diff --git a/ARM.CMSIS-NN.pdsc b/ARM.CMSIS-NN.pdsc
index d5240368..5dbf5495 100644
--- a/ARM.CMSIS-NN.pdsc
+++ b/ARM.CMSIS-NN.pdsc
@@ -38,6 +38,7 @@
+
@@ -53,7 +54,6 @@
-
@@ -69,13 +69,13 @@
-
+
@@ -101,9 +101,9 @@
+
-
@@ -112,6 +112,7 @@
+
diff --git a/Include/arm_nnfunctions.h b/Include/arm_nnfunctions.h
index ab4c0eca..e4604c17 100644
--- a/Include/arm_nnfunctions.h
+++ b/Include/arm_nnfunctions.h
@@ -21,8 +21,8 @@
* Title: arm_nnfunctions.h
* Description: Public header file for CMSIS NN Library
*
- * $Date: 20 February 2024
- * $Revision: V.14.0.0
+ * $Date: 11 March 2024
+ * $Revision: V.15.0.0
*
* Target : Arm(R) M-Profile Architecture
@@ -375,8 +375,10 @@ arm_cmsis_nn_status arm_convolve_s4(const cmsis_nn_context *ctx,
* @param[in] bias_data Optional bias data pointer. Data type: int32
* @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT]
* @param[out] output_data Output data pointer. Data type: int8
-
- * @return The function returns ARM_CMSIS_NN_SUCCESS
+ *
+ * @return The function returns ARM_CMSIS_NN_SUCCESS if successful or
+ * ARM_CMSIS_NN_ARG_ERROR if incorrect arguments or
+ * ARM_CMSIS_NN_NO_IMPL_ERROR
*
* @details
* 1. Supported framework: TensorFlow Lite micro
@@ -522,8 +524,10 @@ int32_t arm_transpose_conv_s8_get_buffer_size_mve(const cmsis_nn_dims *input_dim
* @param[in] bias_data Optional bias data pointer. Data type: int64
* @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT]
* @param[out] output_data Output data pointer. Data type: int16
-
- * @return The function returns ARM_CMSIS_NN_SUCCESS
+ *
+ * @return The function returns ARM_CMSIS_NN_SUCCESS if successful or
+ * ARM_CMSIS_NN_ARG_ERROR if incorrect arguments or
+ * ARM_CMSIS_NN_NO_IMPL_ERROR
*
* @details
* 1. Supported framework: TensorFlow Lite micro
@@ -541,47 +545,6 @@ arm_cmsis_nn_status arm_convolve_s16(const cmsis_nn_context *ctx,
const int64_t *bias_data,
const cmsis_nn_dims *output_dims,
int16_t *output_data);
-/**
- * @brief Optimized s16 convolution function
- * @param[in, out] ctx Function context that contains the additional buffer if required by the function.
- * arm_convolve_fast_s16_get_buffer_size will return the buffer_size if required.
- * The caller is expected to clear the buffer, if applicable, for security reasons.
- * @param[in] conv_params Convolution parameters (e.g. strides, dilations, pads,...).
- * conv_params->input_offset : Not used
- * conv_params->output_offset : Not used
- * @param[in] quant_params Per-channel quantization info.
- * It contains the multiplier and shift values to be applied to each output channel
- * @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
- * @param[in] input_data Input (activation) data pointer. Data type: int16
- * @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the
- * spatial filter dimensions. (filter_dims->w * filter_dims->h * input_dims->c) must not
- exceed 512
- * @param[in] filter_data Filter data pointer. Data type: int8
- * @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT]
- * @param[in] bias_data Optional bias data pointer. Data type: int64
- * @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT]
- * @param[out] output_data Output data pointer. Data type: int16
-
- * @return The function returns ARM_CMSIS_NN_SUCCESS
- *
- * @details
- * 1. Supported framework: TensorFlow Lite micro
- * 2. Additional memory is required for optimization. Refer to argument 'ctx' for details.
- * 3. Implementation supports kernel volumes (filter width * filter height * input channels) < 512.
- *
- */
-
-arm_cmsis_nn_status arm_convolve_fast_s16(const cmsis_nn_context *ctx,
- const cmsis_nn_conv_params *conv_params,
- const cmsis_nn_per_channel_quant_params *quant_params,
- const cmsis_nn_dims *input_dims,
- const int16_t *input_data,
- const cmsis_nn_dims *filter_dims,
- const int8_t *filter_data,
- const cmsis_nn_dims *bias_dims,
- const int64_t *bias_data,
- const cmsis_nn_dims *output_dims,
- int16_t *output_data);
/**
* @brief Get the required buffer size for s16 convolution function
@@ -594,17 +557,6 @@ arm_cmsis_nn_status arm_convolve_fast_s16(const cmsis_nn_context *ctx,
*/
int32_t arm_convolve_s16_get_buffer_size(const cmsis_nn_dims *input_dims, const cmsis_nn_dims *filter_dims);
-/**
- * @brief Get the required buffer size for fast s16 convolution function
- *
- * @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
- * @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK
- * are the spatial filter dimensions
- * @return The function returns required buffer size(bytes)
- *
- */
-int32_t arm_convolve_fast_s16_get_buffer_size(const cmsis_nn_dims *input_dims, const cmsis_nn_dims *filter_dims);
-
/**
* @brief Fast s4 version for 1x1 convolution (non-square shape)
*
diff --git a/Include/arm_nnsupportfunctions.h b/Include/arm_nnsupportfunctions.h
index f2b7f29b..70c57022 100644
--- a/Include/arm_nnsupportfunctions.h
+++ b/Include/arm_nnsupportfunctions.h
@@ -21,8 +21,8 @@
* Title: arm_nnsupportfunctions.h
* Description: Public header file of support functions for CMSIS NN Library
*
- * $Date: 14 February 2024
- * $Revision: V.19.0.0
+ * $Date: 22 March 2024
+ * $Revision: V.20.0.0
*
* Target : Arm(R) M-Profile Architecture
* -------------------------------------------------------------------- */
@@ -63,6 +63,10 @@ extern "C" {
// will result in lower scratch buffer usage.
#define CH_IN_BLOCK_MVE (124)
+// For input of int16 when number of columns are above this limit int64 accumulation is needed
+// to not loose precision.
+#define MAX_COL_COUNT (512)
+
/**
* @brief definition to pack four 8 bit values.
*/
@@ -281,8 +285,8 @@ int16_t *arm_nn_mat_mult_kernel_s16(const int8_t *input_a,
const int32_t output_ch,
const int32_t *out_shift,
const int32_t *out_mult,
- const int16_t activation_min,
- const int16_t activation_max,
+ const int32_t activation_min,
+ const int32_t activation_max,
const int32_t num_col_a,
const int64_t *const output_bias,
int16_t *out_0);
@@ -445,6 +449,48 @@ arm_cmsis_nn_status arm_nn_mat_mult_nt_t_s8(const int8_t *lhs,
const int32_t row_address_offset,
const int32_t lhs_cols_offset);
+/**
+ * @brief General Matrix-multiplication function with per-channel requantization and int16 input (LHS) and output.
+ * This function assumes:
+ * - LHS input matrix NOT transposed (nt)
+ * - RHS input matrix transposed (t)
+ *
+ * @note This operation also performs the broadcast bias addition before the requantization
+ *
+ * @param[in] lhs Pointer to the LHS input matrix
+ * @param[in] rhs Pointer to the RHS input matrix
+ * @param[in] bias Pointer to the bias vector. The length of this vector is equal to the number of
+ * output columns (or RHS input rows)
+ * @param[out] dst Pointer to the output matrix with "m" rows and "n" columns
+ * @param[in] dst_multipliers Pointer to the multipliers vector needed for the per-channel requantization.
+ * The length of this vector is equal to the number of output columns (or RHS input
+ * rows)
+ * @param[in] dst_shifts Pointer to the shifts vector needed for the per-channel requantization. The length
+ * of this vector is equal to the number of output columns (or RHS input rows)
+ * @param[in] lhs_rows Number of LHS input rows
+ * @param[in] rhs_rows Number of RHS input rows
+ * @param[in] rhs_cols Number of LHS/RHS input columns
+ * @param[in] activation_min Minimum value to clamp down the output. Range : int16
+ * @param[in] activation_max Maximum value to clamp up the output. Range : int16
+ *
+ * @details MVE implementation only.
+ *
+ * @return The function returns ARM_CMSIS_NN_SUCCESS or
+ * ARM_CMSIS_NN_NO_IMPL_ERROR if not for MVE
+ *
+ */
+arm_cmsis_nn_status arm_nn_mat_mult_nt_t_s16(const int16_t *lhs,
+ const int8_t *rhs,
+ const int64_t *bias,
+ int16_t *dst,
+ const int32_t *dst_multipliers,
+ const int32_t *dst_shifts,
+ const int32_t lhs_rows,
+ const int32_t rhs_rows,
+ const int32_t rhs_cols,
+ const int32_t activation_min,
+ const int32_t activation_max);
+
/**
* @brief General Matrix-multiplication function with int8 input and int32 output.
* This function assumes:
diff --git a/Source/ConvolutionFunctions/arm_convolve_fast_s16.c b/Source/ConvolutionFunctions/arm_convolve_fast_s16.c
deleted file mode 100644
index 5348c3f7..00000000
--- a/Source/ConvolutionFunctions/arm_convolve_fast_s16.c
+++ /dev/null
@@ -1,232 +0,0 @@
-/*
- * SPDX-FileCopyrightText: Copyright 2010-2023 Arm Limited and/or its affiliates
- *
- * SPDX-License-Identifier: Apache-2.0
- *
- * Licensed under the Apache License, Version 2.0 (the License); you may
- * not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS, WITHOUT
- * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/* ----------------------------------------------------------------------
- * Project: CMSIS NN Library
- * Title: arm_convolve_fast_s16.c
- * Description: Optimized s16 version of convolution.
- *
- * $Date: 23 March 2023
- * $Revision: V.2.3.0
- *
- * Target : Arm(R) M-Profile Architecture
- *
- * -------------------------------------------------------------------- */
-
-#include "arm_nnfunctions.h"
-#include "arm_nnsupportfunctions.h"
-
-/**
- * @ingroup Public
- */
-
-/**
- * @addtogroup NNConv
- * @{
- */
-
-/*
- * Basic s16 convolution function.
- *
- * Refer header file for details. Optimal use case for the DSP/MVE implementation is when input and output channels
- * are multiples of 4 or atleast greater than 4.
- *
- */
-
-arm_cmsis_nn_status arm_convolve_fast_s16(const cmsis_nn_context *ctx,
- const cmsis_nn_conv_params *conv_params,
- const cmsis_nn_per_channel_quant_params *quant_params,
- const cmsis_nn_dims *input_dims,
- const int16_t *input_data,
- const cmsis_nn_dims *filter_dims,
- const int8_t *filter_data,
- const cmsis_nn_dims *bias_dims,
- const int64_t *bias_data,
- const cmsis_nn_dims *output_dims,
- int16_t *output_data)
-{
- (void)bias_dims;
- if (filter_dims->w * filter_dims->h * input_dims->c >= 512)
- {
- return ARM_CMSIS_NN_ARG_ERROR;
- }
-
- if (ctx->buf == NULL && arm_convolve_s8_get_buffer_size(input_dims, filter_dims) > 0)
- {
- return ARM_CMSIS_NN_ARG_ERROR;
- }
- int16_t *buffer_a = (int16_t *)ctx->buf;
-
- const int32_t input_batches = input_dims->n;
- const int32_t input_x = input_dims->w;
- const int32_t input_y = input_dims->h;
- const int32_t input_ch = input_dims->c;
- const int32_t kernel_x = filter_dims->w;
- const int32_t kernel_y = filter_dims->h;
- const int32_t output_x = output_dims->w;
- const int32_t output_y = output_dims->h;
- const int32_t output_ch = output_dims->c;
- const int32_t rhs_cols = input_ch * kernel_y * kernel_x;
-
- const int32_t pad_x = conv_params->padding.w;
- const int32_t pad_y = conv_params->padding.h;
- const int32_t stride_x = conv_params->stride.w;
- const int32_t stride_y = conv_params->stride.h;
-
- const int16_t out_activation_min = conv_params->activation.min;
- const int16_t out_activation_max = conv_params->activation.max;
- int32_t *output_mult = quant_params->multiplier;
- int32_t *output_shift = quant_params->shift;
-
- for (int i_batch = 0; i_batch < input_batches; i_batch++)
- {
-#if defined(ARM_MATH_DSP) && !defined(ARM_MATH_MVEI)
- /* Generate two columns from the input tensor a GEMM computation */
- int16_t *two_column_buf = buffer_a;
- int16_t *out = output_data;
- /* This part implements the im2col function */
- for (int32_t i_out_y = 0; i_out_y < output_y; i_out_y++)
- {
- for (int32_t i_out_x = 0; i_out_x < output_x; i_out_x++)
- {
- for (int32_t i_ker_y = i_out_y * stride_y - pad_y; i_ker_y < i_out_y * stride_y - pad_y + kernel_y;
- i_ker_y++)
- {
- for (int32_t i_ker_x = i_out_x * stride_x - pad_x; i_ker_x < i_out_x * stride_x - pad_x + kernel_x;
- i_ker_x++)
- {
- if (i_ker_y < 0 || i_ker_y >= input_y || i_ker_x < 0 || i_ker_x >= input_x)
- {
- /* Filling 0 for out-of-bound paddings */
- arm_memset_s8((int8_t *)two_column_buf, 0, sizeof(int16_t) * input_ch);
- }
- else
- {
- arm_memcpy_s8((int8_t *)two_column_buf,
- (const int8_t *)(input_data + (i_ker_y * input_x + i_ker_x) * input_ch),
- input_ch * sizeof(int16_t));
- }
- two_column_buf += input_ch;
- }
- }
- /* Computation is filed for every 2 columns */
- if (two_column_buf == buffer_a + 2 * rhs_cols)
- {
- out = arm_nn_mat_mult_kernel_s16(filter_data,
- buffer_a,
- output_ch,
- output_shift,
- output_mult,
- out_activation_min,
- out_activation_max,
- rhs_cols,
- bias_data,
- out);
-
- /* Counter reset */
- two_column_buf = buffer_a;
- }
- }
- }
-
- /* Left-over because odd number of output pixels */
- if (two_column_buf != buffer_a)
- {
- const int8_t *ker_a = filter_data;
- int i;
-
- for (i = 0; i < output_ch; i++)
- {
- /* Init the accumulator*/
- int32_t sum = 0;
-
- /* Point to the beginning of the im2col buffer where the input is available as a rearranged column */
- const int16_t *ip_as_col = buffer_a;
-
- /* 4 multiply and accumulates are done in one loop. */
- int32_t col_count = rhs_cols >> 2;
-
- while (col_count)
- {
- int32_t ker_a1, ker_a2;
- int32_t ip_b1, ip_b2;
-
- ker_a = read_and_pad(ker_a, &ker_a1, &ker_a2);
-
- ip_b1 = arm_nn_read_q15x2_ia(&ip_as_col);
- sum = SMLAD(ker_a1, ip_b1, sum);
- ip_b2 = arm_nn_read_q15x2_ia(&ip_as_col);
- sum = SMLAD(ker_a2, ip_b2, sum);
-
- col_count--;
- }
- /* Handle left over mac */
- col_count = rhs_cols & 0x3;
- while (col_count)
- {
- int8_t ker_a1 = *ker_a++;
- int16_t ip_b1 = *ip_as_col++;
- sum += ker_a1 * ip_b1;
- col_count--;
- }
- if (bias_data)
- {
- int32_t reduced_multiplier = REDUCE_MULTIPLIER(output_mult[i]);
- int64_t acc_64 = sum + bias_data[i];
- sum = arm_nn_requantize_s64(acc_64, reduced_multiplier, output_shift[i]);
- }
- else
- {
- sum = arm_nn_requantize(sum, output_mult[i], output_shift[i]);
- }
- sum = MAX(sum, out_activation_min);
- sum = MIN(sum, out_activation_max);
- *out++ = (int16_t)sum;
- }
- }
-#else
- (void)input_data;
- (void)output_data;
- (void)bias_data;
- (void)filter_data;
- (void)buffer_a;
- (void)kernel_x;
- (void)kernel_y;
- (void)pad_x;
- (void)pad_y;
- (void)stride_x;
- (void)stride_y;
- (void)out_activation_min;
- (void)out_activation_max;
- (void)output_mult;
- (void)output_shift;
- (void)rhs_cols;
- return ARM_CMSIS_NN_ARG_ERROR;
-#endif
- /* Advance to the next batch */
- input_data += (input_x * input_y * input_ch);
- output_data += (output_x * output_y * output_ch);
- }
-
- /* Return to application */
- return ARM_CMSIS_NN_SUCCESS;
-}
-
-/**
- * @} end of NNConv group
- */
diff --git a/Source/ConvolutionFunctions/arm_convolve_get_buffer_sizes_s16.c b/Source/ConvolutionFunctions/arm_convolve_get_buffer_sizes_s16.c
index 56af0e35..ad3291cd 100644
--- a/Source/ConvolutionFunctions/arm_convolve_get_buffer_sizes_s16.c
+++ b/Source/ConvolutionFunctions/arm_convolve_get_buffer_sizes_s16.c
@@ -1,5 +1,5 @@
/*
- * SPDX-FileCopyrightText: Copyright 2023 Arm Limited and/or its affiliates
+ * SPDX-FileCopyrightText: Copyright 2023-2024 Arm Limited and/or its affiliates
*
* SPDX-License-Identifier: Apache-2.0
*
@@ -21,8 +21,8 @@
* Title: arm_convolve_get_buffer_sizes_s16.c
* Description: Collection of get buffer size functions for the various s16 convolution layer functions.
*
- * $Date: 30 January 2023
- * $Revision: V.1.0.0
+ * $Date: 20 March 2024
+ * $Revision: V.2.0.0
*
* Target : Arm(R) M-Profile Architecture
*
@@ -40,30 +40,26 @@
* @{
*/
-__STATIC_INLINE int32_t arm_convolve_fast_s16_get_buffer_size_dsp(const cmsis_nn_dims *input_dims,
- const cmsis_nn_dims *filter_dims)
+__STATIC_INLINE int32_t arm_convolve_s16_get_buffer_size_mve(const cmsis_nn_dims *input_dims,
+ const cmsis_nn_dims *filter_dims)
{
- return (2 * input_dims->c * filter_dims->w * filter_dims->h) * (int32_t)sizeof(int16_t);
+ int32_t col_length = input_dims->c * filter_dims->w * filter_dims->h;
+ // Get number of complete lanes with int16 elements (multiple of 8) for given col_length. This is dependent on
+ // implementation of arm_nn_mat_mult_nt_t_s16
+ col_length = (col_length + 7) / 8;
+ // 4 -> number of im2col buffers, 8 -> 8 elements per Q register
+ return 4 * col_length * 8 * (int32_t)sizeof(int16_t);
}
-int32_t arm_convolve_fast_s16_get_buffer_size(const cmsis_nn_dims *input_dims, const cmsis_nn_dims *filter_dims)
+int32_t arm_convolve_s16_get_buffer_size(const cmsis_nn_dims *input_dims, const cmsis_nn_dims *filter_dims)
{
-#if defined(ARM_MATH_DSP) && !defined(ARM_MATH_MVEI)
- return arm_convolve_fast_s16_get_buffer_size_dsp(input_dims, filter_dims);
+#if defined(ARM_MATH_MVEI)
+ return arm_convolve_s16_get_buffer_size_mve(input_dims, filter_dims);
#else
- (void)input_dims;
- (void)filter_dims;
- return 0;
+ return (2 * input_dims->c * filter_dims->w * filter_dims->h) * (int32_t)sizeof(int16_t);
#endif
}
-int32_t arm_convolve_s16_get_buffer_size(const cmsis_nn_dims *input_dims, const cmsis_nn_dims *filter_dims)
-{
- (void)input_dims;
- (void)filter_dims;
- return 0;
-}
-
/*
* Get the required buffer size for arm_convolve_wrapper_s16. This is the recommended function convolve wrapper s16
* function.
@@ -76,16 +72,10 @@ int32_t arm_convolve_wrapper_s16_get_buffer_size(const cmsis_nn_conv_params *con
const cmsis_nn_dims *filter_dims,
const cmsis_nn_dims *output_dims)
{
-
-#if defined(ARM_MATH_DSP) && !defined(ARM_MATH_MVEI)
- return arm_convolve_wrapper_s16_get_buffer_size_dsp(conv_params, input_dims, filter_dims, output_dims);
-#else
(void)conv_params;
(void)output_dims;
- // MVE and scalar implementation have same buffer requirements
return arm_convolve_s16_get_buffer_size(input_dims, filter_dims);
-#endif
}
int32_t arm_convolve_wrapper_s16_get_buffer_size_dsp(const cmsis_nn_conv_params *conv_params,
@@ -93,18 +83,7 @@ int32_t arm_convolve_wrapper_s16_get_buffer_size_dsp(const cmsis_nn_conv_params
const cmsis_nn_dims *filter_dims,
const cmsis_nn_dims *output_dims)
{
- (void)output_dims;
-
- if (filter_dims->w * filter_dims->h * input_dims->c < 512 &&
- (conv_params->dilation.w == 1 && conv_params->dilation.h == 1))
- {
- return arm_convolve_fast_s16_get_buffer_size_dsp(input_dims, filter_dims);
- }
- else
- {
-
- return arm_convolve_s16_get_buffer_size(input_dims, filter_dims);
- }
+ return arm_convolve_wrapper_s16_get_buffer_size(conv_params, input_dims, filter_dims, output_dims);
}
int32_t arm_convolve_wrapper_s16_get_buffer_size_mve(const cmsis_nn_conv_params *conv_params,
@@ -112,7 +91,10 @@ int32_t arm_convolve_wrapper_s16_get_buffer_size_mve(const cmsis_nn_conv_params
const cmsis_nn_dims *filter_dims,
const cmsis_nn_dims *output_dims)
{
- return arm_convolve_wrapper_s16_get_buffer_size(conv_params, input_dims, filter_dims, output_dims);
+ (void)conv_params;
+ (void)output_dims;
+
+ return arm_convolve_s16_get_buffer_size_mve(input_dims, filter_dims);
}
/**
diff --git a/Source/ConvolutionFunctions/arm_convolve_get_buffer_sizes_s8.c b/Source/ConvolutionFunctions/arm_convolve_get_buffer_sizes_s8.c
index fc48198d..dd09b4ed 100644
--- a/Source/ConvolutionFunctions/arm_convolve_get_buffer_sizes_s8.c
+++ b/Source/ConvolutionFunctions/arm_convolve_get_buffer_sizes_s8.c
@@ -21,8 +21,8 @@
* Title: arm_convolve_get_buffer_sizes_s8.c
* Description: Collection of get buffer size functions for the various s8 convolution layer functions.
*
- * $Date: 14 March 2024
- * $Revision: V.2.1.0
+ * $Date: 28 March 2024
+ * $Revision: V.2.1.1
*
* Target : Arm(R) M-Profile Architecture
*
@@ -45,11 +45,11 @@ __STATIC_INLINE int32_t arm_convolve_s8_get_buffer_size_mve(const cmsis_nn_dims
const cmsis_nn_dims *filter_dims)
{
int32_t col_length = input_dims->c * filter_dims->w * filter_dims->h;
- // Get number of complete int16 lanes(multiple of 8) for given col_length. This is dependent on
+ // Get number of complete lanes with int8 elements (multiple of 16) for given col_length. This is dependent on
// implementation of arm_nn_mat_mult_nt_t_s8
- col_length = (col_length + 7) / 8;
- // 4 -> number of im2col buffers, 8 -> 8 elements per Q register
- return 4 * col_length * 8 * (int32_t)sizeof(int8_t);
+ col_length = (col_length + 15) / 16;
+ // 4 -> number of im2col buffers, 16 -> 16 elements per Q register
+ return 4 * col_length * 16 * (int32_t)sizeof(int8_t);
}
__STATIC_INLINE int32_t arm_convolve_1_x_n_s8_get_buffer_size_mve(const cmsis_nn_conv_params *conv_params,
diff --git a/Source/ConvolutionFunctions/arm_convolve_s16.c b/Source/ConvolutionFunctions/arm_convolve_s16.c
index 75d30f16..08e21663 100644
--- a/Source/ConvolutionFunctions/arm_convolve_s16.c
+++ b/Source/ConvolutionFunctions/arm_convolve_s16.c
@@ -1,5 +1,5 @@
/*
- * SPDX-FileCopyrightText: Copyright 2010-2023 Arm Limited and/or its affiliates
+ * SPDX-FileCopyrightText: Copyright 2010-2024 Arm Limited and/or its affiliates
*
* SPDX-License-Identifier: Apache-2.0
*
@@ -19,10 +19,10 @@
/* ----------------------------------------------------------------------
* Project: CMSIS NN Library
* Title: arm_convolve_s16.c
- * Description: s16 version of convolution using symmetric quantization.
+ * Description: s16 version of convolution.
*
- * $Date: 30 January 2023
- * $Revision: V.2.1.0
+ * $Date: 22 March 2024
+ * $Revision: V.3.0.0
*
* Target : Arm(R) M-Profile Architecture
*
@@ -47,7 +47,6 @@
* are multiples of 4 or atleast greater than 4.
