COMPMID-413: Add support for QS8 and QS16 CLNormalizationLayer.

Change-Id: I1aaa9fb8d05796bbca9cfae584e084646552bb71
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/80155
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>

6 files changed, 107 insertions, 50 deletions

diff --git a/src/core/CL/cl_kernels/fill_border.cl b/src/core/CL/cl_kernels/fill_border.cl
index 5fbe3ea070..f511613a37 100644
--- a/src/core/CL/cl_kernels/fill_border.cl
+++ b/src/core/CL/cl_kernels/fill_border.cl
@@ -21,6 +21,7 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
+#include "fixed_point.h"
 #include "helpers.h"
 
 /** Fill N pixel of the padding edge of a single channel image by replicating the closest valid pixel.
diff --git a/src/core/CL/cl_kernels/fixed_point.h b/src/core/CL/cl_kernels/fixed_point.h
index 509e9d01c2..7038d40e16 100644
--- a/src/core/CL/cl_kernels/fixed_point.h
+++ b/src/core/CL/cl_kernels/fixed_point.h
@@ -378,6 +378,7 @@ EXPQ_IMPL(qs16, qs16x16, 16)
 
 LOGQ_IMPL(qs8, qs8x16, 16)
 LOGQ_IMPL(qs16, qs16x8, 8)
+LOGQ_IMPL(qs16, qs16x16, 16)
 
 #define LOG_OP_EXPAND_STR(a, type, size, position) log_sat_##type##x##size((a), (position))
 #define LOG_OP_EXPAND(a, type, size, position) LOG_OP_EXPAND_STR(a, type, size, position)
diff --git a/src/core/CL/cl_kernels/normalization_layer.cl b/src/core/CL/cl_kernels/normalization_layer.cl
index 2305ae0d15..598b734c26 100644
--- a/src/core/CL/cl_kernels/normalization_layer.cl
+++ b/src/core/CL/cl_kernels/normalization_layer.cl
@@ -23,11 +23,41 @@
  */
 #include "helpers.h"
 
