COMPMID-1104 Add fast math hint in the graph API

Change-Id: I83db135fa94c6884e080f0229a9b6430d908c029
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/129823
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

12 files changed, 230 insertions, 94 deletions

diff --git a/examples/graph_alexnet.cpp b/examples/graph_alexnet.cpp
index 45c2b56cc2..b97ca54c78 100644
--- a/examples/graph_alexnet.cpp
+++ b/examples/graph_alexnet.cpp
@@ -37,7 +37,7 @@ using namespace arm_compute::graph_utils;
 /** Example demonstrating how to implement AlexNet's network using the Compute Library's graph API
  *
  * @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
  */
 class GraphAlexnetExample : public Example
 {
@@ -60,40 +60,51 @@ public:
         const bool        is_neon              = (target_hint == Target::NEON);
         ConvolutionMethod convolution_5x5_hint = is_neon ? ConvolutionMethod::GEMM : ConvolutionMethod::DIRECT;
         ConvolutionMethod convolution_3x3_hint = is_neon ? ConvolutionMethod::GEMM : ConvolutionMethod::DEFAULT;
+        FastMathHint      fast_math_hint       = FastMathHint::DISABLED;
 
         // Parse arguments
         if(argc < 2)
         {
             // Print help
-            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No data folder provided: using random values\n\n";
         }
         else if(argc == 2)
         {
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No data folder provided: using random values\n\n";
         }
         else if(argc == 3)
         {
             data_path = argv[2];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No image provided: using random values\n\n";
         }
         else if(argc == 4)
         {
             data_path = argv[2];
             image     = argv[3];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n";
             std::cout << "No text file with labels provided: skipping output accessor\n\n";
         }
-        else
+        else if(argc == 5)
         {
             data_path = argv[2];
             image     = argv[3];
             label     = argv[4];
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n";
+            std::cout << "No fast math info provided: disabling fast math\n\n";
+        }
+        else
+        {
+            data_path      = argv[2];
+            image          = argv[3];
+            label          = argv[4];
+            fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
         }
 
         graph << target_hint
+              << fast_math_hint
               << InputLayer(TensorDescriptor(TensorShape(227U, 227U, 3U, 1U), DataType::F32),
                             get_input_accessor(image, std::move(preprocessor)))
               // Layer 1
@@ -185,7 +196,7 @@ private:
 /** Main program for AlexNet
  *
  * @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 )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
  */
 int main(int argc, char **argv)
 {
diff --git a/examples/graph_googlenet.cpp b/examples/graph_googlenet.cpp
index deafe5a822..bac3c7c6d5 100644
--- a/examples/graph_googlenet.cpp
+++ b/examples/graph_googlenet.cpp
@@ -36,7 +36,7 @@ using namespace arm_compute::graph_utils;
 /** Example demonstrating how to implement Googlenet's network using the Compute Library's graph API
  *
  * @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
  */
 class GraphGooglenetExample : public Example
 {
@@ -55,40 +55,51 @@ public:
         const int         target           = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
         Target            target_hint      = set_target_hint(target);
         ConvolutionMethod convolution_hint = target_hint == Target::NEON ? ConvolutionMethod::GEMM : ConvolutionMethod::DEFAULT;
+        FastMathHint      fast_math_hint   = FastMathHint::DISABLED;
 
         // Parse arguments
         if(argc < 2)
         {
             // Print help
-            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No data folder provided: using random values\n\n";
         }
         else if(argc == 2)
         {
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No data folder provided: using random values\n\n";
         }
         else if(argc == 3)
         {
             data_path = argv[2];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No image provided: using random values\n\n";
         }
         else if(argc == 4)
         {
             data_path = argv[2];
             image     = argv[3];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n";
             std::cout << "No text file with labels provided: skipping output accessor\n\n";
         }
-        else
+        else if(argc == 5)
         {
             data_path = argv[2];
             image     = argv[3];
             label     = argv[4];
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n";
+            std::cout << "No fast math info provided: disabling fast math\n\n";
+        }
+        else
+        {
+            data_path      = argv[2];
+            image          = argv[3];
+            label          = argv[4];
+            fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
         }
 
