.run();
+        fc0->run();
+        act2.run();
+        softmax->run();
+
+        // Release memory
+        memory_group0->release();
+        memory_group1->release();
+    }
+
+private:
+    // The src tensor should contain the input image
+    Tensor src{};
+
+    // Intermediate tensors used
+    Tensor weights0{};
+    Tensor weights1{};
+    Tensor weights2{};
+    Tensor biases0{};
+    Tensor biases1{};
+    Tensor biases2{};
+    Tensor out_conv0{};
+    Tensor out_conv1{};
+    Tensor out_act0{};
+    Tensor out_act1{};
+    Tensor out_act2{};
+    Tensor out_pool0{};
+    Tensor out_pool1{};
+    Tensor out_fc0{};
+    Tensor out_softmax{};
+
+    // NEON allocator
+    Allocator allocator{};
+
+    // Memory groups
+    std::unique_ptr<MemoryGroup> memory_group0{};
+    std::unique_ptr<MemoryGroup> memory_group1{};
+
+    // Layers
+    std::unique_ptr<NEConvolutionLayer>    conv0{};
+    std::unique_ptr<NEConvolutionLayer>    conv1{};
+    std::unique_ptr<NEFullyConnectedLayer> fc0{};
+    std::unique_ptr<NESoftmaxLayer>        softmax{};
+    NEPoolingLayer                         pool0{};
+    NEPoolingLayer                         pool1{};
+    NEActivationLayer                      act0{};
+    NEActivationLayer                      act1{};
+    NEActivationLayer                      act2{};
+};
 
 /** Main program for cnn test
  *
@@ -293,5 +300,5 @@ void main_cnn(int argc, char **argv)
  */
 int main(int argc, char **argv)
 {
-    return utils::run_example(argc, argv, main_cnn);
+    return utils::run_example<NEONCNNExample>(argc, argv);
 }
diff --git a/examples/neon_convolution.cpp b/examples/neon_convolution.cpp
index 79800ae897..d51d2013f6 100644
--- a/examples/neon_convolution.cpp
+++ b/examples/neon_convolution.cpp
@@ -49,62 +49,72 @@ const int16_t gaussian5x5[] =
     1, 4, 6, 4, 1
 };
 
-void main_neon_convolution(int argc, char **argv)
+class NEONConvolutionExample : public Example
 {
-    /** [Accurate padding] **/
-    PPMLoader ppm;
-    Image     src, tmp, dst;
-
-    if(argc < 2)
-    {
-        // Print help
-        std::cout << "Usage: ./build/neon_convolution [input_image.ppm]\n\n";
-        std::cout << "No input_image provided, creating a dummy 640x480 image\n";
-        // Initialize just the dimensions and format of your buffers:
-        src.allocator()->init(TensorInfo(640, 480, Format::U8));
-    }
-    else
+public:
+    void do_setup(int argc, char **argv) override
     {
-        ppm.open(argv[1]);
-        // Initialize just the dimensions and format of your buffers:
-        ppm.init_image(src, Format::U8);
-    }
+        /** [Accurate padding] **/
+        PPMLoader ppm;
 
-    // Initialize just the dimensions and format of the temporary and destination images:
-    tmp.allocator()->init(*src.info());
-    dst.allocator()->init(*src.info());
+        if(argc < 2)
+        {
+            // Print help
+            std::cout << "Usage: ./build/neon_convolution [input_image.ppm]\n\n";
+            std::cout << "No input_image provided, creating a dummy 640x480 image\n";
+            // Initialize just the dimensions and format of your buffers:
+            src.allocator()->init(TensorInfo(640, 480, Format::U8));
+        }
+        else
+        {
+            ppm.open(argv[1]);
+            // Initialize just the dimensions and format of your buffers:
+            ppm.init_image(src, Format::U8);
+        }
 
-    NEConvolution3x3 conv3x3;
-    NEConvolution5x5 conv5x5;
+        // Initialize just the dimensions and format of the temporary and destination images:
+        tmp.allocator()->init(*src.info());
+        dst.allocator()->init(*src.info());
 
