Fully-connected Layer Functions

GetBufferSizeFC

riscv_nmsis_nn_status riscv_fully_connected_mat_q7_vec_q15(const q15_t *pV, const q7_t *pM, const uint16_t dim_vec, const uint16_t num_of_rows, const uint16_t bias_shift, const uint16_t out_shift, const q7_t *bias, q15_t *pOut, q15_t *vec_buffer)

riscv_nmsis_nn_status riscv_fully_connected_mat_q7_vec_q15_opt(const q15_t *pV, const q7_t *pM, const uint16_t dim_vec, const uint16_t num_of_rows, const uint16_t bias_shift, const uint16_t out_shift, const q7_t *bias, q15_t *pOut, q15_t *vec_buffer)

riscv_nmsis_nn_status riscv_fully_connected_q15(const q15_t *pV, const q15_t *pM, const uint16_t dim_vec, const uint16_t num_of_rows, const uint16_t bias_shift, const uint16_t out_shift, const q15_t *bias, q15_t *pOut, q15_t *vec_buffer)

riscv_nmsis_nn_status riscv_fully_connected_q15_opt(const q15_t *pV, const q15_t *pM, const uint16_t dim_vec, const uint16_t num_of_rows, const uint16_t bias_shift, const uint16_t out_shift, const q15_t *bias, q15_t *pOut, q15_t *vec_buffer)

riscv_nmsis_nn_status riscv_fully_connected_q7(const q7_t *pV, const q7_t *pM, const uint16_t dim_vec, const uint16_t num_of_rows, const uint16_t bias_shift, const uint16_t out_shift, const q7_t *bias, q7_t *pOut, q15_t *vec_buffer)

riscv_nmsis_nn_status riscv_fully_connected_q7_opt(const q7_t *pV, const q7_t *pM, const uint16_t dim_vec, const uint16_t num_of_rows, const uint16_t bias_shift, const uint16_t out_shift, const q7_t *bias, q7_t *pOut, q15_t *vec_buffer)

riscv_nmsis_nn_status riscv_fully_connected_s16(const nmsis_nn_context *ctx, const nmsis_nn_fc_params *fc_params, const nmsis_nn_per_tensor_quant_params *quant_params, const nmsis_nn_dims *input_dims, const int16_t *input, const nmsis_nn_dims *filter_dims, const int8_t *kernel, const nmsis_nn_dims *bias_dims, const int64_t *bias, const nmsis_nn_dims *output_dims, int16_t *output)

riscv_nmsis_nn_status riscv_fully_connected_s4(const nmsis_nn_context *ctx, const nmsis_nn_fc_params *fc_params, const nmsis_nn_per_tensor_quant_params *quant_params, const nmsis_nn_dims *input_dims, const int8_t *input, const nmsis_nn_dims *filter_dims, const int8_t *kernel, const nmsis_nn_dims *bias_dims, const int32_t *bias, const nmsis_nn_dims *output_dims, int8_t *output)

riscv_nmsis_nn_status riscv_fully_connected_s8(const nmsis_nn_context *ctx, const nmsis_nn_fc_params *fc_params, const nmsis_nn_per_tensor_quant_params *quant_params, const nmsis_nn_dims *input_dims, const int8_t *input, const nmsis_nn_dims *filter_dims, const int8_t *kernel, const nmsis_nn_dims *bias_dims, const int32_t *bias, const nmsis_nn_dims *output_dims, int8_t *output)

riscv_nmsis_nn_status riscv_vector_sum_s8(int32_t *vector_sum_buf, const int32_t vector_cols, const int32_t vector_rows, const int8_t *vector_data, const int32_t lhs_offset, const int32_t *bias_data)

riscv_nmsis_nn_status riscv_vector_sum_s8_s64(int64_t *vector_sum_buf, const int32_t vector_cols, const int32_t vector_rows, const int8_t *vector_data, const int32_t lhs_offset, const int64_t *bias_data)

group FC

Collection of fully-connected and matrix multiplication functions.

Fully-connected layer is basically a matrix-vector multiplication with bias. The matrix is the weights and the input/output vectors are the activation values. Supported {weight, activation} precisions include {8-bit, 8-bit} and {8-bit, 16-bit}

Functions

riscv_nmsis_nn_status riscv_fully_connected_mat_q7_vec_q15(const q15_t *pV, const q7_t *pM, const uint16_t dim_vec, const uint16_t num_of_rows, const uint16_t bias_shift, const uint16_t out_shift, const q7_t *bias, q15_t *pOut, q15_t *vec_buffer)

Mixed Q15-Q7 fully-connected layer function.

