Files
file	noodle.h
	Public Noodle API for small CNN/ML inference on microcontrollers.
file	noodle_buffer.h
	Grow-only float buffers used by NoodleBuffer convolution overloads.

Classes
struct	Conv
	File-backed convolution parameter bundle. More...
struct	ConvFile
	File-backed convolution parameter bundle alias. More...
struct	ConvMem
	Memory-backed convolution parameter bundle. More...
struct	ConvProgmem
	Near-PROGMEM convolution parameter bundle. More...
struct	Pool
	Valid-pooling parameter bundle. More...
struct	FCN
	File-backed fully connected parameter bundle. More...
struct	FCNFile
	File-backed fully connected parameter bundle alias. More...
struct	FCNMem
	Memory-backed fully connected parameter bundle. More...
struct	FCNProgmem
	Far-PROGMEM fully connected parameter bundle for AVR. More...
struct	NoodleBuffer
	Grow-only float buffer managed by Noodle. More...

Typedefs
typedef void(*	CBFPtr) (float progress)
	Progress callback used by long-running layer routines.

Enumerations
enum	Activation : uint8_t { ACT_NONE = 0 , ACT_RELU = 1 , ACT_SOFTMAX = 2 }
	Activation applied after bias where supported. More...

Functions
static float	noodle_pgm_float (const float *p, uint32_t idx)
	Read a float from normal memory or near AVR PROGMEM.
bool	noodle_fs_init (uint8_t clk_pin, uint8_t cmd_pin, uint8_t d0_pin)
	Initialize SD_MMC with explicit 1-bit pins, or default-init other backends.
bool	noodle_fs_init (uint8_t clk_pin, uint8_t cmd_pin, uint8_t d0_pin, uint8_t d1_pin, uint8_t d2_pin, uint8_t d3_pin)
	Initialize SD_MMC with explicit 4-bit pins, or default-init other backends.
bool	noodle_fs_init ()
	Initialize the selected filesystem backend with default settings.
bool	noodle_fs_init (uint8_t cs_pin)
	Initialize the selected filesystem backend with an SPI chip-select pin.
bool	noodle_fs_init (uint8_t cs_pin, SPIClass &spi, uint8_t sck_mhz)
	Initialize SdFat with an explicit SPI bus and clock speed.
void	noodle_read_top_line (const char fn, char line, size_t maxlen)
	Read the first line from a text file.
void	noodle_delete_file (const char *fn)
	Delete a file through the selected filesystem backend.
size_t	noodle_read_bytes_until (NDL_File &file, char terminator, char *buffer, size_t length)
	Read bytes until a terminator or until the buffer is full.
void	noodle_write_float (NDL_File &f, float d)
	Write a float using NOODLE_FILE_FORMAT.
float	noodle_read_float (NDL_File &f)
	Read a float using NOODLE_FILE_FORMAT.
byte	noodle_read_byte (NDL_File &f)
	Read a byte using NOODLE_FILE_FORMAT.
void	noodle_write_byte (NDL_File &f, byte d)
	Write a byte using NOODLE_FILE_FORMAT.
void	noodle_setup_temp_buffers (void b1, void b2)
	Install caller-owned internal scratch buffers.
void	noodle_setup_temp_buffers (void *b2)
	Install only the caller-owned accumulation scratch buffer.
void	noodle_temp_buffers_free (void)
	Release automatically allocated internal scratch buffers.
float *	noodle_create_buffer (uint16_t size)
	Allocate a raw byte buffer and return it as a float pointer.
void	noodle_delete_buffer (float *buffer)
	Free a buffer allocated by noodle_create_buffer().
void	noodle_array_to_file (float array, const char fn, uint16_t n)
	Write a float array to a file.
void	noodle_grid_to_file (byte grid, const char fn, uint16_t n)
	Write an n x n byte grid to a file in row-major order.
void	noodle_grid_to_file (float grid, const char fn, uint16_t n)
	Write an n x n float grid to a file in row-major order.
void	noodle_array_from_file (const char fn, float buffer, uint16_t K)
	Read a float array from a file.
void	noodle_grid_from_file (const char fn, byte buffer, uint16_t K)
	Read a K x K grid into a byte buffer.
void	noodle_grid_from_file (const char fn, int8_t buffer, uint16_t K)
	Read a K x K grid into an int8 buffer.
void	noodle_grid_from_file (const char fn, float buffer, uint16_t K)
	Read a K x K grid into a float buffer.
void	noodle_buffer_init (NoodleBuffer *buf)
	Initialize a NoodleBuffer.
float *	noodle_buffer_require (NoodleBuffer *buf, size_t required_floats)
	Ensure that a buffer can hold at least required_floats floats.
void	noodle_buffer_free (NoodleBuffer *buf)
	Release a NoodleBuffer.
size_t	noodle_buffer_capacity (const NoodleBuffer *buf)
	Return the buffer capacity in float elements.
size_t	noodle_buffer_capacity_bytes (const NoodleBuffer *buf)
	Return the buffer capacity in bytes.