+#if defined(FIXED_POINT_POSITION)
+
+#include "fixed_point.h"
+#define MUL_OP(x, y) MUL_SAT_OP_EXPAND((x), (y), DATA_TYPE, VEC_SIZE, FIXED_POINT_POSITION)
+#define ADD_OP(x, y) ADD_SAT_OP_EXPAND((x), (y), DATA_TYPE, VEC_SIZE)
+#define DIV_OP(x, y) DIV_SAT_OP_EXPAND((x), (y), DATA_TYPE, VEC_SIZE, FIXED_POINT_POSITION)
+#define EXP_OP(x) EXP_OP_EXPAND((x), DATA_TYPE, VEC_SIZE, FIXED_POINT_POSITION)
+#define LOG_OP(x) LOG_OP_EXPAND((x), DATA_TYPE, VEC_SIZE, FIXED_POINT_POSITION)
+#define POW_OP(x, y) EXP_OP(MUL_OP(LOG_OP((x)), (y)))
+#define SQCVT_SAT(a) SQCVT_SAT_OP_EXPAND((a), DATA_TYPE, FIXED_POINT_POSITION)
+
+#define LOAD_OP(offset, ptr) vload16(offset, ptr)
+#define STORE_OP(data, offset, ptr) vstore16(data, offset, ptr)
+
+#else // FIXED_POINT_POSITION
+
+#define MUL_OP(x, y) ((x) * (y))
+#define ADD_OP(x, y) ((x) + (y))
+#define DIV_OP(x, y) ((x) / (y))
+#define POW_OP(x, y) pow((x), (y))
+#define SQCVT_SAT(a) (a)
+
+#define LOAD_OP(offset, ptr) vload4(offset, ptr)
+#define STORE_OP(data, offset, ptr) vstore4(data, offset, ptr)
+
+#endif // FIXED_POINT_POSITION
+
 /** Apply cross map normalization.
  *
  * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=short
+ * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size, e.g. -DVEC_SIZE=16
+ * @note In case of fixed-point operation -DFIXED_POINT_POSITION=fixed_point_position must be provided: e.g. -DFIXED_POINT_POSITION=3
+ * @note Scaling coefficient (= alpha/norm_size), beta and kappa need to be passed at compile time using -DCOEFF, -DALPHA and -DKAPPA
  *
- * @param[in]  input_ptr                                   Pointer to the first source tensor. Supported data types: F16, F32
+ * @param[in]  input_ptr                                   Pointer to the first source tensor. Supported data types: QS8/QS16/F16/F32
  * @param[in]  input_stride_x                              Stride of the first source tensor 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 first source tensor in Y dimension (in bytes)
@@ -35,7 +65,7 @@
  * @param[in]  input_stride_z                              Stride of the first 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 first source tensor
- * @param[in]  squared_input_ptr                           Pointer to the second source tensor. Supported data types: F16, F32
+ * @param[in]  squared_input_ptr                           Pointer to the second source tensor. Supported data types: same as @p input_ptr
  * @param[in]  squared_input_stride_x                      Stride of the second source tensor in X dimension (in bytes)
  * @param[in]  squared_input_step_x                        input_stride_x * number of elements along X processed per workitem(in bytes)
  * @param[in]  squared_input_stride_y                      Stride of the second source tensor in Y dimension (in bytes)
@@ -43,7 +73,7 @@
  * @param[in]  squared_input_stride_z                      Stride of the second source tensor in Z dimension (in bytes)
  * @param[in]  squared_input_step_z                        input_stride_z * number of elements along Z processed per workitem(in bytes)
  * @param[in]  squared_input_offset_first_element_in_bytes The offset of the second element in the second source tensor
- * @param[out] output_ptr                                  Pointer to the destination tensor. Supported data types: F16, F32
+ * @param[out] output_ptr                                  Pointer to the destination tensor. Supported data types: same as @p input_ptr
  * @param[in]  output_stride_x                             Stride of the destination tensor 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 tensor in Y dimension (in bytes)
@@ -51,24 +81,25 @@
  * @param[in]  output_stride_z                             Stride of the destination 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 tensor
- * @param[in]  coeff                                       Alpha parameter / norm_size
- * @param[in]  beta                                        Beta parameter in the normalization equation
- * @param[in]  kappa                                       Kappa parameter in the normalization equation
  * @param[in]  radius                                      Number of elements on the right or left side to normalize across
  */
 __kernel void normalization_layer_cross_map(TENSOR3D_DECLARATION(input),
                                             TENSOR3D_DECLARATION(squared_input),
                                             TENSOR3D_DECLARATION(output),
-                                            float coeff,
-                                            float beta,
-                                            float kappa,
-                                            uint  radius)
+                                            uint radius)
 {
     Tensor3D in         = CONVERT_TO_TENSOR3D_STRUCT(input);
     Tensor3D squared_in = CONVERT_TO_TENSOR3D_STRUCT(squared_input);
     Tensor3D out        = CONVERT_TO_TENSOR3D_STRUCT(output);
 
-    DATA_TYPE acc = 0;
+    VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+    acc = (VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE))0;
+    const VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+    coeff_v = (VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE))SQCVT_SAT(COEFF);
+    const VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+    beta_v = (VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE))SQCVT_SAT(BETA);
+    const VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+    kappa_v = (VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE))SQCVT_SAT(KAPPA);
 
     const int num_of_slices = get_global_size(2);
     const int current_slice = get_global_id(2);
@@ -78,21 +109,26 @@ __kernel void normalization_layer_cross_map(TENSOR3D_DECLARATION(input),
 
     for(int i = left_slice; i <= right_slice; i++)
     {
-        acc += *(__global DATA_TYPE *)tensor3D_offset(&squared_in, 0, 0, i - current_slice);
+        acc = ADD_OP(acc, LOAD_OP(0, (__global DATA_TYPE *)tensor3D_offset(&squared_in, 0, 0, i - current_slice)));
     }
 
-    const float normalized = pow(kappa + coeff * (float)acc, beta);
+    acc = ADD_OP(MUL_OP(acc, coeff_v), kappa_v);
+    const VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+    normalized = POW_OP(acc, beta_v);
+    const VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+    normalized_pixel = DIV_OP(LOAD_OP(0, (__global DATA_TYPE *)in.ptr), normalized);
 
-    const float normalized_pixel = (float) * ((__global DATA_TYPE *)in.ptr) / normalized;
-
-    *(__global DATA_TYPE *)out.ptr = CONVERT(normalized_pixel, DATA_TYPE);
+    STORE_OP(normalized_pixel, 0, (__global DATA_TYPE *)out.ptr);
 }
 