Buffer size:

vec_buffer size: 0

Q7_Q15 version of the fully connected layer

Weights are in q7_t and Activations are in q15_t

Parameters

pV – [in] pointer to input vector
pM – [in] pointer to matrix weights
dim_vec – [in] length of the vector
num_of_rows – [in] number of rows in weight matrix
bias_shift – [in] amount of left-shift for bias
out_shift – [in] amount of right-shift for output
bias – [in] pointer to bias
pOut – [inout] pointer to output vector
vec_buffer – [inout] pointer to buffer space for input

Returns

The function returns RISCV_NMSIS_NN_SUCCESS

riscv_nmsis_nn_status riscv_fully_connected_mat_q7_vec_q15_opt(const q15_t *pV, const q7_t *pM, const uint16_t dim_vec, const uint16_t num_of_rows, const uint16_t bias_shift, const uint16_t out_shift, const q7_t *bias, q15_t *pOut, q15_t *vec_buffer)

Mixed Q15-Q7 opt fully-connected layer function.

Buffer size:

vec_buffer size: 0

Q7_Q15 version of the fully connected layer

Weights are in q7_t and Activations are in q15_t

Limitation: x4 version requires weight reordering to work

Here we use only one pointer to read 4 rows in the weight matrix. So if the original q7_t matrix looks like this:

| a11 | a12 | a13 | a14 | a15 | a16 | a17 |

| a21 | a22 | a23 | a24 | a25 | a26 | a27 |

| a31 | a32 | a33 | a34 | a35 | a36 | a37 |

| a41 | a42 | a43 | a44 | a45 | a46 | a47 |

| a51 | a52 | a53 | a54 | a55 | a56 | a57 |

| a61 | a62 | a63 | a64 | a65 | a66 | a67 |

We operates on multiple-of-4 rows, so the first four rows becomes

| a11 | a21 | a12 | a22 | a31 | a41 | a32 | a42 |

| a13 | a23 | a14 | a24 | a33 | a43 | a34 | a44 |

| a15 | a25 | a16 | a26 | a35 | a45 | a36 | a46 |

The column left over will be in-order. which is: | a17 | a27 | a37 | a47 |

For the left-over rows, we do 1x1 computation, so the data remains as its original order.

So the stored weight matrix looks like this:

| a11 | a21 | a12 | a22 | a31 | a41 |

| a32 | a42 | a13 | a23 | a14 | a24 |

| a33 | a43 | a34 | a44 | a15 | a25 |

| a16 | a26 | a35 | a45 | a36 | a46 |

| a17 | a27 | a37 | a47 | a51 | a52 |

| a53 | a54 | a55 | a56 | a57 | a61 |

| a62 | a63 | a64 | a65 | a66 | a67 |

Parameters

pV – [in] pointer to input vector
pM – [in] pointer to matrix weights
dim_vec – [in] length of the vector
num_of_rows – [in] number of rows in weight matrix
bias_shift – [in] amount of left-shift for bias
out_shift – [in] amount of right-shift for output
bias – [in] pointer to bias
pOut – [inout] pointer to output vector
vec_buffer – [inout] pointer to buffer space for input

Returns

The function returns RISCV_NMSIS_NN_SUCCESS

riscv_nmsis_nn_status riscv_fully_connected_q15(const q15_t *pV, const q15_t *pM, const uint16_t dim_vec, const uint16_t num_of_rows, const uint16_t bias_shift, const uint16_t out_shift, const q15_t *bias, q15_t *pOut, q15_t *vec_buffer)

Q15 opt fully-connected layer function.

Q15 basic fully-connected layer function.

Buffer size:

vec_buffer size: 0

Parameters

pV – [in] pointer to input vector
pM – [in] pointer to matrix weights
dim_vec – [in] length of the vector
num_of_rows – [in] number of rows in weight matrix
bias_shift – [in] amount of left-shift for bias
out_shift – [in] amount of right-shift for output
bias – [in] pointer to bias
pOut – [inout] pointer to output vector
vec_buffer – [inout] pointer to buffer space for input

Returns

The function returns RISCV_NMSIS_NN_SUCCESS

riscv_nmsis_nn_status riscv_fully_connected_q15_opt(const q15_t *pV, const q15_t *pM, const uint16_t dim_vec, const uint16_t num_of_rows, const uint16_t bias_shift, const uint16_t out_shift, const q15_t *bias, q15_t *pOut, q15_t *vec_buffer)

Q15 opt fully-connected layer function.