File-backed layers
File-backed convolution APIs read packed input tensors from files, stream parameters from files or memory/PROGMEM, and write packed output tensors to a file. They return the output width/length, or 0 on allocation/open/shape failure.
uint16_t	noodle_conv_byte (const char in_fn, uint16_t n_inputs, uint16_t n_outputs, const char out_fn, uint16_t W, const Conv &conv, const Pool &pool, CBFPtr progress_cb=0)
	Run file-to-file 2D convolution on byte input feature maps.
uint16_t	noodle_conv_float (const char in_fn, uint16_t n_inputs, uint16_t n_outputs, const char out_fn, uint16_t W, const Conv &conv, const Pool &pool, CBFPtr progress_cb=0)
	Run file-to-file 2D convolution on float input feature maps.
uint16_t	noodle_conv_float (const char in_fn, uint16_t n_inputs, uint16_t n_outputs, const char out_fn, uint16_t W, const ConvMem &conv, const Pool &pool, CBFPtr progress_cb=0)
	Run file-to-file 2D convolution with memory-backed parameters.
uint16_t	noodle_conv_float (const char in_fn, uint16_t n_inputs, uint16_t n_outputs, const char out_fn, uint16_t W, const ConvProgmem &conv, const Pool &pool, CBFPtr progress_cb=0)
	Run file-to-file 2D convolution with near-PROGMEM parameters.
uint16_t	noodle_conv1d (const char in_fn, uint16_t n_inputs, const char out_fn, uint16_t n_outputs, uint16_t W, const Conv &conv, const Pool &pool, CBFPtr progress_cb=0)
	Run file-to-file 1D convolution with file-backed parameters and pooling.
uint16_t	noodle_conv1d (const char in_fn, uint16_t n_inputs, const char out_fn, uint16_t n_outputs, uint16_t W, const Conv &conv, CBFPtr progress_cb=0)
	Run file-to-file 1D convolution with file-backed parameters.
uint16_t	noodle_conv1d (const char in_fn, uint16_t n_inputs, const char out_fn, uint16_t n_outputs, uint16_t W, const ConvMem &conv, CBFPtr progress_cb=0)
	Run file-to-file 1D convolution with memory-backed parameters.
uint16_t	noodle_dwconv_float (const char in_fn, uint16_t n_channels, const char out_fn, uint16_t W, const Conv &conv, const Pool &pool, CBFPtr progress_cb=0)
	Run file-to-file depthwise 2D convolution with file-backed parameters.
uint16_t	noodle_dwconv_float (const char in_fn, uint16_t n_channels, const char out_fn, uint16_t W, const ConvProgmem &conv, const Pool &pool, CBFPtr progress_cb=0)
	Run file-to-file depthwise 2D convolution with near-PROGMEM parameters.
uint16_t	noodle_fcn (const int8_t input, uint16_t n_inputs, uint16_t n_outputs, const char out_fn, const FCNFile &fcn, CBFPtr progress_cb=0)
	Run a fully connected layer from int8 memory to a file.
uint16_t	noodle_fcn (const byte input, uint16_t n_inputs, uint16_t n_outputs, const char out_fn, const FCNFile &fcn, CBFPtr progress_cb=0)
	Run a fully connected layer from byte memory to a file.
uint16_t	noodle_fcn (const char in_fn, uint16_t n_inputs, uint16_t n_outputs, const char out_fn, const FCNFile &fcn, CBFPtr progress_cb=0)
	Run a fully connected layer from a file to a file.

NoodleBuffer RAM-to-RAM layers
These overloads read input from input->data, grow `output` with noodle_buffer_require(), and write the result into output->data. They return the output width/length or output count, and return 0 on null input, null storage, invalid shape, allocation failure, or a failing lower-level layer.
uint16_t	noodle_conv_float (NoodleBuffer input, uint16_t n_inputs, uint16_t n_outputs, NoodleBuffer output, uint16_t W, const Conv &conv, const Pool &pool, CBFPtr progress_cb=0)
	Run 2D convolution using file-backed parameters.
uint16_t	noodle_conv_float (NoodleBuffer input, uint16_t n_inputs, uint16_t n_outputs, NoodleBuffer output, uint16_t W, const ConvMem &conv, const Pool &pool, CBFPtr progress_cb=0)
	Run 2D convolution using memory-backed parameters.
uint16_t	noodle_conv_float (NoodleBuffer input, uint16_t n_inputs, uint16_t n_outputs, NoodleBuffer output, uint16_t W, const ConvProgmem &conv, const Pool &pool, CBFPtr progress_cb=0)
	Run 2D convolution using near-PROGMEM parameters.
uint16_t	noodle_conv1d (NoodleBuffer input, uint16_t n_inputs, NoodleBuffer output, uint16_t n_outputs, uint16_t W, const ConvMem &conv, CBFPtr progress_cb=0)
	Run 1D convolution using memory-backed parameters.
uint16_t	noodle_conv1d (NoodleBuffer input, uint16_t n_inputs, NoodleBuffer output, uint16_t n_outputs, uint16_t W, const ConvMem &conv, const Pool &pool, CBFPtr progress_cb=0)
	Run 1D convolution using memory-backed parameters and pooling.
uint16_t	noodle_dwconv_float (NoodleBuffer input, uint16_t n_channels, NoodleBuffer output, uint16_t W, const Conv &conv, const Pool &pool, CBFPtr progress_cb=0)
	Run depthwise 2D convolution using file-backed parameters.
uint16_t	noodle_dwconv_float (NoodleBuffer input, uint16_t n_channels, NoodleBuffer output, uint16_t W, const ConvMem &conv, const Pool &pool, CBFPtr progress_cb=0)
	Run depthwise 2D convolution using memory-backed parameters.
uint16_t	noodle_dwconv_float (NoodleBuffer input, uint16_t n_channels, NoodleBuffer output, uint16_t W, const ConvProgmem &conv, const Pool &pool, CBFPtr progress_cb=0)
	Run depthwise 2D convolution using near-PROGMEM parameters.
uint16_t	noodle_conv_transpose_float (NoodleBuffer input, uint16_t n_inputs, uint16_t n_outputs, NoodleBuffer output, uint16_t W, const ConvMem &conv, CBFPtr progress_cb=0)
	Run 2D transpose convolution using memory-backed parameters.
uint16_t	noodle_fcn (NoodleBuffer input, uint16_t n_inputs, uint16_t n_outputs, NoodleBuffer output, const FCNMem &fcn, CBFPtr progress_cb=0)
	Run a fully connected layer using memory-backed parameters.
uint16_t	noodle_fcn (NoodleBuffer input, uint16_t n_inputs, uint16_t n_outputs, NoodleBuffer output, const FCNFile &fcn, CBFPtr progress_cb=0)
	Run a fully connected layer using file-backed parameters.
uint16_t	noodle_fcn (NoodleBuffer input, uint16_t n_inputs, uint16_t n_outputs, NoodleBuffer output, const FCNProgmem &fcn, CBFPtr progress_cb=0)
	Run a fully connected layer using far-PROGMEM parameters.
uint16_t	noodle_fcn (const byte input, uint16_t n_inputs, uint16_t n_outputs, NoodleBuffer output, const FCNFile &fcn, CBFPtr progress_cb=0)
	Run a fully connected layer from byte memory to a NoodleBuffer.
uint16_t	noodle_fcn (const int8_t input, uint16_t n_inputs, uint16_t n_outputs, NoodleBuffer output, const FCNFile &fcn, CBFPtr progress_cb=0)
	Run a fully connected layer from int8 memory to a NoodleBuffer.
uint16_t	noodle_fcn (const char in_fn, uint16_t n_inputs, uint16_t n_outputs, NoodleBuffer output, const FCNFile &fcn, CBFPtr progress_cb=0)
	Run a fully connected layer from a file to a NoodleBuffer.
uint16_t	noodle_fcn_progmem (NoodleBuffer input, uint16_t n_inputs, uint16_t n_outputs, NoodleBuffer output, const float weight, const float bias, Activation act, CBFPtr progress_cb=0)
	Run a fully connected layer using near-PROGMEM weights.
uint16_t	noodle_flat (const char in_fn, NoodleBuffer output, uint16_t V, uint16_t n_filters)
	Flatten a packed file tensor into a NoodleBuffer.
uint16_t	noodle_flat (NoodleBuffer input, NoodleBuffer output, uint16_t V, uint16_t n_filters)
	Flatten a packed NoodleBuffer tensor into HWC-like order.
uint16_t	noodle_reshape (NoodleBuffer src_hwc, NoodleBuffer dst_chw, uint16_t W, uint16_t C)
	Convert HWC-like NoodleBuffer data to packed channel-first data.
uint16_t	noodle_gap (NoodleBuffer *inout, uint16_t C, uint16_t W)
	Apply global average pooling in place on packed channel-first maps.
uint16_t	noodle_gmp (NoodleBuffer *inout, uint16_t C, uint16_t W)
	Apply global max pooling in place on packed channel-first data.
uint16_t	noodle_soft_max (NoodleBuffer *input_output, uint16_t n)
	Apply numerically stabilized softmax in place on a NoodleBuffer.
uint16_t	noodle_sigmoid (NoodleBuffer *input_output, uint16_t n)
	Apply sigmoid in place on a NoodleBuffer.
float	noodle_sigmoidf (float x)
	Compute sigmoid for one scalar.
uint16_t	noodle_logit (NoodleBuffer *input_output, uint16_t n)
	Apply logistic sigmoid in place on a NoodleBuffer.
uint16_t	noodle_relu (NoodleBuffer *input_output, uint16_t n)
	Apply ReLU in place on a NoodleBuffer.
void	noodle_find_max (NoodleBuffer *input, uint16_t n, float &max_val, uint16_t &max_idx)
	Find the maximum value and its index in a NoodleBuffer vector.
uint16_t	noodle_bn1d (NoodleBuffer x, uint16_t N, const float gamma, const float beta, const float mean, const float *var, float eps)
	Apply 1D batch normalization in place to a NoodleBuffer vector.
uint16_t	noodle_bn1d (NoodleBuffer x, uint16_t N, const float bn_params, float eps)
	Apply 1D batch normalization from a packed parameter array.
uint16_t	noodle_bn1d_relu (NoodleBuffer x, uint16_t N, const float gamma, const float beta, const float mean, const float *var, float eps)
	Apply 1D batch normalization followed by ReLU in place.
uint16_t	noodle_bn1d_relu (NoodleBuffer x, uint16_t N, const float bn_params, float eps)
	Apply packed 1D batch normalization followed by ReLU in place.
uint16_t	noodle_bn2d (NoodleBuffer x, uint16_t C, uint16_t W, const float gamma, const float beta, const float mean, const float *var, float eps)
	Apply 2D channel-wise batch normalization in place.
uint16_t	noodle_bn2d (NoodleBuffer x, uint16_t C, uint16_t W, const float bn_params, float eps)
	Apply 2D channel-wise batch normalization from packed parameters.
uint16_t	noodle_bn2d_relu (NoodleBuffer x, uint16_t C, uint16_t W, const float gamma, const float beta, const float mean, const float *var, float eps)
	Apply 2D channel-wise batch normalization followed by ReLU.
uint16_t	noodle_bn2d_relu (NoodleBuffer x, uint16_t C, uint16_t W, const float bn_params, float eps)
	Apply packed 2D batch normalization followed by ReLU.
uint16_t	noodle_bn (NoodleBuffer x, uint16_t C, uint16_t W, const float gamma, const float beta, const float mean, const float *var, float eps)
	Backward-compatible alias for noodle_bn2d().
uint16_t	noodle_bn (NoodleBuffer x, uint16_t C, uint16_t W, const float bn_params, float eps)
	Backward-compatible alias for packed-parameter noodle_bn2d().
uint16_t	noodle_bn_relu (NoodleBuffer x, uint16_t C, uint16_t W, const float gamma, const float beta, const float mean, const float *var, float eps)
	Backward-compatible alias for noodle_bn2d_relu().
uint16_t	noodle_bn_relu (NoodleBuffer x, uint16_t C, uint16_t W, const float bn_params, float eps)
	Backward-compatible alias for packed-parameter noodle_bn2d_relu().

