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| author | joshua <joshua@joshuayun.com> | 2023-12-30 23:54:31 -0500 |
|---|---|---|
| committer | joshua <joshua@joshuayun.com> | 2023-12-30 23:54:31 -0500 |
| commit | 86608c6770cf08c138a2bdab5855072f64be09ef (patch) | |
| tree | 494a61b3ef37e76f9235a0d10f5c93d97290a35f /Drivers/CMSIS/NN/Source/SoftmaxFunctions | |
| download | sdr-software-86608c6770cf08c138a2bdab5855072f64be09ef.tar.gz | |
Diffstat (limited to 'Drivers/CMSIS/NN/Source/SoftmaxFunctions')
| -rw-r--r-- | Drivers/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q15.c | 120 | ||||
| -rw-r--r-- | Drivers/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q7.c | 121 |
2 files changed, 241 insertions, 0 deletions
diff --git a/Drivers/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q15.c b/Drivers/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q15.c new file mode 100644 index 0000000..22fa62b --- /dev/null +++ b/Drivers/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q15.c @@ -0,0 +1,120 @@ +/*
+ * Copyright (C) 2010-2018 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.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_softmax_q15.c
+ * Description: Q15 softmax function
+ *
+ * $Date: 20. February 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ * @ingroup groupNN
+ */
+
+/**
+ * @addtogroup Softmax
+ * @{
+ */
+
+ /**
+ * @brief Q15 softmax function
+ * @param[in] vec_in pointer to input vector
+ * @param[in] dim_vec input vector dimention
+ * @param[out] p_out pointer to output vector
+ * @return none.
+ *
+ * @details
+ *
+ * Here, instead of typical e based softmax, we use
+ * 2-based softmax, i.e.,:
+ *
+ * y_i = 2^(x_i) / sum(2^x_j)
+ *
+ * The relative output will be different here.
+ * But mathematically, the gradient will be the same
+ * with a log(2) scaling factor.
+ *
+ */
+
+void arm_softmax_q15(const q15_t * vec_in, const uint16_t dim_vec, q15_t * p_out)
+{
+ q31_t sum;
+ int16_t i;
+ uint8_t shift;
+ q31_t base;
+ base = -1 * 0x100000;
+ for (i = 0; i < dim_vec; i++)
+ {
+ if (vec_in[i] > base)
+ {
+ base = vec_in[i];
+ }
+ }
+
+ /* we ignore really small values
+ * anyway, they will be 0 after shrinking
+ * to q15_t
+ */
+ base = base - 16;
+
+ sum = 0;
+
+ for (i = 0; i < dim_vec; i++)
+ {
+ if (vec_in[i] > base)
+ {
+ shift = (uint8_t)__USAT(vec_in[i] - base, 5);
+ sum += 0x1 << shift;
+ }
+ }
+
+ /* This is effectively (0x1 << 32) / sum */
+ int64_t div_base = 0x100000000LL;
+ int output_base = (int32_t)(div_base / sum);
+
+ /* Final confidence will be output_base >> ( 17 - (vec_in[i] - base) )
+ * so 32768 (0x1<<15) -> 100% confidence when sum = 0x1 << 16, output_base = 0x1 << 16
+ * and vec_in[i]-base = 16
+ */
+ for (i = 0; i < dim_vec; i++)
+ {
+ if (vec_in[i] > base)
+ {
+ /* Here minimum value of 17+base-vec[i] will be 1 */
+ shift = (uint8_t)__USAT(17+base-vec_in[i], 5);
+ p_out[i] = (q15_t) __SSAT((output_base >> shift), 16);
+ } else
+ {
+ p_out[i] = 0;
+ }
+ }
+
+}
+
+/**
+ * @} end of Softmax group
+ */
diff --git a/Drivers/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q7.c b/Drivers/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q7.c new file mode 100644 index 0000000..06a69e1 --- /dev/null +++ b/Drivers/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q7.c @@ -0,0 +1,121 @@ +/*
+ * Copyright (C) 2010-2018 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.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_softmax_q7.c
+ * Description: Q7 softmax function
+ *
+ * $Date: 20. February 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ * @ingroup groupNN
+ */
+
+/**
+ * @addtogroup Softmax
+ * @{
+ */
+
+ /**
+ * @brief Q7 softmax function
+ * @param[in] vec_in pointer to input vector
+ * @param[in] dim_vec input vector dimention
+ * @param[out] p_out pointer to output vector
+ * @return none.
+ *
+ * @details
+ *
+ * Here, instead of typical natural logarithm e based softmax, we use
+ * 2-based softmax here, i.e.,:
+ *
+ * y_i = 2^(x_i) / sum(2^x_j)
+ *
+ * The relative output will be different here.
+ * But mathematically, the gradient will be the same
+ * with a log(2) scaling factor.
+ *
+ */
+
+void arm_softmax_q7(const q7_t * vec_in, const uint16_t dim_vec, q7_t * p_out)
+{
+ q31_t sum;
+ int16_t i;
+ uint8_t shift;
+ q15_t base;
+ base = -257;
+
+ /* We first search for the maximum */
+ for (i = 0; i < dim_vec; i++)
+ {
+ if (vec_in[i] > base)
+ {
+ base = vec_in[i];
+ }
+ }
+
+ /*
+ * So the base is set to max-8, meaning
+ * that we ignore really small values.
+ * anyway, they will be 0 after shrinking to q7_t.
+ */
+ base = base - 8;
+
+ sum = 0;
+
+ for (i = 0; i < dim_vec; i++)
+ {
+ if (vec_in[i] > base)
+ {
+ shift = (uint8_t)__USAT(vec_in[i] - base, 5);
+ sum += 0x1 << shift;
+ }
+ }
+
+ /* This is effectively (0x1 << 20) / sum */
+ int output_base = 0x100000 / sum;
+
+ /*
+ * Final confidence will be output_base >> ( 13 - (vec_in[i] - base) )
+ * so 128 (0x1<<7) -> 100% confidence when sum = 0x1 << 8, output_base = 0x1 << 12
+ * and vec_in[i]-base = 8
+ */
+ for (i = 0; i < dim_vec; i++)
+ {
+ if (vec_in[i] > base)
+ {
+ /* Here minimum value of 13+base-vec_in[i] will be 5 */
+ shift = (uint8_t)__USAT(13+base-vec_in[i], 5);
+ p_out[i] = (q7_t) __SSAT((output_base >> shift), 8);
+ } else {
+ p_out[i] = 0;
+ }
+ }
+}
+
+/**
+ * @} end of Softmax group
+ */
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