Buffer size:

vec_buffer size: 0

Here we use only one pointer to read 4 rows in the weight matrix. So if the original matrix looks like this:

| a11 | a12 | a13 |

| a21 | a22 | a23 |

| a31 | a32 | a33 |

| a41 | a42 | a43 |

| a51 | a52 | a53 |

| a61 | a62 | a63 |

We operates on multiple-of-4 rows, so the first four rows becomes

| a11 | a12 | a21 | a22 | a31 | a32 | a41 | a42 |

| a13 | a23 | a33 | a43 |

Remaining rows are kept the same original order.

So the stored weight matrix looks like this:

| a11 | a12 | a21 | a22 | a31 | a32 | a41 | a42 |

| a13 | a23 | a33 | a43 | a51 | a52 | a53 | a61 |

| a62 | a63 |

Parameters

pV – [in] pointer to input vector
pM – [in] pointer to matrix weights
dim_vec – [in] length of the vector
num_of_rows – [in] number of rows in weight matrix
bias_shift – [in] amount of left-shift for bias
out_shift – [in] amount of right-shift for output
bias – [in] pointer to bias
pOut – [inout] pointer to output vector
vec_buffer – [inout] pointer to buffer space for input

Returns

The function returns RISCV_NMSIS_NN_SUCCESS

riscv_nmsis_nn_status riscv_fully_connected_q7(const q7_t *pV, const q7_t *pM, const uint16_t dim_vec, const uint16_t num_of_rows, const uint16_t bias_shift, const uint16_t out_shift, const q7_t *bias, q7_t *pOut, q15_t *vec_buffer)

Q7 basic fully-connected layer function.

Buffer size:

vec_buffer size: dim_vec

This basic function is designed to work with regular weight matrix without interleaving.

Parameters

pV – [in] pointer to input vector
pM – [in] pointer to matrix weights
dim_vec – [in] length of the vector
num_of_rows – [in] number of rows in weight matrix
bias_shift – [in] amount of left-shift for bias
out_shift – [in] amount of right-shift for output
bias – [in] pointer to bias
pOut – [inout] pointer to output vector
vec_buffer – [inout] pointer to buffer space for input

Returns

The function returns RISCV_NMSIS_NN_SUCCESS

riscv_nmsis_nn_status riscv_fully_connected_q7_opt(const q7_t *pV, const q7_t *pM, const uint16_t dim_vec, const uint16_t num_of_rows, const uint16_t bias_shift, const uint16_t out_shift, const q7_t *bias, q7_t *pOut, q15_t *vec_buffer)

Q7 opt fully-connected layer function.

Buffer size:

vec_buffer size: dim_vec

This opt function is designed to work with interleaved weight matrix. The vector input is assumed in q7_t format, we call riscv_q7_to_q15_no_shift_shuffle function to expand into q15_t format with certain weight re-ordering, refer to the function comments for more details. Here we use only one pointer to read 4 rows in the weight matrix. So if the original q7_t matrix looks like this:

| a11 | a12 | a13 | a14 | a15 | a16 | a17 |

| a21 | a22 | a23 | a24 | a25 | a26 | a27 |

| a31 | a32 | a33 | a34 | a35 | a36 | a37 |

| a41 | a42 | a43 | a44 | a45 | a46 | a47 |

| a51 | a52 | a53 | a54 | a55 | a56 | a57 |

| a61 | a62 | a63 | a64 | a65 | a66 | a67 |

We operates on multiple-of-4 rows, so the first four rows becomes

| a11 | a21 | a13 | a23 | a31 | a41 | a33 | a43 |

| a12 | a22 | a14 | a24 | a32 | a42 | a34 | a44 |

| a15 | a25 | a35 | a45 | a16 | a26 | a36 | a46 |

So within the kernel, we first read the re-ordered vector in as:

| b1 | b3 | and | b2 | b4 |

the four q31_t weights will look like

| a11 | a13 |, | a21 | a23 |, | a31 | a33 |, | a41 | a43 |

| a12 | a14 |, | a22 | a24 |, | a32 | a34 |, | a42 | a44 |

The column left over will be in-order. which is:

| a17 | a27 | a37 | a47 |

For the left-over rows, we do 1x1 computation, so the data remains as its original order.