         graph << target_hint
+              << fast_math_hint
               << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32),
                             get_input_accessor(image, std::move(preprocessor)))
               << ConvolutionLayer(
@@ -206,7 +217,7 @@ private:
 /** Main program for Googlenet
  *
  * @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 )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
  */
 int main(int argc, char **argv)
 {
diff --git a/examples/graph_inception_v3.cpp b/examples/graph_inception_v3.cpp
index 7fa0fc74fe..8e30fd97f2 100644
--- a/examples/graph_inception_v3.cpp
+++ b/examples/graph_inception_v3.cpp
@@ -36,7 +36,7 @@ using namespace arm_compute::graph_utils;
 /** Example demonstrating how to implement InceptionV3's network using the Compute Library's graph API
  *
  * @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] image, [optional] labels )
+ * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
  */
 class InceptionV3Example : public Example
 {
@@ -54,41 +54,53 @@ public:
         const int         target           = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
         Target            target_hint      = set_target_hint(target);
         ConvolutionMethod convolution_hint = target_hint == Target::NEON ? ConvolutionMethod::GEMM : ConvolutionMethod::DEFAULT;
+        FastMathHint      fast_math_hint   = FastMathHint::DISABLED;
 
         // Parse arguments
         if(argc < 2)
         {
             // Print help
-            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No data folder provided: using random values\n\n";
         }
         else if(argc == 2)
         {
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No data folder provided: using random values\n\n";
         }
         else if(argc == 3)
         {
             data_path = argv[2];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No image provided: using random values\n\n";
         }
         else if(argc == 4)
         {
             data_path = argv[2];
             image     = argv[3];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n";
             std::cout << "No text file with labels provided: skipping output accessor\n\n";
         }
-        else
+        else if(argc == 5)
         {
             data_path = argv[2];
             image     = argv[3];
             label     = argv[4];
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n";
+            std::cout << "No fast math info provided: disabling fast math\n\n";
+        }
+        else
+        {
+            data_path      = argv[2];
+            image          = argv[3];
+            label          = argv[4];
+            fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
         }
 
-        graph << target_hint << InputLayer(TensorDescriptor(TensorShape(299U, 299U, 3U, 1U), DataType::F32),
-                                           get_input_accessor(image, std::move(preprocessor), false))
+        graph << target_hint
+              << fast_math_hint
+              << InputLayer(TensorDescriptor(TensorShape(299U, 299U, 3U, 1U), DataType::F32),
+                            get_input_accessor(image, std::move(preprocessor), false))
               << ConvolutionLayer(3U, 3U, 32U,
                                   get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_weights.npy"),
                                   std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0))
@@ -759,7 +771,7 @@ private:
 /** Main program for Inception V3
  *
  * @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
  */
 int main(int argc, char **argv)
 {
diff --git a/examples/graph_inception_v4.cpp b/examples/graph_inception_v4.cpp
index 4217c78554..827370ec5e 100644
--- a/examples/graph_inception_v4.cpp
+++ b/examples/graph_inception_v4.cpp
@@ -36,7 +36,7 @@ using namespace arm_compute::graph_utils;
 /** Example demonstrating how to implement InceptionV4's network using the Compute Library's graph API
  *
  * @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
  */
 class InceptionV4Example final : public Example
 {
@@ -57,41 +57,53 @@ public:
         const int         target           = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
         Target            target_hint      = set_target_hint(target);
         ConvolutionMethod convolution_hint = target_hint == Target::NEON ? ConvolutionMethod::GEMM : ConvolutionMethod::DEFAULT;
+        FastMathHint      fast_math_hint   = FastMathHint::DISABLED;
 
         // Parse arguments
         if(argc < 2)
         {
             // Print help
-            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No data folder provided: using random values\n\n";
         }
         else if(argc == 2)
         {
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No data folder provided: using random values\n\n";
         }
         else if(argc == 3)
         {
             data_path = argv[2];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No image provided: using random values\n\n";
         }
         else if(argc == 4)
         {
             data_path = argv[2];
             image     = argv[3];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n";
             std::cout << "No text file with labels provided: skipping output accessor\n\n";
         }
-        else
+        else if(argc == 5)
         {
             data_path = argv[2];
             image     = argv[3];
             label     = argv[4];
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n";
+            std::cout << "No fast math info provided: disabling fast math\n\n";
+        }
+        else
+        {
+            data_path      = argv[2];
+            image          = argv[3];
+            label          = argv[4];
+            fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
         }
 