-    // Apply a Gaussian 3x3 filter to the source image followed by a Gaussian 5x5:
-    // The function will automatically update the padding information inside input and output to match its requirements
-    conv3x3.configure(&src, &tmp, gaussian3x3, 0 /* Let arm_compute calculate the scale */, BorderMode::UNDEFINED);
-    conv5x5.configure(&tmp, &dst, gaussian5x5, 0 /* Let arm_compute calculate the scale */, BorderMode::UNDEFINED);
+        // Apply a Gaussian 3x3 filter to the source image followed by a Gaussian 5x5:
+        // The function will automatically update the padding information inside input and output to match its requirements
+        conv3x3.configure(&src, &tmp, gaussian3x3, 0 /* Let arm_compute calculate the scale */, BorderMode::UNDEFINED);
+        conv5x5.configure(&tmp, &dst, gaussian5x5, 0 /* Let arm_compute calculate the scale */, BorderMode::UNDEFINED);
 
-    // Now that the padding requirements are known we can allocate the images:
-    src.allocator()->allocate();
-    tmp.allocator()->allocate();
-    dst.allocator()->allocate();
+        // Now that the padding requirements are known we can allocate the images:
+        src.allocator()->allocate();
+        tmp.allocator()->allocate();
+        dst.allocator()->allocate();
 
-    // Fill the input image with the content of the PPM image if a filename was provided:
-    if(ppm.is_open())
+        // Fill the input image with the content of the PPM image if a filename was provided:
+        if(ppm.is_open())
+        {
+            ppm.fill_image(src);
+            output_filename = std::string(argv[1]) + "_out.ppm";
+        }
+        /** [Accurate padding] **/
+    }
+    void do_run() override
     {
-        ppm.fill_image(src);
+        //Execute the functions:
+        conv3x3.run();
+        conv5x5.run();
     }
-
-    //Execute the functions:
-    conv3x3.run();
-    conv5x5.run();
-
-    // Save the result to file:
-    if(ppm.is_open())
+    void do_teardown() override
     {
-        const std::string output_filename = std::string(argv[1]) + "_out.ppm";
-        save_to_ppm(dst, output_filename);
+        // Save the result to file:
+        if(!output_filename.empty())
+        {
+            save_to_ppm(dst, output_filename); // save_to_ppm maps and unmaps the image to store as PPM
+        }
     }
-    /** [Accurate padding] **/
-}
+
+private:
+    Image            src{}, tmp{}, dst{};
+    NEConvolution3x3 conv3x3{};
+    NEConvolution5x5 conv5x5{};
+    std::string      output_filename{};
+};
 
 /** Main program for convolution test
  *
@@ -113,5 +123,5 @@ void main_neon_convolution(int argc, char **argv)
  */
 int main(int argc, char **argv)
 {
-    return utils::run_example(argc, argv, main_neon_convolution);
+    return utils::run_example<NEONConvolutionExample>(argc, argv);
 }
diff --git a/examples/neon_copy_objects.cpp b/examples/neon_copy_objects.cpp
index 55d03dd8bf..9409cf366c 100644
--- a/examples/neon_copy_objects.cpp
+++ b/examples/neon_copy_objects.cpp
@@ -31,116 +31,128 @@
 #include <iostream>
 
 using namespace arm_compute;
+using namespace utils;
 
-void main_neon_copy_objects(int argc, char **argv)
+class NEONCopyObjectsExample : public Example
 {
-    ARM_COMPUTE_UNUSED(argc);
-    ARM_COMPUTE_UNUSED(argv);
+public:
+    void do_setup(int argc, char **argv) override
+    {
+        ARM_COMPUTE_UNUSED(argc);
+        ARM_COMPUTE_UNUSED(argv);
 