So the stored weight matrix looks like this:

| a11 | a21 | a13 | a23 | a31 | a41 |

| a33 | a43 | a12 | a22 | a14 | a24 |

| a32 | a42 | a34 | a44 | a15 | a25 |

| a35 | a45 | a16 | a26 | a36 | a46 |

| a17 | a27 | a37 | a47 | a51 | a52 |

| a53 | a54 | a55 | a56 | a57 | a61 |

| a62 | a63 | a64 | a65 | a66 | a67 |

Parameters

pV – [in] pointer to input vector
pM – [in] pointer to matrix weights
dim_vec – [in] length of the vector
num_of_rows – [in] number of rows in weight matrix
bias_shift – [in] amount of left-shift for bias
out_shift – [in] amount of right-shift for output
bias – [in] pointer to bias
pOut – [inout] pointer to output vector
vec_buffer – [inout] pointer to buffer space for input

Returns

The function returns RISCV_NMSIS_NN_SUCCESS

riscv_nmsis_nn_status riscv_fully_connected_s16(const nmsis_nn_context *ctx, const nmsis_nn_fc_params *fc_params, const nmsis_nn_per_tensor_quant_params *quant_params, const nmsis_nn_dims *input_dims, const int16_t *input, const nmsis_nn_dims *filter_dims, const int8_t *kernel, const nmsis_nn_dims *bias_dims, const int64_t *bias, const nmsis_nn_dims *output_dims, int16_t *output)

Basic s16 Fully Connected function.

Supported framework: TensorFlow Lite

Parameters

ctx – [inout] Function context (e.g. temporary buffer). Check the function definition file to see if an additional buffer is required. Optional function {API}_get_buffer_size() provides the buffer size if an additional buffer is required. The caller is expected to clear the buffer, if applicable, for security reasons.
fc_params – [in] Fully Connected layer parameters. fc_params->input_offset : 0 fc_params->filter_offset : 0 fc_params->output_offset : 0
quant_params – [in] Per-tensor quantization info. It contains the multiplier and shift values to be applied to the output tensor.
input_dims – [in] Input (activation) tensor dimensions. Format: [N, H, W, C_IN] Input dimension is taken as Nx(H * W * C_IN)
input_data – [in] Input (activation) data pointer. Data type: int16
filter_dims – [in] Two dimensional filter dimensions. Format: [N, C] N : accumulation depth and equals (H * W * C_IN) from input_dims C : output depth and equals C_OUT in output_dims H & W : Not used
filter_data – [in] Filter data pointer. Data type: int8
bias_dims – [in] Bias tensor dimensions. Format: [C_OUT] N, H, W : Not used
bias_data – [in] Bias data pointer. Data type: int64
output_dims – [in] Output tensor dimensions. Format: [N, C_OUT] N : Batches C_OUT : Output depth H & W : Not used.
output_data – [inout] Output data pointer. Data type: int16

Returns

The function returns RISCV_NMSIS_NN_SUCCESS

riscv_nmsis_nn_status riscv_fully_connected_s4(const nmsis_nn_context *ctx, const nmsis_nn_fc_params *fc_params, const nmsis_nn_per_tensor_quant_params *quant_params, const nmsis_nn_dims *input_dims, const int8_t *input, const nmsis_nn_dims *filter_dims, const int8_t *kernel, const nmsis_nn_dims *bias_dims, const int32_t *bias, const nmsis_nn_dims *output_dims, int8_t *output)

Basic s4 Fully Connected function.

Supported framework: TensorFlow Lite

Parameters

ctx – [inout] Function context (e.g. temporary buffer). Check the function definition file to see if an additional buffer is required. Optional function {API}_get_buffer_size() provides the buffer size if an additional buffer is required. The caller is expected to clear the buffer ,if applicable, for security reasons.
fc_params – [in] Fully Connected layer parameters. Range of fc_params->input_offset : [-127, 128] fc_params->filter_offset : 0 Range of fc_params->output_offset : [-128, 127]
quant_params – [in] Per-tensor quantization info. It contains the multiplier and shift values to be applied to the output tensor.
input_dims – [in] Input (activation) tensor dimensions. Format: [N, H, W, C_IN] Input dimension is taken as Nx(H * W * C_IN)
input_data – [in] Input (activation) data pointer. Data type: int8
filter_dims – [in] Two dimensional filter dimensions. Format: [N, C] N : accumulation depth and equals (H * W * C_IN) from input_dims C : output depth and equals C_OUT in output_dims H & W : Not used
filter_data – [in] Filter data pointer. Data type: int8_t packed 4-bit weights, e.g four sequential weights [0x1, 0x2, 0x3, 0x4] packed as [0x21, 0x43].
bias_dims – [in] Bias tensor dimensions. Format: [C_OUT] N, H, W : Not used
bias_data – [in] Bias data pointer. Data type: int32
output_dims – [in] Output tensor dimensions. Format: [N, C_OUT] N : Batches C_OUT : Output depth H & W : Not used.
output_data – [inout] Output data pointer. Data type: int8