-        graph << target_hint << InputLayer(TensorDescriptor(TensorShape(299U, 299U, 3U, 1U), DataType::F32),
-                                           get_input_accessor(image, std::move(preprocessor), false))
+        graph << target_hint
+              << fast_math_hint
+              << InputLayer(TensorDescriptor(TensorShape(299U, 299U, 3U, 1U), DataType::F32),
+                            get_input_accessor(image, std::move(preprocessor), false))
               // Conv2d_1a_3x3
               << ConvolutionLayer(3U, 3U, 32U,
                                   get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_1a_3x3_weights.npy"),
@@ -737,7 +749,7 @@ private:
 /** Main program for Inception V4
  *
  * @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
  */
 int main(int argc, char **argv)
 {
diff --git a/examples/graph_lenet.cpp b/examples/graph_lenet.cpp
index ea0916b317..92be2d48c1 100644
--- a/examples/graph_lenet.cpp
+++ b/examples/graph_lenet.cpp
@@ -36,7 +36,7 @@ using namespace arm_compute::graph_utils;
 /** 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 )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] batches, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
  */
 class GraphLenetExample : public Example
 {
@@ -50,34 +50,45 @@ public:
         const int target      = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
         Target    target_hint = set_target_hint(target);
 
+        FastMathHint fast_math_hint = FastMathHint::DISABLED;
+
         // Parse arguments
         if(argc < 2)
         {
             // Print help
-            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [batches]\n\n";
+            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [batches] [fast_math_hint]\n\n";
             std::cout << "No data folder provided: using random values\n\n";
         }
         else if(argc == 2)
         {
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [batches]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [batches] [fast_math_hint]\n\n";
             std::cout << "No data folder provided: using random values\n\n";
         }
         else if(argc == 3)
         {
             //Do something with argv[1]
             data_path = argv[2];
-            std::cout << "Usage: " << argv[0] << " [path_to_data] [batches]\n\n";
+            std::cout << "Usage: " << argv[0] << " [path_to_data] [batches] [fast_math_hint]\n\n";
             std::cout << "No number of batches where specified, thus will use the default : " << batches << "\n\n";
         }
-        else
+        else if(argc == 4)
         {
-            //Do something with argv[1] and argv[2]
             data_path = argv[2];
             batches   = std::strtol(argv[3], nullptr, 0);
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [fast_math_hint]\n\n";
+            std::cout << "No fast math info provided: disabling fast math\n\n";
+        }
+        else
+        {
+            //Do something with argv[1] and argv[2]
+            data_path      = argv[2];
+            batches        = std::strtol(argv[3], nullptr, 0);
+            fast_math_hint = (std::strtol(argv[4], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
         }
 
         //conv1 << pool1 << conv2 << pool2 << fc1 << act1 << fc2 << smx
         graph << target_hint
+              << fast_math_hint
               << InputLayer(TensorDescriptor(TensorShape(28U, 28U, 1U, batches), DataType::F32), get_input_accessor(""))
               << ConvolutionLayer(
                   5U, 5U, 20U,
@@ -125,7 +136,7 @@ private:
 /** Main program for LeNet
  *
  * @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] batches )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] batches, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
  */
 int main(int argc, char **argv)
 {
diff --git a/examples/graph_mobilenet.cpp b/examples/graph_mobilenet.cpp
index 813c0bfe1d..7bfc6808fa 100644
--- a/examples/graph_mobilenet.cpp
+++ b/examples/graph_mobilenet.cpp
@@ -35,7 +35,7 @@ using namespace arm_compute::graph_utils;
 /** Example demonstrating how to implement MobileNet's network using the Compute Library's graph API
  *
  * @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] data layout, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
  */
 class GraphMobilenetExample : public Example
 {
@@ -54,6 +54,7 @@ public:
         Target                     target_hint                = set_target_hint(target);
         ConvolutionMethod          convolution_hint           = ConvolutionMethod::GEMM;
         DepthwiseConvolutionMethod depthwise_convolution_hint = DepthwiseConvolutionMethod::OPTIMIZED_3x3;
+        FastMathHint               fast_math_hint             = FastMathHint::DISABLED;
 