-    /** [Copy objects example] */
-    constexpr unsigned int width  = 4;
-    constexpr unsigned int height = 3;
-    constexpr unsigned int batch  = 2;
+        /** [Copy objects example] */
+        constexpr unsigned int width  = 4;
+        constexpr unsigned int height = 3;
+        constexpr unsigned int batch  = 2;
 
-    auto *src_data = new float[width * height * batch];
-    auto *dst_data = new float[width * height * batch];
+        src_data = new float[width * height * batch];
+        dst_data = new float[width * height * batch];
 
-    // Fill src_data with dummy values:
-    for(unsigned int b = 0; b < batch; b++)
-    {
-        for(unsigned int h = 0; h < height; h++)
+        // Fill src_data with dummy values:
+        for(unsigned int b = 0; b < batch; b++)
         {
-            for(unsigned int w = 0; w < width; w++)
+            for(unsigned int h = 0; h < height; h++)
             {
-                src_data[b * (width * height) + h * width + w] = static_cast<float>(100 * b + 10 * h + w);
+                for(unsigned int w = 0; w < width; w++)
+                {
+                    src_data[b * (width * height) + h * width + w] = static_cast<float>(100 * b + 10 * h + w);
+                }
             }
         }
-    }
 
-    Tensor         input, output;
-    NESoftmaxLayer softmax;
-
-    // Initialize the tensors dimensions and type:
-    const TensorShape shape(width, height, batch);
-    input.allocator()->init(TensorInfo(shape, 1, DataType::F32));
-    output.allocator()->init(TensorInfo(shape, 1, DataType::F32));
-
-    // Configure softmax:
-    softmax.configure(&input, &output);
-
-    // Allocate the input / output tensors:
-    input.allocator()->allocate();
-    output.allocator()->allocate();
-
-    // Fill the input tensor:
-    // Simplest way: create an iterator to iterate through each element of the input tensor:
-    Window input_window;
-    input_window.use_tensor_dimensions(input.info()->tensor_shape());
-    std::cout << " Dimensions of the input's iterator:\n";
-    std::cout << " X = [start=" << input_window.x().start() << ", end=" << input_window.x().end() << ", step=" << input_window.x().step() << "]\n";
-    std::cout << " Y = [start=" << input_window.y().start() << ", end=" << input_window.y().end() << ", step=" << input_window.y().step() << "]\n";
-    std::cout << " Z = [start=" << input_window.z().start() << ", end=" << input_window.z().end() << ", step=" << input_window.z().step() << "]\n";
-
-    // Create an iterator:
-    Iterator input_it(&input, input_window);
-
-    // Iterate through the elements of src_data and copy them one by one to the input tensor:
-    // This is equivalent to:
-    // for( unsigned int z = 0; z < batch; ++z)
-    // {
-    //   for( unsigned int y = 0; y < height; ++y)
-    //   {
-    //     for( unsigned int x = 0; x < width; ++x)
-    //     {
-    //       *reinterpret_cast<float*>( input.buffer() + input.info()->offset_element_in_bytes(Coordinates(x,y,z))) = src_data[ z * (width*height) + y * width + x];
-    //     }
-    //   }
-    // }
-    // Except it works for an arbitrary number of dimensions
-    execute_window_loop(input_window, [&](const Coordinates & id)
+        // Initialize the tensors dimensions and type:
+        const TensorShape shape(width, height, batch);
+        input.allocator()->init(TensorInfo(shape, 1, DataType::F32));
+        output.allocator()->init(TensorInfo(shape, 1, DataType::F32));
+
+        // Configure softmax:
+        softmax.configure(&input, &output);
+
+        // Allocate the input / output tensors:
+        input.allocator()->allocate();
+        output.allocator()->allocate();
+
+        // Fill the input tensor:
+        // Simplest way: create an iterator to iterate through each element of the input tensor:
+        Window input_window;
+        input_window.use_tensor_dimensions(input.info()->tensor_shape());
+        std::cout << " Dimensions of the input's iterator:\n";
+        std::cout << " X = [start=" << input_window.x().start() << ", end=" << input_window.x().end() << ", step=" << input_window.x().step() << "]\n";
+        std::cout << " Y = [start=" << input_window.y().start() << ", end=" << input_window.y().end() << ", step=" << input_window.y().step() << "]\n";
+        std::cout << " Z = [start=" << input_window.z().start() << ", end=" << input_window.z().end() << ", step=" << input_window.z().step() << "]\n";
+
+        // Create an iterator:
+        Iterator input_it(&input, input_window);
+
+        // Iterate through the elements of src_data and copy them one by one to the input tensor:
+        // This is equivalent to:
+        // for( unsigned int z = 0; z < batch; ++z)
+        // {
+        //   for( unsigned int y = 0; y < height; ++y)
+        //   {
+        //     for( unsigned int x = 0; x < width; ++x)