 /** Apply in map normalization.
  *
  * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=short
+ * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size, e.g. -DVEC_SIZE=16
+ * @note In case of fixed-point operation -DFIXED_POINT_POSITION=fixed_point_position must be provided: e.g. -DFIXED_POINT_POSITION=3
+ * @note Scaling coefficient (= alpha/norm_size), beta and kappa need to be passed at compile time using -DCOEFF, -DALPHA and -DKAPPA
  *
- * @param[in]  input_ptr                                   Pointer to the first source tensor. Supported data types: F16, F32
+ * @param[in]  input_ptr                                   Pointer to the first source tensor. Supported data types: QS8/F16/F32
  * @param[in]  input_stride_x                              Stride of the first source tensor 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 first source tensor in Y dimension (in bytes)
@@ -100,7 +136,7 @@ __kernel void normalization_layer_cross_map(TENSOR3D_DECLARATION(input),
  * @param[in]  input_stride_z                              Stride of the first 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 first source tensor
- * @param[in]  squared_input_ptr                           Pointer to the second source tensor. Supported data types: F16, F32
+ * @param[in]  squared_input_ptr                           Pointer to the second source tensor. Supported data types: same as @p input_ptr
  * @param[in]  squared_input_stride_x                      Stride of the second source tensor in X dimension (in bytes)
  * @param[in]  squared_input_step_x                        input_stride_x * number of elements along X processed per workitem(in bytes)
  * @param[in]  squared_input_stride_y                      Stride of the second source tensor in Y dimension (in bytes)
@@ -108,7 +144,7 @@ __kernel void normalization_layer_cross_map(TENSOR3D_DECLARATION(input),
  * @param[in]  squared_input_stride_z                      Stride of the second source tensor in Z dimension (in bytes)
  * @param[in]  squared_input_step_z                        input_stride_z * number of elements along Z processed per workitem(in bytes)
  * @param[in]  squared_input_offset_first_element_in_bytes The offset of the second element in the second source tensor
- * @param[out] output_ptr                                  Pointer to the destination tensor. Supported data types: F16, F32
+ * @param[out] output_ptr                                  Pointer to the destination tensor. Supported data types: same as @p input_ptr
  * @param[in]  output_stride_x                             Stride of the destination tensor 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 first destination tensor in Y dimension (in bytes)
@@ -116,25 +152,25 @@ __kernel void normalization_layer_cross_map(TENSOR3D_DECLARATION(input),
  * @param[in]  output_stride_z                             Stride of the first 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 tensor
- * @param[in]  coeff                                       Alpha parameter / norm_size
- * @param[in]  beta                                        Beta parameter in the normalization equation
- * @param[in]  kappa                                       Kappa parameter in the normalization equation
  * @param[in]  radius                                      Number of elements on the right or left side to normalize across
  */
 __kernel void normalization_layer_in_map_1D(TENSOR3D_DECLARATION(input),
                                             TENSOR3D_DECLARATION(squared_input),
                                             TENSOR3D_DECLARATION(output),
-                                            float coeff,
-                                            float beta,
-                                            float kappa,
-                                            uint  radius)
+                                            uint radius)
 {
     Tensor3D in         = CONVERT_TO_TENSOR3D_STRUCT(input);
     Tensor3D squared_in = CONVERT_TO_TENSOR3D_STRUCT(squared_input);
     Tensor3D out        = CONVERT_TO_TENSOR3D_STRUCT(output);
 
-    VEC_DATA_TYPE(DATA_TYPE, 4)
-    acc_vec = 0;
+    VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+    acc = (VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE))0;
+    const VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+    coeff_v = (VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE))SQCVT_SAT(COEFF);
+    const VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+    beta_v = (VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE))SQCVT_SAT(BETA);
+    const VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+    kappa_v = (VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE))SQCVT_SAT(KAPPA);
 
     const int current_pos = get_global_id(0) << 2;
 
@@ -143,12 +179,14 @@ __kernel void normalization_layer_in_map_1D(TENSOR3D_DECLARATION(input),
 
     for(int i = left_pos; i <= right_pos; i += 1)
     {
-        acc_vec += vload4(0, (__global DATA_TYPE *)tensor3D_offset(&squared_in, i - current_pos, 0, 0));
+        acc = ADD_OP(acc, LOAD_OP(0, (__global DATA_TYPE *)tensor3D_offset(&squared_in, i - current_pos, 0, 0)));
     }
 