Detailed Description

Public functions, types, and configuration intended for application code.

Typedef Documentation

◆ CBFPtr

typedef void(* CBFPtr) (float progress)

Progress callback used by long-running layer routines.

Parameters

progress Normalized progress, usually in the range [0, 1].

Enumeration Type Documentation

◆ Activation

enum Activation : uint8_t

Activation applied after bias where supported.

Enumerator
ACT_NONE	Do not apply an activation.
ACT_RELU	Clamp negative values to zero.
ACT_SOFTMAX	Normalize a final output vector where supported.

Function Documentation

◆ noodle_array_from_file()

void noodle_array_from_file	(	const char *	fn,
		float *	buffer,
		uint16_t	K )

Read a float array from a file.

Parameters

fn	Input file.
buffer	Destination array with room for `K` floats.
K	Number of values to read.

◆ noodle_array_to_file()

void noodle_array_to_file	(	float *	array,
		const char *	fn,
		uint16_t	n )

Write a float array to a file.

Parameters

array	Source array with `n` values.
fn	Output file.
n	Number of values to write.

◆ noodle_bn() [1/2]

uint16_t noodle_bn	(	NoodleBuffer *	x,
		uint16_t	C,
		uint16_t	W,
		const float *	bn_params,
		float	eps )

Backward-compatible alias for packed-parameter noodle_bn2d().

Treats x as packed [C][W][W]. bn_params is packed as [gamma[C]][beta[C]][mean[C]][var[C]].

Parameters

x	Tensor buffer updated in place.
C	Number of channels.
W	Plane width and height.
bn_params	Packed batch-normalization parameters.
eps	Small value added to variance before inversion.

Returns: W, or 0 when x has no data.

◆ noodle_bn() [2/2]

uint16_t noodle_bn	(	NoodleBuffer *	x,
		uint16_t	C,
		uint16_t	W,
		const float *	gamma,
		const float *	beta,
		const float *	mean,
		const float *	var,
		float	eps )

Backward-compatible alias for noodle_bn2d().

Treats x as packed [C][W][W].

Parameters

x	Tensor buffer updated in place.
C	Number of channels.
W	Plane width and height.
gamma	Scale parameters.
beta	Offset parameters.
mean	Moving-mean parameters.
var	Moving-variance parameters.
eps	Small value added to variance before inversion.