+        //     {
+        //       *reinterpret_cast<float*>( input.buffer() + input.info()->offset_element_in_bytes(Coordinates(x,y,z))) = src_data[ z * (width*height) + y * width + x];
+        //     }
+        //   }
+        // }
+        // Except it works for an arbitrary number of dimensions
+        execute_window_loop(input_window, [&](const Coordinates & id)
+        {
+            std::cout << "Setting item [" << id.x() << "," << id.y() << "," << id.z() << "]\n";
+            *reinterpret_cast<float *>(input_it.ptr()) = src_data[id.z() * (width * height) + id.y() * width + id.x()];
+        },
+        input_it);
+
+        // More efficient way: create an iterator to iterate through each row (instead of each element) of the output tensor:
+        Window output_window;
+        output_window.use_tensor_dimensions(output.info()->tensor_shape(), /* first_dimension =*/Window::DimY); // Iterate through the rows (not each element)
+        std::cout << " Dimensions of the output's iterator:\n";
+        std::cout << " X = [start=" << output_window.x().start() << ", end=" << output_window.x().end() << ", step=" << output_window.x().step() << "]\n";
+        std::cout << " Y = [start=" << output_window.y().start() << ", end=" << output_window.y().end() << ", step=" << output_window.y().step() << "]\n";
+        std::cout << " Z = [start=" << output_window.z().start() << ", end=" << output_window.z().end() << ", step=" << output_window.z().step() << "]\n";
+
+        // Create an iterator:
+        Iterator output_it(&output, output_window);
+
+        // Iterate through the rows of the output tensor and copy them to dst_data:
+        // This is equivalent to:
+        // for( unsigned int z = 0; z < batch; ++z)
+        // {
+        //   for( unsigned int y = 0; y < height; ++y)
+        //   {
+        //     memcpy( dst_data + z * (width*height) + y * width, input.buffer() + input.info()->offset_element_in_bytes(Coordinates(0,y,z)), width * sizeof(float));
+        //   }
+        // }
+        // Except it works for an arbitrary number of dimensions
+        execute_window_loop(output_window, [&](const Coordinates & id)
+        {
+            std::cout << "Copying one row starting from [" << id.x() << "," << id.y() << "," << id.z() << "]\n";
+            // Copy one whole row:
+            memcpy(dst_data + id.z() * (width * height) + id.y() * width, output_it.ptr(), width * sizeof(float));
+        },
+        output_it);
+
+        /** [Copy objects example] */
+    }
+    void do_run() override
     {
-        std::cout << "Setting item [" << id.x() << "," << id.y() << "," << id.z() << "]\n";
-        *reinterpret_cast<float *>(input_it.ptr()) = src_data[id.z() * (width * height) + id.y() * width + id.x()];
-    },
-    input_it);
-
-    // Run NEON softmax:
-    softmax.run();
-
-    // More efficient way: create an iterator to iterate through each row (instead of each element) of the output tensor:
-    Window output_window;
-    output_window.use_tensor_dimensions(output.info()->tensor_shape(), /* first_dimension =*/Window::DimY); // Iterate through the rows (not each element)
-    std::cout << " Dimensions of the output's iterator:\n";
-    std::cout << " X = [start=" << output_window.x().start() << ", end=" << output_window.x().end() << ", step=" << output_window.x().step() << "]\n";
-    std::cout << " Y = [start=" << output_window.y().start() << ", end=" << output_window.y().end() << ", step=" << output_window.y().step() << "]\n";
-    std::cout << " Z = [start=" << output_window.z().start() << ", end=" << output_window.z().end() << ", step=" << output_window.z().step() << "]\n";
-
-    // Create an iterator:
-    Iterator output_it(&output, output_window);
-
-    // Iterate through the rows of the output tensor and copy them to dst_data:
-    // This is equivalent to:
-    // for( unsigned int z = 0; z < batch; ++z)
-    // {
-    //   for( unsigned int y = 0; y < height; ++y)
-    //   {
-    //     memcpy( dst_data + z * (width*height) + y * width, input.buffer() + input.info()->offset_element_in_bytes(Coordinates(0,y,z)), width * sizeof(float));
-    //   }
-    // }
-    // Except it works for an arbitrary number of dimensions
-    execute_window_loop(output_window, [&](const Coordinates & id)
+        // Run NEON softmax:
+        softmax.run();
+    }
+    void do_teardown() override
     {
-        std::cout << "Copying one row starting from [" << id.x() << "," << id.y() << "," << id.z() << "]\n";
-        // Copy one whole row:
-        memcpy(dst_data + id.z() * (width * height) + id.y() * width, output_it.ptr(), width * sizeof(float));
-    },
-    output_it);
-
-    delete[] src_data;
-    delete[] dst_data;
-    /** [Copy objects example] */
-}
+        delete[] src_data;
+        delete[] dst_data;
+    }
 