*
*/
-
arm_cmsis_nn_status arm_convolve_s16(const cmsis_nn_context *ctx,
const cmsis_nn_conv_params *conv_params,
const cmsis_nn_per_channel_quant_params *quant_params,
@@ -61,7 +60,12 @@ arm_cmsis_nn_status arm_convolve_s16(const cmsis_nn_context *ctx,
int16_t *output_data)
{
(void)bias_dims;
- (void)ctx;
+
+ if (ctx->buf == NULL)
+ {
+ return ARM_CMSIS_NN_ARG_ERROR;
+ }
+ int16_t *buffer_a = (int16_t *)ctx->buf;
const int32_t input_batches = input_dims->n;
const int32_t input_x = input_dims->w;
@@ -72,69 +76,190 @@ arm_cmsis_nn_status arm_convolve_s16(const cmsis_nn_context *ctx,
const int32_t output_x = output_dims->w;
const int32_t output_y = output_dims->h;
const int32_t output_ch = output_dims->c;
+ const int32_t rhs_cols = input_ch * kernel_y * kernel_x;
+ const int32_t dilation_x = conv_params->dilation.w;
+ const int32_t dilation_y = conv_params->dilation.h;
const int32_t pad_x = conv_params->padding.w;
const int32_t pad_y = conv_params->padding.h;
const int32_t stride_x = conv_params->stride.w;
const int32_t stride_y = conv_params->stride.h;
- const int32_t dilation_x = conv_params->dilation.w;
- const int32_t dilation_y = conv_params->dilation.h;
const int32_t out_activation_min = conv_params->activation.min;
const int32_t out_activation_max = conv_params->activation.max;
int32_t *output_mult = quant_params->multiplier;
int32_t *output_shift = quant_params->shift;
+#if defined(ARM_MATH_MVEI)
+ const int32_t rhs_rows = output_dims->c;
+#endif
+
for (int i_batch = 0; i_batch < input_batches; i_batch++)
{
- /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
- for (int32_t i_out_ch = 0; i_out_ch < output_ch; i_out_ch++)
- {
- const int32_t reduced_multiplier = REDUCE_MULTIPLIER(output_mult[i_out_ch]);
+ int16_t *im2col = buffer_a;
+ int16_t *out = output_data;
- for (int32_t base_idx_y = -pad_y, i_out_y = 0; i_out_y < output_y; base_idx_y += stride_y, i_out_y++)
+ int32_t lhs_rows = 0;
+
+ /* This part implements the im2col function */
+ for (int32_t i_out_y = 0; i_out_y < output_y; i_out_y++)
+ {
+ for (int32_t i_out_x = 0; i_out_x < output_x; i_out_x++)
{
- for (int32_t base_idx_x = -pad_x, i_out_x = 0; i_out_x < output_x; base_idx_x += stride_x, i_out_x++)
+ const int32_t base_idx_x = stride_x * i_out_x - pad_x;
+ const int32_t base_idx_y = stride_y * i_out_y - pad_y;
+
+ for (int32_t i_ker_y = 0; i_ker_y < kernel_y; i_ker_y++)
{
- int64_t conv_out_acc = 0;
-
- const int32_t start_y_max = (-base_idx_y + dilation_y - 1) / dilation_y;
- const int32_t ker_y_start = MAX(0, start_y_max);
- const int32_t start_x_max = (-base_idx_x + dilation_x - 1) / dilation_x;
- const int32_t ker_x_start = MAX(0, start_x_max);
- const int32_t end_min_y = (input_y - base_idx_y + dilation_y - 1) / dilation_y;
- const int32_t ker_y_end = MIN(kernel_y, end_min_y);
- const int32_t end_min_x = (input_x - base_idx_x + dilation_x - 1) / dilation_x;
- const int32_t ker_x_end = MIN(kernel_x, end_min_x);
-
- for (int32_t i_ker_y = ker_y_start; i_ker_y < ker_y_end; i_ker_y++)
+ for (int32_t i_ker_x = 0; i_ker_x < kernel_x; i_ker_x++)
{
- for (int32_t i_ker_x = ker_x_start; i_ker_x < ker_x_end; i_ker_x++)
+ const int32_t k_y = base_idx_y + dilation_y * i_ker_y;
+ const int32_t k_x = base_idx_x + dilation_x * i_ker_x;
+
+ if (k_y < 0 || k_y >= input_y || k_x < 0 || k_x >= input_x)
+ {
+ /* Filling 0 for out-of-bound paddings */
+ arm_memset_s8((int8_t *)im2col, 0, sizeof(int16_t) * (uint32_t)input_ch);
+ }
+ else
{
- const int32_t in_row = base_idx_y + dilation_y * i_ker_y;
- const int32_t in_col = base_idx_x + dilation_x * i_ker_x;
-
- for (int32_t i_input_ch = 0; i_input_ch < input_ch; i_input_ch++)
- {
- conv_out_acc += input_data[(in_row * input_x + in_col) * input_ch + i_input_ch] *
- filter_data[i_out_ch * input_ch * kernel_y * kernel_x +
- (i_ker_y * kernel_x + i_ker_x) * input_ch + i_input_ch];
- }
+ arm_memcpy_s8((int8_t *)im2col,
+ (const int8_t *)(input_data + (k_y * input_x + k_x) * input_ch),
+ (uint32_t)input_ch * sizeof(int16_t));
}
+ im2col += input_ch;
}
+ }
- if (bias_data)
- {
- conv_out_acc += bias_data[i_out_ch];
- }
+ lhs_rows++;
+#if defined(ARM_MATH_MVEI)
+ /* Computation is filed for every 4 columns */
+ if (lhs_rows == 4)
+ {
+ arm_nn_mat_mult_nt_t_s16(buffer_a,
+ filter_data,
+ bias_data,
+ out,
+ output_mult,
+ output_shift,
+ lhs_rows,
+ rhs_rows,
+ rhs_cols,
+ out_activation_min,
+ out_activation_max);
+ out += lhs_rows * output_ch;
+
+ lhs_rows = 0;
+ im2col = buffer_a;
+ }
+#else
+ /* Computation is filed for every 2 columns */
+ if (lhs_rows == 2)
+ {
+ out = arm_nn_mat_mult_kernel_s16(filter_data,
+ buffer_a,
+ output_ch,
+ output_shift,
+ output_mult,
+ out_activation_min,
+ out_activation_max,
+ rhs_cols,
+ bias_data,
+ out);
- int32_t conv_out = arm_nn_requantize_s64(conv_out_acc, reduced_multiplier, output_shift[i_out_ch]);
- conv_out = MAX(conv_out, out_activation_min);
- conv_out = MIN(conv_out, out_activation_max);
- output_data[i_out_ch + (i_out_y * output_x + i_out_x) * output_ch] = (int16_t)conv_out;
+ /* Counter reset */
+ im2col = buffer_a;
+ lhs_rows = 0;
}
+#endif
}
+
+ if (out == NULL)
+ {
+ return ARM_CMSIS_NN_NO_IMPL_ERROR;
+ }
+ }
+
+ /* Handle left over columns */
+ if (lhs_rows != 0)
+ {
+#if defined(ARM_MATH_MVEI)
+ arm_nn_mat_mult_nt_t_s16(buffer_a,
+ filter_data,
+ bias_data,
+ out,
+ output_mult,
+ output_shift,
+ lhs_rows,
+ rhs_rows,
+ rhs_cols,
+ out_activation_min,
+ out_activation_max);
+ out += lhs_rows * rhs_rows;
+ lhs_rows = 0;
+ im2col = buffer_a;
+#else // #if defined(ARM_MATH_MVEI)
+
+ const int8_t *ker_a = filter_data;
+ int i;
+
+ for (i = 0; i < output_ch; i++)
+ {
+ /* Init the accumulator*/
+ int32_t sum = 0;
+
+ /* Point to the beginning of the im2col buffer where the input is available as a rearranged column */
+ const int16_t *ip_as_col = buffer_a;
+
+ #if defined(ARM_MATH_DSP)
+ /* 4 multiply and accumulates are done in one loop. */
+ int32_t col_count = rhs_cols >> 2;
+
+ while (col_count)
+ {
+ int32_t ker_a1, ker_a2;
+ int32_t ip_b1, ip_b2;
+
+ ker_a = read_and_pad(ker_a, &ker_a1, &ker_a2);
+
+ ip_b1 = arm_nn_read_q15x2_ia(&ip_as_col);
+ sum = SMLAD(ker_a1, ip_b1, sum);
+ ip_b2 = arm_nn_read_q15x2_ia(&ip_as_col);
+ sum = SMLAD(ker_a2, ip_b2, sum);
+
+ col_count--;
+ }
+ /* Handle left over mac */
+ col_count = rhs_cols & 0x3;
+ #else
+ uint16_t col_count = rhs_cols;
+
+ #endif
+ while (col_count)
+ {
+ int8_t ker_a1 = *ker_a++;
+ int16_t ip_b1 = *ip_as_col++;
+ sum += ker_a1 * ip_b1;
+ col_count--;
+ }
+ if (bias_data)
+ {
+ int32_t reduced_multiplier = REDUCE_MULTIPLIER(output_mult[i]);
+ int64_t acc_64 = sum + bias_data[i];
+ sum = arm_nn_requantize_s64(acc_64, reduced_multiplier, output_shift[i]);
+ }
+ else
+ {
+ sum = arm_nn_requantize(sum, output_mult[i], output_shift[i]);
+ }
+ sum = MAX(sum, out_activation_min);
+ sum = MIN(sum, out_activation_max);
+ *out++ = (int16_t)sum;
+ }
+ lhs_rows = 0;
+#endif // #if defined(ARM_MATH_MVEI)
}
+
/* Advance to the next batch */
input_data += (input_x * input_y * input_ch);
output_data += (output_x * output_y * output_ch);
diff --git a/Source/ConvolutionFunctions/arm_convolve_wrapper_s16.c b/Source/ConvolutionFunctions/arm_convolve_wrapper_s16.c
index b05588ed..0d6d8832 100644
--- a/Source/ConvolutionFunctions/arm_convolve_wrapper_s16.c
+++ b/Source/ConvolutionFunctions/arm_convolve_wrapper_s16.c
@@ -1,5 +1,5 @@
/*
- * SPDX-FileCopyrightText: Copyright 2021-2023 Arm Limited and/or its affiliates
+ * SPDX-FileCopyrightText: Copyright 2021-2024 Arm Limited and/or its affiliates
*
* SPDX-License-Identifier: Apache-2.0
*
@@ -22,8 +22,8 @@
* Description: s16 convolution layer wrapper function with the main purpose to call the optimal kernel available in
* cmsis-nn to perform the convolution.
*
- * $Date: 30 January 2023
- * $Revision: V.2.1.0
+ * $Date: 12 March 2024
+ * $Revision: V.2.2.0
*
* Target : Arm(R) M-Profile Architecture
*
@@ -59,37 +59,6 @@ arm_cmsis_nn_status arm_convolve_wrapper_s16(const cmsis_nn_context *ctx,
const cmsis_nn_dims *output_dims,
int16_t *output_data)
{
-#if defined(ARM_MATH_DSP) && !defined(ARM_MATH_MVEI)
- if (filter_dims->w * filter_dims->h * input_dims->c < 512 &&
- (conv_params->dilation.w == 1 && conv_params->dilation.h == 1))
- {
- return arm_convolve_fast_s16(ctx,
- conv_params,
- quant_params,
- input_dims,
- input_data,
- filter_dims,
- filter_data,
- bias_dims,
- bias_data,
- output_dims,
- output_data);
- }
- else
- {
- return arm_convolve_s16(ctx,
- conv_params,
- quant_params,
- input_dims,
- input_data,
- filter_dims,
- filter_data,
- bias_dims,
- bias_data,
- output_dims,
- output_data);
- }
-#else
return arm_convolve_s16(ctx,
conv_params,
quant_params,
@@ -101,7 +70,6 @@ arm_cmsis_nn_status arm_convolve_wrapper_s16(const cmsis_nn_context *ctx,
bias_data,
output_dims,
output_data);
-#endif
}
/**
diff --git a/Source/ConvolutionFunctions/arm_depthwise_conv_fast_s16.c b/Source/ConvolutionFunctions/arm_depthwise_conv_fast_s16.c
index f15578e3..c8f56a27 100644
--- a/Source/ConvolutionFunctions/arm_depthwise_conv_fast_s16.c
+++ b/Source/ConvolutionFunctions/arm_depthwise_conv_fast_s16.c
@@ -1,5 +1,5 @@
/*
- * SPDX-FileCopyrightText: Copyright 2022-2023 Arm Limited and/or its affiliates
+ * SPDX-FileCopyrightText: Copyright 2022-2024 Arm Limited and/or its affiliates
*
* SPDX-License-Identifier: Apache-2.0
*
@@ -22,8 +22,8 @@
* Description: Optimized s16 depthwise separable convolution function for
* channel multiplier of 1.
*
- * $Date: 30 January 2023
- * $Revision: V.1.3.0
+ * $Date: 19 March 2024
+ * $Revision: V.1.4.0
*
* Target : Arm(R) M-Profile Architecture
*
@@ -69,7 +69,7 @@ arm_cmsis_nn_status arm_depthwise_conv_fast_s16(const cmsis_nn_context *ctx,
return ARM_CMSIS_NN_ARG_ERROR;
}
- if (filter_dims->w * filter_dims->h >= 512)
+ if (filter_dims->w * filter_dims->h >= MAX_COL_COUNT)
{
return ARM_CMSIS_NN_ARG_ERROR;
}
diff --git a/Source/NNSupportFunctions/arm_nn_mat_mul_kernel_s16.c b/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_s16.c
similarity index 62%
rename from Source/NNSupportFunctions/arm_nn_mat_mul_kernel_s16.c
rename to Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_s16.c
index b196eb86..9e9c5337 100644
--- a/Source/NNSupportFunctions/arm_nn_mat_mul_kernel_s16.c
+++ b/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_s16.c
@@ -1,5 +1,5 @@
/*
- * SPDX-FileCopyrightText: Copyright 2010-2023 Arm Limited and/or its affiliates
+ * SPDX-FileCopyrightText: Copyright 2010-2024 Arm Limited and/or its affiliates
*
*
* SPDX-License-Identifier: Apache-2.0
@@ -20,10 +20,10 @@
/* ----------------------------------------------------------------------
* Project: CMSIS NN Library
* Title: arm_nn_mat_mult_kernel_s16.c
- * Description: Matrix-multiplication function for convolution
+ * Description: Matrix-multiplication function for 16 bits convolution
*
- * $Date: 5 Janauray 2023
- * $Revision: V.1.2.0
+ * $Date: 22 March 2024
+ * $Revision: V.2.0.0
*
* Target : Arm(R) M-Profile Architecture
* -------------------------------------------------------------------- */
@@ -46,34 +46,35 @@
* Refer header file for details.
*
*/
-
int16_t *arm_nn_mat_mult_kernel_s16(const int8_t *input_a,
const int16_t *input_b,
const int32_t output_ch,
const int32_t *out_shift,
const int32_t *out_mult,
- const int16_t activation_min,
- const int16_t activation_max,
+ const int32_t activation_min,
+ const int32_t activation_max,
const int32_t num_col_a,
const int64_t *const output_bias,
int16_t *out_0)
{
+#if !defined(ARM_MATH_MVEI)
+ const int32_t num_col_a_fast = num_col_a > MAX_COL_COUNT ? MAX_COL_COUNT : num_col_a;
+ const int32_t num_col_a_slow = num_col_a - MAX_COL_COUNT;
-#if defined(ARM_MATH_DSP) && !defined(ARM_MATH_MVEI)
- /* set up the second output pointers */
int16_t *out_1 = out_0 + output_ch;
const int64_t *bias = output_bias;
- uint16_t row_count = output_ch / 2;
+ int32_t row_count = output_ch / 2;
const int8_t *ip_a0 = input_a;
+ int32_t reduced_multiplier;
- /* this loop over rows in A */
+ /* This loop over rows in A */
while (row_count)
{
- /* setup pointers for B */
+ /* Setup pointers for B */
const int16_t *ip_b0 = input_b;
const int16_t *ip_b1 = ip_b0 + num_col_a;
- /* align the second pointer for A */
+ /* Align the second pointer for A */
const int8_t *ip_a1 = ip_a0 + num_col_a;
/* Init accumulator for channel N and N + 1 */
@@ -82,8 +83,15 @@ int16_t *arm_nn_mat_mult_kernel_s16(const int8_t *input_a,
int32_t ch_1_out_0 = 0;
int32_t ch_1_out_1 = 0;
- uint16_t col_count = num_col_a / 4;
- /* accumulate over the vector */
+ int64_t ch_0_out_0_s64 = 0;
+ int64_t ch_0_out_1_s64 = 0;
+ int64_t ch_1_out_0_s64 = 0;
+ int64_t ch_1_out_1_s64 = 0;
+
+ #if defined(ARM_MATH_DSP)
+ uint16_t col_count = num_col_a_fast / 4;
+
+ /* Accumulate over the vector */
while (col_count)
{
int32_t a01, a02, a11, a12;
@@ -107,8 +115,12 @@ int16_t *arm_nn_mat_mult_kernel_s16(const int8_t *input_a,
ch_1_out_1 = SMLAD(a12, b1, ch_1_out_1);
col_count--;
- } /* while over col_count */
- col_count = num_col_a & 0x3;
+ }
+ col_count = num_col_a_fast & 0x3;
+ #else
+ int32_t col_count = num_col_a_fast;
+ #endif
+
while (col_count)
{
int8_t a0 = *ip_a0++;
@@ -121,22 +133,49 @@ int16_t *arm_nn_mat_mult_kernel_s16(const int8_t *input_a,
ch_1_out_0 += a1 * b0;
ch_1_out_1 += a1 * b1;
col_count--;
- } /* while over col_count */
- if (bias)
+ }
+
+ ch_0_out_0_s64 = ch_0_out_0;
+ ch_0_out_1_s64 = ch_0_out_1;
+ ch_1_out_0_s64 = ch_1_out_0;
+ ch_1_out_1_s64 = ch_1_out_1;
+
+ if (num_col_a > MAX_COL_COUNT)
{
- int32_t reduced_multiplier = REDUCE_MULTIPLIER(*out_mult);
- int64_t acc_64 = ch_0_out_0 + *bias;
- ch_0_out_0 = arm_nn_requantize_s64(acc_64, reduced_multiplier, *out_shift);
- acc_64 = ch_0_out_1 + *bias++;
- ch_0_out_1 = arm_nn_requantize_s64(acc_64, reduced_multiplier, *out_shift);
- out_mult++;
+ col_count = num_col_a_slow;
+ while (col_count)
+ {
+ int8_t a0 = *ip_a0++;
+ int16_t b0 = *ip_b0++;
+ int8_t a1 = *ip_a1++;
+ int16_t b1 = *ip_b1++;
+
+ ch_0_out_0_s64 += a0 * b0;
+ ch_0_out_1_s64 += a0 * b1;
+ ch_1_out_0_s64 += a1 * b0;
+ ch_1_out_1_s64 += a1 * b1;
+ col_count--;
+ }
}
- else
+
+ if (bias)
{
- ch_0_out_0 = arm_nn_requantize(ch_0_out_0, *out_mult, *out_shift);
- ch_0_out_1 = arm_nn_requantize(ch_0_out_1, *out_mult, *out_shift);
- out_mult++;
+ ch_0_out_0_s64 += *bias;
+ ch_0_out_1_s64 += *bias++;
+ ch_1_out_0_s64 += *bias;
+ ch_1_out_1_s64 += *bias++;
}
+
+ reduced_multiplier = REDUCE_MULTIPLIER(*out_mult);
+ ch_0_out_0 = arm_nn_requantize_s64(ch_0_out_0_s64, reduced_multiplier, *out_shift);
+ ch_0_out_1 = arm_nn_requantize_s64(ch_0_out_1_s64, reduced_multiplier, *out_shift);
+ out_mult++;
+ out_shift++;
+
+ reduced_multiplier = REDUCE_MULTIPLIER(*out_mult);
+ ch_1_out_0 = arm_nn_requantize_s64(ch_1_out_0_s64, reduced_multiplier, *out_shift);
+ ch_1_out_1 = arm_nn_requantize_s64(ch_1_out_1_s64, reduced_multiplier, *out_shift);
+
ch_0_out_0 = MAX(ch_0_out_0, activation_min);
ch_0_out_0 = MIN(ch_0_out_0, activation_max);
*out_0++ = (int16_t)ch_0_out_0;
@@ -144,23 +183,7 @@ int16_t *arm_nn_mat_mult_kernel_s16(const int8_t *input_a,
ch_0_out_1 = MAX(ch_0_out_1, activation_min);
ch_0_out_1 = MIN(ch_0_out_1, activation_max);
*out_1++ = (int16_t)ch_0_out_1;
- out_shift++;
- if (bias)
- {
- int32_t reduced_multiplier = REDUCE_MULTIPLIER(*out_mult);
- int64_t acc_64 = ch_1_out_0 + *bias;
- ch_1_out_0 = arm_nn_requantize_s64(acc_64, reduced_multiplier, *out_shift);
- acc_64 = ch_1_out_1 + *bias++;
- ch_1_out_1 = arm_nn_requantize_s64(acc_64, reduced_multiplier, *out_shift);
- out_mult++;
- }
- else
- {
- ch_1_out_0 = arm_nn_requantize(ch_1_out_0, *out_mult, *out_shift);
- ch_1_out_1 = arm_nn_requantize(ch_1_out_1, *out_mult, *out_shift);
- out_mult++;
- }
ch_1_out_0 = MAX(ch_1_out_0, activation_min);
ch_1_out_0 = MIN(ch_1_out_0, activation_max);
*out_0++ = (int16_t)ch_1_out_0;
@@ -168,24 +191,29 @@ int16_t *arm_nn_mat_mult_kernel_s16(const int8_t *input_a,
ch_1_out_1 = MAX(ch_1_out_1, activation_min);
ch_1_out_1 = MIN(ch_1_out_1, activation_max);
*out_1++ = (int16_t)ch_1_out_1;
+
+ out_mult++;
out_shift++;
- /* skip row */
+ /* Skip row */
ip_a0 += num_col_a;
row_count--;
}
- /* compute the last odd numbered row if any */
+ /* Compute the last odd numbered row if any */
if (output_ch & 0x1)
{
- /* setup pointers for B */
+ /* Setup pointers for B */
const int16_t *ip_b0 = input_b;
const int16_t *ip_b1 = ip_b0 + num_col_a;
int32_t ch_0_out_0 = 0;
int32_t ch_0_out_1 = 0;
+ int64_t ch_0_out_0_s64 = 0;
+ int64_t ch_0_out_1_s64 = 0;
- uint16_t col_count = num_col_a >> 2;
+ #if defined(ARM_MATH_DSP)
+ uint16_t col_count = num_col_a_fast >> 2;
while (col_count)
{
int32_t a01, a02;
@@ -205,6 +233,9 @@ int16_t *arm_nn_mat_mult_kernel_s16(const int8_t *input_a,
col_count--;
}
col_count = num_col_a & 0x3;
+ #else
+ int32_t col_count = num_col_a_fast;
+ #endif
while (col_count)
{
int8_t a0 = *ip_a0++;
@@ -215,19 +246,35 @@ int16_t *arm_nn_mat_mult_kernel_s16(const int8_t *input_a,
ch_0_out_1 += a0 * b1;
col_count--;
}
- if (bias)
+
+ ch_0_out_0_s64 = ch_0_out_0;
+ ch_0_out_1_s64 = ch_0_out_1;
+
+ if (num_col_a > MAX_COL_COUNT)
{
- int32_t reduced_multiplier = REDUCE_MULTIPLIER(*out_mult);
- int64_t acc_64 = ch_0_out_0 + *bias;
- ch_0_out_0 = arm_nn_requantize_s64(acc_64, reduced_multiplier, *out_shift);
- acc_64 = ch_0_out_1 + *bias++;
- ch_0_out_1 = arm_nn_requantize_s64(acc_64, reduced_multiplier, *out_shift);
+ col_count = num_col_a_slow;
+ while (col_count)
+ {
+ int8_t a0 = *ip_a0++;
+ int16_t b0 = *ip_b0++;
+ int16_t b1 = *ip_b1++;
+
+ ch_0_out_0_s64 += a0 * b0;
+ ch_0_out_1_s64 += a0 * b1;
+ col_count--;
+ }
}
- else
+
+ if (bias)
{
- ch_0_out_0 = arm_nn_requantize(ch_0_out_0, *out_mult, *out_shift);
- ch_0_out_1 = arm_nn_requantize(ch_0_out_1, *out_mult, *out_shift);
+ ch_0_out_0_s64 += *bias;
+ ch_0_out_1_s64 += *bias++;
}
+
+ reduced_multiplier = REDUCE_MULTIPLIER(*out_mult);
+ ch_0_out_0 = arm_nn_requantize_s64(ch_0_out_0_s64, reduced_multiplier, *out_shift);
+ ch_0_out_1 = arm_nn_requantize_s64(ch_0_out_1_s64, reduced_multiplier, *out_shift);
+
ch_0_out_0 = MAX(ch_0_out_0, activation_min);
ch_0_out_0 = MIN(ch_0_out_0, activation_max);
*out_0++ = (int16_t)ch_0_out_0;
@@ -241,7 +288,7 @@ int16_t *arm_nn_mat_mult_kernel_s16(const int8_t *input_a,
out_0 += output_ch;
- /* return the new output pointer with offset */
+ /* Return the new output pointer with offset */
return out_0;
#else
(void)input_a;
@@ -254,11 +301,11 @@ int16_t *arm_nn_mat_mult_kernel_s16(const int8_t *input_a,
(void)num_col_a;
(void)output_bias;
(void)out_0;
- /* To be completed */
+
return NULL;
#endif
}
/**
* @} end of Doxygen group
- */
\ No newline at end of file
+ */
diff --git a/Source/NNSupportFunctions/arm_nn_mat_mult_nt_t_s16.c b/Source/NNSupportFunctions/arm_nn_mat_mult_nt_t_s16.c
new file mode 100644
index 00000000..78972d5b
--- /dev/null
+++ b/Source/NNSupportFunctions/arm_nn_mat_mult_nt_t_s16.c
@@ -0,0 +1,265 @@
+/*
+ * SPDX-FileCopyrightText: Copyright 2020-2024 Arm Limited and/or its affiliates
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_nn_mat_mult_nt_t_s16
+ * Description: Matrix multiplication support function with the right-hand-side (rhs) matrix transposed
+ *
+ * $Date: 2 April 2024
+ * $Revision: V.1.0.0
+ *
+ * Target : Arm(R) M-Profile Architecture
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_nnsupportfunctions.h"
+
+/**
+ * @ingroup groupSupport
+ */
+
+/**
+ * @addtogroup supportConvolution
+ * @{
+ */
+
+/*
+ * s16 matrix multiplication with the right-hand-side matrix transposed
+ *
+ * Refer header file for details.
+ *
+ */
+arm_cmsis_nn_status arm_nn_mat_mult_nt_t_s16(const int16_t *lhs,
+ const int8_t *rhs,
+ const int64_t *bias,
+ int16_t *dst,
+ const int32_t *dst_multipliers,
+ const int32_t *dst_shifts,
+ const int32_t lhs_rows,
+ const int32_t rhs_rows,
+ const int32_t rhs_cols,
+ const int32_t activation_min,
+ const int32_t activation_max)
+{
+#if defined(ARM_MATH_MVEI)
+
+ const uint32_t rhs_rows_offset = (uint32_t)rhs_rows * sizeof(int16_t);
+ const uint32x4_t scatter_offset = {
+ 0, (uint32_t)rhs_rows_offset, (uint32_t)rhs_rows_offset * 2, (uint32_t)rhs_rows_offset * 3};
+
+ const int32_t rhs_cols_fast = rhs_cols > MAX_COL_COUNT ? MAX_COL_COUNT : rhs_cols;
+ const int32_t rhs_cols_slow = rhs_cols - MAX_COL_COUNT;
+
+ int i_items = 0;
+
+ for (; i_items <= (lhs_rows - 4); i_items += 4)
+ {
+ for (int i = 0; i < rhs_rows; i++)
+ {
+ int32_t acc_n0 = 0;
+ int32_t acc_n1 = 0;
+ int32_t acc_n2 = 0;
+ int32_t acc_n3 = 0;
+
+ int64_t acc_n0_s64 = 0;
+ int64_t acc_n1_s64 = 0;
+ int64_t acc_n2_s64 = 0;
+ int64_t acc_n3_s64 = 0;
+
+ const int16_t *ip_row_0 = lhs;
+ const int16_t *ip_row_1 = lhs + rhs_cols;
+ const int16_t *ip_row_2 = lhs + (2 * rhs_cols);
+ const int16_t *ip_row_3 = lhs + (3 * rhs_cols);
+ const int8_t *col_base = rhs + i * rhs_cols;
+
+ #if defined(ARM_MATH_AUTOVECTORIZE)
+ for (int j = 0; j < rhs_cols_fast; j++)
+ {
+ int8_t col = col_base[j];
+ acc_n0 += ip_row_0[j] * col;
+ acc_n1 += ip_row_1[j] * col;
+ acc_n2 += ip_row_2[j] * col;
+ acc_n3 += ip_row_3[j] * col;
+ }
+ #else
+ // Note: If operand initialization is moved around, use '&' constraint to
+ // specify earlyclobber operands.