Returns: W, or 0 when x has no data.

◆ noodle_bn1d() [1/2]

uint16_t noodle_bn1d	(	NoodleBuffer *	x,
		uint16_t	N,
		const float *	bn_params,
		float	eps )

Apply 1D batch normalization from a packed parameter array.

bn_params is packed as [gamma[N]][beta[N]][mean[N]][var[N]].

Parameters

x	Vector buffer updated in place.
N	Number of vector elements.
bn_params	Packed batch-normalization parameters.
eps	Small value added to variance before inversion.

Returns: N, or 0 when x has no data.

◆ noodle_bn1d() [2/2]

uint16_t noodle_bn1d	(	NoodleBuffer *	x,
		uint16_t	N,
		const float *	gamma,
		const float *	beta,
		const float *	mean,
		const float *	var,
		float	eps )

Apply 1D batch normalization in place to a NoodleBuffer vector.

gamma, beta, mean, and var each contain N values.

Parameters

x	Vector buffer updated in place.
N	Number of vector elements.
gamma	Scale parameters.
beta	Offset parameters.
mean	Moving-mean parameters.
var	Moving-variance parameters.
eps	Small value added to variance before inversion.

Returns: N, or 0 when x has no data.

◆ noodle_bn1d_relu() [1/2]

uint16_t noodle_bn1d_relu	(	NoodleBuffer *	x,
		uint16_t	N,
		const float *	bn_params,
		float	eps )

Apply packed 1D batch normalization followed by ReLU in place.

bn_params is packed as [gamma[N]][beta[N]][mean[N]][var[N]].

Parameters

x	Vector buffer updated in place.
N	Number of vector elements.
bn_params	Packed batch-normalization parameters.
eps	Small value added to variance before inversion.

Returns: N, or 0 when x has no data.

◆ noodle_bn1d_relu() [2/2]

uint16_t noodle_bn1d_relu	(	NoodleBuffer *	x,
		uint16_t	N,
		const float *	gamma,
		const float *	beta,
		const float *	mean,
		const float *	var,
		float	eps )

Apply 1D batch normalization followed by ReLU in place.

gamma, beta, mean, and var each contain N values.

Parameters

x	Vector buffer updated in place.
N	Number of vector elements.
gamma	Scale parameters.
beta	Offset parameters.
mean	Moving-mean parameters.
var	Moving-variance parameters.
eps	Small value added to variance before inversion.

Returns: N, or 0 when x has no data.

◆ noodle_bn2d() [1/2]

uint16_t noodle_bn2d	(	NoodleBuffer *	x,
		uint16_t	C,
		uint16_t	W,
		const float *	bn_params,
		float	eps )

Apply 2D channel-wise batch normalization from packed parameters.

Treats x as packed [C][W][W]. bn_params is packed as [gamma[C]][beta[C]][mean[C]][var[C]].

Parameters

x	Tensor buffer updated in place.
C	Number of channels.
W	Plane width and height.
bn_params	Packed batch-normalization parameters.
eps	Small value added to variance before inversion.

Returns: W, or 0 when x has no data.

◆ noodle_bn2d() [2/2]

uint16_t noodle_bn2d	(	NoodleBuffer *	x,
		uint16_t	C,
		uint16_t	W,
		const float *	gamma,
		const float *	beta,
		const float *	mean,
		const float *	var,
		float	eps )

Apply 2D channel-wise batch normalization in place.

Treats x as packed [C][W][W]. The parameter arrays each contain C values, one per channel.

Parameters

x	Tensor buffer updated in place.
C	Number of channels.
W	Plane width and height.
gamma	Scale parameters.
beta	Offset parameters.
mean	Moving-mean parameters.
var	Moving-variance parameters.
eps	Small value added to variance before inversion.

Returns: W, or 0 when x has no data.

◆ noodle_bn2d_relu() [1/2]

uint16_t noodle_bn2d_relu	(	NoodleBuffer *	x,
		uint16_t	C,
		uint16_t	W,
		const float *	bn_params,
		float	eps )

Apply packed 2D batch normalization followed by ReLU.

Treats x as packed [C][W][W]. bn_params is packed as [gamma[C]][beta[C]][mean[C]][var[C]].

Parameters

x	Tensor buffer updated in place.
C	Number of channels.
W	Plane width and height.
bn_params	Packed batch-normalization parameters.
eps	Small value added to variance before inversion.

Returns: W, or 0 when x has no data.

◆ noodle_bn2d_relu() [2/2]

uint16_t noodle_bn2d_relu	(	NoodleBuffer *	x,
		uint16_t	C,
		uint16_t	W,
		const float *	gamma,
		const float *	beta,
		const float *	mean,
		const float *	var,
		float	eps )

Apply 2D channel-wise batch normalization followed by ReLU.

Treats x as packed [C][W][W]. The parameter arrays each contain C values, one per channel.

Parameters

x	Tensor buffer updated in place.
C	Number of channels.
W	Plane width and height.
gamma	Scale parameters.
beta	Offset parameters.
mean	Moving-mean parameters.
var	Moving-variance parameters.
eps	Small value added to variance before inversion.

Returns: W, or 0 when x has no data.

◆ noodle_bn_relu() [1/2]

uint16_t noodle_bn_relu	(	NoodleBuffer *	x,
		uint16_t	C,
		uint16_t	W,
		const float *	bn_params,
		float	eps )

Backward-compatible alias for packed-parameter noodle_bn2d_relu().

Treats x as packed [C][W][W]. bn_params is packed as [gamma[C]][beta[C]][mean[C]][var[C]].

Parameters

x	Tensor buffer updated in place.
C	Number of channels.
W	Plane width and height.
bn_params	Packed batch-normalization parameters.
eps	Small value added to variance before inversion.

Returns: W, or 0 when x has no data.

◆ noodle_bn_relu() [2/2]

uint16_t noodle_bn_relu	(	NoodleBuffer *	x,
		uint16_t	C,
		uint16_t	W,
		const float *	gamma,
		const float *	beta,
		const float *	mean,
		const float *	var,
		float	eps )

Backward-compatible alias for noodle_bn2d_relu().

Treats x as packed [C][W][W].

Parameters

x	Tensor buffer updated in place.
C	Number of channels.
W	Plane width and height.
gamma	Scale parameters.
beta	Offset parameters.
mean	Moving-mean parameters.
var	Moving-variance parameters.
eps	Small value added to variance before inversion.

