diff --git a/arm_compute/core/CL/kernels/CLPoolingLayerKernel.h b/arm_compute/core/CL/kernels/CLPoolingLayerKernel.h
index 6c5091f..971e150 100644
--- a/arm_compute/core/CL/kernels/CLPoolingLayerKernel.h
+++ b/arm_compute/core/CL/kernels/CLPoolingLayerKernel.h
@@ -49,7 +49,7 @@
 
     /** Set the input and output tensors.
      *
-     * @param[in]  input     Source tensor. Data types supported: F16, F32.
+     * @param[in]  input     Source tensor. Data types supported: F16/F32.
      * @param[out] output    Destination tensor. Data types supported: Same as @p input.
      * @param[in]  pool_info Contains pooling operation information described in @ref PoolingLayerInfo.
      *                       Supported pooling sizes : 2, 3 and 7
@@ -65,6 +65,7 @@
     ICLTensor       *_output;
     PoolingLayerInfo _pool_info;
     BorderSize       _border_size;
+    unsigned int     _num_elems_processed_per_iteration;
 };
 }
 #endif /*__ARM_COMPUTE_CLPOOLINGLAYERKERNEL_H__ */
diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp
index cda2c5a..000cffa 100644
--- a/src/core/CL/CLKernelLibrary.cpp
+++ b/src/core/CL/CLKernelLibrary.cpp
@@ -232,6 +232,7 @@
     { "pixelwise_mul_int", "pixelwise_mul_int.cl" },
     { "pooling_layer_2", "pooling_layer.cl" },
     { "pooling_layer_3", "pooling_layer.cl" },
+    { "pooling_layer_3_optimized", "pooling_layer.cl" },
     { "pooling_layer_7", "pooling_layer.cl" },
     { "remap_nearest_neighbour", "remap.cl" },
     { "remap_bilinear", "remap.cl" },
diff --git a/src/core/CL/cl_kernels/pooling_layer.cl b/src/core/CL/cl_kernels/pooling_layer.cl
index b724520..06989aa 100644
--- a/src/core/CL/cl_kernels/pooling_layer.cl
+++ b/src/core/CL/cl_kernels/pooling_layer.cl
@@ -29,22 +29,143 @@
 #define POOL_OP(x, y) (fmax((x), (y)))
 #endif /* POOL_AVG */
 