+ __ASM volatile(" .p2align 2 \n"
+ " wlstp.16 lr, %[cnt], 1f \n"
+ " mov %[out0], 0 \n"
+ " mov %[out1], 0 \n"
+ " mov %[out2], 0 \n"
+ " mov %[out3], 0 \n"
+ " vldrb.s16 q0, [%[col]], #8 \n"
+ "2: \n"
+ " vldrh.u16 q1, [%[row0]], #16 \n"
+ " vmlava.s16 %[out0], q0, q1 \n"
+ " vldrh.u16 q2, [%[row1]], #16 \n"
+ " vmlava.s16 %[out1], q0, q2 \n"
+ " vldrh.u16 q3, [%[row2]], #16 \n"
+ " vmlava.s16 %[out2], q0, q3 \n"
+ " vldrh.u16 q4, [%[row3]], #16 \n"
+ " vmlava.s16 %[out3], q0, q4 \n"
+ " vldrb.s16 q0, [%[col]], #8 \n"
+ " letp lr, 2b \n"
+ "1: \n"
+ : [col] "+l"(col_base),
+ [row0] "+l"(ip_row_0),
+ [row1] "+l"(ip_row_1),
+ [row2] "+l"(ip_row_2),
+ [row3] "+l"(ip_row_3),
+ [out0] "=Te"(acc_n0),
+ [out1] "=Te"(acc_n1),
+ [out2] "=Te"(acc_n2),
+ [out3] "=Te"(acc_n3)
+ : [cnt] "r"(rhs_cols_fast)
+ : "q0", "q1", "q2", "q3", "q4", "memory", "r14");
+ #endif
+ acc_n0_s64 = acc_n0;
+ acc_n1_s64 = acc_n1;
+ acc_n2_s64 = acc_n2;
+ acc_n3_s64 = acc_n3;
+
+ if (rhs_cols > MAX_COL_COUNT)
+ {
+ ip_row_0 = lhs + MAX_COL_COUNT;
+ ip_row_1 = lhs + rhs_cols + MAX_COL_COUNT;
+ ip_row_2 = lhs + (2 * rhs_cols) + MAX_COL_COUNT;
+ ip_row_3 = lhs + (3 * rhs_cols) + MAX_COL_COUNT;
+ col_base = rhs + i * rhs_cols + MAX_COL_COUNT;
+
+ for (int j = 0; j < rhs_cols_slow; j++)
+ {
+ int8_t col = col_base[j];
+ acc_n0_s64 += ip_row_0[j] * col;
+ acc_n1_s64 += ip_row_1[j] * col;
+ acc_n2_s64 += ip_row_2[j] * col;
+ acc_n3_s64 += ip_row_3[j] * col;
+ }
+ }
+
+ if (bias)
+ {
+ acc_n0_s64 += bias[i];
+ acc_n1_s64 += bias[i];
+ acc_n2_s64 += bias[i];
+ acc_n3_s64 += bias[i];
+ }
+
+ int32_t reduced_multiplier = REDUCE_MULTIPLIER(dst_multipliers[i]);
+ int32_t shift = dst_shifts[i];
+
+ acc_n0 = arm_nn_requantize_s64(acc_n0_s64, reduced_multiplier, shift);
+ acc_n1 = arm_nn_requantize_s64(acc_n1_s64, reduced_multiplier, shift);
+ acc_n2 = arm_nn_requantize_s64(acc_n2_s64, reduced_multiplier, shift);
+ acc_n3 = arm_nn_requantize_s64(acc_n3_s64, reduced_multiplier, shift);
+ int32x4_t res = {acc_n0, acc_n1, acc_n2, acc_n3};
+
+ res = vmaxq_s32(res, vdupq_n_s32(activation_min));
+ res = vminq_s32(res, vdupq_n_s32(activation_max));
+
+ vstrhq_scatter_offset_s32(dst, scatter_offset, res);
+ dst++;
+ }
+
+ lhs += 4 * rhs_cols;
+ dst += (3 * rhs_rows);
+ }
+
+ for (; i_items < lhs_rows; i_items++)
+ {
+ for (int i = 0; i < rhs_rows; i++)
+ {
+ int32_t acc_n0 = 0;
+ int64_t acc_n0_s64 = 0;
+ const int16_t *ip_row_0 = lhs;
+ const int8_t *col_base = rhs + i * rhs_cols;
+
+ #if defined(ARM_MATH_AUTOVECTORIZE)
+ for (int j = 0; j < rhs_cols_fast; j++)
+ {
+ int8_t col = col_base[j];
+ acc_n0 += ip_row_0[j] * col;
+ }
+ #else
+ __ASM volatile(" .p2align 2 \n"
+ " wlstp.32 lr, %[cnt], 1f \n"
+ " mov %[out0], 0 \n"
+ "2: \n"
+ " vldrb.s32 q0, [%[col]], #4 \n"
+ " vldrh.s32 q1, [%[row0]], #8 \n"
+ " vmlava.s32 %[out0], q0, q1 \n"
+ " letp lr, 2b \n"
+ "1: \n"
+ : [col] "+l"(col_base), [row0] "+l"(ip_row_0), [out0] "=Te"(acc_n0)
+ : [cnt] "r"(rhs_cols_fast)
+ : "q0", "q1", "memory", "r14");
+ #endif
+ acc_n0_s64 = acc_n0;
+
+ if (rhs_cols > MAX_COL_COUNT)
+ {
+ ip_row_0 = lhs + MAX_COL_COUNT;
+ col_base = rhs + i * rhs_cols + MAX_COL_COUNT;
+
+ for (int j = 0; j < rhs_cols_slow; j++)
+ {
+ int8_t col = col_base[j];
+ acc_n0_s64 += ip_row_0[j] * col;
+ }
+ }
+
+ if (bias)
+ {
+ acc_n0_s64 += bias[i];
+ }
+
+ int32_t reduced_multiplier = REDUCE_MULTIPLIER(dst_multipliers[i]);
+ int32_t shift = dst_shifts[i];
+
+ acc_n0 = arm_nn_requantize_s64(acc_n0_s64, reduced_multiplier, shift);
+ acc_n0 = MAX(acc_n0, activation_min);
+ acc_n0 = MIN(acc_n0, activation_max);
+ *dst++ = (int16_t)acc_n0;
+ }
+ lhs += rhs_cols;
+ }
+
+#else
+ (void)lhs;
+ (void)rhs;
+ (void)dst_multipliers;
+ (void)dst_shifts;
+ (void)dst;
+ (void)activation_min;
+ (void)activation_max;
+ (void)bias;
+ (void)lhs_rows;
+ (void)lhs_rows;
+ (void)rhs_rows;
+ (void)rhs_cols;
+
+ return ARM_CMSIS_NN_NO_IMPL_ERROR;
+#endif
+ return ARM_CMSIS_NN_SUCCESS;
+}
+
+/**
+ * @} end of Doxygen group
+ */
diff --git a/Source/NNSupportFunctions/arm_nn_vec_mat_mult_t_s16.c b/Source/NNSupportFunctions/arm_nn_vec_mat_mult_t_s16.c
index b51fbf73..4c7983e2 100644
--- a/Source/NNSupportFunctions/arm_nn_vec_mat_mult_t_s16.c
+++ b/Source/NNSupportFunctions/arm_nn_vec_mat_mult_t_s16.c
@@ -1,5 +1,5 @@
/*
- * SPDX-FileCopyrightText: Copyright 2020-2023 Arm Limited and/or its affiliates
+ * SPDX-FileCopyrightText: Copyright 2020-2024 Arm Limited and/or its affiliates
*
* SPDX-License-Identifier: Apache-2.0
*
@@ -21,8 +21,8 @@
* Title: arm_nn_vec_mat_mult_t_s16
* Description: s16 vector by matrix (transposed) multiplication
*
- * $Date: 5 January 2023
- * $Revision: V.2.2.0
+ * $Date: 22 March 2024
+ * $Revision: V.2.3.0
*
* Target : Arm(R) M-Profile Architecture
*
@@ -30,8 +30,6 @@
#include "arm_nnsupportfunctions.h"
-#define MAX_COL_COUNT (512)
-
/**
* @ingroup groupSupport
*/
diff --git a/Tests/UnitTest/CMakeLists.txt b/Tests/UnitTest/CMakeLists.txt
index d8d8ab7a..cb273db5 100644
--- a/Tests/UnitTest/CMakeLists.txt
+++ b/Tests/UnitTest/CMakeLists.txt
@@ -75,7 +75,6 @@ add_subdirectory(TestCases/test_arm_avgpool_s16)
add_subdirectory(TestCases/test_arm_avgpool_s8)
add_subdirectory(TestCases/test_arm_convolve_1x1_s8_fast)
add_subdirectory(TestCases/test_arm_convolve_1x1_s4_fast)
-add_subdirectory(TestCases/test_arm_convolve_fast_s16)
add_subdirectory(TestCases/test_arm_convolve_s16)
add_subdirectory(TestCases/test_arm_convolve_s8)
add_subdirectory(TestCases/test_arm_convolve_s4)
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8/biases_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8/biases_data.h
index 0df67b68..87e0e5d9 100644
--- a/Tests/UnitTest/TestCases/TestData/int16xint8/biases_data.h
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8/biases_data.h
@@ -1,6 +1,6 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
#pragma once
#include
-const int64_t int16xint8_biases[4] = {-2693823, 4268473, -1952572, -1471661};
+const int64_t *int16xint8_biases = NULL;
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8/config_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8/config_data.h
index 274f27fa..5002212f 100644
--- a/Tests/UnitTest/TestCases/TestData/int16xint8/config_data.h
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8/config_data.h
@@ -1,23 +1,23 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
#pragma once
#define INT16XINT8_OUT_CH 4
-#define INT16XINT8_IN_CH 3
-#define INT16XINT8_INPUT_W 7
+#define INT16XINT8_IN_CH 55
+#define INT16XINT8_INPUT_W 10
#define INT16XINT8_INPUT_H 8
-#define INT16XINT8_DST_SIZE 48
-#define INT16XINT8_INPUT_SIZE 168
-#define INT16XINT8_OUT_ACTIVATION_MIN -13335
+#define INT16XINT8_DST_SIZE 40
+#define INT16XINT8_INPUT_SIZE 4400
+#define INT16XINT8_OUT_ACTIVATION_MIN -32768
#define INT16XINT8_OUT_ACTIVATION_MAX 32767
#define INT16XINT8_INPUT_BATCHES 1
-#define INT16XINT8_FILTER_X 2
+#define INT16XINT8_FILTER_X 9
#define INT16XINT8_FILTER_Y 4
-#define INT16XINT8_STRIDE_X 2
-#define INT16XINT8_STRIDE_Y 3
+#define INT16XINT8_STRIDE_X 1
+#define INT16XINT8_STRIDE_Y 1
#define INT16XINT8_PAD_X 0
-#define INT16XINT8_PAD_Y 1
-#define INT16XINT8_OUTPUT_W 4
-#define INT16XINT8_OUTPUT_H 3
+#define INT16XINT8_PAD_Y 0
+#define INT16XINT8_OUTPUT_W 2
+#define INT16XINT8_OUTPUT_H 5
#define INT16XINT8_INPUT_OFFSET 0
#define INT16XINT8_OUTPUT_OFFSET 0
#define INT16XINT8_DILATION_X 1
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8/input_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8/input_data.h
index 332e9a2d..a9857dce 100644
--- a/Tests/UnitTest/TestCases/TestData/int16xint8/input_data.h
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8/input_data.h
@@ -1,18 +1,321 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
#pragma once
#include
-const int16_t int16xint8_input[168] = {
- 10557, -29412, 16306, -5806, -32348, -19960, 2964, -32596, 20924, 18562, -20598, -6976, 27849, 12415,
- -30223, -27312, -7108, -12970, 29648, 26680, -8421, -14587, 18276, 21648, -2320, 12407, -17227, 8336,
- -19571, 21794, -7472, 26556, -18272, 4333, 29296, -8120, -14915, 28821, -21516, 516, 3900, 24368,
- -3683, -2848, -1891, 10821, -26504, 2110, 5717, -24497, 1898, -23262, -11098, -21592, -27834, -30928,
- 15710, -28005, 5185, -24728, -22215, -21817, 1560, -28083, -29076, -26205, -28534, -24994, -5517, 32166,
- 25809, 904, 23030, 32059, -11323, 3203, 21358, -29406, -18602, -14718, -26485, -31259, -2470, -1560,
- 1982, 801, -418, -20854, -22982, -25937, 30793, 10965, 2980, 23705, 16271, 10279, -25793, 11816,
- 30853, 3285, 29763, 20687, -19271, 972, 27703, -8887, -32590, 428, -31855, -24800, -7196, 3695,
- 9322, -12119, 26056, 28964, 11767, -25994, 8310, 25111, 2990, 10228, 9369, 8724, -23360, -21582,
- -6857, -5432, -20932, -18168, 29165, -4251, -30703, -29891, -332, 18088, 22134, 23526, -22889, 18975,
- -20517, 18490, -15958, 11060, -15179, -1603, 28921, -15049, 29725, -21223, 5630, -27275, -8880, -16761,
- 29354, -10064, -15754, 15828, -9302, -11527, -5767, 2286, 623, -805, 24164, 26393, 14253, 11532};
+const int16_t int16xint8_input[4400] = {
+ 2258, -12216, 21955, 28148, 24627, -27487, 10259, 1230, -22204, -20857, -4155, -11625, -12677, -16925,
+ 20804, -2514, 30927, -8927, 31309, 11732, -15134, 4402, 9163, -27708, 4394, -28138, -31148, 12312,
+ 15482, -24145, 30356, 30332, 5787, 32650, 9767, 9098, 9892, -3355, -4309, 28572, 11701, 11084,
+ -14139, 16324, 11331, 7507, 14202, -19123, 5268, 7244, -12431, -12248, 9727, -17937, -336, 26839,
+ 21250, 21610, -14691, -8927, -10025, 12339, -19596, 4049, 20204, -12937, -27421, 17454, -15713, 1844,
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diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8/output_mult_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8/output_mult_data.h
index c7e50e3d..9fd93e90 100644
--- a/Tests/UnitTest/TestCases/TestData/int16xint8/output_mult_data.h
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8/output_mult_data.h
@@ -1,6 +1,6 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
#pragma once
#include
-const int32_t int16xint8_output_mult[4] = {1089212438, 2042635001, 2134124060, 1087333954};
+const int32_t int16xint8_output_mult[4] = {1094038263, 1093426216, 1093614140, 1093822383};
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8/output_ref_data.h
index 15280764..babc7a6e 100644
--- a/Tests/UnitTest/TestCases/TestData/int16xint8/output_ref_data.h
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8/output_ref_data.h
@@ -1,10 +1,9 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
#pragma once
#include
-const int16_t int16xint8_output_ref[48] = {
- -13335, -169, -10400, -1211, -4046, 17113, 6664, 31020, -11016, 14661, -12617, 4871,
- -11590, 6034, -12869, -2736, -13335, 23907, -6623, 2609, 6054, 6400, -2306, -13335,
- -7031, 1919, 13723, -13335, -13335, 4406, 23009, -13335, -8348, 12887, 1828, -13335,
- -13335, 1073, 13718, 617, -2508, 10951, 5453, -11033, -4797, 5220, -9823, -2385};
+const int16_t int16xint8_output_ref[40] = {180, 471, 855, 475, -60, 327, 298, -198, 147, 108,
+ 535, -101, -101, -235, -535, -188, -547, -944, -548, -328,
+ -895, -624, -496, -1029, -809, -671, -453, -410, -266, -563,
+ -818, -799, -512, -485, -639, -968, -102, -9, -414, -314};
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8/output_shift_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8/output_shift_data.h
index 74770b4b..5ca3315b 100644
--- a/Tests/UnitTest/TestCases/TestData/int16xint8/output_shift_data.h
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8/output_shift_data.h
@@ -1,6 +1,6 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
#pragma once
#include
-const int32_t int16xint8_output_shift[4] = {-8, -9, -9, -8};
+const int32_t int16xint8_output_shift[4] = {-16, -16, -16, -16};
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8/test_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8/test_data.h
index 8102687f..4d61c5f2 100644
--- a/Tests/UnitTest/TestCases/TestData/int16xint8/test_data.h
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8/test_data.h
@@ -1,5 +1,5 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
#include "biases_data.h"
#include "config_data.h"
#include "input_data.h"
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8/weights_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8/weights_data.h
index ac8c8768..91884235 100644
--- a/Tests/UnitTest/TestCases/TestData/int16xint8/weights_data.h
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8/weights_data.h
@@ -1,11 +1,351 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
#pragma once
#include
-const int8_t int16xint8_weights[96] = {
- 12, -38, -25, -95, 18, -1, 57, 35, 76, 61, -54, 20, -19, -40, -127, -7, 39, 110, 39, 45,
- -20, -41, 48, -69, 12, -105, 127, 58, -103, 108, 36, -48, -101, 42, -48, 87, 79, -27, 36, 66,
- -42, -114, 80, 40, 115, -126, 94, -9, -50, -121, 45, -29, 118, -111, -96, -23, -85, 71, 82, 16,
- -127, -82, 83, -88, 74, -101, 118, 37, -91, -71, 82, -1, 42, 125, -58, 54, -113, 119, 94, -23,
- 114, 91, -21, 75, 91, -109, -103, -83, 124, 7, 45, -127, -5, -20, -109, -124};
+const int8_t int16xint8_weights[7920] = {
+ 118, 98, 35, 46, 18, 96, 107, 92, 59, 73, 100, 7, 94, 43, 112, 33, 55, 111, 5, 20, 68, 58, 82,
+ 93, 120, 46, 10, 25, 76, 30, 111, 9, 94, 23, 118, 56, 39, 45, 72, 123, 47, 23, 62, 50, 44, 78,
+ 74, 110, 90, 23, 8, 65, 38, 36, 85, 22, 25, 13, 38, 43, 63, 104, 74, 93, 27, 125, 17, 57, 87,
+ 95, 71, 52, 90, 71, 54, 5, 93, 94, 55, 89, 48, 12, 103, 117, 78, 74, 20, 84, 10, 100, 19, 17,
+ 73, 41, 37, 86, 104, 54, 83, 35, 114, 35, 39, 76, 48, 86, 120, 92, 27, 40, 64, 71, 30, 88, 9,
+ 54, 47, 25, 93, 75, 15, 63, 106, 21, 105, 3, 57, 78, 79, 4, 9, 77, 20, 52, 96, 22, 74, 72,
+ 85, 95, 19, 26, 57, 13, 10, 124, 50, 19, 52, 116, 30, 102, 35, 11, 60, 35, 77, 110, 78, 119, 119,
+ 115, 71, 71, 68, 12, 22, 114, 81, 66, 65, 66, 94, 127, 56, 19, 79, 51, 71, 3, 90, 107, 20, 97,
+ 29, 9, 125, 93, 65, 61, 116, 10, 5, 108, 24, 55, 122, 112, 37, 80, 52, 4, 72, 14, 118, 64, 22,
+ 102, 52, 55, 93, 6, 42, 115, 57, 104, 123, 77, 116, 17, 81, 41, 124, 97, 7, 120, 92, 121, 53, 42,
+ 126, 26, 3, 73, 79, 120, 51, 24, 124, 84, 41, 70, 3, 3, 67, 27, 83, 60, 24, 49, 72, 42, 54,
+ 41, 3, 28, 125, 48, 41, 68, 12, 20, 79, 56, 89, 62, 7, 110, 56, 45, 97, 50, 61, 79, 4, 30,
+ 121, 115, 110, 52, 25, 92, 1, 122, 28, 63, 125, 14, 61, 109, 15, 8, 94, 118, 106, 13, 29, 105, 34,
+ 88, 107, 1, 110, 48, 101, 121, 8, 12, 42, 61, 23, 54, 50, 42, 35, 101, 124, 19, 14, 54, 30, 96,
+ 103, 29, 85, 9, 87, 6, 27, 24, 49, 66, 55, 8, 78, 3, 85, 88, 47, 95, 61, 48, 46, 38, 13,
+ 23, 106, 44, 123, 114, 70, 120, 15, 16, 123, 79, 78, 36, 86, 59, 49, 11, 124, 127, 102, 70, 64, 35,
+ 112, 92, 49, 29, 109, 118, 48, 114, 39, 110, 33, 107, 43, 14, 5, 72, 41, 72, 74, 76, 2, 8, 63,
+ 102, 20, 28, 105, 87, 13, 102, 80, 121, 73, 126, 56, 29, 20, 9, 7, 51, 16, 92, 99, 115, 48, 85,
+ 53, 41, 119, 19, 3, 117, 74, 98, 118, 53, 33, 65, 64, 49, 7, 62, 44, 60, 41, 55, 24, 53, 93,
+ 10, 111, 27, 38, 50, 70, 62, 69, 119, 40, 48, 42, 65, 45, 118, 15, 36, 29, 87, 33, 52, 1, 84,
+ 79, 83, 65, 101, 77, 82, 54, 42, 102, 55, 25, 98, 30, 87, 88, 64, 5, 56, 45, 56, 86, 85, 32,
+ 74, 104, 98, 14, 33, 86, 102, 85, 30, 25, 71, 76, 98, 103, 82, 82, 116, 12, 1, 82, 116, 108, 121,
+ 122, 103, 56, 79, 54, 64, 95, 83, 70, 42, 99, 119, 60, 91, 123, 108, 41, 26, 21, 8, 60, 83, 22,
+ 107, 28, 100, 84, 122, 74, 55, 51, 45, 4, 122, 48, 76, 5, 26, 28, 26, 95, 127, 89, 18, 97, 77,
+ 110, 70, 115, 19, 84, 91, 1, 97, 1, 36, 92, 115, 16, 74, 42, 122, 124, 52, 59, 66, 11, 37, 101,
+ 101, 28, 112, 107, 24, 83, 71, 101, 73, 112, 36, 93, 63, 117, 43, 76, 36, 76, 30, 45, 2, 28, 121,
+ 7, 45, 96, 9, 97, 69, 28, 58, 59, 78, 0, 46, 6, 63, 102, 104, 17, 72, 106, 104, 35, 84, 90,
+ 116, 124, 19, 37, 106, 3, 95, 127, 94, 15, 65, 103, 49, 12, 62, 72, 77, 29, 60, 103, 65, 43, 61,
+ 66, 65, 2, 88, 104, 111, 113, 37, 50, 117, 105, 11, 87, 46, 4, 31, 36, 74, 103, 39, 50, 11, 115,
+ 17, 77, 35, 86, 86, 23, 53, 52, 112, 57, 25, 33, 126, 50, 100, 69, 0, 68, 89, 7, 71, 89, 56,
+ 86, 89, 33, 61, 67, 11, 114, 124, 47, 63, 68, 22, 65, 94, 68, 69, 120, 46, 48, 99, 81, 104, 83,
+ 60, 76, 6, 98, 35, 94, 121, 4, 80, 48, 126, 125, 104, 25, 35, 120, 67, 25, 22, 83, 22, 55, 3,
+ 110, 17, 88, 117, 82, 90, 56, 69, 57, 119, 103, 56, 31, 87, 42, 48, 65, 17, 6, 109, 23, 7, 45,
+ 48, 107, 28, 66, 83, 121, 36, 100, 61, 32, 117, 64, 117, 18, 86, 89, 60, 8, 29, 42, 3, 72, 2,
+ 28, 82, 16, 54, 55, 98, 39, 18, 125, 39, 91, 34, 13, 86, 88, 125, 67, 3, 14, 19, 82, 84, 37,
+ 73, 124, 94, 92, 33, 58, 14, 8, 36, 17, 7, 53, 11, 58, 97, 93, 88, 83, 57, 69, 73, 114, 31,
+ 16, 74, 9, 97, 16, 74, 20, 89, 14, 73, 6, 82, 11, 63, 10, 42, 70, 88, 8, 55, 53, 37, 112,
+ 68, 62, 11, 74, 11, 51, 110, 41, 44, 107, 4, 5, 80, 31, 17, 122, 10, 19, 67, 37, 34, 10, 12,
+ 52, 105, 99, 85, 28, 78, 32, 74, 33, 46, 123, 123, 103, 40, 108, 116, 26, 44, 126, 81, 84, 18, 89,
+ 57, 99, 115, 26, 47, 75, 78, 117, 6, 76, 89, 109, 66, 38, 80, 15, 44, 69, 64, 121, 127, 84, 46,
+ 122, 33, 18, 93, 58, 72, 52, 115, 87, 98, 73, 71, 62, 7, 76, 65, 117, 92, 53, 32, 115, 33, 125,
+ 9, 104, 99, 10, 66, 69, 63, 112, 63, 42, 95, 9, 68, 5, 5, 17, 96, 125, 112, 66, 120, 124, 11,
+ 93, 127, 34, 14, 111, 102, 15, 1, 81, 8, 48, 25, 77, 116, 50, 20, 37, 32, 11, 62, 59, 116, 79,
+ 104, 78, 44, 70, 31, 32, 11, 75, 93, 30, 81, 87, 1, 18, 77, 93, 69, 36, 101, 95, 65, 60, 37,
+ 18, 97, 13, 86, 32, 62, 43, 55, 18, 1, 59, 44, 90, 125, 0, 52, 63, 90, 60, 97, 48, 38, 48,
+ 1, 75, 120, 87, 82, 61, 78, 32, 39, 46, 82, 15, 89, 69, 71, 21, 46, 78, 1, 51, 35, 30, 8,
+ 122, 123, 114, 36, 83, 44, 20, 74, 116, 109, 22, 17, 51, 48, 83, 90, 52, 95, 35, 11, 8, 34, 9,
+ 24, 21, 103, 82, 95, 97, 39, 69, 17, 5, 37, 16, 49, 125, 55, 126, 122, 3, 51, 117, 120, 27, 122,
+ 41, 116, 95, 89, 43, 125, 108, 56, 79, 60, 38, 94, 123, 13, 38, 77, 13, 117, 81, 8, 37, 117, 22,
+ 124, 47, 97, 54, 125, 95, 48, 5, 24, 78, 58, 125, 29, 54, 23, 65, 86, 85, 19, 83, 53, 95, 12,
+ 52, 125, 83, 68, 100, 107, 52, 106, 78, 35, 70, 61, 92, 114, 81, 120, 85, 43, 27, 14, 68, 51, 102,
+ 1, 79, 125, 125, 62, 67, 67, 39, 77, 83, 29, 56, 57, 100, 53, 6, 102, 101, 119, 115, 40, 117, 92,
+ 42, 25, 29, 89, 79, 56, 13, 38, 87, 93, 121, 20, 112, 100, 90, 43, 21, 113, 83, 119, 28, 12, 72,
+ 119, 80, 31, 115, 55, 63, 121, 89, 6, 126, 112, 60, 107, 99, 84, 31, 30, 83, 66, 18, 34, 62, 117,
+ 40, 89, 107, 41, 121, 18, 21, 72, 103, 49, 52, 11, 46, 66, 53, 22, 1, 91, 39, 69, 70, 32, 50,
+ 123, 9, 121, 83, 89, 9, 58, 95, 11, 104, 80, 12, 80, 89, 76, 2, 84, 113, 61, 95, 89, 106, 13,
+ 51, 120, 61, 33, 31, 76, 112, 55, 47, 45, 11, 40, 89, 76, 84, 26, 70, 8, 3, 25, 104, 53, 37,
+ 36, 112, 83, 42, 69, 111, 125, 35, 8, 20, 85, 112, 123, 73, 5, 103, 15, 90, 10, 53, 113, 69, 116,
+ 16, 14, 56, 70, 42, 24, 10, 78, 113, 87, 47, 104, 97, 49, 93, 33, 18, 126, 83, 55, 120, 119, 125,
+ 18, 114, 118, 33, 59, 11, 20, 111, 126, 102, 35, 74, 49, 109, 119, 53, 24, 89, 50, 97, 50, 23, 22,
+ 15, 111, 49, 103, 17, 103, 29, 66, 108, 25, 89, 47, 6, 94, 44, 63, 37, 97, 89, 87, 100, 93, 101,
+ 66, 31, 114, 77, 64, 75, 47, 33, 32, 36, 76, 11, 75, 10, 26, 124, 62, 60, 18, 110, 115, 84, 66,
+ 124, 14, 4, 99, 59, 58, 99, 36, 47, 61, 55, 50, 123, 67, 127, 66, 34, 105, 13, 87, 21, 7, 32,
+ 60, 5, 19, 96, 77, 78, 77, 121, 32, 106, 116, 59, 67, 53, 103, 65, 10, 126, 30, 95, 70, 123, 76,
+ 18, 81, 125, 27, 13, 63, 32, 8, 38, 15, 123, 67, 112, 16, 90, 43, 13, 29, 3, 20, 21, 26, 78,
+ 63, 38, 7, 127, 108, 127, 119, 47, 31, 88, 6, 119, 86, 56, 24, 6, 15, 119, 29, 108, 121, 96, 31,
+ 24, 118, 16, 81, 25, 103, 55, 21, 22, 58, 9, 116, 42, 91, 19, 120, 65, 25, 103, 24, 38, 62, 35,
+ 109, 44, 97, 36, 97, 64, 46, 55, 16, 74, 34, 118, 127, 20, 18, 6, 11, 22, 21, 44, 109, 48, 46,
+ 48, 2, 115, 42, 66, 93, 64, 15, 33, 67, 99, 22, 27, 44, 52, 35, 21, 102, 76, 6, 24, 90, 2,
+ 93, 94, 23, 19, 42, 57, 69, 111, 9, 92, 40, 58, 60, 36, 40, 17, 119, 57, 79, 48, 65, 90, 22,
+ 66, 73, 66, 20, 54, 127, 54, 37, 84, 72, 39, 8, 5, 95, 116, 82, 32, 51, 56, 97, 47, 47, 70,
+ 113, 108, 82, 72, 70, 54, 101, 45, 47, 12, 23, 22, 78, 107, 20, 66, 118, 81, 25, 29, 40, 95, 49,
+ 55, 123, 18, 19, 28, 8, 44, 122, 13, 104, 19, 73, 2, 15, 95, 65, 82, 73, 16, 110, 49, 98, 109,