Returns: W, or 0 when x has no data.

◆ noodle_buffer_capacity()

size_t noodle_buffer_capacity ( const NoodleBuffer * buf )

Return the buffer capacity in float elements.

Parameters

buf	Buffer descriptor to inspect.

Returns: Capacity in float elements, or 0 when buf is NULL.

◆ noodle_buffer_capacity_bytes()

size_t noodle_buffer_capacity_bytes ( const NoodleBuffer * buf )

Return the buffer capacity in bytes.

Parameters

buf	Buffer descriptor to inspect.

Returns: Capacity in bytes, or 0 when buf is NULL.

◆ noodle_buffer_free()

void noodle_buffer_free ( NoodleBuffer * buf )

Release a NoodleBuffer.

This frees the internal data pointer and resets the descriptor.

Parameters

buf	Buffer descriptor to release. Passing NULL is allowed.

◆ noodle_buffer_init()

void noodle_buffer_init ( NoodleBuffer * buf )

Initialize a NoodleBuffer.

Sets the data pointer to NULL and the capacity to zero. Call this before the first use of a stack- or static-allocated NoodleBuffer.

Parameters

buf	Buffer descriptor to initialize. Passing NULL is allowed.

◆ noodle_buffer_require()

float * noodle_buffer_require	(	NoodleBuffer *	buf,
		size_t	required_floats )

Ensure that a buffer can hold at least required_floats floats.

If the buffer is NULL, this allocates it. If the buffer is already large enough, this reuses it. If the buffer is too small, this allocates a larger block first, then frees the old block only after the new allocation succeeds.

Parameters

buf	Buffer descriptor.
required_floats	Required capacity in float elements.

Returns: Pointer to usable float storage, or NULL on failure.

◆ noodle_conv1d() [1/5]

uint16_t noodle_conv1d	(	const char *	in_fn,
		uint16_t	n_inputs,
		const char *	out_fn,
		uint16_t	n_outputs,
		uint16_t	W,
		const Conv &	conv,
		CBFPtr	progress_cb = 0 )

Run file-to-file 1D convolution with file-backed parameters.

This overload does not apply pooling.

Parameters

in_fn	Input file with packed [I][W] sequences.
n_inputs	Number of input channels.
out_fn	Output file for packed [O][V] sequences.
n_outputs	Number of output channels.
W	Input sequence length.
conv	File-backed convolution parameters.
progress_cb	Optional progress callback.

Returns: Output length before pooling, or 0 on failure.

◆ noodle_conv1d() [2/5]

uint16_t noodle_conv1d	(	const char *	in_fn,
		uint16_t	n_inputs,
		const char *	out_fn,
		uint16_t	n_outputs,
		uint16_t	W,
		const Conv &	conv,
		const Pool &	pool,
		CBFPtr	progress_cb = 0 )

Run file-to-file 1D convolution with file-backed parameters and pooling.

Input is packed [I][W]; output is packed [O][Vout].

Parameters

in_fn	Input file.
n_inputs	Number of input channels.
out_fn	Output file.
n_outputs	Number of output channels.
W	Input sequence length.
conv	File-backed convolution parameters.
pool	Pooling parameters applied after bias and activation.
progress_cb	Optional progress callback.

Returns: Output length after pooling, or 0 on failure.

◆ noodle_conv1d() [3/5]

uint16_t noodle_conv1d	(	const char *	in_fn,
		uint16_t	n_inputs,
		const char *	out_fn,
		uint16_t	n_outputs,
		uint16_t	W,
		const ConvMem &	conv,
		CBFPtr	progress_cb = 0 )

Run file-to-file 1D convolution with memory-backed parameters.

This overload does not apply pooling. conv.weight uses [O][I][K]; nullptr bias means zero bias.

Parameters

in_fn	Input file with packed [I][W] sequences.
n_inputs	Number of input channels.
out_fn	Output file for packed [O][V] sequences.
n_outputs	Number of output channels.
W	Input sequence length.
conv	Memory-backed convolution parameters.
progress_cb	Optional progress callback.

Returns: Output length before pooling, or 0 on failure.

◆ noodle_conv1d() [4/5]

uint16_t noodle_conv1d	(	NoodleBuffer *	input,
		uint16_t	n_inputs,
		NoodleBuffer *	output,
		uint16_t	n_outputs,
		uint16_t	W,
		const ConvMem &	conv,
		CBFPtr	progress_cb = 0 )

Run 1D convolution using memory-backed parameters.

This overload does not apply pooling. Input is [I][W]; output is [O][V].

Parameters

input	Input NoodleBuffer with packed 1D feature maps.
n_inputs	Number of input channels.
output	Output NoodleBuffer grown as needed.
n_outputs	Number of output channels.
W	Input sequence length.
conv	Memory-backed 1D convolution parameters.
progress_cb	Optional progress callback.

Returns: Output length before pooling, or 0 on failure.

◆ noodle_conv1d() [5/5]

uint16_t noodle_conv1d	(	NoodleBuffer *	input,
		uint16_t	n_inputs,
		NoodleBuffer *	output,
		uint16_t	n_outputs,
		uint16_t	W,
		const ConvMem &	conv,
		const Pool &	pool,
		CBFPtr	progress_cb = 0 )

Run 1D convolution using memory-backed parameters and pooling.

Input is [I][W]; output is [O][Vout].

Parameters

input	Input NoodleBuffer with packed 1D feature maps.
n_inputs	Number of input channels.
output	Output NoodleBuffer grown as needed.
n_outputs	Number of output channels.
W	Input sequence length.
conv	Memory-backed 1D convolution parameters.
pool	Pooling parameters applied after bias and activation.
progress_cb	Optional progress callback.

Returns: Output length after pooling, or 0 on failure.

◆ noodle_conv_byte()

uint16_t noodle_conv_byte	(	const char *	in_fn,
		uint16_t	n_inputs,
		uint16_t	n_outputs,
		const char *	out_fn,
		uint16_t	W,
		const Conv &	conv,
		const Pool &	pool,
		CBFPtr	progress_cb = 0 )

Run file-to-file 2D convolution on byte input feature maps.

Input is packed [I][W][W]; output is packed [O][Vout][Vout].

Parameters

in_fn	Input file.
n_inputs	Number of input channels.
n_outputs	Number of output channels.
out_fn	Output file.
W	Input width and height.
conv	File-backed convolution parameters.
pool	Pooling parameters applied after bias and activation.
progress_cb	Optional progress callback.