         // Set model to execute. 0 (MobileNetV1_1.0_224), 1 (MobileNetV1_0.75_160)
         int model_id = (argc > 2) ? std::strtol(argv[2], nullptr, 10) : 0;
@@ -72,33 +73,33 @@ public:
         if(argc < 2)
         {
             // Print help
-            std::cout << "Usage: " << argv[0] << " [target] [model] [layout] [path_to_data] [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " [target] [model] [layout] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No model ID provided: using MobileNetV1_1.0_224\n\n";
             std::cout << "No data layout provided: using NCHW\n\n";
             std::cout << "No data folder provided: using random values\n\n";
         }
         else if(argc == 2)
         {
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [model] [layout] [path_to_data] [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [model] [layout] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No model ID provided: using MobileNetV1_1.0_224\n\n";
             std::cout << "No data layout provided: using NCHW\n\n";
             std::cout << "No data folder provided: using random values\n\n";
         }
         else if(argc == 3)
         {
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [layout] [path_to_data] [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [layout] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No data layout provided: using NCHW\n\n";
             std::cout << "No data folder provided: using random values\n\n";
         }
         else if(argc == 4)
         {
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [path_to_data] [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [path_to_data] [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No data folder provided: using random values\n\n";
         }
         else if(argc == 5)
         {
             data_path = argv[4];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No image provided: using random values\n\n";
             std::cout << "No text file with labels provided: skipping output accessor\n\n";
         }
@@ -106,14 +107,23 @@ public:
         {
             data_path = argv[4];
             image     = argv[5];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n";
             std::cout << "No text file with labels provided: skipping output accessor\n\n";
         }
-        else
+        else if(argc == 7)
         {
             data_path = argv[4];
             image     = argv[5];
             label     = argv[6];
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n";
+            std::cout << "No fast math info provided: disabling fast math\n\n";
+        }
+        else
+        {
+            data_path      = argv[4];
+            image          = argv[5];
+            label          = argv[6];
+            fast_math_hint = (std::strtol(argv[7], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
         }
 
         // Add model path to data path
@@ -125,6 +135,7 @@ public:
         graph << target_hint
               << convolution_hint
               << depthwise_convolution_hint
+              << fast_math_hint
               << InputLayer(input_descriptor,
                             get_input_accessor(image, std::move(preprocessor), false))
               << ConvolutionLayer(
@@ -225,7 +236,9 @@ private:
  *                             [optional] Model ID (0 = MobileNetV1_1.0_224, 1 = MobileNetV1_0.75_160),
  *                             [optional] Path to the weights folder,
  *                             [optional] image,
- *                             [optional] labels )
+ *                             [optional] labels,
+ *                             [optional] data layout,
+ *                             [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
  */
 int main(int argc, char **argv)
 {
diff --git a/examples/graph_mobilenet_qasymm8.cpp b/examples/graph_mobilenet_qasymm8.cpp
index 7edd1822ae..2801209985 100644
--- a/examples/graph_mobilenet_qasymm8.cpp
+++ b/examples/graph_mobilenet_qasymm8.cpp
@@ -36,7 +36,7 @@ using namespace arm_compute::graph_utils;
 /** Example demonstrating how to implement QASYMM8 MobileNet's network using the Compute Library's graph API
  *
  * @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] npy_input, [optional] labels )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] npy_input, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
  */
 class GraphMobileNetQASYMM8Example : public Example
 {
@@ -92,37 +92,48 @@ public:
         };
 
         // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
-        const int target      = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
-        Target    target_hint = set_target_hint(target);
+        const int    target         = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
+        Target       target_hint    = set_target_hint(target);
+        FastMathHint fast_math_hint = FastMathHint::DISABLED;
 