-float calculate_avg_scale(const int pool_size, const int upper_bound_w, const int upper_bound_h,
-                          const int pad_x, const int pad_y, const int stride_x, const int stride_y)
+#if STRIDE_X == 1
+#define POOLING3x3(res, input, output) POOLING3x3_STRIDE1(res, input, output)
+#elif STRIDE_X == 2 /* STRIDE_X == 1 */
+#define POOLING3x3(res, input, output) POOLING3x3_STRIDE2(res, input, output)
+#elif STRIDE_X == 3 /* STRIDE_X not equals 1 or 2 */
+#define POOLING3x3(res, input, output) POOLING3x3_STRIDE3(res, input, output)
+#endif /* STRIDE_X == 3 */
+
+#define CONVERT_OP(data_type) convert_##data_type##4
+#define CONVERT_VECTOR4(data_type) CONVERT_OP(data_type)
+
+#define POOLING3x3_STRIDE1(res, input, output)                                                                                               \
+    ({                                                                                                                                       \
+        VEC_DATA_TYPE(DATA_TYPE, 4)                                                                                                          \
+        data00 = vload4(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0));                                                          \
+        VEC_DATA_TYPE(DATA_TYPE, 2)                                                                                                          \
+        data01 = vload2(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0) + 4);                                                      \
+        VEC_DATA_TYPE(DATA_TYPE, 4)                                                                                                          \
+        data10 = vload4(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0));                                                          \
+        VEC_DATA_TYPE(DATA_TYPE, 2)                                                                                                          \
+        data11 = vload2(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0) + 4);                                                      \
+        VEC_DATA_TYPE(DATA_TYPE, 4)                                                                                                          \
+        data20 = vload4(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0));                                                          \
+        VEC_DATA_TYPE(DATA_TYPE, 2)                                                                                                          \
+        data21 = vload2(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0) + 4);                                                      \
+        \
+        VEC_DATA_TYPE(DATA_TYPE, 8)                                                                                                          \
+        values00 = (VEC_DATA_TYPE(DATA_TYPE, 8))(data00.s01212323);                                                                          \
+        VEC_DATA_TYPE(DATA_TYPE, 4)                                                                                                          \
+        values01 = (VEC_DATA_TYPE(DATA_TYPE, 4))(data01.s0, data00.s3, data01.s01);                                                          \
+        VEC_DATA_TYPE(DATA_TYPE, 8)                                                                                                          \
+        values10 = (VEC_DATA_TYPE(DATA_TYPE, 8))(data10.s01212323);                                                                          \
+        VEC_DATA_TYPE(DATA_TYPE, 4)                                                                                                          \
+        values11 = (VEC_DATA_TYPE(DATA_TYPE, 4))(data11.s0, data10.s3, data11.s01);                                                          \
+        VEC_DATA_TYPE(DATA_TYPE, 8)                                                                                                          \
+        values20 = (VEC_DATA_TYPE(DATA_TYPE, 8))(data20.s01212323);                                                                          \
+        VEC_DATA_TYPE(DATA_TYPE, 4)                                                                                                          \
+        values21 = (VEC_DATA_TYPE(DATA_TYPE, 4))(data21.s0, data20.s3, data21.s01);                                                          \
+        \
+        values00 = POOL_OP(values00, values10);                                                                                              \
+        values01 = POOL_OP(values01, values11);                                                                                              \
+        values00 = POOL_OP(values00, values20);                                                                                              \
+        values01 = POOL_OP(values01, values21);                                                                                              \
+        \
+        res = POOL_OP((VEC_DATA_TYPE(DATA_TYPE, 4))(values00.s036, values01.s1), (VEC_DATA_TYPE(DATA_TYPE, 4))(values00.s147, values01.s2)); \
+        res = POOL_OP(res, (VEC_DATA_TYPE(DATA_TYPE, 4))(values00.s25, values01.s03));                                                       \
+    })
+
+#define POOLING3x3_STRIDE2(res, input, output)                                                                                               \
+    ({                                                                                                                                       \
+        VEC_DATA_TYPE(DATA_TYPE, 8)                                                                                                          \
+        data00           = vload8(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0));                                                \
+        DATA_TYPE data01 = *((__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0) + 8);                                                    \
+        VEC_DATA_TYPE(DATA_TYPE, 8)                                                                                                          \
+        data10           = vload8(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0));                                                \
+        DATA_TYPE data11 = *((__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0) + 8);                                                    \