+ 73, 42, 112, 112, 49, 43, 18, 2, 22, 40, 95, 19, 57, 15, 76, 66, 43, 91, 112, 127, 71, 21, 98,
+ 105, 96, 72, 118, 103, 9, 2, 74, 37, 75, 42, 98, 0, 16, 34, 63, 40, 73, 15, 117, 117, 13, 108,
+ 60, 55, 40, 106, 4, 19, 42, 77, 1, 40, 97, 74, 50, 13, 26, 100, 113, 40, 84, 98, 3, 112, 29,
+ 35, 74, 81, 22, 9, 45, 113, 69, 91, 109, 48, 24, 17, 15, 97, 125, 75, 57, 72, 57, 117, 27, 72,
+ 126, 100, 77, 62, 44, 28, 65, 53, 14, 126, 96, 47, 14, 94, 40, 83, 77, 5, 40, 39, 124, 98, 110,
+ 122, 58, 79, 38, 91, 51, 81, 101, 30, 60, 11, 3, 20, 90, 57, 63, 76, 69, 45, 89, 97, 79, 127,
+ 63, 8, 21, 14, 99, 90, 76, 33, 38, 35, 7, 103, 28, 71, 53, 24, 38, 20, 5, 123, 70, 30, 99,
+ 88, 3, 4, 56, 35, 103, 124, 81, 61, 96, 50, 12, 56, 96, 46, 45, 116, 44, 43, 53, 111, 2, 92,
+ 112, 116, 69, 113, 28, 72, 4, 52, 85, 105, 75, 3, 71, 77, 107, 15, 28, 117, 112, 67, 100, 103, 18,
+ 123, 40, 72, 16, 39, 1, 2, 57, 24, 20, 35, 29, 39, 46, 28, 86, 86, 5, 93, 65, 85, 97, 116,
+ 61, 31, 56, 82, 7, 59, 74, 125, 81, 115, 112, 87, 16, 40, 81, 25, 86, 125, 68, 117, 6, 124, 114,
+ 119, 44, 16, 11, 23, 53, 18, 16, 48, 46, 96, 82, 20, 27, 112, 20, 72, 48, 35, 49, 70, 39, 78,
+ 121, 35, 112, 97, 79, 60, 121, 17, 96, 115, 93, 40, 70, 64, 100, 47, 75, 120, 98, 102, 76, 65, 122,
+ 46, 23, 89, 68, 96, 110, 23, 1, 108, 12, 65, 48, 69, 40, 80, 19, 49, 18, 59, 126, 45, 34, 87,
+ 41, 50, 23, 2, 72, 106, 29, 34, 32, 43, 88, 109, 104, 118, 92, 82, 110, 30, 23, 54, 95, 28, 56,
+ 30, 81, 113, 78, 30, 27, 17, 62, 4, 22, 89, 78, 65, 69, 37, 109, 40, 36, 31, 91, 87, 37, 56,
+ 17, 81, 31, 7, 55, 51, 75, 4, 39, 98, 36, 8, 50, 117, 73, 71, 123, 48, 57, 22, 124, 40, 54,
+ 99, 71, 116, 74, 23, 69, 106, 93, 40, 100, 92, 76, 105, 72, 37, 6, 17, 55, 60, 91, 26, 80, 59,
+ 82, 35, 117, 99, 86, 23, 10, 14, 72, 124, 74, 20, 105, 42, 81, 41, 116, 61, 75, 19, 38, 69, 36,
+ 94, 77, 95, 114, 64, 11, 108, 96, 76, 124, 58, 8, 22, 54, 4, 47, 65, 34, 66, 116, 125, 79, 45,
+ 73, 14, 15, 78, 66, 68, 93, 108, 113, 44, 60, 125, 77, 86, 92, 94, 125, 53, 4, 85, 114, 33, 99,
+ 98, 115, 59, 4, 124, 95, 56, 21, 71, 119, 71, 74, 121, 29, 17, 94, 22, 30, 80, 29, 14, 64, 22,
+ 87, 115, 99, 48, 47, 40, 52, 112, 27, 51, 120, 118, 38, 6, 85, 56, 86, 3, 101, 52, 69, 91, 111,
+ 120, 99, 8, 123, 38, 16, 74, 88, 61, 87, 73, 7, 8, 8, 116, 65, 106, 30, 62, 73, 13, 13, 28,
+ 68, 101, 57, 13, 24, 102, 70, 20, 18, 27, 124, 0, 93, 85, 43, 93, 67, 114, 10, 67, 46, 58, 118,
+ 36, 30, 106, 74, 70, 21, 105, 88, 125, 78, 62, 112, 120, 70, 70, 73, 90, 17, 23, 41, 81, 111, 103,
+ 91, 37, 9, 27, 112, 10, 82, 30, 122, 8, 52, 61, 93, 71, 68, 71, 90, 36, 62, 117, 86, 79, 88,
+ 60, 99, 19, 9, 4, 9, 14, 43, 111, 63, 4, 19, 23, 70, 124, 16, 70, 30, 111, 115, 122, 85, 89,
+ 42, 36, 49, 32, 23, 91, 87, 31, 18, 114, 18, 59, 124, 23, 72, 6, 95, 112, 56, 99, 61, 42, 103,
+ 121, 123, 86, 83, 18, 65, 51, 105, 26, 10, 103, 107, 28, 41, 29, 11, 31, 34, 102, 20, 106, 12, 113,
+ 32, 93, 89, 53, 58, 83, 68, 82, 87, 113, 35, 56, 8, 23, 85, 76, 13, 67, 44, 53, 91, 9, 40,
+ 86, 37, 76, 65, 15, 45, 93, 99, 43, 64, 24, 115, 86, 40, 14, 12, 42, 112, 71, 99, 93, 7, 92,
+ 103, 72, 76, 73, 124, 119, 57, 48, 14, 64, 109, 70, 104, 3, 46, 75, 33, 78, 64, 22, 74, 83, 118,
+ 115, 74, 1, 122, 32, 24, 38, 83, 41, 63, 121, 22, 65, 127, 96, 104, 110, 35, 7, 2, 109, 57, 68,
+ 40, 87, 8, 44, 23, 111, 98, 45, 105, 11, 78, 31, 116, 48, 97, 120, 83, 95, 109, 46, 116, 95, 125,
+ 124, 25, 6, 101, 87, 70, 27, 74, 17, 104, 92, 15, 96, 47, 89, 102, 116, 94, 17, 34, 110, 85, 61,
+ 114, 126, 119, 112, 45, 98, 47, 16, 59, 74, 127, 83, 64, 91, 112, 47, 12, 3, 53, 7, 69, 75, 2,
+ 120, 60, 6, 31, 103, 112, 94, 101, 123, 69, 6, 58, 6, 103, 8, 46, 63, 6, 43, 59, 75, 125, 112,
+ 100, 95, 5, 115, 44, 62, 74, 53, 104, 69, 51, 96, 38, 95, 66, 96, 9, 6, 94, 126, 100, 93, 46,
+ 40, 122, 83, 12, 63, 25, 46, 115, 26, 26, 67, 34, 29, 41, 126, 29, 122, 72, 55, 68, 95, 87, 73,
+ 103, 109, 64, 10, 109, 92, 69, 3, 42, 54, 93, 21, 91, 77, 34, 110, 6, 118, 80, 9, 44, 20, 69,
+ 113, 47, 26, 88, 105, 64, 36, 5, 29, 73, 76, 46, 35, 111, 115, 122, 25, 94, 96, 64, 122, 127, 48,
+ 83, 93, 91, 76, 58, 127, 87, 1, 73, 21, 109, 29, 74, 113, 104, 28, 106, 25, 45, 113, 61, 104, 20,
+ 43, 96, 119, 36, 5, 4, 64, 71, 112, 61, 43, 124, 104, 86, 69, 51, 69, 33, 42, 60, 8, 111, 102,
+ 77, 20, 114, 16, 122, 113, 28, 52, 113, 81, 54, 84, 47, 96, 39, 72, 78, 55, 126, 119, 89, 117, 107,
+ 103, 81, 64, 27, 4, 76, 67, 40, 85, 47, 65, 30, 6, 115, 25, 56, 125, 91, 58, 18, 94, 70, 57,
+ 96, 70, 87, 59, 118, 102, 86, 114, 25, 5, 105, 117, 88, 94, 107, 108, 93, 54, 37, 31, 87, 52, 17,
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+ 54, 102, 9, 118, 98, 40, 17, 35, 79, 37, 4, 2, 45, 21, 99, 86, 48, 105, 83, 2, 115, 88, 81,
+ 32, 28, 104, 94, 63, 11, 3, 102, 76, 77, 95, 23, 115, 36, 93, 55, 116, 21, 7, 21, 12, 6, 103,
+ 39, 87, 94, 118, 119, 29, 48, 6, 112, 48, 112, 10, 42, 103, 125, 110, 54, 49, 16, 2, 63, 107, 41,
+ 113, 95, 121, 58, 26, 15, 58, 8, 78, 73, 99, 4, 3, 21, 103, 67, 103, 110, 101, 99, 77, 83, 107,
+ 17, 14, 72, 121, 32, 100, 105, 34, 88, 99, 77, 114, 81, 87, 71, 10, 58, 114, 41, 82, 64, 79, 97,
+ 99, 89, 36, 22, 36, 86, 51, 100, 57, 70, 2, 104, 20, 58, 15, 42, 20, 71, 108, 95, 17, 82, 45,
+ 91, 68, 8, 15, 6, 36, 58, 50, 86, 62, 70, 121, 14, 26, 91, 60, 56, 27, 21, 19, 32, 68, 125,
+ 76, 28, 110, 87, 110, 73, 92, 114, 100, 64, 100, 59, 85, 54, 39, 78, 115, 97, 91, 50, 83, 88, 0,
+ 117, 67, 117, 126, 78, 80, 90, 78, 21, 18, 48, 90, 102, 91, 88, 113, 124, 103, 25, 53, 38, 105, 38,
+ 2, 101, 110, 98, 23, 70, 33, 114, 36, 78, 75, 118, 37, 101, 43, 22, 82, 28, 68, 126, 18, 111, 96,
+ 76, 2, 92, 90, 71, 6, 12, 70, 25, 72, 32, 39, 115, 42, 21, 91, 94, 35, 9, 45, 65, 16, 119,
+ 37, 55, 15, 30, 110, 43, 3, 46, 53, 77, 44, 81, 34, 38, 30, 46, 96, 113, 125, 9, 17, 120, 2,
+ 50, 50, 19, 73, 59, 22, 121, 107, 62, 69, 44, 97, 95, 35, 40, 59, 24, 44, 54, 117, 4, 0, 68,
+ 31, 77, 18, 14, 56, 54, 110, 109, 34, 76, 21, 76, 3, 4, 56, 21, 65, 15, 122, 48, 3, 56, 97,
+ 68, 50, 114, 101, 87, 87, 75, 25, 64, 51, 40, 93, 36, 120, 62, 77, 7, 111, 109, 73, 5, 3, 47,
+ 48, 95, 105, 52, 91, 54, 103, 71, 49, 6, 4, 6, 108, 7, 110, 62, 95, 70, 73, 45, 107, 23, 44,
+ 89, 65, 60, 27, 90, 55, 18, 111, 89, 46, 48, 113, 34, 81, 90, 104, 22, 107, 44, 38, 45, 102, 123,
+ 75, 78, 11, 50, 89, 56, 13, 92, 97, 41, 73, 58, 95, 120, 36, 102, 59, 124, 5, 114, 109, 87, 55,
+ 6, 55, 16, 76, 53, 58, 49, 7, 70, 57, 59, 10, 126, 125, 10, 113, 127, 118, 69, 21, 68, 59, 125,
+ 2, 40, 74, 16, 85, 116, 4, 33, 5, 29, 55, 63, 115, 114, 84, 95, 111, 43, 91, 73, 90, 39, 108,
+ 44, 36, 72, 104, 126, 112, 109, 30, 95, 33, 73, 83, 1, 116, 99, 26, 54, 3, 83, 82, 119, 79, 108,
+ 11, 118, 90, 42, 110, 9, 15, 110, 93, 6, 105, 103, 27, 70, 23, 97, 53, 8, 40, 42, 79, 38, 86,
+ 14, 79, 64, 121, 5, 106, 70, 3, 112, 75, 17, 58, 3, 79, 100, 114, 51, 88, 21, 79, 58, 99, 106,
+ 113, 110, 92, 109, 21, 94, 83, 3, 61, 117, 94, 32, 126, 66, 46, 12, 48, 53, 81, 29, 120, 84, 105,
+ 101, 49, 71, 20, 19, 25, 49, 126, 21, 86, 111, 69, 51, 45, 53, 40, 11, 91, 20, 77, 32, 5, 90,
+ 23, 19, 74, 121, 113, 118, 33, 19, 112, 17, 34, 30, 108, 77, 59, 39, 91, 90, 18, 83, 63, 105, 43,
+ 30, 54, 3, 106, 52, 41, 61, 49, 112, 5, 98, 40, 24, 14, 117, 20, 85, 66, 32, 42, 59, 66, 2,
+ 98, 16, 76, 27, 85, 58, 17, 42, 69, 109, 97, 2, 18, 112, 86, 127, 114, 45, 69, 45, 46, 45, 28,
+ 97, 42, 24, 39, 126, 42, 74, 47, 101, 58, 106, 42, 58, 123, 7, 111, 102, 36, 87, 126, 28, 115, 68,
+ 38, 125, 49, 7, 87, 114, 125, 27, 60, 57, 124, 89, 21, 109, 74, 64, 88, 11, 28, 4, 58, 29, 13,
+ 119, 1, 15, 26, 40, 99, 7, 68, 17, 105, 103, 75, 29, 48, 40, 1, 116, 0, 76, 65, 37, 52, 102,
+ 69, 12, 71, 19, 108, 57, 103, 45, 84, 46, 53, 39, 82, 3, 113, 75, 18, 116, 13, 40, 93, 78, 5,
+ 94, 112, 90, 40, 126, 79, 91, 89, 29, 58, 83, 48, 66, 101, 4, 32, 68, 96, 98, 36, 45, 111, 29,
+ 60, 95, 123, 38, 111, 123, 38, 35, 98, 12, 57, 126, 43, 19, 21, 108, 77, 71, 40, 109, 112, 122, 83,
+ 77, 70, 76, 5, 14, 115, 100, 88, 1, 2, 75, 12, 15, 74, 8, 91, 108, 96, 27, 114, 49, 19, 71,
+ 126, 33, 81, 93, 93, 11, 9, 87, 76, 32, 28, 102, 16, 41, 99, 75, 87, 11, 120, 126, 89, 62, 91,
+ 123, 89, 44, 44, 22, 2, 127, 101, 122, 90, 27, 74, 45, 107, 119, 103, 123, 68, 12, 7, 79, 104, 102,
+ 88, 78, 84, 119, 2, 41, 57, 31, 40, 109, 34, 64, 51, 110, 87, 125, 59, 32, 66, 95, 8, 114, 65,
+ 11, 53, 113, 59, 24, 57, 14, 60, 28, 50, 107, 50, 28, 106, 8, 127, 95, 61, 116, 32, 114, 22, 90,
+ 72, 106, 6, 49, 83, 97, 84, 3, 70, 75, 124, 88, 112, 23, 77, 2, 114, 84, 64, 103, 118, 85, 4,
+ 109, 40, 15, 30, 31, 6, 117, 95, 99, 107, 41, 23, 124, 76, 86, 127, 89, 86, 87, 76, 106, 73, 63,
+ 31, 84, 34, 102, 51, 48, 99, 58, 38, 124, 20, 20, 98, 8, 53, 1, 106, 126, 48, 103, 117, 26, 43,
+ 106, 115, 35, 73, 50, 48, 78, 3, 108, 66, 119, 39, 109, 53, 58, 6, 87, 103, 54, 98, 83, 56, 76,
+ 37, 9, 99, 8, 69, 37, 46, 19, 87, 13, 97, 42, 29, 56, 110, 28, 41, 20, 34, 119, 107, 56, 53,
+ 124, 59, 120, 13, 85, 1, 121, 22, 38, 96, 47, 117, 44, 51, 87, 102, 110, 119, 116, 83, 52, 47, 17,
+ 113, 3, 83, 95, 15, 72, 119, 127, 28, 41, 20, 36, 23, 34, 90, 40, 13, 124, 80, 0, 51, 122, 20,
+ 66, 14, 122, 83, 35, 115, 115, 31, 77, 42, 82, 104, 50, 91, 90, 106, 57, 5, 53, 42, 41, 35, 58,
+ 94, 43, 113, 88, 42, 9, 119, 125, 114, 89, 90, 83, 28, 90, 30, 87, 111, 35, 42, 6, 63, 49, 96,
+ 33, 61, 13, 54, 110, 113, 12, 54, 93, 117, 122, 105, 89, 34, 120, 117, 6, 97, 111, 72, 96, 71, 1,
+ 92, 24, 5, 50, 29, 100, 96, 61, 72, 35, 118, 93, 93, 72, 21, 95, 5, 104, 104, 90, 110, 24, 51,
+ 120, 10, 64, 34, 122, 31, 34, 90, 8, 61, 11, 119, 61, 111, 125, 24, 23, 72, 85, 100, 72, 54, 113,
+ 18, 6, 56, 9, 27, 26, 97, 127, 110, 49, 106, 76, 11, 83, 98, 123, 92, 14, 68, 121, 26, 3, 120,
+ 17, 24, 73, 8, 14, 39, 27, 95, 71, 67, 4, 58, 58, 5, 111, 92, 127, 6, 10, 94, 75, 69, 78,
+ 70, 107, 11, 117, 98, 99, 95, 95, 50, 3, 91, 71, 27, 35, 117, 56, 18, 92, 51, 66, 28, 96, 103,
+ 109, 111, 10, 95, 109, 110, 127, 27, 46, 67, 43, 79, 82, 31, 19, 8, 45, 9, 50, 30, 79, 97, 78,
+ 69, 119, 2, 46, 18, 50, 31, 85, 126, 78, 7, 67, 42, 22, 45, 25, 3, 103, 59, 36, 91, 41, 6,
+ 60, 42, 79, 95, 123, 4, 27, 58, 120, 6, 28, 81, 79, 112, 106, 96, 52, 18, 68, 69, 38, 74, 27,
+ 22, 50, 74, 103, 34, 120, 93, 122};
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill/biases_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/biases_data.h
new file mode 100644
index 00000000..e6f7ade2
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/biases_data.h
@@ -0,0 +1,6 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#pragma once
+#include
+
+const int64_t *int16xint8_spill_biases = NULL;
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill/config_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/config_data.h
new file mode 100644
index 00000000..7aa2ee73
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/config_data.h
@@ -0,0 +1,24 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#pragma once
+#define INT16XINT8_SPILL_OUT_CH 9
+#define INT16XINT8_SPILL_IN_CH 55
+#define INT16XINT8_SPILL_INPUT_W 10
+#define INT16XINT8_SPILL_INPUT_H 8
+#define INT16XINT8_SPILL_DST_SIZE 90
+#define INT16XINT8_SPILL_INPUT_SIZE 4400
+#define INT16XINT8_SPILL_OUT_ACTIVATION_MIN 32667
+#define INT16XINT8_SPILL_OUT_ACTIVATION_MAX 32767
+#define INT16XINT8_SPILL_INPUT_BATCHES 1
+#define INT16XINT8_SPILL_FILTER_X 9
+#define INT16XINT8_SPILL_FILTER_Y 4
+#define INT16XINT8_SPILL_STRIDE_X 1
+#define INT16XINT8_SPILL_STRIDE_Y 1
+#define INT16XINT8_SPILL_PAD_X 0
+#define INT16XINT8_SPILL_PAD_Y 0
+#define INT16XINT8_SPILL_OUTPUT_W 2
+#define INT16XINT8_SPILL_OUTPUT_H 5
+#define INT16XINT8_SPILL_INPUT_OFFSET 0
+#define INT16XINT8_SPILL_OUTPUT_OFFSET 0
+#define INT16XINT8_SPILL_DILATION_X 1
+#define INT16XINT8_SPILL_DILATION_Y 1
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill/input_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/input_data.h
new file mode 100644
index 00000000..dd1ccfce
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/input_data.h
@@ -0,0 +1,281 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#pragma once
+#include
+
+const int16_t int16xint8_spill_input[4400] = {
+ 32755, 32727, 32721, 32721, 32701, 32721, 32750, 32682, 32669, 32691, 32714, 32670, 32699, 32722, 32730, 32726,
+ 32684, 32730, 32734, 32682, 32699, 32743, 32697, 32740, 32677, 32731, 32721, 32667, 32712, 32747, 32674, 32684,
+ 32751, 32713, 32737, 32672, 32696, 32728, 32761, 32690, 32740, 32687, 32672, 32757, 32691, 32742, 32719, 32687,
+ 32672, 32672, 32717, 32719, 32753, 32722, 32695, 32695, 32741, 32673, 32701, 32721, 32750, 32739, 32731, 32743,
+ 32679, 32762, 32678, 32725, 32675, 32716, 32741, 32735, 32757, 32762, 32719, 32709, 32766, 32738, 32694, 32761,
+ 32702, 32764, 32694, 32758, 32716, 32744, 32736, 32701, 32720, 32766, 32710, 32685, 32730, 32743, 32676, 32756,
+ 32685, 32692, 32693, 32700, 32715, 32708, 32761, 32693, 32732, 32726, 32674, 32766, 32676, 32680, 32705, 32678,
+ 32707, 32704, 32713, 32733, 32761, 32753, 32731, 32692, 32731, 32704, 32753, 32667, 32740, 32738, 32755, 32683,
+ 32673, 32671, 32681, 32723, 32751, 32671, 32766, 32692, 32724, 32764, 32723, 32673, 32679, 32697, 32678, 32761,
+ 32726, 32698, 32677, 32735, 32690, 32754, 32692, 32752, 32737, 32761, 32674, 32744, 32762, 32731, 32720, 32732,
+ 32749, 32713, 32691, 32742, 32675, 32707, 32736, 32712, 32681, 32700, 32737, 32752, 32764, 32742, 32725, 32708,
+ 32732, 32687, 32681, 32680, 32672, 32680, 32760, 32712, 32719, 32689, 32713, 32757, 32710, 32757, 32681, 32704,
+ 32674, 32670, 32714, 32683, 32740, 32757, 32728, 32751, 32720, 32722, 32725, 32676, 32684, 32720, 32688, 32681,
+ 32725, 32705, 32679, 32714, 32750, 32758, 32721, 32742, 32760, 32691, 32698, 32761, 32708, 32667, 32670, 32745,
+ 32740, 32748, 32733, 32727, 32739, 32763, 32716, 32683, 32754, 32669, 32746, 32746, 32696, 32764, 32736, 32720,
+ 32720, 32683, 32714, 32727, 32756, 32706, 32761, 32691, 32718, 32762, 32692, 32736, 32716, 32733, 32761, 32719,
+ 32720, 32698, 32710, 32733, 32732, 32722, 32764, 32732, 32690, 32766, 32694, 32667, 32688, 32695, 32689, 32737,
+ 32706, 32705, 32711, 32676, 32683, 32730, 32685, 32741, 32762, 32742, 32692, 32696, 32674, 32757, 32721, 32762,
+ 32673, 32673, 32758, 32708, 32758, 32722, 32682, 32674, 32748, 32745, 32688, 32760, 32683, 32756, 32724, 32668,
+ 32694, 32753, 32687, 32746, 32738, 32680, 32759, 32676, 32688, 32689, 32736, 32702, 32689, 32762, 32713, 32752,
+ 32679, 32682, 32682, 32677, 32685, 32737, 32695, 32766, 32667, 32759, 32731, 32714, 32668, 32732, 32715, 32700,
+ 32715, 32755, 32737, 32694, 32729, 32766, 32734, 32762, 32701, 32674, 32726, 32682, 32766, 32748, 32749, 32743,
+ 32762, 32679, 32713, 32756, 32751, 32677, 32734, 32759, 32727, 32668, 32750, 32695, 32738, 32742, 32702, 32755,
+ 32698, 32710, 32733, 32745, 32765, 32713, 32680, 32710, 32681, 32763, 32725, 32697, 32668, 32723, 32689, 32711,
+ 32686, 32743, 32685, 32724, 32750, 32722, 32679, 32751, 32726, 32763, 32686, 32751, 32703, 32668, 32745, 32748,
+ 32739, 32691, 32673, 32756, 32735, 32727, 32715, 32747, 32734, 32722, 32706, 32716, 32721, 32766, 32702, 32744,
+ 32704, 32668, 32726, 32763, 32713, 32720, 32676, 32738, 32684, 32672, 32747, 32735, 32680, 32693, 32739, 32748,
+ 32689, 32718, 32704, 32722, 32676, 32676, 32692, 32756, 32766, 32713, 32713, 32690, 32749, 32754, 32707, 32751,
+ 32707, 32760, 32699, 32759, 32735, 32696, 32745, 32743, 32736, 32755, 32686, 32766, 32710, 32667, 32712, 32701,
+ 32733, 32669, 32755, 32756, 32685, 32762, 32737, 32754, 32733, 32728, 32722, 32700, 32690, 32722, 32751, 32668,
+ 32695, 32737, 32676, 32741, 32724, 32758, 32714, 32684, 32706, 32703, 32699, 32743, 32682, 32719, 32759, 32737,
+ 32685, 32752, 32757, 32753, 32721, 32748, 32753, 32679, 32760, 32681, 32708, 32691, 32747, 32732, 32708, 32679,
+ 32678, 32737, 32716, 32700, 32723, 32694, 32707, 32721, 32728, 32725, 32749, 32723, 32722, 32713, 32688, 32675,
+ 32717, 32764, 32709, 32733, 32738, 32755, 32678, 32676, 32714, 32706, 32689, 32727, 32730, 32720, 32681, 32676,
+ 32676, 32763, 32692, 32722, 32714, 32694, 32746, 32682, 32723, 32712, 32678, 32742, 32722, 32731, 32761, 32756,
+ 32677, 32688, 32741, 32729, 32694, 32730, 32722, 32679, 32754, 32674, 32739, 32739, 32700, 32739, 32669, 32730,
+ 32756, 32744, 32689, 32765, 32682, 32689, 32685, 32680, 32758, 32764, 32678, 32668, 32679, 32743, 32697, 32725,
+ 32690, 32696, 32763, 32689, 32734, 32737, 32699, 32687, 32675, 32760, 32695, 32692, 32677, 32679, 32706, 32725,
+ 32693, 32762, 32763, 32675, 32685, 32672, 32748, 32749, 32701, 32746, 32730, 32753, 32731, 32738, 32681, 32677,
+ 32676, 32687, 32747, 32761, 32759, 32719, 32676, 32733, 32753, 32686, 32686, 32758, 32706, 32703, 32759, 32704,
+ 32669, 32725, 32744, 32686, 32721, 32764, 32754, 32729, 32731, 32677, 32741, 32728, 32679, 32722, 32678, 32674,
+ 32672, 32672, 32676, 32681, 32729, 32706, 32726, 32669, 32694, 32677, 32762, 32702, 32745, 32739, 32729, 32702,
+ 32673, 32739, 32715, 32720, 32723, 32715, 32753, 32704, 32683, 32732, 32763, 32766, 32683, 32724, 32733, 32759,
+ 32764, 32704, 32724, 32747, 32751, 32719, 32673, 32752, 32765, 32737, 32707, 32677, 32679, 32688, 32681, 32671,
+ 32721, 32689, 32720, 32708, 32702, 32722, 32707, 32691, 32716, 32710, 32764, 32672, 32762, 32742, 32749, 32749,
+ 32735, 32753, 32718, 32677, 32753, 32672, 32765, 32699, 32694, 32736, 32728, 32743, 32696, 32739, 32740, 32705,
+ 32677, 32670, 32758, 32674, 32676, 32766, 32702, 32750, 32696, 32674, 32720, 32725, 32701, 32679, 32760, 32712,
+ 32714, 32714, 32698, 32669, 32763, 32755, 32669, 32765, 32754, 32735, 32729, 32676, 32682, 32741, 32704, 32698,
+ 32725, 32748, 32757, 32685, 32748, 32680, 32671, 32718, 32733, 32742, 32737, 32693, 32701, 32675, 32700, 32698,
+ 32702, 32766, 32685, 32742, 32697, 32728, 32677, 32747, 32678, 32764, 32676, 32731, 32763, 32684, 32720, 32747,
+ 32695, 32765, 32753, 32726, 32711, 32686, 32734, 32699, 32765, 32704, 32765, 32719, 32690, 32714, 32739, 32763,
+ 32748, 32761, 32760, 32748, 32763, 32758, 32755, 32757, 32746, 32766, 32672, 32711, 32714, 32691, 32744, 32762,
+ 32671, 32737, 32752, 32753, 32739, 32687, 32726, 32726, 32750, 32687, 32673, 32754, 32676, 32765, 32690, 32714,
+ 32762, 32724, 32766, 32733, 32715, 32674, 32715, 32731, 32699, 32674, 32694, 32746, 32743, 32705, 32689, 32668,
+ 32745, 32718, 32675, 32678, 32743, 32706, 32730, 32679, 32680, 32704, 32692, 32717, 32689, 32761, 32724, 32729,
+ 32753, 32668, 32762, 32691, 32675, 32702, 32678, 32673, 32711, 32668, 32718, 32675, 32671, 32736, 32729, 32743,
+ 32692, 32730, 32721, 32710, 32759, 32750, 32731, 32698, 32688, 32699, 32671, 32763, 32673, 32713, 32671, 32749,
+ 32717, 32694, 32745, 32694, 32694, 32707, 32743, 32705, 32762, 32750, 32750, 32753, 32722, 32689, 32687, 32721,
+ 32739, 32725, 32738, 32741, 32706, 32702, 32739, 32684, 32727, 32704, 32691, 32682, 32709, 32738, 32760, 32686,
+ 32736, 32719, 32727, 32673, 32753, 32707, 32684, 32701, 32741, 32726, 32737, 32761, 32702, 32723, 32729, 32673,
+ 32676, 32691, 32687, 32759, 32678, 32765, 32667, 32733, 32757, 32676, 32751, 32720, 32757, 32722, 32720, 32736,
+ 32736, 32735, 32704, 32756, 32670, 32710, 32750, 32682, 32765, 32752, 32741, 32746, 32706, 32733, 32741, 32756,
+ 32668, 32678, 32726, 32711, 32731, 32721, 32743, 32749, 32711, 32727, 32692, 32721, 32680, 32711, 32759, 32747,
+ 32667, 32710, 32762, 32758, 32678, 32685, 32676, 32740, 32722, 32685, 32733, 32763, 32667, 32736, 32701, 32681,
+ 32680, 32693, 32690, 32762, 32673, 32713, 32744, 32694, 32747, 32723, 32671, 32709, 32722, 32762, 32694, 32709,
+ 32705, 32741, 32757, 32686, 32696, 32706, 32716, 32730, 32708, 32758, 32750, 32691, 32714, 32755, 32709, 32699,
+ 32728, 32677, 32761, 32718, 32752, 32699, 32738, 32739, 32724, 32707, 32667, 32679, 32761, 32711, 32685, 32761,
+ 32673, 32742, 32679, 32678, 32718, 32752, 32743, 32678, 32727, 32726, 32693, 32707, 32739, 32687, 32667, 32738,
+ 32669, 32676, 32684, 32694, 32730, 32723, 32727, 32687, 32689, 32679, 32749, 32690, 32758, 32675, 32721, 32716,
+ 32756, 32732, 32721, 32700, 32757, 32684, 32702, 32694, 32679, 32698, 32740, 32758, 32705, 32759, 32698, 32709,
+ 32716, 32669, 32751, 32717, 32756, 32681, 32689, 32685, 32724, 32757, 32732, 32705, 32669, 32749, 32709, 32728,