-    const float4 normalized = pow((float4)kappa + coeff * CONVERT(acc_vec, float4), beta);
-
-    const float4 normalized_pixel = CONVERT(vload4(0, (__global DATA_TYPE *)in.ptr), float4) / normalized;
+    acc = ADD_OP(MUL_OP(acc, coeff_v), kappa_v);
+    const VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+    normalized = POW_OP(acc, beta_v);
+    const VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+    normalized_pixel = DIV_OP(LOAD_OP(0, (__global DATA_TYPE *)in.ptr), normalized);
 
-    vstore4(CONVERT(normalized_pixel, VEC_DATA_TYPE(DATA_TYPE, 4)), 0, (__global DATA_TYPE *)out.ptr);
+    STORE_OP(normalized_pixel, 0, (__global DATA_TYPE *)out.ptr);
 }
diff --git a/src/core/CL/kernels/CLNormalizationLayerKernel.cpp b/src/core/CL/kernels/CLNormalizationLayerKernel.cpp
index 1afd76a375..a0607c2ba0 100644
--- a/src/core/CL/kernels/CLNormalizationLayerKernel.cpp
+++ b/src/core/CL/kernels/CLNormalizationLayerKernel.cpp
@@ -26,6 +26,7 @@
 #include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/CLKernelLibrary.h"
 #include "arm_compute/core/CL/ICLTensor.h"
+#include "arm_compute/core/FixedPoint.h"
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/Utils.h"
@@ -46,7 +47,7 @@ BorderSize CLNormalizationLayerKernel::border_size() const
 
 void CLNormalizationLayerKernel::configure(const ICLTensor *input, const ICLTensor *squared_input, ICLTensor *output, NormalizationLayerInfo norm_info)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
+    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);
 
     // Output tensor auto initialization if not yet initialized
@@ -56,34 +57,46 @@ void CLNormalizationLayerKernel::configure(const ICLTensor *input, const ICLTens
     ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, squared_input, output);
     ARM_COMPUTE_ERROR_ON_MSG(!(norm_info.norm_size() % 2), "Normalization size should be odd");
     ARM_COMPUTE_ERROR_ON_MSG(norm_info.type() == NormType::IN_MAP_2D, "2D In-Map Normalization not implemented");
-
-    // Set build options
-    std::set<std::string> build_opts;
-    build_opts.emplace(("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())));
+    if(is_data_type_fixed_point(input->info()->data_type()))
+    {
+        ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, squared_input, output);
+        ARM_COMPUTE_ERROR_ON_VALUE_NOT_REPRESENTABLE_IN_FIXED_POINT(norm_info.beta(), input);
+        ARM_COMPUTE_ERROR_ON_VALUE_NOT_REPRESENTABLE_IN_FIXED_POINT(norm_info.kappa(), input);
+        ARM_COMPUTE_ERROR_ON_VALUE_NOT_REPRESENTABLE_IN_FIXED_POINT(norm_info.scale_coeff(), input);
+    }
 
     _input         = input;
     _squared_input = squared_input;
     _output        = output;
 
-    _is_in_map                      = (norm_info.type() == NormType::IN_MAP_1D);
+    _is_in_map                      = (norm_info.type() != NormType::CROSS_MAP);
     const unsigned int border_width = _is_in_map ? std::min(norm_info.norm_size() / 2, 3U) : 0;
     _border_size                    = BorderSize(0, border_width);
 
+    const unsigned int num_elems_processed_per_iteration = (is_data_type_fixed_point(input->info()->data_type())) ? 16 : 4;
+    const unsigned int num_elems_read_per_iteration      = num_elems_processed_per_iteration + 2 * (norm_info.norm_size() / 2);
+
+    // Set build options
+    std::set<std::string> build_opts;
+    build_opts.emplace(("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())));
+    if(is_data_type_fixed_point(input->info()->data_type()))
+    {
+        build_opts.emplace(("-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position())));
+    }
+    build_opts.emplace(("-DCOEFF=" + support::cpp11::to_string(norm_info.scale_coeff())));
+    build_opts.emplace(("-DBETA=" + support::cpp11::to_string(norm_info.beta())));
+    build_opts.emplace(("-DKAPPA=" + support::cpp11::to_string(norm_info.kappa())));
+    build_opts.emplace(("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration)));
+
     // Create kernel
     std::string kernel_name = (norm_info.type() == NormType::IN_MAP_1D) ? "normalization_layer_in_map_1D" : "normalization_layer_cross_map";
     _kernel                 = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts));
 