Returns: Output width after pooling, or 0 on failure.

◆ noodle_conv_float() [1/6]

uint16_t noodle_conv_float	(	const char *	in_fn,
		uint16_t	n_inputs,
		uint16_t	n_outputs,
		const char *	out_fn,
		uint16_t	W,
		const Conv &	conv,
		const Pool &	pool,
		CBFPtr	progress_cb = 0 )

Run file-to-file 2D convolution on float input feature maps.

Input is packed [I][W][W]; output is packed [O][Vout][Vout].

Parameters

in_fn	Input file.
n_inputs	Number of input channels.
n_outputs	Number of output channels.
out_fn	Output file.
W	Input width and height.
conv	File-backed convolution parameters.
pool	Pooling parameters applied after bias and activation.
progress_cb	Optional progress callback.

Returns: Output width after pooling, or 0 on failure.

◆ noodle_conv_float() [2/6]

uint16_t noodle_conv_float	(	const char *	in_fn,
		uint16_t	n_inputs,
		uint16_t	n_outputs,
		const char *	out_fn,
		uint16_t	W,
		const ConvMem &	conv,
		const Pool &	pool,
		CBFPtr	progress_cb = 0 )

Run file-to-file 2D convolution with memory-backed parameters.

conv.weight uses [O][I][K][K]; nullptr bias means zero bias.

Parameters

in_fn	Input file with packed [I][W][W] planes.
n_inputs	Number of input channels.
n_outputs	Number of output channels.
out_fn	Output file for packed [O][Vout][Vout] planes.
W	Input width and height.
conv	Memory-backed convolution parameters.
pool	Pooling parameters applied after bias and activation.
progress_cb	Optional progress callback.

Returns: Output width after pooling, or 0 on failure.

◆ noodle_conv_float() [3/6]

uint16_t noodle_conv_float	(	const char *	in_fn,
		uint16_t	n_inputs,
		uint16_t	n_outputs,
		const char *	out_fn,
		uint16_t	W,
		const ConvProgmem &	conv,
		const Pool &	pool,
		CBFPtr	progress_cb = 0 )

Run file-to-file 2D convolution with near-PROGMEM parameters.

conv.weight uses [O][I][K][K]; nullptr bias means zero bias.

Parameters

in_fn	Input file with packed [I][W][W] planes.
n_inputs	Number of input channels.
n_outputs	Number of output channels.
out_fn	Output file for packed [O][Vout][Vout] planes.
W	Input width and height.
conv	Near-PROGMEM convolution parameters.
pool	Pooling parameters applied after bias and activation.
progress_cb	Optional progress callback.

Returns: Output width after pooling, or 0 on failure.

◆ noodle_conv_float() [4/6]

uint16_t noodle_conv_float	(	NoodleBuffer *	input,
		uint16_t	n_inputs,
		uint16_t	n_outputs,
		NoodleBuffer *	output,
		uint16_t	W,
		const Conv &	conv,
		const Pool &	pool,
		CBFPtr	progress_cb = 0 )

Run 2D convolution using file-backed parameters.

Input is packed [I][W][W]; output is packed [O][Vout][Vout].

Parameters

input	Input NoodleBuffer with packed feature maps.
n_inputs	Number of input channels.
n_outputs	Number of output channels.
output	Output NoodleBuffer grown as needed.
W	Input width and height.
conv	File-backed convolution parameters.
pool	Pooling parameters.
progress_cb	Optional progress callback.

Returns: Output width after pooling, or 0 on failure.

◆ noodle_conv_float() [5/6]

uint16_t noodle_conv_float	(	NoodleBuffer *	input,
		uint16_t	n_inputs,
		uint16_t	n_outputs,
		NoodleBuffer *	output,
		uint16_t	W,
		const ConvMem &	conv,
		const Pool &	pool,
		CBFPtr	progress_cb = 0 )

Run 2D convolution using memory-backed parameters.

conv.weight uses [O][I][K][K]; nullptr bias means zero bias.

Parameters

input	Input NoodleBuffer with packed [I][W][W] feature maps.
n_inputs	Number of input channels.
n_outputs	Number of output channels.
output	Output NoodleBuffer grown as needed.
W	Input width and height.
conv	Memory-backed convolution parameters.
pool	Pooling parameters.
progress_cb	Optional progress callback.

Returns: Output width after pooling, or 0 on failure.

◆ noodle_conv_float() [6/6]

uint16_t noodle_conv_float	(	NoodleBuffer *	input,
		uint16_t	n_inputs,
		uint16_t	n_outputs,
		NoodleBuffer *	output,
		uint16_t	W,
		const ConvProgmem &	conv,
		const Pool &	pool,
		CBFPtr	progress_cb = 0 )

Run 2D convolution using near-PROGMEM parameters.

conv.weight uses [O][I][K][K]; nullptr bias means zero bias.

Parameters

input	Input NoodleBuffer with packed [I][W][W] feature maps.
n_inputs	Number of input channels.
n_outputs	Number of output channels.
output	Output NoodleBuffer grown as needed.
W	Input width and height.
conv	Near-PROGMEM convolution parameters.
pool	Pooling parameters.
progress_cb	Optional progress callback.

Returns: Output width after pooling, or 0 on failure.

◆ noodle_conv_transpose_float()

uint16_t noodle_conv_transpose_float	(	NoodleBuffer *	input,
		uint16_t	n_inputs,
		uint16_t	n_outputs,
		NoodleBuffer *	output,
		uint16_t	W,
		const ConvMem &	conv,
		CBFPtr	progress_cb = 0 )

Run 2D transpose convolution using memory-backed parameters.

Input is packed [I][W][W]; output is packed [O][Vt][Vt]. Weights use [O][I][K][K].

For explicit padding, callers set conv.OP so Vt = (W - 1) * conv.S - 2 * conv.P + conv.K + conv.OP matches the desired output width. With conv.P == 65535, SAME-style output uses Vt = W * conv.S.

Parameters

input	Input NoodleBuffer with packed feature maps.
n_inputs	Number of input channels.
n_outputs	Number of output channels.
output	Output NoodleBuffer grown as needed.
W	Input width and height.
conv	Memory-backed transpose convolution parameters.
progress_cb	Optional progress callback.

Returns: Output width, or 0 on failure.

◆ noodle_create_buffer()

float * noodle_create_buffer ( uint16_t size )

Allocate a raw byte buffer and return it as a float pointer.

This compatibility wrapper calls malloc() with size bytes.

Parameters

size	Number of bytes to allocate.