+ 32696, 32674, 32754, 32766, 32705, 32722, 32759, 32712, 32674, 32742, 32686, 32673, 32742, 32703, 32751, 32697,
+ 32706, 32718, 32763, 32745, 32719, 32717, 32668, 32719, 32744, 32744, 32674, 32717, 32683, 32686, 32671, 32668,
+ 32727, 32732, 32731, 32738, 32707, 32713, 32720, 32686, 32669, 32715, 32739, 32748, 32757, 32712, 32765, 32723,
+ 32717, 32678, 32741, 32703, 32687, 32692, 32722, 32738, 32669, 32672, 32734, 32765, 32683, 32698, 32680, 32708,
+ 32668, 32695, 32704, 32724, 32745, 32675, 32673, 32718, 32738, 32681, 32714, 32744, 32697, 32709, 32680, 32667,
+ 32703, 32762, 32741, 32738, 32757, 32723, 32717, 32762, 32725, 32765, 32731, 32700, 32689, 32760, 32672, 32670,
+ 32734, 32672, 32738, 32678, 32681, 32686, 32705, 32761, 32742, 32745, 32753, 32751, 32731, 32742, 32766, 32759,
+ 32755, 32675, 32688, 32676, 32703, 32760, 32738, 32747, 32690, 32686, 32760, 32724, 32754, 32724, 32676, 32683,
+ 32671, 32716, 32725, 32728, 32674, 32719, 32670, 32673, 32720, 32679, 32695, 32729, 32762, 32719, 32717, 32746,
+ 32719, 32758, 32668, 32668, 32705, 32763, 32684, 32719, 32674, 32680, 32707, 32719, 32738, 32723, 32738, 32741,
+ 32714, 32719, 32691, 32685, 32752, 32746, 32679, 32711, 32673, 32765, 32691, 32698, 32669, 32694, 32700, 32763,
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+ 32750, 32732, 32695, 32683, 32694, 32715, 32697, 32698, 32734, 32698, 32734, 32718, 32758, 32721, 32676, 32685,
+ 32678, 32690, 32734, 32719, 32752, 32704, 32707, 32752, 32685, 32754, 32737, 32733, 32745, 32762, 32709, 32746,
+ 32726, 32758, 32750, 32673, 32670, 32735, 32701, 32761, 32732, 32702, 32747, 32669, 32744, 32757, 32717, 32704,
+ 32675, 32676, 32695, 32760, 32695, 32703, 32743, 32748, 32745, 32696, 32736, 32751, 32735, 32695, 32763, 32674,
+ 32736, 32668, 32709, 32689, 32678, 32720, 32720, 32690, 32686, 32686, 32714, 32751, 32752, 32698, 32763, 32670,
+ 32697, 32700, 32720, 32719, 32681, 32728, 32728, 32714, 32739, 32687, 32682, 32696, 32720, 32700, 32728, 32764,
+ 32713, 32698, 32715, 32674, 32721, 32709, 32696, 32688, 32733, 32762, 32752, 32739, 32703, 32723, 32750, 32736,
+ 32730, 32676, 32742, 32685, 32762, 32766, 32673, 32693, 32753, 32702, 32758, 32716, 32679, 32690, 32701, 32672,
+ 32746, 32727, 32745, 32741, 32677, 32707, 32759, 32694, 32753, 32678, 32716, 32736, 32717, 32739, 32720, 32667,
+ 32748, 32753, 32683, 32684, 32746, 32719, 32751, 32711, 32762, 32680, 32733, 32742, 32672, 32738, 32743, 32735,
+ 32709, 32763, 32738, 32679, 32722, 32751, 32710, 32688, 32745, 32759, 32680, 32750, 32766, 32667, 32667, 32729,
+ 32764, 32699, 32712, 32760, 32704, 32693, 32669, 32694, 32703, 32701, 32669, 32744, 32721, 32762, 32711, 32740,
+ 32677, 32688, 32763, 32726, 32713, 32703, 32704, 32704, 32742, 32681, 32748, 32693, 32739, 32754, 32700, 32728,
+ 32698, 32738, 32736, 32696, 32716, 32729, 32709, 32694, 32675, 32723, 32694, 32685, 32676, 32748, 32674, 32698,
+ 32673, 32704, 32703, 32752, 32694, 32754, 32757, 32687, 32734, 32693, 32706, 32694, 32729, 32695, 32690, 32667,
+ 32710, 32678, 32744, 32671, 32686, 32733, 32682, 32672, 32712, 32763, 32727, 32710, 32678, 32712, 32733, 32708,
+ 32680, 32736, 32670, 32682, 32691, 32759, 32726, 32693, 32684, 32678, 32720, 32715, 32699, 32698, 32745, 32683,
+ 32687, 32714, 32684, 32685, 32687, 32683, 32740, 32742, 32681, 32764, 32714, 32698, 32752, 32734, 32755, 32697,
+ 32671, 32682, 32722, 32721, 32726, 32690, 32699, 32696, 32681, 32717, 32670, 32670, 32676, 32674, 32677, 32764,
+ 32675, 32739, 32766, 32682, 32697, 32717, 32679, 32725, 32682, 32669, 32667, 32756, 32741, 32688, 32717, 32677,
+ 32685, 32759, 32670, 32702, 32687, 32703, 32737, 32714, 32711, 32734, 32737, 32755, 32744, 32719, 32698, 32756,
+ 32708, 32686, 32764, 32747, 32677, 32727, 32678, 32697, 32701, 32719, 32677, 32712, 32678, 32743, 32713, 32712,
+ 32708, 32668, 32692, 32695, 32688, 32766, 32668, 32745, 32752, 32681, 32704, 32707, 32730, 32708, 32681, 32732,
+ 32703, 32705, 32730, 32761, 32741, 32740, 32689, 32752, 32703, 32695, 32697, 32667, 32765, 32706, 32669, 32750,
+ 32678, 32731, 32677, 32741, 32669, 32726, 32716, 32670, 32751, 32701, 32720, 32741, 32689, 32724, 32704, 32737,
+ 32714, 32704, 32739, 32682, 32760, 32728, 32736, 32710, 32703, 32749, 32751, 32677, 32711, 32748, 32748, 32756,
+ 32704, 32727, 32763, 32765, 32683, 32714, 32674, 32764, 32728, 32720, 32736, 32698, 32719, 32749, 32711, 32680,
+ 32696, 32714, 32689, 32690, 32708, 32687, 32756, 32747, 32720, 32722, 32736, 32704, 32738, 32729, 32743, 32723,
+ 32760, 32727, 32713, 32709, 32736, 32713, 32750, 32670, 32747, 32734, 32754, 32673, 32722, 32670, 32720, 32684,
+ 32688, 32669, 32764, 32677, 32693, 32764, 32756, 32713, 32736, 32732, 32737, 32695, 32728, 32760, 32684, 32736,
+ 32680, 32724, 32748, 32747, 32721, 32687, 32718, 32672, 32699, 32758, 32700, 32686, 32668, 32723, 32751, 32698,
+ 32751, 32684, 32748, 32734, 32766, 32734, 32754, 32721, 32675, 32734, 32727, 32670, 32734, 32685, 32737, 32673,
+ 32762, 32732, 32712, 32761, 32757, 32715, 32693, 32754, 32750, 32687, 32696, 32757, 32758, 32737, 32683, 32757,
+ 32709, 32696, 32696, 32757, 32752, 32671, 32678, 32729, 32671, 32681, 32686, 32724, 32714, 32668, 32674, 32694,
+ 32695, 32763, 32725, 32701, 32750, 32682, 32683, 32754, 32712, 32762, 32716, 32732, 32701, 32749, 32761, 32670,
+ 32671, 32716, 32755, 32727, 32696, 32758, 32732, 32715, 32687, 32696, 32694, 32720, 32716, 32727, 32715, 32724,
+ 32740, 32739, 32696, 32747, 32758, 32707, 32697, 32731, 32713, 32716, 32672, 32733, 32713, 32753, 32713, 32742};
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill/output_mult_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/output_mult_data.h
new file mode 100644
index 00000000..9b36b1a9
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/output_mult_data.h
@@ -0,0 +1,7 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#pragma once
+#include
+
+const int32_t int16xint8_spill_output_mult[9] =
+ {1100398998, 1100624659, 1100035049, 1099990516, 1100661379, 1100714636, 1100682734, 1100519706, 1100868940};
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/output_ref_data.h
new file mode 100644
index 00000000..ee02443f
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/output_ref_data.h
@@ -0,0 +1,12 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#pragma once
+#include
+
+const int16_t int16xint8_spill_output_ref[90] = {
+ 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32719, 32667, 32667, 32667, 32667, 32667, 32667,
+ 32667, 32667, 32719, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32719, 32667, 32667, 32667,
+ 32667, 32667, 32667, 32667, 32667, 32719, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32720,
+ 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32719, 32667, 32667, 32667, 32667, 32667, 32667,
+ 32667, 32667, 32720, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32719, 32667, 32667, 32667,
+ 32667, 32667, 32667, 32667, 32667, 32719, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32718};
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill/output_shift_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/output_shift_data.h
new file mode 100644
index 00000000..b9beffc0
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/output_shift_data.h
@@ -0,0 +1,6 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#pragma once
+#include
+
+const int32_t int16xint8_spill_output_shift[9] = {-16, -16, -16, -16, -16, -16, -16, -16, -16};
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill/test_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/test_data.h
new file mode 100644
index 00000000..4d61c5f2
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/test_data.h
@@ -0,0 +1,9 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#include "biases_data.h"
+#include "config_data.h"
+#include "input_data.h"
+#include "output_mult_data.h"
+#include "output_ref_data.h"
+#include "output_shift_data.h"
+#include "weights_data.h"
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill/weights_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/weights_data.h
new file mode 100644
index 00000000..35ff86df
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/weights_data.h
@@ -0,0 +1,781 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#pragma once
+#include
+
+const int8_t int16xint8_spill_weights[17820] = {
+ 79, 101, 58, 81, 42, 71, 35, 44, 102, 13, 8, 58, 12, 100, 15, 59, 85, 94, 86, 9, 50, 53, 63,
+ 17, 76, 82, 122, 74, 26, 41, 46, 92, 125, 70, 75, 32, 62, 53, 80, 21, 102, 110, 17, 21, 82, 104,
+ 120, 114, 26, 19, 33, 74, 116, 60, 96, 45, 110, 53, 102, 97, 124, 47, 8, 100, 25, 2, 75, 16, 36,
+ 115, 33, 86, 59, 8, 62, 108, 101, 35, 62, 106, 51, 87, 94, 102, 87, 68, 107, 60, 75, 24, 79, 50,
+ 96, 26, 21, 29, 26, 34, 102, 102, 31, 35, 118, 117, 104, 53, 82, 124, 2, 27, 62, 117, 70, 93, 14,
+ 7, 3, 76, 15, 27, 120, 17, 10, 1, 93, 75, 101, 63, 19, 37, 119, 11, 16, 109, 35, 91, 68, 58,
+ 45, 84, 25, 39, 18, 55, 118, 64, 45, 112, 95, 67, 121, 109, 75, 26, 62, 37, 82, 19, 46, 20, 108,
+ 91, 108, 47, 34, 102, 22, 126, 2, 30, 44, 98, 28, 58, 77, 20, 83, 17, 65, 101, 101, 11, 87, 42,
+ 101, 36, 55, 81, 24, 82, 101, 42, 2, 86, 17, 120, 10, 51, 75, 41, 66, 123, 73, 102, 9, 64, 14,
+ 57, 14, 3, 75, 91, 37, 92, 11, 35, 44, 28, 21, 124, 36, 13, 25, 3, 122, 24, 17, 46, 18, 113,
+ 32, 20, 3, 118, 89, 124, 20, 120, 44, 51, 74, 100, 120, 75, 68, 35, 106, 111, 7, 108, 99, 110, 113,
+ 28, 21, 68, 42, 119, 108, 9, 14, 39, 47, 59, 115, 21, 109, 93, 95, 53, 21, 18, 35, 106, 79, 43,
+ 54, 52, 118, 77, 63, 65, 105, 67, 12, 27, 12, 126, 71, 103, 23, 100, 72, 22, 7, 113, 1, 71, 66,
+ 84, 96, 57, 76, 117, 72, 6, 85, 126, 35, 22, 111, 28, 117, 12, 80, 82, 115, 40, 59, 38, 115, 7,
+ 100, 111, 20, 33, 77, 60, 119, 16, 123, 96, 94, 66, 99, 115, 47, 8, 109, 105, 56, 9, 8, 17, 116,
+ 97, 112, 47, 40, 100, 48, 16, 44, 61, 24, 107, 34, 67, 26, 92, 2, 78, 107, 89, 11, 8, 123, 74,
+ 90, 108, 84, 18, 28, 29, 70, 16, 115, 23, 106, 102, 64, 123, 56, 101, 125, 53, 122, 39, 70, 4, 39,
+ 41, 25, 72, 71, 69, 70, 62, 106, 57, 105, 125, 64, 113, 96, 27, 102, 37, 8, 83, 91, 38, 90, 63,
+ 118, 10, 35, 40, 121, 89, 50, 106, 30, 110, 120, 45, 18, 10, 85, 119, 23, 8, 15, 33, 34, 100, 84,
+ 74, 6, 111, 70, 47, 12, 30, 54, 102, 92, 92, 119, 83, 50, 95, 70, 106, 88, 77, 45, 73, 116, 127,
+ 104, 70, 22, 25, 83, 46, 67, 3, 82, 120, 93, 73, 43, 72, 81, 105, 100, 105, 84, 3, 32, 14, 53,
+ 117, 52, 106, 45, 31, 30, 123, 67, 95, 66, 53, 16, 58, 18, 19, 50, 109, 123, 25, 61, 13, 17, 91,
+ 25, 47, 116, 22, 64, 50, 28, 13, 22, 109, 1, 71, 102, 16, 25, 29, 38, 12, 32, 87, 65, 52, 2,
+ 83, 54, 46, 111, 48, 63, 36, 44, 44, 37, 31, 106, 33, 54, 39, 8, 103, 40, 15, 92, 14, 3, 75,
+ 26, 27, 107, 99, 93, 124, 22, 94, 38, 68, 25, 67, 108, 9, 25, 2, 14, 108, 2, 41, 36, 24, 125,
+ 41, 76, 119, 87, 103, 64, 60, 52, 98, 71, 40, 104, 89, 77, 49, 12, 122, 18, 23, 82, 93, 103, 94,
+ 78, 30, 68, 57, 23, 79, 113, 40, 56, 118, 22, 78, 22, 67, 53, 37, 29, 75, 30, 79, 31, 101, 104,
+ 68, 117, 19, 72, 5, 89, 39, 102, 4, 9, 36, 29, 106, 57, 98, 19, 28, 126, 10, 52, 9, 16, 29,
+ 103, 38, 126, 64, 78, 51, 30, 32, 103, 46, 48, 118, 59, 100, 31, 42, 7, 78, 16, 78, 124, 49, 16,
+ 36, 59, 22, 85, 42, 107, 45, 12, 68, 51, 104, 57, 77, 113, 101, 89, 43, 91, 97, 76, 114, 76, 126,
+ 29, 15, 102, 35, 65, 88, 46, 98, 103, 15, 64, 89, 18, 47, 77, 22, 59, 23, 29, 74, 105, 16, 33,
+ 74, 110, 3, 18, 113, 127, 12, 4, 84, 96, 66, 24, 9, 118, 71, 33, 35, 100, 67, 36, 21, 83, 101,
+ 32, 124, 82, 14, 84, 27, 59, 20, 34, 25, 84, 85, 114, 79, 72, 5, 42, 86, 42, 105, 109, 44, 119,
+ 29, 110, 1, 98, 43, 94, 77, 43, 117, 51, 29, 12, 125, 23, 26, 75, 32, 43, 58, 11, 108, 82, 53,
+ 63, 67, 109, 1, 30, 92, 53, 124, 45, 45, 15, 116, 70, 95, 31, 121, 44, 5, 92, 79, 48, 56, 40,
+ 65, 101, 52, 125, 85, 19, 114, 44, 64, 19, 105, 88, 26, 38, 39, 124, 43, 54, 76, 13, 86, 93, 45,
+ 3, 33, 105, 53, 108, 61, 118, 59, 125, 93, 113, 22, 97, 96, 4, 79, 113, 39, 123, 93, 114, 87, 28,
+ 92, 90, 27, 8, 103, 108, 6, 87, 31, 119, 74, 89, 39, 71, 123, 116, 108, 92, 83, 92, 64, 54, 23,
+ 46, 98, 15, 48, 120, 18, 124, 80, 79, 27, 24, 95, 90, 53, 87, 59, 65, 14, 40, 107, 74, 62, 74,
+ 104, 77, 36, 104, 86, 0, 73, 103, 18, 93, 12, 87, 102, 62, 110, 34, 20, 17, 69, 3, 101, 31, 37,
+ 117, 117, 113, 51, 24, 76, 78, 64, 51, 61, 39, 26, 45, 61, 75, 16, 57, 39, 79, 59, 106, 35, 76,
+ 27, 18, 73, 42, 6, 58, 92, 102, 59, 30, 17, 103, 24, 88, 61, 70, 41, 101, 8, 118, 97, 106, 69,
+ 10, 0, 97, 8, 11, 89, 63, 13, 65, 81, 55, 51, 10, 11, 17, 105, 13, 113, 123, 113, 93, 54, 123,
+ 35, 113, 98, 14, 99, 40, 68, 106, 45, 48, 116, 115, 66, 71, 17, 46, 123, 49, 92, 122, 110, 103, 56,
+ 54, 17, 102, 16, 103, 35, 117, 23, 18, 41, 11, 67, 107, 13, 64, 59, 73, 23, 39, 69, 10, 27, 93,
+ 61, 2, 26, 45, 80, 8, 85, 64, 60, 110, 74, 77, 72, 67, 50, 38, 6, 52, 113, 55, 84, 62, 6,
+ 90, 109, 15, 43, 72, 55, 106, 8, 89, 5, 120, 120, 38, 110, 37, 93, 59, 126, 35, 117, 59, 86, 125,
+ 74, 75, 76, 95, 19, 82, 20, 91, 7, 39, 64, 5, 14, 57, 39, 1, 13, 22, 8, 53, 66, 6, 13,
+ 83, 96, 32, 83, 82, 99, 121, 104, 65, 109, 79, 35, 3, 44, 1, 90, 1, 116, 72, 43, 5, 109, 59,
+ 76, 127, 25, 85, 72, 34, 126, 114, 40, 43, 117, 61, 92, 52, 117, 62, 124, 102, 6, 26, 95, 112, 9,
+ 56, 121, 13, 102, 86, 27, 110, 71, 6, 123, 97, 123, 87, 52, 118, 107, 37, 42, 10, 46, 12, 82, 126,
+ 8, 71, 16, 40, 84, 18, 50, 124, 67, 68, 65, 18, 117, 58, 108, 67, 66, 55, 87, 60, 102, 34, 77,
+ 27, 63, 101, 12, 46, 84, 82, 50, 110, 87, 66, 10, 17, 110, 60, 111, 57, 105, 123, 27, 99, 85, 92,
+ 87, 66, 58, 33, 16, 21, 15, 57, 61, 121, 52, 94, 8, 109, 111, 24, 22, 31, 125, 94, 33, 90, 19,
+ 64, 89, 124, 20, 60, 126, 39, 101, 69, 81, 42, 18, 64, 100, 50, 62, 108, 66, 58, 80, 38, 72, 40,
+ 22, 42, 2, 47, 95, 35, 22, 78, 46, 21, 8, 75, 84, 98, 99, 13, 127, 87, 108, 52, 62, 104, 49,
+ 43, 102, 63, 35, 68, 37, 30, 47, 49, 1, 105, 114, 116, 62, 19, 121, 105, 5, 113, 111, 26, 70, 122,
+ 4, 119, 95, 122, 75, 52, 107, 45, 1, 42, 27, 40, 108, 97, 117, 103, 13, 118, 15, 65, 90, 25, 116,
+ 33, 20, 9, 116, 80, 37, 11, 39, 92, 33, 81, 100, 19, 60, 20, 11, 95, 114, 85, 116, 23, 52, 52,
+ 7, 39, 120, 43, 34, 25, 116, 39, 101, 106, 10, 65, 84, 54, 87, 85, 55, 41, 44, 123, 97, 49, 16,
+ 76, 91, 24, 114, 92, 51, 79, 34, 5, 40, 21, 72, 82, 120, 68, 20, 113, 101, 50, 51, 27, 56, 22,
+ 100, 48, 21, 113, 48, 90, 30, 8, 52, 76, 18, 113, 54, 51, 22, 120, 29, 105, 58, 51, 98, 99, 40,
+ 56, 45, 47, 125, 42, 91, 112, 72, 124, 81, 70, 99, 87, 1, 97, 40, 20, 8, 57, 112, 31, 59, 124,
+ 101, 44, 0, 40, 93, 36, 6, 106, 70, 48, 60, 100, 112, 38, 91, 22, 103, 31, 19, 10, 46, 70, 75,
+ 29, 10, 120, 96, 5, 37, 101, 124, 0, 46, 89, 60, 2, 95, 27, 58, 7, 46, 63, 3, 14, 61, 10,
+ 123, 7, 51, 22, 18, 39, 38, 9, 125, 9, 75, 47, 46, 24, 53, 4, 80, 35, 98, 38, 114, 36, 86,
+ 68, 89, 11, 73, 16, 117, 75, 81, 25, 98, 127, 97, 79, 92, 22, 16, 25, 32, 74, 9, 52, 54, 95,
+ 33, 60, 66, 101, 23, 57, 7, 77, 33, 121, 37, 6, 68, 29, 71, 79, 33, 106, 117, 85, 46, 49, 3,
+ 84, 68, 126, 49, 119, 8, 79, 57, 16, 40, 82, 91, 60, 94, 3, 49, 4, 40, 125, 27, 7, 14, 78,
+ 19, 83, 19, 117, 23, 83, 0, 39, 107, 65, 63, 44, 61, 112, 65, 68, 55, 19, 52, 11, 79, 82, 119,
+ 59, 8, 114, 92, 32, 120, 27, 119, 48, 57, 102, 46, 94, 38, 46, 62, 26, 81, 106, 34, 103, 25, 108,
+ 51, 38, 20, 8, 78, 76, 33, 76, 72, 92, 74, 114, 112, 55, 104, 16, 89, 60, 89, 39, 124, 65, 49,
+ 118, 15, 21, 81, 21, 73, 97, 111, 66, 89, 122, 104, 82, 9, 25, 17, 81, 56, 27, 117, 58, 101, 41,
+ 92, 72, 18, 2, 16, 40, 42, 59, 46, 106, 59, 91, 75, 117, 7, 82, 95, 69, 45, 54, 22, 41, 94,
+ 55, 0, 36, 73, 75, 14, 0, 17, 81, 74, 47, 127, 13, 60, 56, 34, 92, 20, 53, 41, 64, 20, 55,
+ 102, 118, 15, 105, 7, 22, 70, 93, 53, 48, 84, 107, 16, 89, 86, 52, 39, 26, 114, 51, 67, 71, 87,
+ 6, 71, 49, 70, 59, 9, 50, 105, 121, 77, 100, 47, 119, 8, 61, 12, 2, 50, 121, 89, 46, 109, 56,
+ 114, 69, 96, 49, 104, 58, 80, 35, 125, 25, 114, 106, 36, 111, 34, 118, 93, 16, 48, 9, 85, 61, 39,
+ 87, 50, 88, 14, 87, 48, 84, 110, 74, 61, 3, 33, 76, 94, 96, 57, 110, 52, 38, 29, 18, 101, 111,
+ 68, 83, 6, 9, 12, 124, 3, 30, 62, 98, 83, 116, 11, 127, 91, 60, 105, 23, 79, 47, 63, 35, 63,
+ 16, 62, 57, 59, 105, 103, 65, 50, 103, 3, 120, 5, 66, 87, 30, 84, 127, 87, 113, 15, 78, 96, 1,
+ 0, 107, 9, 116, 41, 82, 111, 53, 97, 114, 91, 55, 34, 77, 38, 54, 105, 88, 42, 50, 27, 54, 68,
+ 54, 79, 114, 71, 97, 34, 6, 28, 6, 3, 88, 125, 50, 114, 39, 48, 29, 104, 87, 108, 100, 44, 50,
+ 39, 64, 50, 14, 80, 50, 70, 11, 21, 72, 38, 96, 44, 115, 15, 32, 77, 70, 124, 94, 116, 42, 95,
+ 38, 100, 102, 2, 28, 124, 66, 64, 11, 116, 112, 15, 11, 74, 111, 85, 27, 8, 71, 46, 59, 63, 19,
+ 65, 70, 62, 70, 76, 95, 51, 71, 74, 49, 43, 108, 38, 19, 74, 79, 83, 118, 24, 104, 120, 59, 65,
+ 107, 36, 97, 28, 15, 24, 7, 114, 70, 109, 43, 36, 36, 6, 63, 30, 18, 120, 115, 86, 39, 61, 127,
+ 69, 57, 126, 34, 94, 60, 49, 6, 18, 14, 1, 96, 77, 45, 119, 19, 118, 123, 120, 34, 81, 22, 61,
+ 61, 22, 80, 18, 75, 84, 49, 43, 86, 45, 28, 36, 97, 16, 1, 0, 85, 118, 61, 17, 96, 21, 10,
+ 81, 20, 3, 117, 29, 57, 2, 81, 62, 17, 76, 77, 64, 78, 110, 44, 107, 29, 48, 47, 66, 84, 36,
+ 112, 125, 12, 108, 62, 95, 119, 121, 56, 122, 93, 35, 52, 37, 120, 89, 18, 73, 127, 100, 14, 62, 74,
+ 53, 15, 13, 51, 42, 64, 80, 116, 50, 42, 22, 65, 88, 23, 17, 7, 83, 125, 4, 40, 109, 37, 1,
+ 93, 85, 85, 88, 80, 5, 47, 17, 60, 100, 68, 95, 104, 26, 69, 111, 104, 29, 101, 57, 115, 43, 64,
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+ 48, 8, 45, 87, 42, 28, 49, 56, 98, 107, 111, 63, 55, 73, 28, 43, 96, 120, 62, 77, 25, 47, 32,
+ 40, 48, 101, 106, 115, 32, 5, 92, 86, 27, 115, 127, 107, 9, 14, 49, 121, 24, 60, 61, 26, 57, 10,
+ 21, 51, 56, 69, 69, 35, 79, 5, 109, 25, 79, 56, 52, 88, 88, 4, 38, 22, 54, 102, 85, 32, 80,
+ 72, 36, 58, 67, 80, 4, 6, 104, 82, 7, 53, 37, 48, 93, 42, 49, 49, 27, 61, 56, 7, 38, 83,
+ 113, 99, 83, 30, 82, 110, 25, 63, 88, 58, 70, 97, 50, 34, 20, 45, 71, 77, 89, 76, 12, 59, 107,
+ 79, 80, 125, 47, 85, 86, 43, 70, 80, 22, 36, 124, 44, 23, 7, 37, 50, 51, 46, 122, 5, 89, 1,
+ 47, 72, 30, 89, 10, 102, 103, 72, 75, 116, 121, 13, 104, 75, 43, 79, 106, 50, 56, 35, 25, 27, 10,
+ 73, 17, 58, 112, 102, 49, 72, 63, 84, 36, 126, 26, 76, 118, 50, 90, 113, 90, 118, 16, 10, 85, 127,
+ 79, 30, 50, 85, 29, 45, 103, 99, 52, 111, 57, 110, 85, 25, 16, 36, 118, 64, 41, 48, 26, 29, 62,
+ 40, 60, 37, 3, 73, 87, 112, 57, 64, 4, 97, 90, 84, 57, 18, 94, 92, 62, 97, 101, 50, 86, 34,
+ 29, 116, 59, 44, 124, 49, 9, 27, 71, 118, 53, 18, 76, 55, 11, 100, 61, 108, 115, 120, 49, 73, 80,
+ 42, 23, 74, 113, 35, 31, 38, 35, 121, 3, 96, 122, 28, 26, 20, 12, 67, 46, 84, 117, 30, 81, 58,
+ 125, 14, 39, 19, 95, 58, 44, 96, 122, 60, 39, 77, 109, 58, 10, 35, 73, 120, 101, 24, 87, 126, 72,
+ 90, 9, 1, 18, 12, 16, 87, 32, 71, 12, 10, 5, 123, 127, 64, 98, 81, 27, 68, 89, 57, 126, 31,
+ 94, 126, 60, 31, 109, 71, 120, 34, 1, 115, 75, 119, 26, 122, 104, 9, 101, 106, 52, 75, 46, 46, 54,
+ 37, 69, 38, 38, 65, 67, 68, 31, 40, 60, 110, 117, 8, 42, 45, 117, 89, 114, 11, 86, 15, 58, 27,
+ 113, 47, 71, 15, 49, 79, 50, 106, 10, 38, 80, 39, 86, 87, 74, 49, 126, 42, 99, 127, 80, 26, 110,
+ 31, 83, 110, 67, 32, 48, 66, 48, 110, 121, 120, 90, 36, 68, 67, 124, 68, 115, 26, 60, 112, 51, 25,
+ 83, 17, 84, 73, 125, 44, 42, 48, 75, 24, 33, 98, 88, 102, 110, 124, 6, 119, 118, 65, 47, 68, 88,
+ 19, 32, 22, 53, 51, 64, 11, 8, 25, 16, 8, 102, 18, 118, 97, 110, 52, 19, 70, 5, 111, 61, 103,
+ 17, 91, 11, 16, 39, 11, 43, 107, 115, 47, 95, 8, 8, 72, 31, 9, 62, 7, 50, 103, 67, 86, 121,
+ 83, 57, 123, 59, 67, 71, 40, 6, 119, 11, 98, 51, 103, 26, 54, 107, 100, 89};
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/biases_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/biases_data.h
new file mode 100644
index 00000000..ace28d6c
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/biases_data.h
@@ -0,0 +1,6 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#pragma once
+#include
+
+const int64_t *int16xint8_spill2_biases = NULL;
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/config_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/config_data.h
new file mode 100644
index 00000000..b79e38d8
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/config_data.h
