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. More...

int32_t	arm_fully_connected_s16_get_buffer_size (const cmsis_nn_dims *filter_dims)
	Get the required buffer size for S16 basic fully-connected and matrix multiplication layer function for TF Lite. More...

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. More...

int32_t	arm_fully_connected_s8_get_buffer_size (const cmsis_nn_dims *filter_dims)
	Get the required buffer size for S8 basic fully-connected and matrix multiplication layer function for TF Lite. More...

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
	)

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_s16_get_buffer_size()

int32_t arm_fully_connected_s16_get_buffer_size ( const cmsis_nn_dims * filter_dims )

Parameters

[in] filter_dims dimension of filter

Returns: The function returns required buffer size in bytes

◆ 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
	)

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 ARM_CMSIS_NN_SUCCESS

Supported framework: TensorFlow Lite

◆ arm_fully_connected_s8_get_buffer_size()

int32_t arm_fully_connected_s8_get_buffer_size ( const cmsis_nn_dims * filter_dims )

Parameters

[in] filter_dims dimension of filter

Returns: The function returns required buffer size in bytes

Functions

Description

Function Documentation

◆ arm_fully_connected_s16()

◆ arm_fully_connected_s16_get_buffer_size()

◆ arm_fully_connected_s8()

◆ arm_fully_connected_s8_get_buffer_size()