+        VEC_DATA_TYPE(DATA_TYPE, 8)                                                                                                          \
+        data20           = vload8(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0));                                                \
+        DATA_TYPE data21 = *((__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0) + 8);                                                    \
+        \
+        VEC_DATA_TYPE(DATA_TYPE, 8)                                                                                                          \
+        values00 = (VEC_DATA_TYPE(DATA_TYPE, 8))(data00.s01223445);                                                                          \
+        VEC_DATA_TYPE(DATA_TYPE, 4)                                                                                                          \
+        values01 = (VEC_DATA_TYPE(DATA_TYPE, 4))(data00.s667, data01);                                                                       \
+        VEC_DATA_TYPE(DATA_TYPE, 8)                                                                                                          \
+        values10 = (VEC_DATA_TYPE(DATA_TYPE, 8))(data10.s01223445);                                                                          \
+        VEC_DATA_TYPE(DATA_TYPE, 4)                                                                                                          \
+        values11 = (VEC_DATA_TYPE(DATA_TYPE, 4))(data10.s667, data11);                                                                       \
+        VEC_DATA_TYPE(DATA_TYPE, 8)                                                                                                          \
+        values20 = (VEC_DATA_TYPE(DATA_TYPE, 8))(data20.s01223445);                                                                          \
+        VEC_DATA_TYPE(DATA_TYPE, 4)                                                                                                          \
+        values21 = (VEC_DATA_TYPE(DATA_TYPE, 4))(data20.s667, data21);                                                                       \
+        \
+        values00 = POOL_OP(values00, values10);                                                                                              \
+        values01 = POOL_OP(values01, values11);                                                                                              \
+        values00 = POOL_OP(values00, values20);                                                                                              \
+        values01 = POOL_OP(values01, values21);                                                                                              \
+        \
+        res = POOL_OP((VEC_DATA_TYPE(DATA_TYPE, 4))(values00.s036, values01.s1), (VEC_DATA_TYPE(DATA_TYPE, 4))(values00.s147, values01.s2)); \
+        res = POOL_OP(res, (VEC_DATA_TYPE(DATA_TYPE, 4))(values00.s25, values01.s03));                                                       \
+    })
+
+#define POOLING3x3_STRIDE3(res, input, output)                                                                                       \
+    ({                                                                                                                               \
+        VEC_DATA_TYPE(DATA_TYPE, 8)                                                                                                  \
+        data00 = vload8(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0));                                                  \
+        VEC_DATA_TYPE(DATA_TYPE, 4)                                                                                                  \
+        data01 = vload4(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0) + 8);                                              \
+        VEC_DATA_TYPE(DATA_TYPE, 8)                                                                                                  \
+        data10 = vload8(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0));                                                  \
+        VEC_DATA_TYPE(DATA_TYPE, 4)                                                                                                  \
+        data11 = vload4(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0) + 8);                                              \
+        VEC_DATA_TYPE(DATA_TYPE, 8)                                                                                                  \
+        data20 = vload8(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0));                                                  \
+        VEC_DATA_TYPE(DATA_TYPE, 4)                                                                                                  \
+        data21 = vload4(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0) + 8);                                              \
+        \
+        data00 = POOL_OP(data00, data10);                                                                                            \
+        data01 = POOL_OP(data01, data11);                                                                                            \
+        data00 = POOL_OP(data00, data20);                                                                                            \
+        data01 = POOL_OP(data01, data21);                                                                                            \
+        \
+        res = POOL_OP((VEC_DATA_TYPE(DATA_TYPE, 4))(data00.s036, data01.s1), (VEC_DATA_TYPE(DATA_TYPE, 4))(data00.s147, data01.s2)); \
+        res = POOL_OP(res, (VEC_DATA_TYPE(DATA_TYPE, 4))(data00.s25, data01.s03));                                                   \
+    })
+
+DATA_TYPE calculate_avg_scale(const int pool_size, const int upper_bound_w, const int upper_bound_h,
+                              const int pad_x, const int pad_y, const int stride_x, const int stride_y)
 {
-    int start_x = get_global_id(0) * stride_x - pad_x;
-    int start_y = get_global_id(1) * stride_y - pad_y;
-    int end_x   = min(start_x + pool_size, upper_bound_w);
-    int end_y   = min(start_y + pool_size, upper_bound_h);
+    const int start_x = get_global_id(0) * stride_x - pad_x;
+    const int start_y = get_global_id(1) * stride_y - pad_y;
+    const int end_x   = min(start_x + pool_size, upper_bound_w);
+    const int end_y   = min(start_y + pool_size, upper_bound_h);
     return 1.f / ((end_y - start_y) * (end_x - start_x));
 }
 