@@ -0,0 +1,24 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#pragma once
+#define INT16XINT8_SPILL2_OUT_CH 9
+#define INT16XINT8_SPILL2_IN_CH 12
+#define INT16XINT8_SPILL2_INPUT_W 10
+#define INT16XINT8_SPILL2_INPUT_H 8
+#define INT16XINT8_SPILL2_DST_SIZE 90
+#define INT16XINT8_SPILL2_INPUT_SIZE 960
+#define INT16XINT8_SPILL2_OUT_ACTIVATION_MIN 32667
+#define INT16XINT8_SPILL2_OUT_ACTIVATION_MAX 32767
+#define INT16XINT8_SPILL2_INPUT_BATCHES 1
+#define INT16XINT8_SPILL2_FILTER_X 9
+#define INT16XINT8_SPILL2_FILTER_Y 4
+#define INT16XINT8_SPILL2_STRIDE_X 1
+#define INT16XINT8_SPILL2_STRIDE_Y 1
+#define INT16XINT8_SPILL2_PAD_X 0
+#define INT16XINT8_SPILL2_PAD_Y 0
+#define INT16XINT8_SPILL2_OUTPUT_W 2
+#define INT16XINT8_SPILL2_OUTPUT_H 5
+#define INT16XINT8_SPILL2_INPUT_OFFSET 0
+#define INT16XINT8_SPILL2_OUTPUT_OFFSET 0
+#define INT16XINT8_SPILL2_DILATION_X 1
+#define INT16XINT8_SPILL2_DILATION_Y 1
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/input_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/input_data.h
new file mode 100644
index 00000000..d81c3483
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/input_data.h
@@ -0,0 +1,66 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#pragma once
+#include
+
+const int16_t int16xint8_spill2_input[960] = {
+ 32732, 32696, 32712, 32689, 32702, 32755, 32747, 32756, 32690, 32671, 32733, 32703, 32714, 32668, 32687, 32730,
+ 32742, 32735, 32699, 32674, 32711, 32675, 32719, 32718, 32737, 32694, 32748, 32669, 32735, 32676, 32743, 32755,
+ 32740, 32719, 32746, 32688, 32719, 32733, 32739, 32689, 32766, 32703, 32674, 32687, 32728, 32704, 32708, 32691,
+ 32730, 32684, 32701, 32683, 32731, 32712, 32723, 32722, 32724, 32700, 32733, 32703, 32745, 32732, 32714, 32694,
+ 32732, 32754, 32695, 32741, 32750, 32739, 32688, 32722, 32756, 32674, 32681, 32759, 32741, 32697, 32706, 32747,
+ 32687, 32730, 32741, 32667, 32682, 32684, 32726, 32691, 32721, 32755, 32735, 32715, 32732, 32689, 32716, 32749,
+ 32677, 32692, 32694, 32739, 32715, 32717, 32723, 32717, 32716, 32736, 32678, 32701, 32694, 32705, 32688, 32718,
+ 32701, 32758, 32700, 32709, 32688, 32737, 32687, 32736, 32676, 32727, 32731, 32688, 32713, 32730, 32698, 32686,
+ 32762, 32722, 32685, 32720, 32754, 32753, 32711, 32726, 32760, 32759, 32680, 32702, 32683, 32761, 32713, 32699,
+ 32674, 32679, 32727, 32757, 32672, 32691, 32720, 32728, 32704, 32700, 32680, 32704, 32749, 32765, 32719, 32730,
+ 32683, 32751, 32736, 32718, 32696, 32674, 32674, 32690, 32747, 32690, 32763, 32706, 32758, 32720, 32710, 32735,
+ 32721, 32698, 32738, 32668, 32718, 32694, 32681, 32707, 32706, 32761, 32756, 32687, 32700, 32686, 32686, 32680,
+ 32751, 32761, 32715, 32744, 32697, 32712, 32686, 32725, 32764, 32746, 32739, 32739, 32733, 32763, 32718, 32720,
+ 32714, 32681, 32696, 32711, 32734, 32761, 32746, 32697, 32729, 32711, 32677, 32740, 32741, 32715, 32729, 32685,
+ 32685, 32726, 32728, 32707, 32763, 32731, 32676, 32729, 32711, 32714, 32739, 32753, 32729, 32716, 32735, 32712,
+ 32724, 32722, 32731, 32699, 32765, 32745, 32695, 32739, 32755, 32726, 32750, 32756, 32693, 32700, 32697, 32752,
+ 32726, 32732, 32722, 32684, 32724, 32706, 32737, 32755, 32684, 32764, 32703, 32687, 32733, 32671, 32699, 32726,
+ 32703, 32747, 32670, 32667, 32724, 32697, 32719, 32739, 32760, 32740, 32700, 32756, 32716, 32754, 32696, 32744,
+ 32750, 32732, 32761, 32760, 32751, 32668, 32753, 32698, 32748, 32667, 32760, 32746, 32725, 32708, 32711, 32706,
+ 32715, 32752, 32667, 32734, 32724, 32745, 32743, 32723, 32682, 32706, 32718, 32762, 32733, 32676, 32708, 32680,
+ 32761, 32673, 32710, 32749, 32704, 32738, 32684, 32681, 32756, 32669, 32691, 32726, 32760, 32742, 32725, 32667,
+ 32760, 32704, 32728, 32706, 32719, 32709, 32671, 32759, 32720, 32669, 32742, 32695, 32694, 32765, 32717, 32761,
+ 32730, 32712, 32714, 32687, 32750, 32734, 32730, 32720, 32745, 32688, 32752, 32679, 32749, 32723, 32708, 32688,
+ 32683, 32712, 32668, 32690, 32669, 32749, 32734, 32678, 32678, 32698, 32713, 32727, 32702, 32694, 32697, 32759,
+ 32730, 32749, 32745, 32696, 32733, 32701, 32672, 32730, 32748, 32707, 32747, 32758, 32711, 32691, 32688, 32681,
+ 32755, 32748, 32748, 32735, 32727, 32707, 32677, 32712, 32751, 32741, 32724, 32674, 32689, 32752, 32740, 32710,
+ 32764, 32676, 32729, 32674, 32688, 32757, 32709, 32724, 32710, 32683, 32764, 32762, 32713, 32765, 32669, 32667,
+ 32704, 32736, 32749, 32732, 32712, 32748, 32733, 32695, 32743, 32693, 32761, 32740, 32675, 32682, 32744, 32714,
+ 32765, 32668, 32757, 32711, 32756, 32701, 32721, 32706, 32733, 32715, 32720, 32744, 32739, 32740, 32670, 32757,
+ 32675, 32687, 32722, 32753, 32683, 32752, 32685, 32685, 32688, 32747, 32727, 32701, 32734, 32677, 32669, 32674,
+ 32675, 32706, 32753, 32695, 32759, 32731, 32687, 32754, 32738, 32680, 32702, 32752, 32714, 32731, 32730, 32749,
+ 32721, 32699, 32760, 32684, 32719, 32687, 32694, 32720, 32671, 32723, 32679, 32741, 32690, 32737, 32737, 32689,
+ 32684, 32685, 32687, 32734, 32729, 32755, 32736, 32742, 32756, 32710, 32734, 32766, 32725, 32701, 32756, 32682,
+ 32683, 32749, 32741, 32736, 32695, 32723, 32726, 32758, 32678, 32689, 32681, 32765, 32708, 32718, 32693, 32737,
+ 32724, 32677, 32720, 32721, 32680, 32683, 32752, 32765, 32696, 32765, 32745, 32684, 32699, 32681, 32731, 32729,
+ 32678, 32711, 32667, 32668, 32756, 32713, 32745, 32737, 32741, 32673, 32719, 32678, 32759, 32673, 32714, 32698,
+ 32670, 32759, 32702, 32691, 32710, 32744, 32686, 32715, 32739, 32726, 32721, 32758, 32701, 32682, 32727, 32737,
+ 32743, 32704, 32762, 32743, 32701, 32685, 32720, 32763, 32729, 32752, 32763, 32696, 32735, 32697, 32700, 32707,
+ 32695, 32700, 32697, 32744, 32714, 32704, 32708, 32741, 32739, 32669, 32719, 32743, 32674, 32712, 32715, 32701,
+ 32704, 32708, 32684, 32707, 32736, 32731, 32697, 32729, 32689, 32741, 32730, 32760, 32689, 32674, 32766, 32693,
+ 32689, 32736, 32678, 32685, 32672, 32753, 32683, 32761, 32691, 32700, 32709, 32708, 32672, 32672, 32763, 32742,
+ 32696, 32714, 32722, 32752, 32735, 32697, 32724, 32678, 32712, 32668, 32707, 32678, 32679, 32696, 32759, 32760,
+ 32709, 32741, 32724, 32717, 32689, 32705, 32725, 32722, 32681, 32736, 32684, 32695, 32721, 32669, 32706, 32740,
+ 32693, 32704, 32737, 32694, 32738, 32720, 32690, 32763, 32689, 32675, 32725, 32765, 32669, 32676, 32688, 32723,
+ 32750, 32669, 32763, 32707, 32755, 32720, 32740, 32688, 32766, 32721, 32734, 32723, 32685, 32700, 32756, 32736,
+ 32702, 32721, 32698, 32722, 32745, 32718, 32675, 32752, 32702, 32726, 32668, 32675, 32714, 32694, 32745, 32676,
+ 32743, 32731, 32745, 32759, 32758, 32708, 32700, 32703, 32752, 32725, 32708, 32683, 32720, 32732, 32686, 32760,
+ 32766, 32733, 32750, 32737, 32746, 32702, 32684, 32709, 32731, 32733, 32689, 32713, 32730, 32762, 32688, 32710,
+ 32716, 32674, 32722, 32667, 32678, 32710, 32747, 32688, 32715, 32708, 32738, 32679, 32720, 32709, 32705, 32713,
+ 32703, 32681, 32732, 32684, 32717, 32715, 32711, 32736, 32685, 32695, 32669, 32758, 32725, 32680, 32758, 32678,
+ 32670, 32720, 32705, 32715, 32743, 32703, 32711, 32717, 32683, 32736, 32732, 32700, 32728, 32726, 32717, 32716,
+ 32713, 32763, 32697, 32711, 32736, 32704, 32728, 32761, 32677, 32679, 32731, 32720, 32712, 32723, 32762, 32718,
+ 32699, 32751, 32755, 32694, 32736, 32670, 32679, 32676, 32701, 32760, 32735, 32716, 32726, 32714, 32703, 32667,
+ 32671, 32668, 32729, 32738, 32727, 32704, 32748, 32717, 32716, 32717, 32701, 32748, 32721, 32679, 32747, 32707,
+ 32757, 32696, 32712, 32757, 32693, 32711, 32741, 32687, 32763, 32747, 32730, 32727, 32698, 32702, 32678, 32699,
+ 32756, 32733, 32697, 32691, 32732, 32732, 32680, 32690, 32739, 32740, 32750, 32717, 32674, 32686, 32703, 32671,
+ 32726, 32672, 32738, 32724, 32765, 32690, 32749, 32714, 32694, 32691, 32725, 32676, 32699, 32725, 32713, 32762,
+ 32724, 32690, 32676, 32710, 32697, 32765, 32670, 32758, 32728, 32681, 32744, 32712, 32741, 32765, 32756, 32766,
+ 32713, 32706, 32737, 32758, 32733, 32763, 32670, 32667, 32674, 32726, 32718, 32732, 32735, 32734, 32703, 32673,
+ 32709, 32723, 32717, 32759, 32754, 32671, 32723, 32719, 32740, 32677, 32697, 32668, 32671, 32739, 32704, 32744};
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/output_mult_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/output_mult_data.h
new file mode 100644
index 00000000..0fd80c3c
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/output_mult_data.h
@@ -0,0 +1,7 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#pragma once
+#include
+
+const int32_t int16xint8_spill2_output_mult[9] =
+ {1231320420, 1228194292, 1228950954, 1231269879, 1231112138, 1229528901, 1230557932, 1230167876, 1229640034};
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/output_ref_data.h
new file mode 100644
index 00000000..ba5623b5
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/output_ref_data.h
@@ -0,0 +1,12 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#pragma once
+#include
+
+const int16_t int16xint8_spill2_output_ref[90] = {
+ 32716, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32718, 32667, 32667, 32667, 32667, 32667,
+ 32667, 32667, 32667, 32716, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32714, 32667, 32667,
+ 32667, 32667, 32667, 32667, 32667, 32667, 32716, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32667,
+ 32716, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32713, 32667, 32667, 32667, 32667, 32667,
+ 32667, 32667, 32667, 32714, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32713, 32667, 32667,
+ 32667, 32667, 32667, 32667, 32667, 32667, 32714, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32667};
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/output_shift_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/output_shift_data.h
new file mode 100644
index 00000000..074e382c
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/output_shift_data.h
@@ -0,0 +1,6 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#pragma once
+#include
+
+const int32_t int16xint8_spill2_output_shift[9] = {-14, -14, -14, -14, -14, -14, -14, -14, -14};
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/test_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/test_data.h
new file mode 100644
index 00000000..4d61c5f2
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/test_data.h
@@ -0,0 +1,9 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#include "biases_data.h"
+#include "config_data.h"
+#include "input_data.h"
+#include "output_mult_data.h"
+#include "output_ref_data.h"
+#include "output_shift_data.h"
+#include "weights_data.h"
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/weights_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/weights_data.h
new file mode 100644
index 00000000..4aee9323
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/weights_data.h
@@ -0,0 +1,176 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#pragma once
+#include
+
+const int8_t int16xint8_spill2_weights[3888] = {
+ 41, 91, 43, 84, 71, 88, 125, 36, 81, 15, 18, 116, 85, 94, 126, 54, 95, 93, 126, 68, 45, 123, 116,
+ 107, 119, 74, 90, 36, 77, 114, 21, 70, 67, 112, 103, 34, 32, 85, 37, 115, 67, 55, 97, 113, 86, 103,
+ 0, 82, 21, 101, 37, 67, 124, 6, 50, 83, 119, 75, 66, 83, 114, 75, 56, 105, 119, 33, 31, 75, 124,
+ 40, 40, 12, 28, 115, 37, 11, 121, 87, 95, 68, 18, 67, 88, 42, 53, 38, 127, 14, 5, 93, 17, 25,
+ 29, 22, 78, 106, 19, 106, 7, 50, 28, 120, 73, 5, 76, 17, 19, 102, 126, 36, 99, 95, 40, 79, 34,
+ 98, 0, 107, 123, 41, 122, 88, 53, 94, 38, 40, 41, 42, 117, 75, 8, 115, 107, 104, 35, 46, 1, 105,
+ 88, 15, 30, 124, 121, 41, 60, 6, 48, 1, 93, 111, 34, 125, 113, 121, 47, 11, 10, 78, 55, 60, 114,
+ 66, 69, 72, 80, 47, 68, 13, 81, 75, 41, 53, 119, 50, 103, 123, 55, 125, 10, 91, 113, 5, 125, 6,
+ 24, 11, 37, 5, 124, 2, 89, 89, 43, 53, 13, 2, 54, 40, 125, 70, 117, 74, 60, 69, 22, 99, 96,
+ 103, 126, 98, 39, 99, 10, 86, 31, 36, 30, 2, 34, 71, 91, 105, 10, 39, 126, 15, 103, 91, 83, 13,
+ 46, 60, 54, 9, 124, 40, 19, 65, 50, 100, 117, 116, 46, 103, 118, 24, 104, 109, 57, 28, 93, 49, 96,
+ 85, 127, 104, 8, 105, 127, 118, 58, 8, 13, 92, 104, 60, 71, 113, 20, 55, 76, 82, 30, 59, 48, 48,
+ 19, 105, 80, 117, 100, 58, 64, 121, 72, 10, 56, 88, 26, 49, 60, 5, 90, 99, 51, 108, 77, 35, 121,
+ 24, 126, 80, 123, 89, 56, 49, 3, 13, 26, 104, 55, 19, 105, 95, 24, 102, 111, 24, 84, 33, 55, 68,
+ 106, 88, 3, 76, 98, 57, 67, 22, 49, 40, 42, 108, 115, 7, 123, 54, 125, 5, 5, 27, 74, 111, 108,
+ 22, 109, 88, 69, 36, 26, 8, 17, 39, 81, 63, 91, 78, 70, 123, 110, 122, 88, 63, 7, 111, 32, 26,
+ 111, 109, 92, 76, 63, 13, 35, 37, 119, 91, 31, 59, 99, 115, 44, 5, 127, 98, 10, 51, 5, 97, 89,
+ 126, 73, 10, 24, 65, 15, 21, 28, 93, 46, 87, 5, 66, 57, 5, 79, 43, 64, 101, 44, 91, 118, 39,
+ 94, 59, 55, 87, 42, 99, 58, 11, 38, 10, 17, 43, 85, 61, 45, 37, 89, 106, 85, 80, 70, 124, 13,
+ 29, 85, 3, 104, 13, 106, 117, 123, 17, 76, 5, 14, 37, 58, 44, 88, 74, 63, 62, 73, 115, 23, 47,
+ 126, 62, 4, 9, 34, 41, 74, 40, 97, 3, 35, 91, 26, 102, 86, 29, 32, 78, 122, 59, 13, 5, 43,
+ 89, 84, 19, 72, 5, 104, 53, 71, 47, 3, 67, 20, 4, 33, 46, 17, 52, 56, 28, 124, 13, 22, 107,
+ 127, 70, 38, 20, 65, 98, 2, 4, 17, 41, 12, 54, 108, 71, 3, 7, 54, 89, 122, 3, 58, 88, 100,
+ 0, 37, 54, 41, 17, 2, 83, 89, 74, 103, 78, 54, 93, 10, 71, 2, 73, 113, 89, 112, 44, 1, 14,
+ 42, 40, 27, 12, 13, 85, 126, 61, 106, 27, 92, 43, 77, 88, 36, 124, 102, 28, 106, 127, 50, 26, 81,
+ 59, 78, 70, 84, 39, 105, 90, 66, 56, 97, 119, 86, 65, 40, 12, 62, 116, 70, 108, 118, 82, 1, 111,
+ 124, 90, 0, 72, 91, 117, 111, 19, 71, 80, 42, 36, 115, 46, 44, 83, 40, 32, 59, 85, 84, 19, 3,
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+ 37, 50, 15, 121, 67, 36, 37, 52, 23, 3, 114, 109, 76, 25, 37, 101, 76, 87, 25, 88, 105, 42, 91,
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+ 40, 10, 105, 105, 103, 33, 119, 103, 89, 122, 86, 126, 18, 64, 3, 82, 104, 32, 26, 30, 82, 57, 47,
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+ 122, 80, 23, 1, 121, 76, 82, 102, 92, 84, 6, 126, 125, 45, 83, 85, 3, 125, 124, 14, 89, 71, 56,
+ 115, 64, 69, 83, 55, 124, 118, 11, 38, 94, 73, 97, 3, 112, 45, 5, 51, 77, 56, 1, 101, 46, 10,
+ 53, 77, 46, 28, 26, 104, 105, 45, 86, 81, 3, 7, 104, 95, 5, 15, 51, 11, 95, 9, 87, 30, 100,
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+ 12, 123, 102, 14, 64, 68, 83, 94, 127, 84, 124, 64, 36, 46, 13, 39, 67, 71, 9, 44, 122, 36, 125,
+ 16, 45, 90, 98, 35, 125, 20, 25, 81, 21, 8, 76, 45, 76, 26, 115, 52, 66, 53, 120, 107, 54, 115,
+ 63, 9, 2, 2, 27, 38, 43, 90, 67, 91, 56, 82, 109, 17, 30, 46, 21, 49, 60, 19, 6, 10, 122,
+ 33, 68, 86, 23, 87, 92, 62, 105, 110, 7, 8, 77, 120, 67, 90, 18, 57, 44, 3, 61, 68, 92, 42,
+ 116, 106, 92, 71, 5, 22, 105, 69, 6, 112, 32, 44, 91, 66, 111, 53, 67, 66, 110, 25, 123, 71, 125,
+ 113, 35, 75, 96, 32, 1, 88, 43, 115, 93, 59, 78, 116, 39, 16, 46, 29, 75, 58, 99, 69, 106, 31,
+ 102, 104, 94, 29, 81, 113, 59, 91, 43, 100, 74, 83, 85, 3, 26, 6, 56, 66, 69, 87, 83, 18, 88,
+ 107, 15, 117, 35, 97, 69, 52, 40, 69, 86, 82, 114, 84, 97, 76, 11, 49, 18, 62, 53, 114, 88, 71,
+ 112, 46, 40, 91, 17, 5, 34, 5, 5, 102, 4, 15, 109, 57, 16, 38, 26, 61, 43, 13, 111, 112, 55,
+ 15, 35, 9, 67, 62, 26, 35, 105, 103, 13, 55, 84, 48, 60, 70, 102, 123, 121, 28, 105, 73, 5, 89,
+ 44, 3, 64, 106, 95, 68, 41, 40, 117, 4, 51, 25, 100, 4, 9, 115, 21, 51, 36, 81, 21, 90, 75,
+ 20, 25, 125, 122, 88, 16, 52, 107, 3, 54, 7, 7, 39, 102, 91, 76, 104, 118, 64, 127, 72, 35, 54,
+ 33, 50, 121, 29, 85, 0, 43, 14, 11, 53, 76, 49, 49, 127, 116, 77, 117, 107, 42, 89, 81, 14, 117,
+ 59, 98, 115, 46, 57, 126, 80, 76, 75, 80, 121, 102, 64, 29, 126, 97, 46, 13, 46, 110, 102, 82, 4,
+ 102, 8, 51, 61, 76, 69, 69, 60, 13, 22, 95, 16, 89, 20, 24, 119, 12, 33, 72, 57, 58, 17, 44,
+ 76, 126, 106, 74, 5, 90, 95, 51, 95, 126, 76, 103, 126, 6, 19, 49, 10, 43, 11, 26, 53, 108, 71,
+ 38, 57, 122, 12, 75, 67, 55, 34, 88, 94, 51, 96, 5, 120, 1, 9, 6, 110, 60, 122, 83, 127, 95,
+ 125, 87, 90, 4, 105, 66, 45, 3, 7, 73, 29, 97, 112, 66, 100, 92, 113, 21, 6, 116, 53, 46, 74,
+ 39, 90, 30, 114, 100, 75, 66, 89, 35, 51, 28, 89, 76, 5, 116, 96, 49, 118, 43, 14, 89, 63, 5,
+ 0, 106, 96, 82, 83, 58, 103, 69, 48, 31, 64, 108, 78, 122, 86, 82, 113, 10, 51, 21, 59, 21, 122,
+ 49, 71, 62, 8, 4, 3, 125, 105, 61, 8, 78, 33, 36, 7, 66, 89, 109, 45, 92, 6, 102, 94, 32,
+ 79, 9, 68, 28, 85, 57, 126, 84, 38, 87, 40, 46, 76, 103, 39, 86, 117, 83, 36, 87, 6, 0, 108,
+ 122, 12, 14, 2, 99, 42, 45, 99, 21, 106, 32, 127, 91, 13, 38, 61, 57, 93, 85, 3, 10, 24, 34,
+ 25, 10, 85, 116, 61, 68, 67, 11, 124, 20, 44, 21, 71, 60, 49, 114, 62, 91, 115, 51, 44, 72, 29,
+ 57, 71, 3, 22, 22, 74, 20, 38, 66, 105, 62, 76, 50, 126, 49, 73, 107, 23, 121, 58, 103, 6, 41,
+ 56, 11, 45, 36, 4, 76, 62, 60, 52, 95, 72, 60, 52, 46, 30, 10, 109, 57, 91, 28, 110, 59, 60,
+ 114, 29, 28, 115, 61, 29, 51, 48, 92, 3, 119, 64, 81, 78, 106, 36, 69, 15, 80, 96, 32, 11, 19,
+ 71, 84, 82, 36, 100, 13, 107, 98, 99, 1, 20, 23, 83, 30, 73, 105, 23, 113, 31, 78, 83, 17, 29,
+ 85, 24, 64, 77, 7, 28, 92, 73, 121, 108, 118, 26, 106, 103, 32, 125, 125, 61, 64, 34, 22, 29, 16,
+ 68, 29, 37, 59, 54, 64, 122, 23, 58, 121, 84, 74, 96, 28, 126, 77, 51, 13, 35, 93, 57, 105, 27,
+ 3, 103, 19, 66, 82, 26, 40, 87, 49, 48, 117, 73, 56, 91, 79, 113, 91, 40, 77, 17, 68, 66, 117,
+ 45, 88, 0, 123, 19, 82, 36, 8, 78, 94, 39, 28, 126, 39, 35, 10, 22, 79, 43, 45, 63, 50, 97,
+ 30, 71, 44, 37, 106, 67, 13, 65, 111, 114, 60, 27, 109, 97, 27, 22, 114, 94, 103, 77, 88, 95, 13,
+ 118, 81, 25, 35, 126, 72, 6, 40, 85, 32, 64, 107, 85, 83, 71, 20, 91, 87, 5, 26, 107, 16, 64,
+ 42, 127, 7, 90, 81, 79, 86, 29, 96, 91, 71, 30, 42, 106, 9, 42, 111, 12, 30, 116, 106, 126, 69,
+ 99, 9, 11, 27, 69, 17, 106, 77, 114, 69, 74, 94, 72, 8, 116, 4, 127, 15, 64, 47, 120, 116, 71,
+ 52, 89, 123, 105, 43, 37, 79, 18, 59, 107, 88, 88, 76, 50, 38, 63, 6, 110, 75, 63, 68, 51, 124,
+ 99, 76, 77, 55, 23, 110, 51, 34, 94, 102, 14, 70, 83, 70, 75, 78, 67, 82, 35, 70, 84, 64, 29,
+ 1, 47, 110, 123, 10, 61, 28, 108, 64, 81, 120, 99, 85, 75, 36, 123, 107, 35, 62, 83, 46, 101, 26,
+ 15, 15, 107, 17, 57, 99, 19, 74, 64, 113, 70, 21, 79, 87, 87, 7, 107, 36, 93, 127, 25, 118, 88,
+ 9, 34, 124, 51, 87, 17, 83, 8, 66, 5, 4, 49, 120, 64, 58, 115, 62, 126, 46, 29, 10, 114, 42,
+ 18, 26, 61, 78, 39, 69, 38, 57, 47, 48, 35, 36, 17, 70, 36, 71, 52, 117, 33, 85, 52, 40, 117,
+ 41, 42, 9, 88, 111, 74, 123, 95, 1, 69, 23, 84, 54, 44, 15, 20, 32, 20, 43, 33, 71, 41, 105,
+ 91, 88, 23, 13, 67, 45, 62, 116, 54, 30, 93, 71, 91, 92, 10, 77, 7, 54, 11, 104, 94, 45, 82,
+ 33, 78, 55, 40, 103, 32, 61, 98, 108, 38, 111, 79, 11, 13, 58, 26, 93, 14, 14, 99, 33, 30, 101,
+ 67, 117, 121, 97, 52, 95, 87, 105, 23, 24, 3, 64, 114, 50, 39, 61, 55, 86, 52, 68, 72, 100, 104,
+ 84, 102, 97, 19, 83, 35, 57, 89, 32, 6, 98, 80, 118, 26, 110, 96, 107, 41, 91, 9, 78, 62, 102,
+ 66, 103, 80, 60, 14, 75, 127, 26, 66, 14, 14, 65, 105, 100, 28, 13, 74, 102, 98, 125, 85, 6, 10,
+ 81, 39, 23, 58, 65, 106, 15, 37, 85, 89, 47, 14, 80, 74, 75, 13, 67, 55, 93, 93, 83, 28, 78,
+ 108, 6, 105, 76, 59, 3, 99, 127, 54, 99, 112, 55, 18, 125, 13, 115, 119, 70, 39, 30, 69, 72, 66,
+ 119, 80, 39, 114, 111, 90, 84, 118, 64, 9, 91, 105, 21, 106, 121, 76, 65, 63, 53, 9, 93, 57, 32,
+ 31, 35, 38, 7, 12, 102, 13, 40, 5, 7, 23, 49, 2, 32, 9, 18, 13, 4, 63, 21, 118, 120, 14,
+ 121, 73, 41, 8, 83, 54, 57, 76, 9, 126, 90, 109, 41, 98, 45, 50, 92, 27, 18, 20, 76, 5, 119,
+ 53, 120, 32, 39, 77, 108, 109, 79, 67, 80, 2, 11, 5, 25, 40, 13, 62, 96, 31, 100, 62, 112, 52,
+ 95, 18, 99, 8, 91, 84, 78, 53, 10, 10, 68, 26, 107, 11, 66, 69, 83, 6, 41, 17, 8, 46, 1,
+ 114, 25, 99, 53, 120, 44, 83, 32, 94, 75, 96, 55, 52, 110, 57, 12, 3, 121, 57, 74, 105, 39, 55,
+ 108, 4, 30, 71, 61, 112, 52, 121, 126, 104, 81, 124, 58, 70, 63, 3, 12, 109, 42, 79, 8, 113, 28,
+ 65, 84, 81, 61, 74, 43, 100, 0, 22, 80, 30, 46, 117, 3, 122, 60, 13, 69, 13, 23, 99, 33, 80,
+ 43, 127, 8, 32, 98, 9, 10, 2, 97, 21, 19, 92, 32, 88, 103, 13, 116, 98, 120, 83, 81, 4, 63,
+ 3, 1, 64, 79, 98, 38, 52, 101, 65, 105, 113, 102, 41, 45, 28, 101, 114, 116, 70, 50, 65, 22, 38,
+ 53, 91, 1, 100, 92, 75, 10, 105, 116, 64, 52, 126, 74, 119, 1, 103, 120, 89, 88, 72, 12, 20, 76,