Returns

The function returns RISCV_NMSIS_NN_SUCCESS

riscv_nmsis_nn_status riscv_fully_connected_s8(const nmsis_nn_context *ctx, const nmsis_nn_fc_params *fc_params, const nmsis_nn_per_tensor_quant_params *quant_params, const nmsis_nn_dims *input_dims, const int8_t *input, const nmsis_nn_dims *filter_dims, const int8_t *kernel, const nmsis_nn_dims *bias_dims, const int32_t *bias, const nmsis_nn_dims *output_dims, int8_t *output)

Basic s8 Fully Connected function.

Supported framework: TensorFlow Lite

Parameters

ctx – [inout] Function context (e.g. temporary buffer). Check the function definition file to see if an additional buffer is required. Optional function {API}_get_buffer_size() provides the buffer size if an additional buffer is required. The caller is expected to clear the buffer, if applicable, for security reasons.
fc_params – [in] Fully Connected layer parameters. Range of fc_params->input_offset : [-127, 128] fc_params->filter_offset : 0 Range of fc_params->output_offset : [-128, 127]
quant_params – [in] Per-tensor quantization info. It contains the multiplier and shift values to be applied to the output tensor.
input_dims – [in] Input (activation) tensor dimensions. Format: [N, H, W, C_IN] Input dimension is taken as Nx(H * W * C_IN)
input_data – [in] Input (activation) data pointer. Data type: int8
filter_dims – [in] Two dimensional filter dimensions. Format: [N, C] N : accumulation depth and equals (H * W * C_IN) from input_dims C : output depth and equals C_OUT in output_dims H & W : Not used
filter_data – [in] Filter data pointer. Data type: int8
bias_dims – [in] Bias tensor dimensions. Format: [C_OUT] N, H, W : Not used
bias_data – [in] Bias data pointer. Data type: int32
output_dims – [in] Output tensor dimensions. Format: [N, C_OUT] N : Batches C_OUT : Output depth H & W : Not used.
output_data – [inout] Output data pointer. Data type: int8

Returns

The function returns either RISCV_NMSIS_NN_ARG_ERROR if argument constraints fail. or, RISCV_NMSIS_NN_SUCCESS on successful completion.

riscv_nmsis_nn_status riscv_vector_sum_s8(int32_t *vector_sum_buf, const int32_t vector_cols, const int32_t vector_rows, const int8_t *vector_data, const int32_t lhs_offset, const int32_t *bias_data)

Calculate the sum of each row in vector_data, multiply by lhs_offset and optionally add s32 bias_data.

Parameters

vector_sum_buf – [inout] Buffer for vector sums
vector_cols – [in] Number of vector columns
vector_rows – [in] Number of vector rows
vector_data – [in] Vector of weigths data
lhs_offset – [in] Constant multiplied with each sum
bias_data – [in] Vector of bias data, added to each sum.

Returns

The function returns RISCV_NMSIS_NN_SUCCESS - Successful operation

riscv_nmsis_nn_status riscv_vector_sum_s8_s64(int64_t *vector_sum_buf, const int32_t vector_cols, const int32_t vector_rows, const int8_t *vector_data, const int32_t lhs_offset, const int64_t *bias_data)

Calculate the sum of each row in vector_data, multiply by lhs_offset and optionally add s64 bias_data.

Parameters

vector_sum_buf – [inout] Buffer for vector sums
vector_cols – [in] Number of vector columns
vector_rows – [in] Number of vector rows
vector_data – [in] Vector of weigths data
lhs_offset – [in] Constant multiplied with each sum
bias_data – [in] Vector of bias data, added to each sum.

Returns

The function returns RISCV_NMSIS_NN_SUCCESS - Successful operation