Content
	GetBufferSizeFC

Functions
arm_cmsis_nn_status	arm_fully_connected_s16 (const cmsis_nn_context ctx, const cmsis_nn_fc_params fc_params, const cmsis_nn_per_tensor_quant_params quant_params, const cmsis_nn_dims input_dims, const int16_t input, const cmsis_nn_dims filter_dims, const int8_t kernel, const cmsis_nn_dims bias_dims, const int64_t bias, const cmsis_nn_dims output_dims, int16_t *output)
	Basic s16 Fully Connected function.

arm_cmsis_nn_status	arm_fully_connected_s4 (const cmsis_nn_context ctx, const cmsis_nn_fc_params fc_params, const cmsis_nn_per_tensor_quant_params quant_params, const cmsis_nn_dims input_dims, const int8_t input, const cmsis_nn_dims filter_dims, const int8_t kernel, const cmsis_nn_dims bias_dims, const int32_t bias, const cmsis_nn_dims output_dims, int8_t *output)
	Basic s4 Fully Connected function.

arm_cmsis_nn_status	arm_fully_connected_s8 (const cmsis_nn_context ctx, const cmsis_nn_fc_params fc_params, const cmsis_nn_per_tensor_quant_params quant_params, const cmsis_nn_dims input_dims, const int8_t input, const cmsis_nn_dims filter_dims, const int8_t kernel, const cmsis_nn_dims bias_dims, const int32_t bias, const cmsis_nn_dims output_dims, int8_t *output)
	Basic s8 Fully Connected function.

arm_cmsis_nn_status	arm_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.

arm_cmsis_nn_status	arm_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.

Description

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}

Function Documentation

◆ arm_fully_connected_s16()

arm_cmsis_nn_status arm_fully_connected_s16	(	const cmsis_nn_context *	ctx,
		const cmsis_nn_fc_params *	fc_params,
		const cmsis_nn_per_tensor_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
	)

Basic s16 Fully Connected function.

Parameters

[in,out]	ctx	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.
[in]	fc_params	Fully Connected layer parameters. fc_params->input_offset : 0 fc_params->filter_offset : 0 fc_params->output_offset : 0
[in]	quant_params	Per-tensor quantization info. It contains the multiplier and shift values to be applied to the output tensor.
[in]	input_dims	Input (activation) tensor dimensions. Format: [N, H, W, C_IN] Input dimension is taken as Nx(H * W * C_IN)
[in]	input_data	Input (activation) data pointer. Data type: int16
[in]	filter_dims	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
[in]	filter_data	Filter data pointer. Data type: int8
[in]	bias_dims	Bias tensor dimensions. Format: [C_OUT] N, H, W : Not used
[in]	bias_data	Bias data pointer. Data type: int64
[in]	output_dims	Output tensor dimensions. Format: [N, C_OUT] N : Batches C_OUT : Output depth H & W : Not used.
[in,out]	output_data	Output data pointer. Data type: int16

Returns: The function returns ARM_CMSIS_NN_SUCCESS

Supported framework: TensorFlow Lite

◆ arm_fully_connected_s4()

arm_cmsis_nn_status arm_fully_connected_s4	(	const cmsis_nn_context *	ctx,
		const cmsis_nn_fc_params *	fc_params,
		const cmsis_nn_per_tensor_quant_params *	quant_params,
		const cmsis_nn_dims *	input_dims,
		const int8_t *	input_data,
		const cmsis_nn_dims *	filter_dims,
		const int8_t *	filter_data,
		const cmsis_nn_dims *	bias_dims,
		const int32_t *	bias_data,
		const cmsis_nn_dims *	output_dims,
		int8_t *	output_data
	)

Basic s4 Fully Connected function.

Parameters

[in,out]	ctx	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.
[in]	fc_params	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]
[in]	quant_params	Per-tensor quantization info. It contains the multiplier and shift values to be applied to the output tensor.
[in]	input_dims	Input (activation) tensor dimensions. Format: [N, H, W, C_IN] Input dimension is taken as Nx(H * W * C_IN)
[in]	input_data	Input (activation) data pointer. Data type: int8
[in]	filter_dims	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
[in]	filter_data	Filter data pointer. Data type: int8_t packed 4-bit weights, e.g four sequential weights [0x1, 0x2, 0x3, 0x4] packed as [0x21, 0x43].
[in]	bias_dims	Bias tensor dimensions. Format: [C_OUT] N, H, W : Not used
[in]	bias_data	Bias data pointer. Data type: int32
[in]	output_dims	Output tensor dimensions. Format: [N, C_OUT] N : Batches C_OUT : Output depth H & W : Not used.
[in,out]	output_data	Output data pointer. Data type: int8

Returns: The function returns ARM_CMSIS_NN_SUCCESS

Supported framework: TensorFlow Lite

◆ arm_fully_connected_s8()

arm_cmsis_nn_status arm_fully_connected_s8	(	const cmsis_nn_context *	ctx,
		const cmsis_nn_fc_params *	fc_params,
		const cmsis_nn_per_tensor_quant_params *	quant_params,
		const cmsis_nn_dims *	input_dims,
		const int8_t *	input_data,
		const cmsis_nn_dims *	filter_dims,
		const int8_t *	filter_data,
		const cmsis_nn_dims *	bias_dims,
		const int32_t *	bias_data,
		const cmsis_nn_dims *	output_dims,
		int8_t *	output_data
	)

Basic s8 Fully Connected function.

Parameters

[in,out]	ctx	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.
[in]	fc_params	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]
[in]	quant_params	Per-tensor quantization info. It contains the multiplier and shift values to be applied to the output tensor.
[in]	input_dims	Input (activation) tensor dimensions. Format: [N, H, W, C_IN] Input dimension is taken as Nx(H * W * C_IN)
[in]	input_data	Input (activation) data pointer. Data type: int8
[in]	filter_dims	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
[in]	filter_data	Filter data pointer. Data type: int8
[in]	bias_dims	Bias tensor dimensions. Format: [C_OUT] N, H, W : Not used
[in]	bias_data	Bias data pointer. Data type: int32
[in]	output_dims	Output tensor dimensions. Format: [N, C_OUT] N : Batches C_OUT : Output depth H & W : Not used.
[in,out]	output_data	Output data pointer. Data type: int8

Returns: The function returns either ARM_CMSIS_NN_ARG_ERROR if argument constraints fail. or, ARM_CMSIS_NN_SUCCESS on successful completion.

Supported framework: TensorFlow Lite

◆ arm_vector_sum_s8()

arm_cmsis_nn_status arm_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

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

Returns: The function returns ARM_CMSIS_NN_SUCCESS - Successful operation

◆ arm_vector_sum_s8_s64()

arm_cmsis_nn_status arm_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

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

Returns: The function returns ARM_CMSIS_NN_SUCCESS - Successful operation

Content

Functions

Description

Function Documentation

◆ arm_fully_connected_s16()

◆ arm_fully_connected_s4()

◆ arm_fully_connected_s8()

◆ arm_vector_sum_s8()

◆ arm_vector_sum_s8_s64()