diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp
index 6602b4d..4cd0a78 100644
--- a/src/core/CL/CLKernelLibrary.cpp
+++ b/src/core/CL/CLKernelLibrary.cpp
@@ -239,6 +239,7 @@
     { "pooling_layer_3", "pooling_layer.cl" },
     { "pooling_layer_3_optimized", "pooling_layer.cl" },
     { "pooling_layer_7", "pooling_layer.cl" },
+    { "pooling_layer_N", "pooling_layer.cl" },
     { "quantization_layer", "quantization_layer.cl" },
     { "reduction_operation", "reduction_operation.cl" },
     { "remap_nearest_neighbour", "remap.cl" },
diff --git a/src/core/CL/cl_kernels/pooling_layer.cl b/src/core/CL/cl_kernels/pooling_layer.cl
index 18ad4a6..0497bf4 100644
--- a/src/core/CL/cl_kernels/pooling_layer.cl
+++ b/src/core/CL/cl_kernels/pooling_layer.cl
@@ -415,3 +415,101 @@
     // Store result
     *(__global DATA_TYPE *)output.ptr = res;
 }
+
+#if defined(POOL_SIZE)
+
+// Set the initial value for the pooling operation accordingly with the data type
+#if defined(POOL_AVG)
+#define INITIAL_VALUE 0
+#else // POOL_AVG
+#ifdef FIXED_POINT_POSITION
+#define MIN_VAL_EXPAND(type) type##_MIN
+#define MIN_VAL(type) MIN_VAL_EXPAND(type)
+#define INITIAL_VALUE MIN_VAL(DATA_TYPE)
+#define INITIAL_VALUE 0
+#else // FIXED_POINT_POSITION
+#if FP16
+#define INITIAL_VALUE -HALF_MAX
+#else // FP16
+#define INITIAL_VALUE -FLT_MAX
+#endif // FP16
+#endif // FIXED_POINT_POSITION
+
+#endif // POOL_AVG
+
+/** Performs a pooling function of pool size equal to N
+ *
+ * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16/F32;
+ * @note -DFP16 must be passed at compile time if half float data type is used
+ * @note Pool size must be passed using -DPOOL_SIZE e.g. -DPOOL_SIZE=13;
+ * @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_N(
+    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, 8)
+    vdata           = INITIAL_VALUE;
+    DATA_TYPE sdata = INITIAL_VALUE;
+
+    // Load data
+    for(int y = 0; y < POOL_SIZE; y++)
+    {
+        int x = 0;
+        for(; x <= ((int)POOL_SIZE - 8); x += 8)
+        {
+            VEC_DATA_TYPE(DATA_TYPE, 8)
+            data0 = vload8(0, (__global DATA_TYPE *)tensor3D_offset(&input, x, y, 0));
+            vdata = POOL_OP(vdata, data0);
+        }
+
+        // Leftover
+        for(; x < (int)POOL_SIZE; ++x)
+        {
+            DATA_TYPE data0 = *((__global DATA_TYPE *)tensor3D_offset(&input, x, y, 0));
+            sdata           = POOL_OP(sdata, data0);
+        }
+    }
+
+    // Reduce result
+    VEC_DATA_TYPE(DATA_TYPE, 4)
+    reduce4 = POOL_OP(vdata.s0123, vdata.s4567);
+    VEC_DATA_TYPE(DATA_TYPE, 2)
+    reduce2       = POOL_OP(reduce4.s01, reduce4.s23);
+    DATA_TYPE res = POOL_OP(reduce2.s0, reduce2.s1);
+    res           = POOL_OP(res, sdata);
+
+    // Divide by pool region in case of average pooling
+#ifdef POOL_AVG
+    res = DIV_OP(res, calculate_avg_scale(POOL_SIZE, MAX_WIDTH, MAX_HEIGHT, PAD_X, PAD_Y, STRIDE_X, STRIDE_Y));
+#endif /* POOL_AVG */
+
+    // Store result
+    *(__global DATA_TYPE *)output.ptr = res;
+}
+#endif // defined(POOL_SIZE)
\ No newline at end of file
diff --git a/src/core/CL/kernels/CLPoolingLayerKernel.cpp b/src/core/CL/kernels/CLPoolingLayerKernel.cpp
index 22d29c6..22c7730 100644
--- a/src/core/CL/kernels/CLPoolingLayerKernel.cpp
+++ b/src/core/CL/kernels/CLPoolingLayerKernel.cpp
@@ -64,13 +64,10 @@
     std::tie(pool_pad_x, pool_pad_y)       = pad_stride_info.pad();
     std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride();
 
-    static const std::set<int> supported_pool_sizes = { 2, 3, 7 };
-    ARM_COMPUTE_UNUSED(supported_pool_sizes);
-
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
     ARM_COMPUTE_ERROR_ON_NULLPTR(output);
-    ARM_COMPUTE_ERROR_ON(supported_pool_sizes.find(pool_size) == supported_pool_sizes.end());
     ARM_COMPUTE_ERROR_ON(pool_pad_x >= pool_size || pool_pad_y >= pool_size);
+    ARM_COMPUTE_ERROR_ON(pool_size > 7 && is_data_type_fixed_point(input->info()->data_type()));
 
     // Check output dimensions
     std::tie(pooled_w, pooled_h) = scaled_dimensions(input->info()->dimension(0),
@@ -92,29 +89,14 @@
     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));
 
-    // 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) && !is_data_type_fixed_point(input->info()->data_type());
-
-    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;
+    const int input_width  = input->info()->dimension(0);
+    const int input_height = input->info()->dimension(1);
 
     // Set instance variables
-    _input              = input;
-    _output             = output;
-    _pool_info          = pool_info;
-    _border_size        = BorderSize(pool_pad_y, pool_pad_x);
-    _border_size.right  = std::max(upper_bound_w, pool_pad_x);
-    _border_size.bottom = std::max(upper_bound_h, pool_pad_y);
+    _input       = input;
+    _output      = output;
+    _pool_info   = pool_info;
+    _border_size = BorderSize(pool_pad_y, pool_pad_x);
 
     // Set build options
     std::set<std::string> build_opts;
@@ -136,14 +118,52 @@
     }
 
     // Create kernel
-    std::string kernel_name = "pooling_layer_" + support::cpp11::to_string(pool_size);
-    if(is_pool3x3_stride_le3)
+    if(pool_size <= 7)
     {
-        _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name + "_optimized", build_opts));
+        // 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) && !is_data_type_fixed_point(input->info()->data_type());
+
+        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 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;
+
+        _border_size.right  = std::max(upper_bound_w, pool_pad_x);
+        _border_size.bottom = std::max(upper_bound_h, pool_pad_y);
+
+        std::string kernel_name = "pooling_layer_" + support::cpp11::to_string(pool_size);
+        if(is_pool3x3_stride_le3)
+        {
+            _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));
+        }
     }
-    else
+    else // Run general case
     {
-        _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts));
+        _num_elems_processed_per_iteration = 1;
+
+        const int upper_bound_w = ((pooled_w - 1) * pool_stride_x - pool_pad_x + pool_size) - input_width;
+        const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height;
+
+        _border_size.right  = std::max(upper_bound_w, pool_pad_x);
+        _border_size.bottom = std::max(upper_bound_h, pool_pad_y);
+
+        build_opts.emplace(("-DPOOL_SIZE=" + support::cpp11::to_string(pool_size)));
+        if(input->info()->data_type() == DataType::F16)
+        {
+            build_opts.emplace("-DFP16");
+        }
+        _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("pooling_layer_N", build_opts));
     }
 
     // Configure kernel window