     // Set kernel static arguments
     unsigned int idx = 3 * num_arguments_per_3D_tensor(); // Skip the input and output parameters
-    _kernel.setArg<cl_float>(idx++, norm_info.scale_coeff());
-    _kernel.setArg<cl_float>(idx++, norm_info.beta());
-    _kernel.setArg<cl_float>(idx++, norm_info.kappa());
     _kernel.setArg<cl_uint>(idx++, norm_info.norm_size() / 2);
 
     // Configure kernel window
-    const unsigned int num_elems_processed_per_iteration = (_is_in_map) ? 4 : 1;
-    const unsigned int num_elems_read_per_iteration      = num_elems_processed_per_iteration + 2 * (norm_info.norm_size() / 2);
-
     Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
 
     AccessWindowHorizontal input_access(input->info(), -_border_size.left, num_elems_read_per_iteration);
diff --git a/src/core/NEON/kernels/NENormalizationLayerKernel.cpp b/src/core/NEON/kernels/NENormalizationLayerKernel.cpp
index 085d412558..0e15244f0e 100644
--- a/src/core/NEON/kernels/NENormalizationLayerKernel.cpp
+++ b/src/core/NEON/kernels/NENormalizationLayerKernel.cpp
@@ -51,12 +51,15 @@ void NENormalizationLayerKernel::configure(const ITensor *input, const ITensor *
     // Output tensor auto initialization if not yet initialized
     auto_init_if_empty(*output->info(), input->info()->tensor_shape(), 1, input->info()->data_type(), input->info()->fixed_point_position());
     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, input_squared, output);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, input_squared, output);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, input_squared, output);
     ARM_COMPUTE_ERROR_ON_MSG(!(norm_info.norm_size() % 2), "Normalization size should be odd");
-    ARM_COMPUTE_ERROR_ON_VALUE_NOT_REPRESENTABLE_IN_FIXED_POINT(norm_info.beta(), input);
-    ARM_COMPUTE_ERROR_ON_VALUE_NOT_REPRESENTABLE_IN_FIXED_POINT(norm_info.kappa(), input);
-    ARM_COMPUTE_ERROR_ON_VALUE_NOT_REPRESENTABLE_IN_FIXED_POINT(norm_info.scale_coeff(), input);
+    if(is_data_type_fixed_point(input->info()->data_type()))
+    {
+        ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, input_squared, output);
+        ARM_COMPUTE_ERROR_ON_VALUE_NOT_REPRESENTABLE_IN_FIXED_POINT(norm_info.beta(), input);
+        ARM_COMPUTE_ERROR_ON_VALUE_NOT_REPRESENTABLE_IN_FIXED_POINT(norm_info.kappa(), input);
+        ARM_COMPUTE_ERROR_ON_VALUE_NOT_REPRESENTABLE_IN_FIXED_POINT(norm_info.scale_coeff(), input);
+    }
 
     const unsigned int border_width = (norm_info.type() == NormType::CROSS_MAP) ? 0 : std::min(norm_info.norm_size() / 2, 3U);
 
diff --git a/src/runtime/CL/functions/CLNormalizationLayer.cpp b/src/runtime/CL/functions/CLNormalizationLayer.cpp
index 2d89ebd676..69cef334e8 100644
--- a/src/runtime/CL/functions/CLNormalizationLayer.cpp
+++ b/src/runtime/CL/functions/CLNormalizationLayer.cpp
@@ -41,7 +41,8 @@ void CLNormalizationLayer::configure(const ICLTensor *input, ICLTensor *output,
 {
     ARM_COMPUTE_ERROR_ON(input == nullptr);
 
-    _squared_input.allocator()->init(TensorInfo(input->info()->tensor_shape(), 1, input->info()->data_type()));
+    TensorInfo tensor_info(input->info()->tensor_shape(), 1, input->info()->data_type(), input->info()->fixed_point_position());
+    _squared_input.allocator()->init(tensor_info);
 
     _norm_kernel.configure(input, &_squared_input, output, norm_info);
     _multiply_kernel.configure(input, input, &_squared_input, 1.0f, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);