+VEC_DATA_TYPE(DATA_TYPE, 4)
+calculate_avg_scale4(const int pool_size, const int upper_bound_w, const int upper_bound_h,
+                     const int pad_x, const int pad_y, const int stride_x, const int stride_y)
+{
+    const int4 start_x = ((int4)get_global_id(0) * 4 + (int4)(0, 1, 2, 3)) * (int4)stride_x - (int4)pad_x;
+    const int  start_y = get_global_id(1) * stride_y - pad_y;
+    const int4 end_x   = min(start_x + (int4)pool_size, (int4)upper_bound_w);
+    const int  end_y   = min(start_y + pool_size, upper_bound_h);
+    return (VEC_DATA_TYPE(DATA_TYPE, 4))(1.f) / CONVERT_VECTOR4(DATA_TYPE)(((int4)(end_y - start_y)) * (end_x - start_x));
+}
+
 /** Performs a pooling function of pool size equal to 2.
  *
- * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16, F32;
- * @note In case of average pooling -DPOOL_AVG must be provided otherwise max pooling will be performed.
+ * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16/F32;
+ * @note In case of average pooling the following information must be passed at compile time:
+ *       -DPOOL_AVG must be provided otherwise max pooling will be performed.
+ *       -DMAX_WIDTH and -DMAX_HEIGHT which are the maximum accessible indeces in x and y dimensions (width + pad)
+ *       -DSTRIDE_X and -DSTRIDE_Y which are the steps of the window along the x and y directions
+ *       -DPAD_X and -DPAD_Y which are the pooling paddings in x and y dimension
  *
- * @param[in]  input_ptr                            Pointer to the source image. Supported data types: F16, F32
+ * @param[in]  input_ptr                            Pointer to the source image. Supported data types: F16/F32
  * @param[in]  input_stride_x                       Stride of the source image in X dimension (in bytes)
  * @param[in]  input_step_x                         input_stride_x * number of elements along X processed per workitem(in bytes)
  * @param[in]  input_stride_y                       Stride of the source image in Y dimension (in bytes)
@@ -52,7 +173,7 @@
  * @param[in]  input_stride_z                       Stride of the source tensor in Z dimension (in bytes)
  * @param[in]  input_step_z                         input_stride_z * number of elements along Z processed per workitem(in bytes)
  * @param[in]  input_offset_first_element_in_bytes  The offset of the first element in the source image
- * @param[out] output_ptr                           Pointer to the destination image. Supported data types: F16, F32
+ * @param[out] output_ptr                           Pointer to the destination image. Supported data types: same as @p input_ptr
  * @param[in]  output_stride_x                      Stride of the destination image in X dimension (in bytes)
  * @param[in]  output_step_x                        output_stride_x * number of elements along X processed per workitem(in bytes)
  * @param[in]  output_stride_y                      Stride of the destination image in Y dimension (in bytes)
@@ -60,18 +181,10 @@
  * @param[in]  output_stride_z                      Stride of the source tensor in Z dimension (in bytes)
  * @param[in]  output_step_z                        output_stride_z * number of elements along Z processed per workitem(in bytes)
  * @param[in]  output_offset_first_element_in_bytes The offset of the first element in the destination image
- * @param[in]  max_dims                             The maximum index that can be accessed in x and y dimension (width + pad)
- * @param[in]  strides                              The pooling operation strides in each dimension
- * @param[in]  paddings                             The pooling operation paddings in each dimension
  */
 __kernel void pooling_layer_2(
     TENSOR3D_DECLARATION(input),
-    TENSOR3D_DECLARATION(output)
-#ifdef POOL_AVG
-    ,
-    int2 max_dims, int2 strides, int2 paddings
-#endif /* POOL_AVG */
-)
+    TENSOR3D_DECLARATION(output))
 {
     // Get pixels pointer
     Tensor3D input  = CONVERT_TO_TENSOR3D_STRUCT(input);
@@ -89,19 +202,23 @@
 