         // Parse arguments
         if(argc < 2)
         {
             // Print help
-            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [npy_input] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [npy_input] [labels] [fast_math_hint]\n\n";
             std::cout << "No data folder provided: using random values\n\n";
         }
         else if(argc == 2)
         {
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [npy_input] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [npy_input] [labels] [fast_math_hint]\n\n";
             std::cout << "No input provided: using random values\n\n";
         }
         else if(argc == 4)
         {
             data_path = argv[2];
             input     = argv[3];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n";
             std::cout << "No text file with labels provided: skipping output accessor\n\n";
         }
-        else
+        else if(argc == 5)
         {
             data_path = argv[2];
             input     = argv[3];
             label     = argv[4];
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n";
+            std::cout << "No fast math info provided: disabling fast math\n\n";
+        }
+        else
+        {
+            data_path      = argv[2];
+            input          = argv[3];
+            label          = argv[4];
+            fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
         }
 
         graph << target_hint
               << DepthwiseConvolutionMethod::OPTIMIZED_3x3 // FIXME(COMPMID-1073): Add heuristics to automatically call the optimized 3x3 method
+              << fast_math_hint
               << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::QASYMM8, in_quant_info),
                             get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/" + input))
               << ConvolutionLayer(
@@ -220,7 +231,7 @@ private:
 /** Main program for MobileNetQASYMM8
  *
  * @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] npy_input, [optional] labels )
+ * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] npy_input, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
  */
 int main(int argc, char **argv)
 {
diff --git a/examples/graph_resnet50.cpp b/examples/graph_resnet50.cpp
index 18a028d48c..6fac372550 100644
--- a/examples/graph_resnet50.cpp
+++ b/examples/graph_resnet50.cpp
@@ -35,7 +35,7 @@ using namespace arm_compute::graph_utils;
 /** Example demonstrating how to implement Microsoft's ResNet50 network using the Compute Library's graph API
  *
  * @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
  */
 class GraphResNet50Example : public Example
 {
@@ -55,40 +55,51 @@ public:
         const int         target           = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
         Target            target_hint      = set_target_hint(target);
         ConvolutionMethod convolution_hint = target_hint == Target::NEON ? ConvolutionMethod::GEMM : ConvolutionMethod::DEFAULT;
+        FastMathHint      fast_math_hint   = FastMathHint::DISABLED;
 
         // Parse arguments
         if(argc < 2)
         {
             // Print help
-            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No data folder provided: using random values\n\n";
         }
         else if(argc == 2)
         {
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No data folder provided: using random values\n\n";
         }
         else if(argc == 3)
         {
             data_path = argv[2];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No image provided: using random values\n\n";
         }
         else if(argc == 4)
         {
             data_path = argv[2];
             image     = argv[3];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n";
             std::cout << "No text file with labels provided: skipping output accessor\n\n";
         }
-        else
+        else if(argc == 5)
         {
             data_path = argv[2];
             image     = argv[3];
             label     = argv[4];
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n";
+            std::cout << "No fast math info provided: disabling fast math\n\n";
+        }
+        else
+        {
+            data_path      = argv[2];
+            image          = argv[3];
+            label          = argv[4];
+            fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
         }
 
         graph << target_hint
+              << fast_math_hint
               << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32),
                             get_input_accessor(image, std::move(preprocessor), false /* Do not convert to BGR */))
               << ConvolutionLayer(
@@ -243,7 +254,7 @@ private:
 /** Main program for ResNet50
  *
  * @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 )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
  */
 int main(int argc, char **argv)
 {
diff --git a/examples/graph_squeezenet.cpp b/examples/graph_squeezenet.cpp
index 8ed43f707d..2627c96774 100644
--- a/examples/graph_squeezenet.cpp
+++ b/examples/graph_squeezenet.cpp
@@ -37,7 +37,7 @@ using namespace arm_compute::logging;
 /** Example demonstrating how to implement Squeezenet's network using the Compute Library's graph API
  *
  * @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
  */
 class GraphSqueezenetExample : public Example
 {
@@ -56,40 +56,51 @@ public:
         const int         target           = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
         Target            target_hint      = set_target_hint(target);
         ConvolutionMethod convolution_hint = target_hint == Target::NEON ? ConvolutionMethod::GEMM : ConvolutionMethod::DEFAULT;
+        FastMathHint      fast_math_hint   = FastMathHint::DISABLED;
 