+private:
+    Tensor         input{}, output{};
+    float         *src_data{};
+    float         *dst_data{};
+    NESoftmaxLayer softmax{};
+};
 /** Main program for the copy objects test
  *
  * @param[in] argc Number of arguments
@@ -148,5 +160,5 @@ void main_neon_copy_objects(int argc, char **argv)
  */
 int main(int argc, char **argv)
 {
-    return utils::run_example(argc, argv, main_neon_copy_objects);
+    return utils::run_example<NEONCopyObjectsExample>(argc, argv);
 }
diff --git a/examples/neon_scale.cpp b/examples/neon_scale.cpp
index 3911f5b3ef..bec277da43 100644
--- a/examples/neon_scale.cpp
+++ b/examples/neon_scale.cpp
@@ -29,55 +29,68 @@
 using namespace arm_compute;
 using namespace utils;
 
-void main_neon_scale(int argc, char **argv)
+class NEONScaleExample : public Example
 {
-    PPMLoader ppm;
-    Image     src, dst;
-
-    if(argc < 2)
-    {
-        // Print help
-        std::cout << "Usage: ./build/neon_scale[input_image.ppm]\n\n";
-        std::cout << "No input_image provided, creating a dummy 640x480 image\n";
-        // Create an empty grayscale 640x480 image
-        src.allocator()->init(TensorInfo(640, 480, Format::U8));
-    }
-    else
+public:
+    void do_setup(int argc, char **argv) override
     {
-        ppm.open(argv[1]);
-        ppm.init_image(src, Format::U8);
-    }
+        PPMLoader ppm;
 
-    constexpr int scale_factor = 2;
+        if(argc < 2)
+        {
+            // Print help
+            std::cout << "Usage: ./build/neon_scale[input_image.ppm]\n\n";
+            std::cout << "No input_image provided, creating a dummy 640x480 image\n";
+            // Create an empty grayscale 640x480 image
+            src.allocator()->init(TensorInfo(640, 480, Format::U8));
+        }
+        else
+        {
+            ppm.open(argv[1]);
+            ppm.init_image(src, Format::U8);
+        }
 