     // Divide by pool region in case of average pooling
 #ifdef POOL_AVG
-    res *= calculate_avg_scale(2, max_dims.x, max_dims.y, paddings.x, paddings.y, strides.x, strides.y);
+    res *= calculate_avg_scale(2, MAX_WIDTH, MAX_HEIGHT, PAD_X, PAD_Y, STRIDE_X, STRIDE_Y);
 #endif /* POOL_AVG */
 
     // Store result
     *(__global DATA_TYPE *)output.ptr = res;
 }
 
-/** Performs a pooling function of pool size equal to 3.
+/** Performs a pooling function of pool size equal to 3
  *
- * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16, F32;
- * @note In case of average pooling -DPOOL_AVG must be provided otherwise max pooling will be performed.
+ * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16/F32;
+ * @note In case of average pooling the following information must be passed at compile time:
+ *       -DPOOL_AVG must be provided otherwise max pooling will be performed.
+ *       -DMAX_WIDTH and -DMAX_HEIGHT which are the maximum accessible indeces in x and y dimensions (width + pad)
+ *       -DSTRIDE_X and -DSTRIDE_Y which are the steps of the window along the x and y directions
+ *       -DPAD_X and -DPAD_Y which are the pooling paddings in x and y dimension
  *
- * @param[in]  input_ptr                            Pointer to the source image. Supported data types: F16, F32
+ * @param[in]  input_ptr                            Pointer to the source image. Supported data types: F16/F32
  * @param[in]  input_stride_x                       Stride of the source image in X dimension (in bytes)
  * @param[in]  input_step_x                         input_stride_x * number of elements along X processed per workitem(in bytes)
  * @param[in]  input_stride_y                       Stride of the source image in Y dimension (in bytes)
@@ -109,7 +226,7 @@
  * @param[in]  input_stride_z                       Stride of the source tensor in Z dimension (in bytes)
  * @param[in]  input_step_z                         input_stride_z * number of elements along Z processed per workitem(in bytes)
  * @param[in]  input_offset_first_element_in_bytes  The offset of the first element in the source image
- * @param[out] output_ptr                           Pointer to the destination image. Supported data types: F16, F32
+ * @param[out] output_ptr                           Pointer to the destination image. Supported data types: same as @p input_ptr
  * @param[in]  output_stride_x                      Stride of the destination image in X dimension (in bytes)
  * @param[in]  output_step_x                        output_stride_x * number of elements along X processed per workitem(in bytes)
  * @param[in]  output_stride_y                      Stride of the destination image in Y dimension (in bytes)
@@ -117,18 +234,10 @@
  * @param[in]  output_stride_z                      Stride of the source tensor in Z dimension (in bytes)
  * @param[in]  output_step_z                        output_stride_z * number of elements along Z processed per workitem(in bytes)
  * @param[in]  output_offset_first_element_in_bytes The offset of the first element in the destination image
- * @param[in]  max_dims                             The maximum index that can be accessed in x and y dimension (width + pad)
- * @param[in]  strides                              The pooling operation strides in each dimension
- * @param[in]  paddings                             The pooling operation paddings in each dimension
  */
 __kernel void pooling_layer_3(
     TENSOR3D_DECLARATION(input),
-    TENSOR3D_DECLARATION(output)
-#ifdef POOL_AVG
-    ,
-    int2 max_dims, int2 strides, int2 paddings
-#endif /* POOL_AVG */
-)
+    TENSOR3D_DECLARATION(output))
 {
     // Get pixels pointer
     Tensor3D input  = CONVERT_TO_TENSOR3D_STRUCT(input);
@@ -149,19 +258,24 @@
 
     // Divide by pool region in case of average pooling
 #ifdef POOL_AVG
-    res *= calculate_avg_scale(3, max_dims.x, max_dims.y, paddings.x, paddings.y, strides.x, strides.y);
-#endif /* POOL_AVG */
+    res *= calculate_avg_scale(3, MAX_WIDTH, MAX_HEIGHT, PAD_X, PAD_Y, STRIDE_X, STRIDE_Y);
+#endif //POOL_AVG
 