+ 81, 84, 65, 49, 68, 15, 56, 114, 28, 43, 94, 65, 69, 119, 87, 0, 115, 26, 74, 95, 92, 99, 52,
+ 56, 122, 22, 92, 73, 4, 112, 102, 105, 97, 80, 14, 84, 74, 36, 1, 105, 27, 42, 92, 95, 15, 72,
+ 80, 28, 57, 42, 72, 111, 81, 121, 98, 49, 105, 88, 28, 30, 77, 119, 115, 1, 126, 38, 22, 41, 87,
+ 96, 117, 93, 28, 30, 36, 91, 92, 79, 3, 88, 66, 36, 75, 15, 98, 25, 88, 104, 23, 36, 5, 4,
+ 26, 35, 47, 77, 16, 17, 105, 110, 106, 70, 25, 50, 1, 33, 77, 96, 8, 24, 104, 80, 40, 9, 97,
+ 99, 17, 82, 88, 41, 10, 107, 50, 11, 48, 80, 4, 27, 123, 14, 42, 99, 84, 75, 65, 7, 80, 115,
+ 114, 90, 99, 46, 11, 34, 95, 12, 85, 25, 58, 29, 22, 88, 45, 36, 101, 67, 1, 10, 112, 39, 101,
+ 43, 9, 112, 23, 39, 87, 113, 57, 29, 91, 35, 90, 53, 113, 27, 52, 50, 44, 75, 78, 108, 102, 89,
+ 127, 97, 94, 27, 89, 121, 46, 35, 23, 87, 100, 83, 39, 65, 68, 67, 91, 126, 120, 29, 115, 16, 20,
+ 105, 36, 85, 46, 9, 13, 28, 81, 7, 37, 120, 88, 72, 27, 78, 116, 31, 66, 73, 21, 13, 101, 37,
+ 59, 15, 28, 50, 61, 90, 11, 102, 99, 98, 68, 42, 107, 98, 88, 80, 98, 113, 23, 4, 63, 36, 115,
+ 61, 91, 96, 46, 18, 102, 83, 11, 106, 49, 50, 108, 120, 110, 74, 95, 7, 6, 114, 99, 74, 4, 60,
+ 42, 66, 9, 90, 19, 118, 19, 23, 127, 116, 10, 48, 106, 18, 49, 19, 81, 62, 32, 98, 26, 39, 41,
+ 5, 113, 112, 32, 57, 46, 76, 57, 106, 15, 32, 48, 84, 28, 60, 28, 32, 38, 23, 29, 81, 13, 97,
+ 51, 69, 59, 8, 73, 13, 41, 71, 54, 6, 55, 64, 74, 74, 126, 93, 80, 56, 33, 43, 76, 18, 93,
+ 31, 77, 38, 118, 68, 122, 13, 80, 63, 60, 105, 2, 122, 60, 37, 76, 32, 77, 95, 107, 41, 108, 42,
+ 70, 77, 124, 33, 33, 119, 19, 102, 103, 63, 44, 83, 85, 118, 37, 118, 6, 0, 103, 39, 4, 109, 93,
+ 43, 24, 83, 62, 86, 69, 124, 6, 91, 114, 95, 90, 5, 124, 10, 96, 45, 71, 28, 120, 96, 77, 84,
+ 46, 26, 108, 111, 105, 63, 35, 36, 18, 3, 85, 63, 119, 49, 118, 37, 42, 80, 107, 6, 52, 19, 39,
+ 37, 125, 35, 101, 30, 66, 71, 98, 76, 98, 28, 107, 58, 109, 65, 104, 94, 9, 41, 47, 52, 101, 62,
+ 29, 66, 81, 102, 26, 77, 83, 123, 91, 65, 77, 54, 45, 80, 33, 112, 57, 79, 98, 85, 88, 60, 5,
+ 49, 63, 78, 53, 36, 95, 119, 42, 49, 89, 65, 13, 85, 50, 31, 1, 112, 109, 77, 73, 62, 87, 62,
+ 107, 112, 56, 47, 56, 76, 119, 70, 94, 0, 76, 82, 19, 120, 97, 2, 10, 121, 86, 70, 41, 86, 106,
+ 7, 68, 19, 39, 108, 109, 18, 11, 5, 77, 100, 117, 64, 9, 77, 61, 48, 0, 80, 120, 64, 121, 21,
+ 80, 105, 30, 19, 114, 39, 1, 90, 98, 19, 114, 120, 54, 57, 52, 112, 54, 46, 7, 15, 2, 50, 116,
+ 44, 11, 20, 31, 118, 114, 57, 69, 124, 15, 87, 11, 90, 73, 1, 45, 20, 2, 4, 118, 60, 24, 88,
+ 30, 95, 125, 127, 112, 111, 42, 39, 113, 25, 22, 59, 4, 85, 86, 69, 113, 5, 76, 86, 23, 95, 105,
+ 113, 104, 25, 18, 50, 108, 20, 56, 14, 117, 24, 20, 77, 5, 13, 72, 109, 10, 69, 103, 117, 82, 98,
+ 81, 93, 7, 5, 11, 44, 22, 45, 120, 59, 126, 112, 77, 100, 38, 86, 3, 121, 50, 75, 77, 3, 118,
+ 3, 87, 19, 12, 23, 1, 71, 83, 41, 63, 91, 38, 110, 8, 113, 58, 48, 46, 94, 47, 127, 110, 114,
+ 77, 5, 18, 84, 68, 53, 34, 27, 57, 17, 44, 21, 49, 43, 127, 65, 74, 9, 70, 59, 86, 37, 49,
+ 98, 38, 49, 116, 81, 56, 116, 25, 32, 57, 44, 14, 31, 7, 103, 90, 92, 98, 120, 55, 70, 16, 84,
+ 38, 66, 24, 120, 83, 35, 78, 6, 91, 20, 100, 81, 68, 49, 84, 102, 16, 116, 75, 111, 20, 94, 115,
+ 103, 9, 106, 102, 97, 29, 117, 7, 63, 127, 75, 124, 32, 75, 89, 76, 25, 105, 29, 59, 33, 118, 97,
+ 125, 58, 108, 18, 10, 84, 112, 33, 91, 111, 16, 2, 94, 122, 23, 22, 121, 80, 90, 2, 42, 77, 119,
+ 79, 113, 22, 86, 36, 101, 73, 79, 67, 43, 58, 5, 53, 69, 61, 119, 114, 102, 29, 5, 52, 101, 46,
+ 27, 91, 74, 123, 58, 59, 116, 121, 3, 88, 112, 41, 114, 61, 32, 63, 12, 109, 49, 124, 97, 42, 61,
+ 42, 39, 32, 22, 118, 126, 60, 104, 97, 78, 106, 23, 82, 97, 94, 126, 60, 21, 37, 115, 56, 48, 126,
+ 101, 48, 91, 66, 20, 111, 31, 51, 21, 53, 55, 68, 104, 85, 27, 18, 35, 30, 23, 22, 44, 73, 33,
+ 30, 45, 26, 92, 27, 116, 126, 51, 44, 101, 23, 33, 38, 50, 53, 79, 36, 71, 0, 42, 112, 23, 124,
+ 48, 49, 29, 6, 58, 95, 29, 45, 100, 112, 100, 54, 57, 125, 105, 92, 117, 14, 34, 121, 36, 121, 85,
+ 39, 24, 108, 52, 19, 41, 116, 40, 15, 13, 116, 52, 19, 126, 111, 33, 19, 73, 60, 100, 25, 35, 127,
+ 27, 45, 49, 12, 98, 32, 43, 52, 71, 29, 48, 70, 88, 39, 39, 16, 27, 16, 8, 44, 23, 77, 116,
+ 78, 24, 72, 69, 122, 94, 82, 122, 28, 114, 96, 58, 95, 42, 99, 104, 97, 31, 45, 15, 50, 86, 53,
+ 84, 63, 40, 55, 34, 11, 93, 61, 3, 125, 70, 32, 108, 93, 7, 122, 7, 16, 29, 51, 98, 28, 42,
+ 64, 69, 22, 64, 80, 53, 116, 75, 33, 36, 14, 41, 2, 113, 57, 53, 105, 109, 83, 122, 119, 81, 109,
+ 3, 38, 45, 110, 71, 49, 9, 1, 105, 95, 71, 45, 116, 14, 51, 63, 63, 30, 97, 35, 23, 109, 8,
+ 27, 92, 10, 49, 66, 84, 32, 106, 73, 24, 100, 90, 77, 6, 118, 99, 20, 96, 41, 19, 35, 45, 66,
+ 78, 36, 60, 29, 67, 25, 0, 57, 104, 11, 116, 89, 73, 66, 88, 61, 59, 40, 104, 14, 4, 43, 125,
+ 34, 80, 122, 11, 109, 49, 52, 37, 25, 53, 71, 55, 96, 24, 55, 84, 118, 2, 56, 46, 33, 75, 20,
+ 37, 20, 49, 109, 115, 65, 26, 83, 45, 118, 5, 7, 51, 53, 22, 96, 80, 89, 72, 80, 107, 75, 127,
+ 33, 74, 106, 74, 49, 95, 94, 32, 35, 70, 41, 111, 46, 116, 67, 98, 98, 116, 88, 39, 41, 115, 17,
+ 90, 103, 20, 44, 40, 41, 103, 68, 30, 16, 8, 60, 74, 10, 118, 72, 17, 117, 110, 102, 67, 85, 98,
+ 31, 13, 105, 105, 60, 11, 106, 42, 79, 1, 27, 53, 123, 18, 94, 40, 79, 65, 57, 66, 91, 121, 29,
+ 61, 26, 43, 93, 99, 7, 78, 58, 53, 125, 24, 10, 116, 67, 72, 27, 20, 124, 105, 101, 79, 62, 51,
+ 58, 114, 32, 2, 6, 80, 34, 125, 88, 93, 48, 57, 1, 3, 41, 47, 70, 72, 73, 6, 114, 76, 93,
+ 18, 95, 61, 29, 53, 88, 27, 64, 15, 87, 36, 87, 60, 54, 49, 125, 106, 87, 95, 30, 38, 114, 83,
+ 51, 11, 82, 83, 46, 2, 91, 125, 26, 83, 112, 15, 32, 33, 17, 0, 121, 27, 87, 85, 29, 19, 30,
+ 41, 16, 88, 115, 2, 100, 85, 108, 89, 73, 53, 30, 70, 88, 20, 52, 102, 44, 30, 2, 107, 66, 120,
+ 7, 72, 79, 93, 36, 73, 46, 120, 118, 4, 73, 24, 124, 66, 110, 21, 46, 92, 111, 17, 96, 66, 29,
+ 44, 21, 41, 54, 71, 33, 34, 77, 17, 54, 23, 13, 97, 2, 0, 106, 31, 27, 126, 70, 82, 38, 14,
+ 107, 8, 127, 0, 100, 31, 52, 103, 26, 10, 106, 88, 51, 119, 28, 42, 0, 28, 68, 14, 55, 41, 80,
+ 104, 1, 6, 18, 63, 24, 107, 122, 24, 21, 119, 27, 60, 119, 74, 102, 10, 29, 5, 13, 5, 64, 1,
+ 12, 28, 115, 113, 96, 39, 84, 99, 73, 18, 25, 25, 79, 75, 115, 39, 94, 24, 6, 15, 87, 28, 40,
+ 106, 114, 92, 2, 53, 21, 52, 71, 25, 43, 118, 69, 86, 6, 88, 95, 10, 93, 105, 93, 127, 125, 61,
+ 36, 117, 104, 76, 47, 22, 84, 103, 23, 95, 95, 109, 39, 60, 1, 68, 31, 4, 72, 54, 74, 21, 118,
+ 40};
diff --git a/Tests/UnitTest/TestCases/TestData/requantize_s64/biases_data.h b/Tests/UnitTest/TestCases/TestData/requantize_s64/biases_data.h
index 42d1ae86..bbd481c9 100644
--- a/Tests/UnitTest/TestCases/TestData/requantize_s64/biases_data.h
+++ b/Tests/UnitTest/TestCases/TestData/requantize_s64/biases_data.h
@@ -1,6 +1,6 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.15.0 (Keras version 2.15.0).
+// Interpreter from tensorflow version 2.15.0 and revision v2.15.0-2-g0b15fdfcb3f.
#pragma once
#include
-const int64_t requantize_s64_biases[2] = {47, 37};
+const int64_t requantize_s64_biases[8] = {2718092, 2556575, 646371, 1427241, 2010306, 3766647, 1886211, 1482898};
diff --git a/Tests/UnitTest/TestCases/TestData/requantize_s64/config_data.h b/Tests/UnitTest/TestCases/TestData/requantize_s64/config_data.h
index e014cca9..359f3f95 100644
--- a/Tests/UnitTest/TestCases/TestData/requantize_s64/config_data.h
+++ b/Tests/UnitTest/TestCases/TestData/requantize_s64/config_data.h
@@ -1,12 +1,12 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.15.0 (Keras version 2.15.0).
+// Interpreter from tensorflow version 2.15.0 and revision v2.15.0-2-g0b15fdfcb3f.
#pragma once
-#define REQUANTIZE_S64_OUT_CH 2
-#define REQUANTIZE_S64_IN_CH 2
-#define REQUANTIZE_S64_INPUT_W 3
-#define REQUANTIZE_S64_INPUT_H 2
-#define REQUANTIZE_S64_DST_SIZE 4
-#define REQUANTIZE_S64_INPUT_SIZE 12
+#define REQUANTIZE_S64_OUT_CH 8
+#define REQUANTIZE_S64_IN_CH 8
+#define REQUANTIZE_S64_INPUT_W 4
+#define REQUANTIZE_S64_INPUT_H 4
+#define REQUANTIZE_S64_DST_SIZE 72
+#define REQUANTIZE_S64_INPUT_SIZE 128
#define REQUANTIZE_S64_OUT_ACTIVATION_MIN -32768
#define REQUANTIZE_S64_OUT_ACTIVATION_MAX 32767
#define REQUANTIZE_S64_INPUT_BATCHES 1
@@ -16,8 +16,8 @@
#define REQUANTIZE_S64_STRIDE_Y 1
#define REQUANTIZE_S64_PAD_X 0
#define REQUANTIZE_S64_PAD_Y 0
-#define REQUANTIZE_S64_OUTPUT_W 2
-#define REQUANTIZE_S64_OUTPUT_H 1
+#define REQUANTIZE_S64_OUTPUT_W 3
+#define REQUANTIZE_S64_OUTPUT_H 3
#define REQUANTIZE_S64_INPUT_OFFSET 0
#define REQUANTIZE_S64_OUTPUT_OFFSET 0
#define REQUANTIZE_S64_DILATION_X 1
diff --git a/Tests/UnitTest/TestCases/TestData/requantize_s64/input_data.h b/Tests/UnitTest/TestCases/TestData/requantize_s64/input_data.h
index 1a058e6d..16b016d2 100644
--- a/Tests/UnitTest/TestCases/TestData/requantize_s64/input_data.h
+++ b/Tests/UnitTest/TestCases/TestData/requantize_s64/input_data.h
@@ -1,6 +1,13 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.15.0 (Keras version 2.15.0).
+// Interpreter from tensorflow version 2.15.0 and revision v2.15.0-2-g0b15fdfcb3f.
#pragma once
#include
-const int16_t requantize_s64_input[12] = {97, 100, 51, 39, 29, 117, -111, -104, 41, 38, 115, -113};
+const int16_t requantize_s64_input[128] = {
+ 12, 97, 80, 37, -72, 16, -110, -46, 109, -28, -21, 20, 24, 66, -110, -71, -31, -109, 90,
+ 11, 64, 77, 83, -38, -26, 44, 86, -109, -88, -39, 43, -36, 29, -95, -90, -36, -30, -41,
+ 8, 62, 25, -124, -107, -104, -113, -7, 18, 6, 44, -18, 5, -81, -18, 64, -49, -29, 82,
+ 124, 66, -63, -111, -103, 99, 109, -15, 101, -2, 39, 21, -42, -51, -115, -59, 25, -121, -69,
+ -46, 104, -20, -8, -91, 31, -114, 25, -27, 39, 79, -117, -28, -93, -31, 19, 107, 61, -8,
+ -125, -66, 97, -87, -98, -113, -111, 51, 65, 104, 76, 90, 110, -39, -98, -74, -56, -56, -48,
+ 34, -46, -107, 12, -74, 14, 96, 77, 85, -61, -17, 40, -18, 97};
diff --git a/Tests/UnitTest/TestCases/TestData/requantize_s64/output_mult_data.h b/Tests/UnitTest/TestCases/TestData/requantize_s64/output_mult_data.h
index a2fc73d1..1f5cffeb 100644
--- a/Tests/UnitTest/TestCases/TestData/requantize_s64/output_mult_data.h
+++ b/Tests/UnitTest/TestCases/TestData/requantize_s64/output_mult_data.h
@@ -1,6 +1,7 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.15.0 (Keras version 2.15.0).
+// Interpreter from tensorflow version 2.15.0 and revision v2.15.0-2-g0b15fdfcb3f.
#pragma once
#include
-const int32_t requantize_s64_output_mult[2] = {1853463365, 1838451297};
+const int32_t requantize_s64_output_mult[8] =
+ {1414739540, 1453951389, 1391394644, 1429974075, 1417631388, 1463499963, 1461229183, 1369934873};
diff --git a/Tests/UnitTest/TestCases/TestData/requantize_s64/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/requantize_s64/output_ref_data.h
index 75a7fa4c..8037cbdd 100644
--- a/Tests/UnitTest/TestCases/TestData/requantize_s64/output_ref_data.h
+++ b/Tests/UnitTest/TestCases/TestData/requantize_s64/output_ref_data.h
@@ -1,6 +1,10 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.15.0 (Keras version 2.15.0).
+// Interpreter from tensorflow version 2.15.0 and revision v2.15.0-2-g0b15fdfcb3f.
#pragma once
#include
-const int16_t requantize_s64_output_ref[4] = {-68, 186, -134, -116};
+const int16_t requantize_s64_output_ref[72] = {
+ 3491, 3462, 833, 1853, 2589, 5061, 2542, 1794, 3482, 3408, 813, 1895, 2613, 4994, 2499, 1816, 3508, 3371,
+ 766, 1820, 2610, 4960, 2545, 1878, 3523, 3346, 811, 1887, 2611, 5067, 2517, 1843, 3481, 3363, 787, 1903,
+ 2590, 4992, 2493, 1863, 3501, 3384, 896, 1931, 2619, 4995, 2398, 1810, 3497, 3405, 835, 1929, 2571, 5008,
+ 2538, 1832, 3566, 3349, 806, 1854, 2573, 4989, 2502, 1870, 3500, 3393, 754, 1804, 2560, 4972, 2570, 1898};
diff --git a/Tests/UnitTest/TestCases/TestData/requantize_s64/output_shift_data.h b/Tests/UnitTest/TestCases/TestData/requantize_s64/output_shift_data.h
index c73834a4..de069ed5 100644
--- a/Tests/UnitTest/TestCases/TestData/requantize_s64/output_shift_data.h
+++ b/Tests/UnitTest/TestCases/TestData/requantize_s64/output_shift_data.h
@@ -1,6 +1,6 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.15.0 (Keras version 2.15.0).
+// Interpreter from tensorflow version 2.15.0 and revision v2.15.0-2-g0b15fdfcb3f.
#pragma once
#include
-const int32_t requantize_s64_output_shift[2] = {-7, -7};
+const int32_t requantize_s64_output_shift[8] = {-9, -9, -9, -9, -9, -9, -9, -9};
diff --git a/Tests/UnitTest/TestCases/TestData/requantize_s64/test_data.h b/Tests/UnitTest/TestCases/TestData/requantize_s64/test_data.h
index 8102687f..7930b388 100644
--- a/Tests/UnitTest/TestCases/TestData/requantize_s64/test_data.h
+++ b/Tests/UnitTest/TestCases/TestData/requantize_s64/test_data.h
@@ -1,5 +1,5 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.15.0 (Keras version 2.15.0).
+// Interpreter from tensorflow version 2.15.0 and revision v2.15.0-2-g0b15fdfcb3f.
#include "biases_data.h"
#include "config_data.h"
#include "input_data.h"
diff --git a/Tests/UnitTest/TestCases/TestData/requantize_s64/weights_data.h b/Tests/UnitTest/TestCases/TestData/requantize_s64/weights_data.h
index 816357f5..f263b88a 100644
--- a/Tests/UnitTest/TestCases/TestData/requantize_s64/weights_data.h
+++ b/Tests/UnitTest/TestCases/TestData/requantize_s64/weights_data.h
@@ -1,7 +1,19 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.15.0 (Keras version 2.15.0).
+// Interpreter from tensorflow version 2.15.0 and revision v2.15.0-2-g0b15fdfcb3f.
#pragma once
#include
-const int8_t requantize_s64_weights[16] =
- {-95, -110, -39, 99, -127, 73, -58, 108, 101, 127, -17, -89, 24, -99, -34, 87};
+const int8_t requantize_s64_weights[256] = {
+ 74, 87, -103, 86, 32, 107, -13, -53, -118, 113, -41, -13, -45, -91, -26, -106, 89, 86, 49, -127,
+ 51, -58, -80, -117, 16, -69, -115, 83, 14, 86, 79, 76, -82, 41, 36, 77, -120, 40, -97, -75,
+ -14, -81, 16, 22, -60, -55, -100, -22, -56, -127, -77, -30, 75, -13, 49, -37, 33, -52, 19, -8,
+ -6, -79, -48, 2, -54, 80, 39, -14, -45, -126, -52, -86, 86, 114, 28, 107, -53, -109, 107, 121,
+ 72, -10, -84, -7, -43, -104, 69, -13, -83, 69, -103, -4, 22, 26, -127, 12, 116, 94, -50, 54,
+ -55, 6, -45, 90, -103, 8, 28, -114, -44, 127, 96, 25, -20, 107, -102, -52, -96, -39, 61, -100,
+ 8, -88, -79, 76, 80, -53, 65, -22, 60, 18, 112, -48, 2, 127, -19, 3, -54, 1, -43, -93,
+ -51, 84, -1, 88, 44, -44, -29, -40, 117, 68, -17, -15, 0, 37, -29, -5, 60, 112, -68, 64,
+ -17, 54, -81, 122, -49, -8, -121, 68, 80, -127, -104, -117, -56, -49, -53, 122, -6, -93, 115, -46,
+ 117, -82, 48, 44, -38, -23, -34, -84, 29, -70, 8, -48, -66, -47, 104, 61, -8, -122, 109, -70,
+ -28, -101, 38, -119, 23, 114, -98, -33, 73, 58, -14, 96, -67, -87, -120, 127, 56, -23, 37, -56,
+ 5, 39, 61, 47, -71, -88, -49, -111, -54, 110, -68, 56, -2, -50, -62, 87, -58, -15, -12, -56,
+ -114, -28, 50, -22, 48, 39, -56, 63, -38, 115, 127, 61, 52, -22, 58, 118};
diff --git a/Tests/UnitTest/TestCases/test_arm_convolve_fast_s16/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_convolve_fast_s16/CMakeLists.txt
deleted file mode 100644
index 1f1a3093..00000000
--- a/Tests/UnitTest/TestCases/test_arm_convolve_fast_s16/CMakeLists.txt
+++ /dev/null
@@ -1,23 +0,0 @@
-#
-# Copyright (C) 2010-2021 Arm Limited or its affiliates. All rights reserved.
-#
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the License); you may
-# not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an AS IS BASIS, WITHOUT
-# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-add_cmsis_nn_unit_test_executable(test_arm_convolve_fast_s16)
-
-target_sources(test_arm_convolve_fast_s16 PRIVATE
- Unity/unity_test_arm_convolve_fast_s16.c
- Unity/TestRunner/unity_test_arm_convolve_fast_s16_runner.c)
diff --git a/Tests/UnitTest/TestCases/test_arm_convolve_fast_s16/Unity/unity_test_arm_convolve_fast_s16.c b/Tests/UnitTest/TestCases/test_arm_convolve_fast_s16/Unity/unity_test_arm_convolve_fast_s16.c
deleted file mode 100644
index cff35bc8..00000000
--- a/Tests/UnitTest/TestCases/test_arm_convolve_fast_s16/Unity/unity_test_arm_convolve_fast_s16.c
+++ /dev/null
@@ -1,51 +0,0 @@
-/*
- * Copyright (C) 2010-2021, 2023 Arm Limited or its affiliates. All rights reserved.
- *
- * SPDX-License-Identifier: Apache-2.0
- *
- * Licensed under the Apache License, Version 2.0 (the License); you may
- * not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS, WITHOUT
- * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include
-#include
-#include
-#include
-#include
-
-#include "../test_arm_convolve_fast_s16.c"
-#include "unity.h"
-
-#ifdef USING_FVP_CORSTONE_300
-extern void uart_init(void);
-#endif
-
-/* This function is called from the autogenerated file.
- * The name must be exactly like this
- */
-void setUp(void)
-{ /* This is run before EACH TEST */
-#ifdef USING_FVP_CORSTONE_300
- uart_init();
-#endif
-}
-
-/* This function is called from the autogenerated file.
- * The name must be exactly like this
- */
-void tearDown(void) {}
-
-void test_int16xint8_arm_convolve_fast_s16(void) { int16xint8_arm_convolve_fast_s16(); }
-void test_requantize_s64_arm_convolve_fast_s16(void) { requantize_s64_arm_convolve_fast_s16(); }
-void test_buffer_size_arm_convolve_fast_s16(void) { buffer_size_arm_convolve_fast_s16(); }
-void test_buffer_size_mve_arm_convolve_fast_s16(void) { buffer_size_mve_arm_convolve_fast_s16(); }
-void test_buffer_size_dsp_arm_convolve_fast_s16(void) { buffer_size_dsp_arm_convolve_fast_s16(); }
diff --git a/Tests/UnitTest/TestCases/test_arm_convolve_fast_s16/test_arm_convolve_fast_s16.c b/Tests/UnitTest/TestCases/test_arm_convolve_fast_s16/test_arm_convolve_fast_s16.c
deleted file mode 100644
index 97854c58..00000000
--- a/Tests/UnitTest/TestCases/test_arm_convolve_fast_s16/test_arm_convolve_fast_s16.c
+++ /dev/null
@@ -1,328 +0,0 @@
-/*
- * SPDX-FileCopyrightText: Copyright 2010-2023 Arm Limited and/or its affiliates All rights
- * reserved.
- *
- * SPDX-License-Identifier: Apache-2.0
- *
- * Licensed under the Apache License, Version 2.0 (the License); you may
- * not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS, WITHOUT
- * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include
-
-#include
-#include
-
-#include "../TestData/int16xint8/test_data.h"
-#include "../TestData/requantize_s64/test_data.h"
-#include "../Utils/validate.h"
-
-void int16xint8_arm_convolve_fast_s16(void)
-{
- int16_t output[INT16XINT8_DST_SIZE] = {0};
-
- cmsis_nn_context ctx;
- cmsis_nn_conv_params conv_params;
- cmsis_nn_per_channel_quant_params quant_params;
- cmsis_nn_dims input_dims;
- cmsis_nn_dims filter_dims;
- cmsis_nn_dims bias_dims;
- cmsis_nn_dims output_dims;
-
- const int64_t *bias_data = int16xint8_biases;
- const int8_t *kernel_data = int16xint8_weights;
- const int16_t *input_data = int16xint8_input;
- const int16_t *output_ref = int16xint8_output_ref;
- const int32_t output_ref_size = INT16XINT8_DST_SIZE;
-
- input_dims.n = INT16XINT8_INPUT_BATCHES;
- input_dims.w = INT16XINT8_INPUT_W;
- input_dims.h = INT16XINT8_INPUT_H;
- input_dims.c = INT16XINT8_IN_CH;
- filter_dims.w = INT16XINT8_FILTER_X;
- filter_dims.h = INT16XINT8_FILTER_Y;
- output_dims.w = INT16XINT8_OUTPUT_W;
- output_dims.h = INT16XINT8_OUTPUT_H;
- output_dims.c = INT16XINT8_OUT_CH;
-
- conv_params.padding.w = INT16XINT8_PAD_X;
- conv_params.padding.h = INT16XINT8_PAD_Y;
- conv_params.stride.w = INT16XINT8_STRIDE_X;
- conv_params.stride.h = INT16XINT8_STRIDE_Y;
- conv_params.dilation.w = INT16XINT8_DILATION_X;
- conv_params.dilation.h = INT16XINT8_DILATION_X;
-
- conv_params.input_offset = 0;
- conv_params.output_offset = 0;
- conv_params.activation.min = INT16XINT8_OUT_ACTIVATION_MIN;
- conv_params.activation.max = INT16XINT8_OUT_ACTIVATION_MAX;
- quant_params.multiplier = (int32_t *)int16xint8_output_mult;
- quant_params.shift = (int32_t *)int16xint8_output_shift;
-
- int buf_size = arm_convolve_wrapper_s16_get_buffer_size(&conv_params, &input_dims, &filter_dims, &output_dims);
- ctx.buf = malloc(buf_size);
-
- arm_cmsis_nn_status result = arm_convolve_wrapper_s16(&ctx,
- &conv_params,
- &quant_params,
- &input_dims,
- input_data,
- &filter_dims,
- kernel_data,
- &bias_dims,
- bias_data,
- &output_dims,
- output);
- if (ctx.buf)
- {
- // The caller is responsible to clear the scratch buffers for security reasons if applicable.