         // Parse arguments
         if(argc < 2)
         {
             // Print help
-            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No data folder provided: using random values\n\n";
         }
         else if(argc == 2)
         {
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No data folder provided: using random values\n\n";
         }
         else if(argc == 3)
         {
             data_path = argv[2];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No image provided: using random values\n\n";
         }
         else if(argc == 4)
         {
             data_path = argv[2];
             image     = argv[3];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n";
             std::cout << "No text file with labels provided: skipping output accessor\n\n";
         }
-        else
+        else if(argc == 5)
         {
             data_path = argv[2];
             image     = argv[3];
             label     = argv[4];
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n";
+            std::cout << "No fast math info provided: disabling fast math\n\n";
+        }
+        else
+        {
+            data_path      = argv[2];
+            image          = argv[3];
+            label          = argv[4];
+            fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
         }
 
         graph << target_hint
+              << fast_math_hint
               << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32),
                             get_input_accessor(image, std::move(preprocessor)))
               << ConvolutionLayer(
@@ -209,7 +220,7 @@ private:
 /** Main program for Squeezenet v1.0
  *
  * @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 )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
  */
 int main(int argc, char **argv)
 {
diff --git a/examples/graph_squeezenet_v1_1.cpp b/examples/graph_squeezenet_v1_1.cpp
index 529f4fe80a..c4a5433352 100644
--- a/examples/graph_squeezenet_v1_1.cpp
+++ b/examples/graph_squeezenet_v1_1.cpp
@@ -40,7 +40,7 @@ namespace
 /** Example demonstrating how to implement Squeezenet's v1.1 network using the Compute Library's graph API
  *
  * @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
  */
 class GraphSqueezenet_v1_1Example : public Example
 {
@@ -59,40 +59,51 @@ public:
         const int         target           = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
         Target            target_hint      = set_target_hint(target);
         ConvolutionMethod convolution_hint = target_hint == Target::NEON ? ConvolutionMethod::GEMM : ConvolutionMethod::DEFAULT;
+        FastMathHint      fast_math_hint   = FastMathHint::DISABLED;
 
         // Parse arguments
         if(argc < 2)
         {
             // Print help
-            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No data folder provided: using random values\n\n";
         }
         else if(argc == 2)
         {
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No data folder provided: using random values\n\n";
         }
         else if(argc == 3)
         {
             data_path = argv[2];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No image provided: using random values\n\n";
         }
         else if(argc == 4)
         {
             data_path = argv[2];
             image     = argv[3];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n";
             std::cout << "No text file with labels provided: skipping output accessor\n\n";
         }
-        else
+        else if(argc == 5)
         {
             data_path = argv[2];
             image     = argv[3];
             label     = argv[4];
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n";
+            std::cout << "No fast math info provided: disabling fast math\n\n";
+        }
+        else
+        {
+            data_path      = argv[2];
+            image          = argv[3];
+            label          = argv[4];
+            fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
         }
 
         graph << target_hint
+              << fast_math_hint
               << InputLayer(TensorDescriptor(TensorShape(227U, 227U, 3U, 1U), DataType::F32),
                             get_input_accessor(image, std::move(preprocessor)))
               << ConvolutionMethod::DIRECT
@@ -214,7 +225,7 @@ private:
 /** Main program for Squeezenet v1.1
  *
  * @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 )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
  */
 int main(int argc, char **argv)
 {
diff --git a/examples/graph_vgg16.cpp b/examples/graph_vgg16.cpp
index 44b4c4c3f2..6db4e386de 100644
--- a/examples/graph_vgg16.cpp
+++ b/examples/graph_vgg16.cpp
@@ -35,7 +35,7 @@ using namespace arm_compute::graph_utils;
 /** Example demonstrating how to implement VGG16's network using the Compute Library's graph API
  *
  * @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
  */
 class GraphVGG16Example : public Example
 {
@@ -57,40 +57,51 @@ public:
 