     // Store result
     *(__global DATA_TYPE *)output.ptr = res;
 }
 
-/** Performs a pooling function of pool size equal to 7.
+#if defined(POOLING3x3)
+/** Performs an optimized pooling function of pool size equal to 3 when the stride_x is less equal than 3
  *
- * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16, F32;
- * @note In case of average pooling -DPOOL_AVG must be provided otherwise max pooling will be performed.
+ * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16/F32;
+ * @note In case of average pooling the following information must be passed at compile time:
+ *       -DPOOL_AVG must be provided otherwise max pooling will be performed.
+ *       -DMAX_WIDTH and -DMAX_HEIGHT which are the maximum accessible indeces in x and y dimensions (width + pad)
+ *       -DSTRIDE_X and -DSTRIDE_Y which are the steps of the window along the x and y directions
+ *       -DPAD_X and -DPAD_Y which are the pooling paddings in x and y dimension
  *
- * @param[in]  input_ptr                            Pointer to the source image. Supported data types: F16, F32
+ * @param[in]  input_ptr                            Pointer to the source image. Supported data types: F16/F32
  * @param[in]  input_stride_x                       Stride of the source image in X dimension (in bytes)
  * @param[in]  input_step_x                         input_stride_x * number of elements along X processed per workitem(in bytes)
  * @param[in]  input_stride_y                       Stride of the source image in Y dimension (in bytes)
@@ -169,7 +283,7 @@
  * @param[in]  input_stride_z                       Stride of the source tensor in Z dimension (in bytes)
  * @param[in]  input_step_z                         input_stride_z * number of elements along Z processed per workitem(in bytes)
  * @param[in]  input_offset_first_element_in_bytes  The offset of the first element in the source image
- * @param[out] output_ptr                           Pointer to the destination image. Supported data types: F16, F32
+ * @param[out] output_ptr                           Pointer to the destination image. Supported data types: same as @p input_ptr
  * @param[in]  output_stride_x                      Stride of the destination image in X dimension (in bytes)
  * @param[in]  output_step_x                        output_stride_x * number of elements along X processed per workitem(in bytes)
  * @param[in]  output_stride_y                      Stride of the destination image in Y dimension (in bytes)
@@ -177,18 +291,59 @@
  * @param[in]  output_stride_z                      Stride of the source tensor in Z dimension (in bytes)
  * @param[in]  output_step_z                        output_stride_z * number of elements along Z processed per workitem(in bytes)
  * @param[in]  output_offset_first_element_in_bytes The offset of the first element in the destination image
- * @param[in]  max_dims                             The maximum index that can be accessed in x and y dimension (width + pad)
- * @param[in]  strides                              The pooling operation strides in each dimension
- * @param[in]  paddings                             The pooling operation paddings in each dimension
+ */
+__kernel void pooling_layer_3_optimized(
+    TENSOR3D_DECLARATION(input),
+    TENSOR3D_DECLARATION(output))
+{
+    // Get pixels pointer
+    Tensor3D input  = CONVERT_TO_TENSOR3D_STRUCT(input);
+    Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output);
+
+    VEC_DATA_TYPE(DATA_TYPE, 4)
+    res;
+
+    // Perform pooling 3x3 for 4 output elements
+    POOLING3x3(res, input, output);
+
+    // Divide by pool region in case of average pooling
+#ifdef POOL_AVG
+    res *= calculate_avg_scale4(3, MAX_WIDTH, MAX_HEIGHT, PAD_X, PAD_Y, STRIDE_X, STRIDE_Y);
+#endif // POOL_AVG
+
+    vstore4(res, 0, (__global DATA_TYPE *)output.ptr);
+}
+#endif // defined(POOLING3x3)
+
+/** Performs a pooling function of pool size equal to 7.
+ *
+ * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16/F32;
+ * @note In case of average pooling the following information must be passed at compile time:
+ *       -DPOOL_AVG must be provided otherwise max pooling will be performed.
+ *       -DMAX_WIDTH and -DMAX_HEIGHT which are the maximum accessible indeces in x and y dimensions (width + pad)
+ *       -DSTRIDE_X and -DSTRIDE_Y which are the steps of the window along the x and y directions
+ *       -DPAD_X and -DPAD_Y which are the pooling paddings in x and y dimension
+ *
+ * @param[in]  input_ptr                            Pointer to the source image. Supported data types: F16/F32
+ * @param[in]  input_stride_x                       Stride of the source image in X dimension (in bytes)
+ * @param[in]  input_step_x                         input_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  input_stride_y                       Stride of the source image in Y dimension (in bytes)
+ * @param[in]  input_step_y                         input_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  input_stride_z                       Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  input_step_z                         input_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  input_offset_first_element_in_bytes  The offset of the first element in the source image
+ * @param[out] output_ptr                           Pointer to the destination image. Supported data types: same as @p input_ptr
+ * @param[in]  output_stride_x                      Stride of the destination image in X dimension (in bytes)
+ * @param[in]  output_step_x                        output_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  output_stride_y                      Stride of the destination image in Y dimension (in bytes)
+ * @param[in]  output_step_y                        output_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  output_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  output_step_z                        output_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  output_offset_first_element_in_bytes The offset of the first element in the destination image
  */
 __kernel void pooling_layer_7(
     TENSOR3D_DECLARATION(input),
-    TENSOR3D_DECLARATION(output)
-#ifdef POOL_AVG
-    ,
-    int2 max_dims, int2 strides, int2 paddings
-#endif /* POOL_AVG */
-)
+    TENSOR3D_DECLARATION(output))
 {
     // Get pixels pointer
     Tensor3D input  = CONVERT_TO_TENSOR3D_STRUCT(input);
@@ -234,7 +389,7 @@
 