- memset(ctx.buf, 0, buf_size);
- free(ctx.buf);
- }
-
- TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, result);
- TEST_ASSERT_TRUE(validate_s16(output, output_ref, output_ref_size));
- memset(output, 0, sizeof(output));
-
- buf_size = arm_convolve_fast_s16_get_buffer_size(&input_dims, &filter_dims);
- ctx.buf = malloc(buf_size);
-
- result = arm_convolve_fast_s16(&ctx,
- &conv_params,
- &quant_params,
- &input_dims,
- input_data,
- &filter_dims,
- kernel_data,
- &bias_dims,
- bias_data,
- &output_dims,
- output);
- if (ctx.buf)
- {
- memset(ctx.buf, 0, buf_size);
- free(ctx.buf);
- }
-#if defined(ARM_MATH_DSP) && !defined(ARM_MATH_MVEI)
- TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, result);
- TEST_ASSERT_TRUE(validate_s16(output, output_ref, output_ref_size));
-#else
- TEST_ASSERT_EQUAL(ARM_CMSIS_NN_ARG_ERROR, result);
-#endif
-}
-
-void requantize_s64_arm_convolve_fast_s16(void)
-{
- int16_t output[REQUANTIZE_S64_DST_SIZE] = {0};
-
- cmsis_nn_context ctx;
- cmsis_nn_conv_params conv_params;
- cmsis_nn_per_channel_quant_params quant_params;
- cmsis_nn_dims input_dims;
- cmsis_nn_dims filter_dims;
- cmsis_nn_dims bias_dims;
- cmsis_nn_dims output_dims;
-
- const int64_t *bias_data = requantize_s64_biases;
- const int8_t *kernel_data = requantize_s64_weights;
- const int16_t *input_data = requantize_s64_input;
- const int16_t *output_ref = requantize_s64_output_ref;
- const int32_t output_ref_size = REQUANTIZE_S64_DST_SIZE;
-
- input_dims.n = REQUANTIZE_S64_INPUT_BATCHES;
- input_dims.w = REQUANTIZE_S64_INPUT_W;
- input_dims.h = REQUANTIZE_S64_INPUT_H;
- input_dims.c = REQUANTIZE_S64_IN_CH;
- filter_dims.w = REQUANTIZE_S64_FILTER_X;
- filter_dims.h = REQUANTIZE_S64_FILTER_Y;
- output_dims.w = REQUANTIZE_S64_OUTPUT_W;
- output_dims.h = REQUANTIZE_S64_OUTPUT_H;
- output_dims.c = REQUANTIZE_S64_OUT_CH;
-
- conv_params.padding.w = REQUANTIZE_S64_PAD_X;
- conv_params.padding.h = REQUANTIZE_S64_PAD_Y;
- conv_params.stride.w = REQUANTIZE_S64_STRIDE_X;
- conv_params.stride.h = REQUANTIZE_S64_STRIDE_Y;
- conv_params.dilation.w = REQUANTIZE_S64_DILATION_X;
- conv_params.dilation.h = REQUANTIZE_S64_DILATION_Y;
-
- conv_params.input_offset = REQUANTIZE_S64_INPUT_OFFSET;
- conv_params.output_offset = REQUANTIZE_S64_OUTPUT_OFFSET;
- conv_params.activation.min = REQUANTIZE_S64_OUT_ACTIVATION_MIN;
- conv_params.activation.max = REQUANTIZE_S64_OUT_ACTIVATION_MAX;
- quant_params.multiplier = (int32_t *)requantize_s64_output_mult;
- quant_params.shift = (int32_t *)requantize_s64_output_shift;
-
- int buf_size = arm_convolve_wrapper_s16_get_buffer_size(&conv_params, &input_dims, &filter_dims, &output_dims);
- ctx.buf = malloc(buf_size);
-
- arm_cmsis_nn_status result = arm_convolve_wrapper_s16(&ctx,
- &conv_params,
- &quant_params,
- &input_dims,
- input_data,
- &filter_dims,
- kernel_data,
- &bias_dims,
- bias_data,
- &output_dims,
- output);
-
- if (ctx.buf)
- {
- memset(ctx.buf, 0, buf_size);
- free(ctx.buf);
- }
- TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, result);
- TEST_ASSERT_TRUE(validate_s16(output, output_ref, output_ref_size));
- memset(output, 0, sizeof(output));
-
- buf_size = arm_convolve_fast_s16_get_buffer_size(&input_dims, &filter_dims);
- ctx.buf = malloc(buf_size);
-
- result = arm_convolve_fast_s16(&ctx,
- &conv_params,
- &quant_params,
- &input_dims,
- input_data,
- &filter_dims,
- kernel_data,
- &bias_dims,
- bias_data,
- &output_dims,
- output);
- if (ctx.buf)
- {
- memset(ctx.buf, 0, buf_size);
- free(ctx.buf);
- }
-#if defined(ARM_MATH_DSP) && !defined(ARM_MATH_MVEI)
- TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, result);
- TEST_ASSERT_TRUE(validate_s16(output, output_ref, output_ref_size));
-#else
- TEST_ASSERT_EQUAL(ARM_CMSIS_NN_ARG_ERROR, result);
-#endif
-}
-
-void buffer_size_arm_convolve_fast_s16(void)
-{
- cmsis_nn_conv_params conv_params;
- cmsis_nn_dims input_dims;
- cmsis_nn_dims filter_dims;
- cmsis_nn_dims output_dims;
-
- input_dims.n = REQUANTIZE_S64_INPUT_BATCHES;
- input_dims.w = REQUANTIZE_S64_INPUT_W;
- input_dims.h = REQUANTIZE_S64_INPUT_H;
- input_dims.c = REQUANTIZE_S64_IN_CH;
- filter_dims.w = REQUANTIZE_S64_FILTER_X;
- filter_dims.h = REQUANTIZE_S64_FILTER_Y;
- output_dims.w = REQUANTIZE_S64_OUTPUT_W;
- output_dims.h = REQUANTIZE_S64_OUTPUT_H;
- output_dims.c = REQUANTIZE_S64_OUT_CH;
-
- conv_params.padding.w = REQUANTIZE_S64_PAD_X;
- conv_params.padding.h = REQUANTIZE_S64_PAD_Y;
- conv_params.stride.w = REQUANTIZE_S64_STRIDE_X;
- conv_params.stride.h = REQUANTIZE_S64_STRIDE_Y;
- conv_params.dilation.w = REQUANTIZE_S64_DILATION_X;
- conv_params.dilation.h = REQUANTIZE_S64_DILATION_Y;
-
- conv_params.input_offset = REQUANTIZE_S64_INPUT_OFFSET;
- conv_params.output_offset = REQUANTIZE_S64_OUTPUT_OFFSET;
- conv_params.activation.min = REQUANTIZE_S64_OUT_ACTIVATION_MIN;
- conv_params.activation.max = REQUANTIZE_S64_OUT_ACTIVATION_MAX;
-
- const int32_t buf_size = arm_convolve_fast_s16_get_buffer_size(&input_dims, &filter_dims);
- const int32_t wrapper_buf_size =
- arm_convolve_wrapper_s16_get_buffer_size(&conv_params, &input_dims, &filter_dims, &output_dims);
-
- TEST_ASSERT_EQUAL(wrapper_buf_size, buf_size);
-}
-
-void buffer_size_mve_arm_convolve_fast_s16(void)
-{
-#if defined(ARM_MATH_MVEI)
- cmsis_nn_conv_params conv_params;
- cmsis_nn_dims input_dims;
- cmsis_nn_dims filter_dims;
- cmsis_nn_dims output_dims;
-
- input_dims.n = REQUANTIZE_S64_INPUT_BATCHES;
- input_dims.w = REQUANTIZE_S64_INPUT_W;
- input_dims.h = REQUANTIZE_S64_INPUT_H;
- input_dims.c = REQUANTIZE_S64_IN_CH;
- filter_dims.w = REQUANTIZE_S64_FILTER_X;
- filter_dims.h = REQUANTIZE_S64_FILTER_Y;
- output_dims.w = REQUANTIZE_S64_OUTPUT_W;
- output_dims.h = REQUANTIZE_S64_OUTPUT_H;
- output_dims.c = REQUANTIZE_S64_OUT_CH;
-
- conv_params.padding.w = REQUANTIZE_S64_PAD_X;
- conv_params.padding.h = REQUANTIZE_S64_PAD_Y;
- conv_params.stride.w = REQUANTIZE_S64_STRIDE_X;
- conv_params.stride.h = REQUANTIZE_S64_STRIDE_Y;
- conv_params.dilation.w = REQUANTIZE_S64_DILATION_X;
- conv_params.dilation.h = REQUANTIZE_S64_DILATION_Y;
-
- conv_params.input_offset = REQUANTIZE_S64_INPUT_OFFSET;
- conv_params.output_offset = REQUANTIZE_S64_OUTPUT_OFFSET;
- conv_params.activation.min = REQUANTIZE_S64_OUT_ACTIVATION_MIN;
- conv_params.activation.max = REQUANTIZE_S64_OUT_ACTIVATION_MAX;
-
- const int32_t wrapper_buf_size =
- arm_convolve_wrapper_s16_get_buffer_size(&conv_params, &input_dims, &filter_dims, &output_dims);
- const int32_t mve_wrapper_buf_size =
- arm_convolve_wrapper_s16_get_buffer_size_mve(&conv_params, &input_dims, &filter_dims, &output_dims);
-
- TEST_ASSERT_EQUAL(wrapper_buf_size, mve_wrapper_buf_size);
-#endif
-}
-
-void buffer_size_dsp_arm_convolve_fast_s16(void)
-{
-#if defined(ARM_MATH_DSP) && !defined(ARM_MATH_MVEI)
- cmsis_nn_conv_params conv_params;
- cmsis_nn_dims input_dims;
- cmsis_nn_dims filter_dims;
- cmsis_nn_dims output_dims;
-
- input_dims.n = REQUANTIZE_S64_INPUT_BATCHES;
- input_dims.w = REQUANTIZE_S64_INPUT_W;
- input_dims.h = REQUANTIZE_S64_INPUT_H;
- input_dims.c = REQUANTIZE_S64_IN_CH;
- filter_dims.w = REQUANTIZE_S64_FILTER_X;
- filter_dims.h = REQUANTIZE_S64_FILTER_Y;
- output_dims.w = REQUANTIZE_S64_OUTPUT_W;
- output_dims.h = REQUANTIZE_S64_OUTPUT_H;
- output_dims.c = REQUANTIZE_S64_OUT_CH;
-
- conv_params.padding.w = REQUANTIZE_S64_PAD_X;
- conv_params.padding.h = REQUANTIZE_S64_PAD_Y;
- conv_params.stride.w = REQUANTIZE_S64_STRIDE_X;
- conv_params.stride.h = REQUANTIZE_S64_STRIDE_Y;
- conv_params.dilation.w = REQUANTIZE_S64_DILATION_X;
- conv_params.dilation.h = REQUANTIZE_S64_DILATION_Y;
-
- conv_params.input_offset = REQUANTIZE_S64_INPUT_OFFSET;
- conv_params.output_offset = REQUANTIZE_S64_OUTPUT_OFFSET;
- conv_params.activation.min = REQUANTIZE_S64_OUT_ACTIVATION_MIN;
- conv_params.activation.max = REQUANTIZE_S64_OUT_ACTIVATION_MAX;
-
- const int32_t wrapper_buf_size =
- arm_convolve_wrapper_s16_get_buffer_size(&conv_params, &input_dims, &filter_dims, &output_dims);
- const int32_t dsp_wrapper_buf_size =
- arm_convolve_wrapper_s16_get_buffer_size_dsp(&conv_params, &input_dims, &filter_dims, &output_dims);
-
- TEST_ASSERT_EQUAL(wrapper_buf_size, dsp_wrapper_buf_size);
-#endif
-}
diff --git a/Tests/UnitTest/TestCases/test_arm_convolve_s16/Unity/unity_test_arm_convolve_s16.c b/Tests/UnitTest/TestCases/test_arm_convolve_s16/Unity/unity_test_arm_convolve_s16.c
index b3df59b7..96232496 100644
--- a/Tests/UnitTest/TestCases/test_arm_convolve_s16/Unity/unity_test_arm_convolve_s16.c
+++ b/Tests/UnitTest/TestCases/test_arm_convolve_s16/Unity/unity_test_arm_convolve_s16.c
@@ -1,5 +1,5 @@
/*
- * Copyright (C) 2010-2023 Arm Limited or its affiliates. All rights reserved.
+ * SPDX-FileCopyrightText: Copyright 2010-2024 Arm Limited and/or its affiliates All rights
*
* SPDX-License-Identifier: Apache-2.0
*
@@ -52,3 +52,5 @@ void test_int16xint8_dilation_3_arm_convolve_s16(void) { int16xint8_dilation_3_a
void test_buffer_size_arm_convolve_s16(void) { buffer_size_arm_convolve_s16(); }
void test_buffer_size_mve_arm_convolve_s16(void) { buffer_size_mve_arm_convolve_s16(); }
void test_buffer_size_dsp_arm_convolve_s16(void) { buffer_size_dsp_arm_convolve_s16(); }
+void test_int16xint8_spill_arm_convolve_s16(void) { int16xint8_spill_arm_convolve_s16(); }
+void test_int16xint8_spill2_arm_convolve_s16(void) { int16xint8_spill2_arm_convolve_s16(); }
diff --git a/Tests/UnitTest/TestCases/test_arm_convolve_s16/test_arm_convolve_s16.c b/Tests/UnitTest/TestCases/test_arm_convolve_s16/test_arm_convolve_s16.c
index 00b74342..25194e8b 100644
--- a/Tests/UnitTest/TestCases/test_arm_convolve_s16/test_arm_convolve_s16.c
+++ b/Tests/UnitTest/TestCases/test_arm_convolve_s16/test_arm_convolve_s16.c
@@ -1,5 +1,5 @@
/*
- * SPDX-FileCopyrightText: Copyright 2010-2023 Arm Limited and/or its affiliates All rights
+ * SPDX-FileCopyrightText: Copyright 2010-2024 Arm Limited and/or its affiliates All rights
* reserved.
*
* SPDX-License-Identifier: Apache-2.0
@@ -26,6 +26,8 @@
#include "../TestData/int16xint8_dilation_1/test_data.h"
#include "../TestData/int16xint8_dilation_2/test_data.h"
#include "../TestData/int16xint8_dilation_3/test_data.h"
+#include "../TestData/int16xint8_spill/test_data.h"
+#include "../TestData/int16xint8_spill2/test_data.h"
#include "../TestData/requantize_s64/test_data.h"
#include "../Utils/validate.h"
@@ -73,18 +75,18 @@ void int16xint8_arm_convolve_s16(void)
int buf_size = arm_convolve_s16_get_buffer_size(&input_dims, &filter_dims);
ctx.buf = malloc(buf_size);
-
- arm_cmsis_nn_status result = arm_convolve_s16(&ctx,
- &conv_params,
- &quant_params,
- &input_dims,
- input_data,
- &filter_dims,
- kernel_data,
- &bias_dims,
- bias_data,
- &output_dims,
- output);
+ arm_cmsis_nn_status result;
+ result = arm_convolve_s16(&ctx,
+ &conv_params,
+ &quant_params,
+ &input_dims,
+ input_data,
+ &filter_dims,
+ kernel_data,
+ &bias_dims,
+ bias_data,
+ &output_dims,
+ output);
if (ctx.buf)
{
// The caller is responsible to clear the scratch buffers for security reasons if applicable.
@@ -109,7 +111,6 @@ void int16xint8_arm_convolve_s16(void)
bias_data,
&output_dims,
output);
-
if (ctx.buf)
{
memset(ctx.buf, 0, buf_size);
@@ -588,3 +589,181 @@ void buffer_size_dsp_arm_convolve_s16(void)
TEST_ASSERT_EQUAL(wrapper_buf_size, dsp_wrapper_buf_size);
#endif
}
+
+void int16xint8_spill_arm_convolve_s16(void)
+{
+ int16_t output[INT16XINT8_SPILL_DST_SIZE] = {0};
+
+ cmsis_nn_context ctx;
+ cmsis_nn_conv_params conv_params;
+ cmsis_nn_per_channel_quant_params quant_params;
+ cmsis_nn_dims input_dims;
+ cmsis_nn_dims filter_dims;
+ cmsis_nn_dims bias_dims;
+ cmsis_nn_dims output_dims;
+
+ const int64_t *bias_data = int16xint8_spill_biases;
+ const int8_t *kernel_data = int16xint8_spill_weights;
+ const int16_t *input_data = int16xint8_spill_input;
+ const int16_t *output_ref = int16xint8_spill_output_ref;
+ const int32_t output_ref_size = INT16XINT8_SPILL_DST_SIZE;
+
+ input_dims.n = INT16XINT8_SPILL_INPUT_BATCHES;
+ input_dims.w = INT16XINT8_SPILL_INPUT_W;
+ input_dims.h = INT16XINT8_SPILL_INPUT_H;
+ input_dims.c = INT16XINT8_SPILL_IN_CH;
+ filter_dims.w = INT16XINT8_SPILL_FILTER_X;
+ filter_dims.h = INT16XINT8_SPILL_FILTER_Y;
+ output_dims.w = INT16XINT8_SPILL_OUTPUT_W;
+ output_dims.h = INT16XINT8_SPILL_OUTPUT_H;
+ output_dims.c = INT16XINT8_SPILL_OUT_CH;
+
+ conv_params.padding.w = INT16XINT8_SPILL_PAD_X;
+ conv_params.padding.h = INT16XINT8_SPILL_PAD_Y;
+ conv_params.stride.w = INT16XINT8_SPILL_STRIDE_X;
+ conv_params.stride.h = INT16XINT8_SPILL_STRIDE_Y;
+ conv_params.dilation.w = INT16XINT8_SPILL_DILATION_X;
+ conv_params.dilation.h = INT16XINT8_SPILL_DILATION_Y;
+
+ conv_params.input_offset = 0;
+ conv_params.output_offset = 0;
+ conv_params.activation.min = INT16XINT8_SPILL_OUT_ACTIVATION_MIN;
+ conv_params.activation.max = INT16XINT8_SPILL_OUT_ACTIVATION_MAX;
+ quant_params.multiplier = (int32_t *)int16xint8_spill_output_mult;
+ quant_params.shift = (int32_t *)int16xint8_spill_output_shift;
+
+ int buf_size = arm_convolve_s16_get_buffer_size(&input_dims, &filter_dims);
+ ctx.buf = malloc(buf_size);
+ arm_cmsis_nn_status result;
+ result = arm_convolve_s16(&ctx,
+ &conv_params,
+ &quant_params,
+ &input_dims,
+ input_data,
+ &filter_dims,
+ kernel_data,
+ &bias_dims,
+ bias_data,
+ &output_dims,
+ output);
+ if (ctx.buf)
+ {
+ // The caller is responsible to clear the scratch buffers for security reasons if applicable.
+ memset(ctx.buf, 0, buf_size);
+ free(ctx.buf);
+ }
+ TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, result);
+ TEST_ASSERT_TRUE(validate_s16(output, output_ref, output_ref_size));
+ memset(output, 0, sizeof(output));
+
+ buf_size = arm_convolve_wrapper_s16_get_buffer_size(&conv_params, &input_dims, &filter_dims, &output_dims);
+ ctx.buf = malloc(buf_size);
+
+ result = arm_convolve_wrapper_s16(&ctx,
+ &conv_params,
+ &quant_params,
+ &input_dims,
+ input_data,
+ &filter_dims,
+ kernel_data,
+ &bias_dims,
+ bias_data,
+ &output_dims,
+ output);
+ if (ctx.buf)
+ {
+ memset(ctx.buf, 0, buf_size);
+ free(ctx.buf);
+ }
+ TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, result);
+ TEST_ASSERT_TRUE(validate_s16(output, output_ref, output_ref_size));
+}
+
+void int16xint8_spill2_arm_convolve_s16(void)
+{
+ int16_t output[INT16XINT8_SPILL2_DST_SIZE] = {0};
+
+ cmsis_nn_context ctx;
+ cmsis_nn_conv_params conv_params;
+ cmsis_nn_per_channel_quant_params quant_params;
+ cmsis_nn_dims input_dims;
+ cmsis_nn_dims filter_dims;
+ cmsis_nn_dims bias_dims;
+ cmsis_nn_dims output_dims;
+
+ const int64_t *bias_data = int16xint8_spill2_biases;
+ const int8_t *kernel_data = int16xint8_spill2_weights;
+ const int16_t *input_data = int16xint8_spill2_input;
+ const int16_t *output_ref = int16xint8_spill2_output_ref;
+ const int32_t output_ref_size = INT16XINT8_SPILL2_DST_SIZE;
+
+ input_dims.n = INT16XINT8_SPILL2_INPUT_BATCHES;
+ input_dims.w = INT16XINT8_SPILL2_INPUT_W;
+ input_dims.h = INT16XINT8_SPILL2_INPUT_H;
+ input_dims.c = INT16XINT8_SPILL2_IN_CH;
+ filter_dims.w = INT16XINT8_SPILL2_FILTER_X;
+ filter_dims.h = INT16XINT8_SPILL2_FILTER_Y;
+ output_dims.w = INT16XINT8_SPILL2_OUTPUT_W;
+ output_dims.h = INT16XINT8_SPILL2_OUTPUT_H;
+ output_dims.c = INT16XINT8_SPILL2_OUT_CH;
+
+ conv_params.padding.w = INT16XINT8_SPILL2_PAD_X;
+ conv_params.padding.h = INT16XINT8_SPILL2_PAD_Y;
+ conv_params.stride.w = INT16XINT8_SPILL2_STRIDE_X;
+ conv_params.stride.h = INT16XINT8_SPILL2_STRIDE_Y;
+ conv_params.dilation.w = INT16XINT8_SPILL2_DILATION_X;
+ conv_params.dilation.h = INT16XINT8_SPILL2_DILATION_Y;
+
+ conv_params.input_offset = 0;
+ conv_params.output_offset = 0;
+ conv_params.activation.min = INT16XINT8_SPILL2_OUT_ACTIVATION_MIN;
+ conv_params.activation.max = INT16XINT8_SPILL2_OUT_ACTIVATION_MAX;
+ quant_params.multiplier = (int32_t *)int16xint8_spill2_output_mult;
+ quant_params.shift = (int32_t *)int16xint8_spill2_output_shift;
+
+ int buf_size = arm_convolve_s16_get_buffer_size(&input_dims, &filter_dims);
+ ctx.buf = malloc(buf_size);
+ arm_cmsis_nn_status result;
+ result = arm_convolve_s16(&ctx,
+ &conv_params,
+ &quant_params,
+ &input_dims,
+ input_data,
+ &filter_dims,
+ kernel_data,
+ &bias_dims,
+ bias_data,
+ &output_dims,
+ output);
+ if (ctx.buf)
+ {
+ // The caller is responsible to clear the scratch buffers for security reasons if applicable.
+ memset(ctx.buf, 0, buf_size);
+ free(ctx.buf);
+ }
+ TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, result);
+ TEST_ASSERT_TRUE(validate_s16(output, output_ref, output_ref_size));
+ memset(output, 0, sizeof(output));
+
+ buf_size = arm_convolve_wrapper_s16_get_buffer_size(&conv_params, &input_dims, &filter_dims, &output_dims);
+ ctx.buf = malloc(buf_size);
+
+ result = arm_convolve_wrapper_s16(&ctx,
+ &conv_params,
+ &quant_params,
+ &input_dims,
+ input_data,
+ &filter_dims,
+ kernel_data,
+ &bias_dims,
+ bias_data,
+ &output_dims,
+ output);
+ if (ctx.buf)
+ {
+ memset(ctx.buf, 0, buf_size);
+ free(ctx.buf);
+ }
+ TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, result);
+ TEST_ASSERT_TRUE(validate_s16(output, output_ref, output_ref_size));
+}
diff --git a/Tests/UnitTest/conv_settings.py b/Tests/UnitTest/conv_settings.py
index c18bdd3d..5b7831d7 100644
--- a/Tests/UnitTest/conv_settings.py
+++ b/Tests/UnitTest/conv_settings.py
@@ -53,7 +53,9 @@ def __init__(self,
dilation_x=1,
dilation_y=1,
interpreter="tensorflow",
- int4_weights=False):
+ int4_weights=False,
+ weights_min=TestSettings.INT32_MIN,
+ weights_max=TestSettings.INT32_MAX):
super().__init__(dataset,
testtype,
regenerate_weights,
@@ -87,6 +89,9 @@ def __init__(self,
self.scaling_factors = []
self.groups = groups
+ self.weights_min = weights_min
+ self.weights_max = weights_max
+
if self.test_type == 'depthwise_conv':
self.channel_multiplier = self.output_ch // self.input_ch
if self.output_ch % self.input_ch != 0:
@@ -311,8 +316,8 @@ def generate_data(self, input_data=None, weights=None, biases=None) -> None:
else:
weights = self.get_randomized_data(weight_shape,
self.kernel_table_file,
- minrange=TestSettings.INT32_MIN,
- maxrange=TestSettings.INT32_MAX,
+ minrange=self.weights_min,
+ maxrange=self.weights_max,
decimals=1,
regenerate=self.regenerate_new_weights)
@@ -327,18 +332,26 @@ def generate_data(self, input_data=None, weights=None, biases=None) -> None:
padding=self.padding,
input_shape=input_shape[1:],
dilation_rate=(self.dilation_y, self.dilation_x),
- groups=self.groups)
+ groups=self.groups,
+ use_bias=self.generate_bias)
model.add(conv_layer)
- conv_layer.set_weights([weights, biases])
+ if self.generate_bias:
+ conv_layer.set_weights([weights, biases])
+ else:
+ conv_layer.set_weights([weights])
elif self.test_type == 'depthwise_conv':
depthwise_layer = keras.layers.DepthwiseConv2D(kernel_size=(self.filter_y, self.filter_x),
strides=(self.stride_y, self.stride_x),
padding=self.padding,
depth_multiplier=self.channel_multiplier,
input_shape=input_shape[1:],
- dilation_rate=(self.dilation_y, self.dilation_x))
+ dilation_rate=(self.dilation_y, self.dilation_x),
+ use_bias=self.generate_bias)
model.add(depthwise_layer)
- depthwise_layer.set_weights([weights, biases])
+ if self.generate_bias:
+ depthwise_layer.set_weights([weights, biases])
+ else:
+ depthwise_layer.set_weights([weights])
elif self.test_type == 'transpose_conv':
transposed_conv_layer = keras.layers.Conv2DTranspose(self.output_ch,
kernel_size=(self.filter_y, self.filter_x),
@@ -371,8 +384,7 @@ def generate_data(self, input_data=None, weights=None, biases=None) -> None:
all_layers_details = interpreter.get_tensor_details()
filter_layer = all_layers_details[filter_index]
- if self.test_type == 'transpose_conv' and not self.generate_bias:
- # TODO: real null bias for all operators and not only transpose conv.
+ if not self.int4_weights and not self.generate_bias:
bias_layer = None
biases = []
else:
diff --git a/Tests/UnitTest/generate_test_data.py b/Tests/UnitTest/generate_test_data.py
index 6d8b375c..a51ebe60 100755
--- a/Tests/UnitTest/generate_test_data.py
+++ b/Tests/UnitTest/generate_test_data.py
@@ -595,21 +595,74 @@ def load_testdata_sets(regenerate_input, regenerate_weights, regenerate_biases,
regenerate_input,
regenerate_biases,
schema_file,
- in_ch=3,
+ in_ch=55,
out_ch=4,
- x_in=7,
+ x_in=10,
y_in=8,
- w_x=2,
+ w_x=9,
w_y=4,
- stride_x=2,
- stride_y=3,
- pad=True,
+ stride_x=1,
+ stride_y=1,
+ pad=False,
+ generate_bias=False,
randmin=TestSettings.INT16_MIN,
randmax=TestSettings.INT16_MAX,
- out_activation_min=-13335,
- out_activation_max=32767,
+ out_activation_min=TestSettings.INT16_MIN,
+ out_activation_max=TestSettings.INT16_MAX,
int16xint8=True,
interpreter=interpreter)
+ dataset = 'int16xint8_spill'
+ testdata_sets[dataset] = ConvSettings(dataset,
+ type_of_test,
+ regenerate_weights,
+ regenerate_input,
+ regenerate_biases,
+ schema_file,
+ in_ch=55,
+ out_ch=9,
+ x_in=10,
+ y_in=8,
+ w_x=9,
+ w_y=4,
+ stride_x=1,
+ stride_y=1,
+ pad=False,
+ generate_bias=False,
+ randmin=TestSettings.INT16_MAX - 100,
+ randmax=TestSettings.INT16_MAX,
+ out_activation_min=TestSettings.INT16_MAX - 100,
+ out_activation_max=TestSettings.INT16_MAX,
+ bias_min=TestSettings.INT64_MIN,
+ bias_max=TestSettings.INT64_MAX,
+ int16xint8=True,
+ interpreter=interpreter,
+ weights_min=100)
+ dataset = 'int16xint8_spill2'
+ testdata_sets[dataset] = ConvSettings(dataset,
+ type_of_test,
+ regenerate_weights,
+ regenerate_input,
+ regenerate_biases,
+ schema_file,
+ in_ch=12,
+ out_ch=9,
+ x_in=10,
+ y_in=8,
+ w_x=9,
+ w_y=4,
+ stride_x=1,
+ stride_y=1,
+ pad=False,
+ generate_bias=False,
+ randmin=TestSettings.INT16_MAX - 100,
+ randmax=TestSettings.INT16_MAX,
+ out_activation_min=TestSettings.INT16_MAX - 100,
+ out_activation_max=TestSettings.INT16_MAX,
+ bias_min=TestSettings.INT64_MIN,
+ bias_max=TestSettings.INT64_MAX,
+ int16xint8=True,
+ interpreter=interpreter,
+ weights_min=100)
dataset = 'requantize_s64'
testdata_sets[dataset] = ConvSettings(dataset,
type_of_test,
@@ -617,20 +670,21 @@ def load_testdata_sets(regenerate_input, regenerate_weights, regenerate_biases,
regenerate_input,
regenerate_biases,
schema_file,
- in_ch=2,
- out_ch=2,
- x_in=3,
- y_in=2,
+ in_ch=8,
+ out_ch=8,
+ x_in=4,
+ y_in=4,
w_x=2,
w_y=2,
stride_x=1,
stride_y=1,
pad=False,
+ generate_bias=True,
out_activation_min=TestSettings.INT16_MIN,
out_activation_max=TestSettings.INT16_MAX,
int16xint8=True,
bias_min=-0x300,
- bias_max=0x9fff,
+ bias_max=TestSettings.INT32_MAX + 1000,
interpreter=interpreter)
dataset = 'int16xint8_dilation_1'
testdata_sets[dataset] = ConvSettings(dataset,