-    TensorInfo dst_tensor_info(src.info()->dimension(0) / scale_factor, src.info()->dimension(1) / scale_factor, Format::U8);
+        constexpr int scale_factor = 2;
 
-    // Configure the destination image
-    dst.allocator()->init(dst_tensor_info);
+        TensorInfo dst_tensor_info(src.info()->dimension(0) / scale_factor, src.info()->dimension(1) / scale_factor,
+                                   Format::U8);
 
-    // Create and initialize a Scale function object:
-    NEScale scale;
-    scale.configure(&src, &dst, InterpolationPolicy::NEAREST_NEIGHBOR, BorderMode::UNDEFINED);
+        // Configure the destination image
+        dst.allocator()->init(dst_tensor_info);
 
-    // Allocate all the images
-    src.allocator()->allocate();
-    dst.allocator()->allocate();
-    // Fill the input image with the content of the PPM image if a filename was provided:
-    if(ppm.is_open())
-    {
-        ppm.fill_image(src);
-    }
+        // Configure Scale function object:
+        scale.configure(&src, &dst, InterpolationPolicy::NEAREST_NEIGHBOR, BorderMode::UNDEFINED);
 
-    // Run the scale operation:
-    scale.run();
+        // Allocate all the images
+        src.allocator()->allocate();
+        dst.allocator()->allocate();
 
-    // Save the result to file:
-    if(ppm.is_open())
+        // Fill the input image with the content of the PPM image if a filename was provided:
+        if(ppm.is_open())
+        {
+            ppm.fill_image(src);
+            output_filename = std::string(argv[1]) + "_out.ppm";
+        }
+    }
+    void do_run() override
     {
-        const std::string output_filename = std::string(argv[1]) + "_out.ppm";
-        save_to_ppm(dst, output_filename);
+        // Run the scale operation:
+        scale.run();
     }
-}
+    void do_teardown() override
+    {
+        // Save the result to file:
+        if(!output_filename.empty())
+        {
+            save_to_ppm(dst, output_filename); // save_to_ppm maps and unmaps the image to store as PPM
+        }
+    }
+
+private:
+    Image       src{}, dst{};
+    NEScale     scale{};
+    std::string output_filename{};
+};
 