         ConvolutionMethod first_convolution3x3_hint = is_opencl ? ConvolutionMethod::DIRECT : ConvolutionMethod::GEMM;
         ConvolutionMethod convolution3x3_hint       = ConvolutionMethod::DEFAULT;
+        FastMathHint      fast_math_hint            = FastMathHint::DISABLED;
 
         // Parse arguments
         if(argc < 2)
         {
             // Print help
-            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No data folder provided: using random values\n\n";
         }
         else if(argc == 2)
         {
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No data folder provided: using random values\n\n";
         }
         else if(argc == 3)
         {
             data_path = argv[2];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No image provided: using random values\n\n";
         }
         else if(argc == 4)
         {
             data_path = argv[2];
             image     = argv[3];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n";
             std::cout << "No text file with labels provided: skipping output accessor\n\n";
         }
-        else
+        else if(argc == 5)
         {
             data_path = argv[2];
             image     = argv[3];
             label     = argv[4];
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n";
+            std::cout << "No fast math info provided: disabling fast math\n\n";
+        }
+        else
+        {
+            data_path      = argv[2];
+            image          = argv[3];
+            label          = argv[4];
+            fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
         }
 
         graph << target_hint
+              << fast_math_hint
               << first_convolution3x3_hint
               << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32),
                             get_input_accessor(image, std::move(preprocessor)))
@@ -246,7 +257,7 @@ private:
 /** Main program for VGG16
  *
  * @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 )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
  */
 int main(int argc, char **argv)
 {
diff --git a/examples/graph_vgg19.cpp b/examples/graph_vgg19.cpp
index 229112bb6f..5a281ea86a 100644
--- a/examples/graph_vgg19.cpp
+++ b/examples/graph_vgg19.cpp
@@ -35,7 +35,7 @@ using namespace arm_compute::graph_utils;
 /** Example demonstrating how to implement VGG19's network using the Compute Library's graph API
  *
  * @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
  */
 class GraphVGG19Example : public Example
 {
@@ -51,9 +51,10 @@ public:
         std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
 
         // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
-        const int  target      = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
-        Target     target_hint = set_target_hint(target);
-        const bool is_opencl   = target_hint == Target::CL;
+        const int    target         = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
+        Target       target_hint    = set_target_hint(target);
+        FastMathHint fast_math_hint = FastMathHint::DISABLED;
+        const bool   is_opencl      = target_hint == Target::CL;
 
         ConvolutionMethod first_convolution3x3_hint = is_opencl ? ConvolutionMethod::DIRECT : ConvolutionMethod::GEMM;
         ConvolutionMethod convolution3x3_hint       = ConvolutionMethod::DEFAULT;
@@ -62,36 +63,46 @@ public:
         if(argc < 2)
         {
             // Print help
-            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No data folder provided: using random values\n\n";
         }
         else if(argc == 2)
         {
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No data folder provided: using random values\n\n";
         }
         else if(argc == 3)
         {
             data_path = argv[2];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\n\n";
             std::cout << "No image provided: using random values\n\n";
         }
         else if(argc == 4)
         {
             data_path = argv[2];
             image     = argv[3];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n";
             std::cout << "No text file with labels provided: skipping output accessor\n\n";
         }
-        else
+        else if(argc == 5)
         {
             data_path = argv[2];
             image     = argv[3];
             label     = argv[4];
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n";
+            std::cout << "No fast math info provided: disabling fast math\n\n";
+        }
+        else
+        {
+            data_path      = argv[2];
+            image          = argv[3];
+            label          = argv[4];
+            fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
         }
 
         graph << target_hint
               << first_convolution3x3_hint
+              << fast_math_hint
               << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32),
                             get_input_accessor(image, std::move(preprocessor)))
               // Layer 1
@@ -259,7 +270,7 @@ private:
 /** Main program for VGG19
  *
  * @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 )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
  */
 int main(int argc, char **argv)
 {