Returns: Allocated pointer, or nullptr on allocation failure.

◆ noodle_delete_buffer()

void noodle_delete_buffer ( float * buffer )

Free a buffer allocated by noodle_create_buffer().

Parameters

buffer Buffer pointer. Passing nullptr is allowed.

◆ noodle_delete_file()

void noodle_delete_file ( const char * fn )

Delete a file through the selected filesystem backend.

Parameters

fn	File to remove.

◆ noodle_dwconv_float() [1/5]

uint16_t noodle_dwconv_float	(	const char *	in_fn,
		uint16_t	n_channels,
		const char *	out_fn,
		uint16_t	W,
		const Conv &	conv,
		const Pool &	pool,
		CBFPtr	progress_cb = 0 )

Run file-to-file depthwise 2D convolution with file-backed parameters.

Input and output are packed channel-first. conv.weight_fn stores [C][K][K], and conv.bias_fn stores one scalar per channel.

Parameters

in_fn	Input file with packed [C][W][W] planes.
n_channels	Number of channels.
out_fn	Output file for packed [C][Vout][Vout] planes.
W	Input width and height.
conv	File-backed depthwise parameters.
pool	Pooling parameters applied after bias and activation.
progress_cb	Optional progress callback.

Returns: Output width after pooling, or 0 on failure.

◆ noodle_dwconv_float() [2/5]

uint16_t noodle_dwconv_float	(	const char *	in_fn,
		uint16_t	n_channels,
		const char *	out_fn,
		uint16_t	W,
		const ConvProgmem &	conv,
		const Pool &	pool,
		CBFPtr	progress_cb = 0 )

Run file-to-file depthwise 2D convolution with near-PROGMEM parameters.

conv.weight stores [C][K][K], and nullptr bias means zero bias.

Parameters

in_fn	Input file with packed [C][W][W] planes.
n_channels	Number of channels.
out_fn	Output file for packed [C][Vout][Vout] planes.
W	Input width and height.
conv	Near-PROGMEM depthwise parameters.
pool	Pooling parameters applied after bias and activation.
progress_cb	Optional progress callback.

Returns: Output width after pooling, or 0 on failure.

◆ noodle_dwconv_float() [3/5]

uint16_t noodle_dwconv_float	(	NoodleBuffer *	input,
		uint16_t	n_channels,
		NoodleBuffer *	output,
		uint16_t	W,
		const Conv &	conv,
		const Pool &	pool,
		CBFPtr	progress_cb = 0 )

Run depthwise 2D convolution using file-backed parameters.

Input and output are packed channel-first. conv.weight_fn stores [C][K][K].

Parameters

input	Input NoodleBuffer with packed [C][W][W] planes.
n_channels	Number of channels.
output	Output NoodleBuffer grown as needed.
W	Input width and height.
conv	File-backed depthwise parameters.
pool	Pooling parameters.
progress_cb	Optional progress callback.

Returns: Output width after pooling, or 0 on failure.

◆ noodle_dwconv_float() [4/5]

uint16_t noodle_dwconv_float	(	NoodleBuffer *	input,
		uint16_t	n_channels,
		NoodleBuffer *	output,
		uint16_t	W,
		const ConvMem &	conv,
		const Pool &	pool,
		CBFPtr	progress_cb = 0 )

Run depthwise 2D convolution using memory-backed parameters.

conv.weight stores [C][K][K]; nullptr bias means zero bias.

Parameters

input	Input NoodleBuffer with packed [C][W][W] planes.
n_channels	Number of channels.
output	Output NoodleBuffer grown as needed.
W	Input width and height.
conv	Memory-backed depthwise parameters.
pool	Pooling parameters.
progress_cb	Optional progress callback.

Returns: Output width after pooling, or 0 on failure.

◆ noodle_dwconv_float() [5/5]

uint16_t noodle_dwconv_float	(	NoodleBuffer *	input,
		uint16_t	n_channels,
		NoodleBuffer *	output,
		uint16_t	W,
		const ConvProgmem &	conv,
		const Pool &	pool,
		CBFPtr	progress_cb = 0 )

Run depthwise 2D convolution using near-PROGMEM parameters.

conv.weight stores [C][K][K]; nullptr bias means zero bias.

Parameters

input	Input NoodleBuffer with packed [C][W][W] planes.
n_channels	Number of channels.
output	Output NoodleBuffer grown as needed.
W	Input width and height.
conv	Near-PROGMEM depthwise parameters.
pool	Pooling parameters.
progress_cb	Optional progress callback.

Returns: Output width after pooling, or 0 on failure.

◆ noodle_fcn() [1/9]

uint16_t noodle_fcn	(	const byte *	input,
		uint16_t	n_inputs,
		uint16_t	n_outputs,
		const char *	out_fn,
		const FCNFile &	fcn,
		CBFPtr	progress_cb = 0 )

Run a fully connected layer from byte memory to a file.

Input is a flat vector. Weights are read in [O][I] order from fcn.

Parameters

input	Input vector with `n_inputs` values.
n_inputs	Input vector length.
n_outputs	Number of output neurons.
out_fn	Output file for `n_outputs` values.
fcn	File-backed FCN parameters.
progress_cb	Optional progress callback.

Returns: n_outputs, or 0 on failure.

◆ noodle_fcn() [2/9]

uint16_t noodle_fcn	(	const byte *	input,
		uint16_t	n_inputs,
		uint16_t	n_outputs,
		NoodleBuffer *	output,
		const FCNFile &	fcn,
		CBFPtr	progress_cb = 0 )

Run a fully connected layer from byte memory to a NoodleBuffer.

Parameters

input	Input vector with `n_inputs` values.
n_inputs	Input vector length.
n_outputs	Number of output neurons.
output	Output NoodleBuffer grown to `n_outputs` floats.
fcn	File-backed FCN parameters.
progress_cb	Optional progress callback.

Returns: n_outputs, or 0 on failure.

◆ noodle_fcn() [3/9]

uint16_t noodle_fcn	(	const char *	in_fn,
		uint16_t	n_inputs,
		uint16_t	n_outputs,
		const char *	out_fn,
		const FCNFile &	fcn,
		CBFPtr	progress_cb = 0 )

Run a fully connected layer from a file to a file.

Input and output are flat vectors. Weights are read in [O][I] order from fcn.

Parameters

in_fn	Input file with `n_inputs` values.
n_inputs	Input vector length.
n_outputs	Number of output neurons.
out_fn	Output file for `n_outputs` values.
fcn	File-backed FCN parameters.
progress_cb	Optional progress callback.