 /** Main program for convolution test
  *
@@ -86,5 +99,5 @@ void main_neon_scale(int argc, char **argv)
  */
 int main(int argc, char **argv)
 {
-    return utils::run_example(argc, argv, main_neon_scale);
+    return utils::run_example<NEONScaleExample>(argc, argv);
 }
diff --git a/examples/neoncl_scale_median_gaussian.cpp b/examples/neoncl_scale_median_gaussian.cpp
index 0b72314f7b..084005fd1b 100644
--- a/examples/neoncl_scale_median_gaussian.cpp
+++ b/examples/neoncl_scale_median_gaussian.cpp
@@ -39,91 +39,100 @@ using namespace utils;
  * @param[in] argc Number of arguments
  * @param[in] argv Arguments ( [optional] Path to PPM image to process )
  */
-void main_neoncl_scale_median_gaussian(int argc, char **argv)
+class NEONCLScaleMedianGaussianExample : public Example
 {
-    /** [NEON / OpenCL Interop] */
-    PPMLoader ppm;
-    CLImage   src, scale_median, median_gauss, dst;
-
-    CLScheduler::get().default_init();
-
-    if(argc < 2)
+public:
+    void do_setup(int argc, char **argv) override
     {
-        // Print help
-        std::cout << "Usage: ./build/cl_convolution [input_image.ppm]\n\n";
-        std::cout << "No input_image provided, creating a dummy 640x480 image\n";
-        // Create an empty grayscale 640x480 image
-        src.allocator()->init(TensorInfo(640, 480, Format::U8));
+        /** [NEON / OpenCL Interop] */
+        PPMLoader ppm;
+
+        CLScheduler::get().default_init();
+
+        if(argc < 2)
+        {
+            // Print help
+            std::cout << "Usage: ./build/cl_convolution [input_image.ppm]\n\n";
+            std::cout << "No input_image provided, creating a dummy 640x480 image\n";
+            // Create an empty grayscale 640x480 image
+            src.allocator()->init(TensorInfo(640, 480, Format::U8));
+        }
+        else
+        {
+            ppm.open(argv[1]);
+            ppm.init_image(src, Format::U8);
+        }
+
+        TensorInfo scale_median_info(TensorInfo(src.info()->dimension(0) / 2, src.info()->dimension(1) / 2, Format::U8));
+
+        // Configure the temporary and destination images
+        scale_median.allocator()->init(scale_median_info);
+        median_gauss.allocator()->init(scale_median_info);
+        dst.allocator()->init(scale_median_info);
+
+        scale.configure(&src, &scale_median, InterpolationPolicy::NEAREST_NEIGHBOR, BorderMode::REPLICATE);
+        median.configure(&scale_median, &median_gauss, BorderMode::REPLICATE);
+        gauss.configure(&median_gauss, &dst, BorderMode::REPLICATE);
+
+        // Allocate all the images
+        src.allocator()->allocate();
+        scale_median.allocator()->allocate();
+        median_gauss.allocator()->allocate();
+        dst.allocator()->allocate();
+
+        // Fill the input image with the content of the PPM image if a filename was provided:
+        if(ppm.is_open())
+        {
+            ppm.fill_image(src);
+            const std::string output_filename = std::string(argv[1]) + "_out.ppm";
+        }
+        /** [NEON / OpenCL Interop] */
     }
-    else
+    void do_run() override
     {
-        ppm.open(argv[1]);
-        ppm.init_image(src, Format::U8);
-    }
-
-    TensorInfo scale_median_info(TensorInfo(src.info()->dimension(0) / 2, src.info()->dimension(1) / 2, Format::U8));
+        // Enqueue and flush the OpenCL kernel:
+        scale.run();
 
-    // Configure the temporary and destination images
-    scale_median.allocator()->init(scale_median_info);
-    median_gauss.allocator()->init(scale_median_info);
-    dst.allocator()->init(scale_median_info);
+        // Do a blocking map of the input and output buffers of the NEON function:
+        scale_median.map();
+        median_gauss.map();
 
-    // Declare and configure the functions to create the following pipeline: scale -> median -> gauss
-    CLScale       scale;
-    NEMedian3x3   median;
-    CLGaussian5x5 gauss;
+        // Run the NEON function:
+        median.run();
 
-    scale.configure(&src, &scale_median, InterpolationPolicy::NEAREST_NEIGHBOR, BorderMode::REPLICATE);
-    median.configure(&scale_median, &median_gauss, BorderMode::REPLICATE);
-    gauss.configure(&median_gauss, &dst, BorderMode::REPLICATE);
+        // Unmap the output buffer before it's used again by OpenCL:
+        scale_median.unmap();
+        median_gauss.unmap();
 
-    // Allocate all the images
-    src.allocator()->allocate();
-    scale_median.allocator()->allocate();
-    median_gauss.allocator()->allocate();
-    dst.allocator()->allocate();
+        // Run the final OpenCL function:
+        gauss.run();
 
-    // Fill the input image with the content of the PPM image if a filename was provided:
-    if(ppm.is_open())
-    {
-        ppm.fill_image(src);
+        // Make sure all the OpenCL jobs are done executing:
+        CLScheduler::get().sync();
     }
-
-    // Enqueue and flush the OpenCL kernel:
-    scale.run();
-
-    // Do a blocking map of the input and output buffers of the NEON function:
-    scale_median.map();
-    median_gauss.map();
-
-    // Run the NEON function:
-    median.run();
-
-    // Unmap the output buffer before it's used again by OpenCL:
-    scale_median.unmap();
-    median_gauss.unmap();
-
-    // Run the final OpenCL function:
-    gauss.run();
-
-    // Make sure all the OpenCL jobs are done executing:
-    CLScheduler::get().sync();
-
-    // Save the result to file:
-    if(ppm.is_open())
+    void do_teardown() override
     {
-        const std::string output_filename = std::string(argv[1]) + "_out.ppm";
-        save_to_ppm(dst, output_filename); // save_to_ppm maps and unmaps the image to store as PPM
+        // Save the result to file:
+        if(!output_filename.empty())
+        {
+            save_to_ppm(dst, output_filename); // save_to_ppm maps and unmaps the image to store as PPM
+        }
     }
-    /** [NEON / OpenCL Interop] */
-}
 