     // Divide by pool region in case of average pooling
 #ifdef POOL_AVG
-    res *= calculate_avg_scale(7, max_dims.x, max_dims.y, paddings.x, paddings.y, strides.x, strides.y);
+    res *= calculate_avg_scale(7, MAX_WIDTH, MAX_HEIGHT, PAD_X, PAD_Y, STRIDE_X, STRIDE_Y);
 #endif /* POOL_AVG */
 
     // Store result
diff --git a/src/core/CL/kernels/CLPoolingLayerKernel.cpp b/src/core/CL/kernels/CLPoolingLayerKernel.cpp
index ca75fd5..6b2e881 100644
--- a/src/core/CL/kernels/CLPoolingLayerKernel.cpp
+++ b/src/core/CL/kernels/CLPoolingLayerKernel.cpp
@@ -41,7 +41,7 @@
 using namespace arm_compute;
 
 CLPoolingLayerKernel::CLPoolingLayerKernel()
-    : _input(nullptr), _output(nullptr), _pool_info(), _border_size(0)
+    : _input(nullptr), _output(nullptr), _pool_info(), _border_size(0), _num_elems_processed_per_iteration(1)
 {
 }
 
@@ -92,11 +92,21 @@
     ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
     ARM_COMPUTE_ERROR_ON((output->info()->dimension(0) != pooled_w) || (output->info()->dimension(1) != pooled_h));
 
-    const int num_elements_read_per_iteration = (pool_size == 7) ? 8 : pool_size;
-    const int input_width                     = input->info()->dimension(0);
-    const int input_height                    = input->info()->dimension(1);
-    const int upper_bound_w                   = ((pooled_w - 1) * pool_stride_x - pool_pad_x + num_elements_read_per_iteration) - input_width;
-    const int upper_bound_h                   = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height;
+    // Check if we have pool3x3 with stride_x less equal than 3. In these cases, run an optimized OpenCL kernel where
+    // each thread computes 4 output elements
+    const bool is_pool3x3_stride_le3 = (pool_size == 3) && (pool_stride_x <= 3);
+
+    int num_elements_read_per_iteration = (pool_size == 7) ? 8 : pool_size;
+    if(is_pool3x3_stride_le3)
+    {
+        // Change the number of elements processed and number of elements read per iteration for pooling 3x3 with stride less equal than 3
+        _num_elems_processed_per_iteration = 4;
+        num_elements_read_per_iteration    = pool_size * (pool_stride_x + 1);
+    }
+    const int input_width   = input->info()->dimension(0);
+    const int input_height  = input->info()->dimension(1);
+    const int upper_bound_w = ((pooled_w - 1) * pool_stride_x - pool_pad_x + num_elements_read_per_iteration) - input_width;
+    const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height;
 