-/** Main program for convolution test
+private:
+    CLImage       src{}, scale_median{}, median_gauss{}, dst{};
+    CLScale       scale{};
+    NEMedian3x3   median{};
+    CLGaussian5x5 gauss{};
+    std::string   output_filename{};
+};
+
+/** Main program for neon/cl scale median gaussian test
  *
  * @param[in] argc Number of arguments
  * @param[in] argv Arguments ( [optional] Path to PPM image to process )
  */
 int main(int argc, char **argv)
 {
-    return utils::run_example(argc, argv, main_neoncl_scale_median_gaussian);
+    return utils::run_example<NEONCLScaleMedianGaussianExample>(argc, argv);
 }
diff --git a/utils/Utils.cpp b/utils/Utils.cpp
index c2f5449a28..32d5e3a6c0 100644
--- a/utils/Utils.cpp
+++ b/utils/Utils.cpp
@@ -66,41 +66,6 @@ void discard_comments_and_spaces(std::ifstream &fs)
 }
 } // namespace
 
-//FIXME: Delete once tests have been ported (COMPMID-782)
-int run_example(int argc, char **argv, example &func)
-{
-    std::cout << "\n"
-              << argv[0] << "\n\n";
-
-    try
-    {
-        func(argc, argv);
-
-        std::cout << "\nTest passed\n";
-        return 0;
-    }
-#ifdef ARM_COMPUTE_CL
-    catch(cl::Error &err)
-    {
-        std::cerr << "!!!!!!!!!!!!!!!!!!!!!!!!!!!" << std::endl;
-        std::cerr << std::endl
-                  << "ERROR " << err.what() << "(" << err.err() << ")" << std::endl;
-        std::cerr << "!!!!!!!!!!!!!!!!!!!!!!!!!!!" << std::endl;
-    }
-#endif /* ARM_COMPUTE_CL */
-    catch(std::runtime_error &err)
-    {
-        std::cerr << "!!!!!!!!!!!!!!!!!!!!!!!!!!!" << std::endl;
-        std::cerr << std::endl
-                  << "ERROR " << err.what() << " " << (errno ? strerror(errno) : "") << std::endl;
-        std::cerr << "!!!!!!!!!!!!!!!!!!!!!!!!!!!" << std::endl;
-    }
-
-    std::cout << "\nTest FAILED\n";
-
-    return -1;
-}
-
 #ifndef BENCHMARK_EXAMPLES
 int run_example(int argc, char **argv, Example &example)
 {
diff --git a/utils/Utils.h b/utils/Utils.h
index 9b5d0c4aa9..01e5137669 100644
--- a/utils/Utils.h
+++ b/utils/Utils.h
@@ -55,23 +55,6 @@ namespace arm_compute
 {
 namespace utils
 {
-//FIXME: Delete once tests have been ported (COMPMID-782)
-/** Signature of an example to run
- *
- * @param[in] argc Number of command line arguments
- * @param[in] argv Command line arguments
- */
-using example = void(int argc, char **argv);
-
-//FIXME: Delete once tests have been ported (COMPMID-782)
-/** Run an example and handle the potential exceptions it throws
- *
- * @param[in] argc Number of command line arguments
- * @param[in] argv Command line arguments
- * @param[in] func Pointer to the function containing the code to run
- */
-int run_example(int argc, char **argv, example &func);
-
 /** Abstract Example class.
  *
  * All examples have to inherit from this class.