     // Set instance variables
     _input              = input;
@@ -110,49 +120,31 @@
     std::set<std::string> build_opts;
     build_opts.emplace(("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())));
     build_opts.emplace(("-DPOOL_" + ((PoolingType::MAX == pool_type) ? std::string("MAX") : std::string("AVG"))));
+    build_opts.emplace(("-DSTRIDE_X=" + support::cpp11::to_string(pool_stride_x)));
+    if(pool_type == PoolingType::AVG)
+    {
+        build_opts.emplace(("-DMAX_WIDTH=" + support::cpp11::to_string(input->info()->dimension(0) + pool_pad_x)));
+        build_opts.emplace(("-DMAX_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(1) + pool_pad_y)));
+        build_opts.emplace(("-DSTRIDE_Y=" + support::cpp11::to_string(pool_stride_y)));
+        build_opts.emplace(("-DPAD_X=" + support::cpp11::to_string(pool_pad_x)));
+        build_opts.emplace(("-DPAD_Y=" + support::cpp11::to_string(pool_pad_y)));
+    }
 
     // Create kernel
     std::string kernel_name = "pooling_layer_" + support::cpp11::to_string(pool_size);
-    _kernel                 = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts));
-
-    // Set static kernel arguments
-    if(pool_type == PoolingType::AVG)
+    if(is_pool3x3_stride_le3)
     {
-        // Create static kernel arguments
-        const cl_int2 max_dims =
-        {
-            {
-                static_cast<cl_int>(input->info()->dimension(0)) + pool_pad_x,
-                static_cast<cl_int>(input->info()->dimension(1)) + pool_pad_y,
-            }
-        };
-        const cl_int2 strides =
-        {
-            {
-                pool_stride_x,
-                pool_stride_y,
-            }
-        };
-        const cl_int2 paddings =
-        {
-            {
-                pool_pad_x,
-                pool_pad_y,
-            }
-        };
-
-        // Set static kernel arguments
-        unsigned int idx = 2 * num_arguments_per_3D_tensor();
-        _kernel.setArg<cl_int2>(idx++, max_dims);
-        _kernel.setArg<cl_int2>(idx++, strides);
-        _kernel.setArg<cl_int2>(idx++, paddings);
+        _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name + "_optimized", build_opts));
+    }
+    else
+    {
+        _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts));
     }
 
     // Configure kernel window
-    const unsigned int     num_elems_processed_per_iteration = 1;
-    Window                 win                               = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration));
+    Window                 win = calculate_max_window(*output->info(), Steps(_num_elems_processed_per_iteration));
     AccessWindowStatic     input_access(input->info(), -pool_pad_x, -pool_pad_y, input_width + _border_size.right, input_height + _border_size.bottom);
-    AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
+    AccessWindowHorizontal output_access(output->info(), 0, _num_elems_processed_per_iteration);
     update_window_and_padding(win, input_access, output_access);
     output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
     ICLKernel::configure(win);
@@ -174,7 +166,7 @@
     {
         // Upsample input by pool size
         Window in_slice(slice);
-        in_slice.set(Window::DimX, Window::Dimension(in_slice.x().start() - pool_pad_x, in_slice.x().end() * pool_stride_x, pool_stride_x));
+        in_slice.set(Window::DimX, Window::Dimension(in_slice.x().start() - pool_pad_x, in_slice.x().end() * pool_stride_x, pool_stride_x * _num_elems_processed_per_iteration));
         in_slice.set(Window::DimY, Window::Dimension(in_slice.y().start() - pool_pad_y, in_slice.y().end() * pool_stride_y, pool_stride_y));
 
         // Set inputs