Returns: n_outputs, or 0 on failure.

◆ noodle_fcn() [4/9]

uint16_t noodle_fcn	(	const char *	in_fn,
		uint16_t	n_inputs,
		uint16_t	n_outputs,
		NoodleBuffer *	output,
		const FCNFile &	fcn,
		CBFPtr	progress_cb = 0 )

Run a fully connected layer from a file to a NoodleBuffer.

Parameters

in_fn	Input file with `n_inputs` values.
n_inputs	Input vector length.
n_outputs	Number of output neurons.
output	Output NoodleBuffer grown to `n_outputs` floats.
fcn	File-backed FCN parameters.
progress_cb	Optional progress callback.

Returns: n_outputs, or 0 on failure.

◆ noodle_fcn() [5/9]

uint16_t noodle_fcn	(	const int8_t *	input,
		uint16_t	n_inputs,
		uint16_t	n_outputs,
		const char *	out_fn,
		const FCNFile &	fcn,
		CBFPtr	progress_cb = 0 )

Run a fully connected layer from int8 memory to a file.

Input is a flat vector. Weights are read in [O][I] order from fcn.

Parameters

input	Input vector with `n_inputs` values.
n_inputs	Input vector length.
n_outputs	Number of output neurons.
out_fn	Output file for `n_outputs` values.
fcn	File-backed FCN parameters.
progress_cb	Optional progress callback.

Returns: n_outputs, or 0 on failure.

◆ noodle_fcn() [6/9]

uint16_t noodle_fcn	(	const int8_t *	input,
		uint16_t	n_inputs,
		uint16_t	n_outputs,
		NoodleBuffer *	output,
		const FCNFile &	fcn,
		CBFPtr	progress_cb = 0 )

Run a fully connected layer from int8 memory to a NoodleBuffer.

Parameters

input	Input vector with `n_inputs` values.
n_inputs	Input vector length.
n_outputs	Number of output neurons.
output	Output NoodleBuffer grown to `n_outputs` floats.
fcn	File-backed FCN parameters.
progress_cb	Optional progress callback.

Returns: n_outputs, or 0 on failure.

◆ noodle_fcn() [7/9]

uint16_t noodle_fcn	(	NoodleBuffer *	input,
		uint16_t	n_inputs,
		uint16_t	n_outputs,
		NoodleBuffer *	output,
		const FCNFile &	fcn,
		CBFPtr	progress_cb = 0 )

Run a fully connected layer using file-backed parameters.

Parameters

input	Input NoodleBuffer containing a flat vector.
n_inputs	Input vector length.
n_outputs	Number of output neurons.
output	Output NoodleBuffer grown to `n_outputs` floats.
fcn	File-backed FCN parameters.
progress_cb	Optional progress callback.

Returns: n_outputs, or 0 on failure.

◆ noodle_fcn() [8/9]

uint16_t noodle_fcn	(	NoodleBuffer *	input,
		uint16_t	n_inputs,
		uint16_t	n_outputs,
		NoodleBuffer *	output,
		const FCNMem &	fcn,
		CBFPtr	progress_cb = 0 )

Run a fully connected layer using memory-backed parameters.

Parameters

input	Input NoodleBuffer containing a flat vector.
n_inputs	Input vector length.
n_outputs	Number of output neurons.
output	Output NoodleBuffer grown to `n_outputs` floats.
fcn	Memory-backed FCN parameters.
progress_cb	Optional progress callback.

Returns: n_outputs, or 0 on failure.

◆ noodle_fcn() [9/9]

uint16_t noodle_fcn	(	NoodleBuffer *	input,
		uint16_t	n_inputs,
		uint16_t	n_outputs,
		NoodleBuffer *	output,
		const FCNProgmem &	fcn,
		CBFPtr	progress_cb = 0 )

Run a fully connected layer using far-PROGMEM parameters.

Parameters

input	Input NoodleBuffer containing a flat vector.
n_inputs	Input vector length.
n_outputs	Number of output neurons.
output	Output NoodleBuffer grown to `n_outputs` floats.
fcn	Far-PROGMEM FCN parameters.
progress_cb	Optional progress callback.

Returns: n_outputs on AVR, or 0 on failure/non-AVR.

◆ noodle_fcn_progmem()

uint16_t noodle_fcn_progmem	(	NoodleBuffer *	input,
		uint16_t	n_inputs,
		uint16_t	n_outputs,
		NoodleBuffer *	output,
		const float *	weight,
		const float *	bias,
		Activation	act,
		CBFPtr	progress_cb = 0 )

Run a fully connected layer using near-PROGMEM weights.

weight is read with noodle_pgm_float() in [O][I] order. bias may be nullptr for zero bias.

Parameters

input	Input NoodleBuffer containing a flat vector.
n_inputs	Input vector length.
n_outputs	Number of output neurons.
output	Output NoodleBuffer grown to `n_outputs` floats.
weight	Near-PROGMEM row-major [O][I] weights.
bias	Near-PROGMEM output biases, or nullptr.
act	Activation applied after each output.
progress_cb	Optional progress callback.

Returns: n_outputs, or 0 on failure.

◆ noodle_find_max()

void noodle_find_max	(	NoodleBuffer *	input,
		uint16_t	n,
		float &	max_val,
		uint16_t &	max_idx )

Find the maximum value and its index in a NoodleBuffer vector.

Parameters

input	Input vector buffer.
n	Number of vector elements to inspect.
max_val	Receives the maximum value, or 0.0 for null/empty input.
max_idx	Receives the maximum index, or 0 for null/empty input.

◆ noodle_flat() [1/2]

uint16_t noodle_flat	(	const char *	in_fn,
		NoodleBuffer *	output,
		uint16_t	V,
		uint16_t	n_filters )

Flatten a packed file tensor into a NoodleBuffer.

Reads packed [C][V][V] input and writes HWC-like order: output[pixel * C + channel]. The output buffer grows automatically to V * V * n_filters floats.

Parameters

in_fn	Input file containing packed channel-first planes.
output	Destination buffer grown as needed.
V	Input plane width and height.
n_filters	Number of channel planes.

Returns: Number of floats written, or 0 on null input/allocation failure.

◆ noodle_flat() [2/2]

uint16_t noodle_flat	(	NoodleBuffer *	input,
		NoodleBuffer *	output,
		uint16_t	V,
		uint16_t	n_filters )

Flatten a packed NoodleBuffer tensor into HWC-like order.

Reads input as packed [C][V][V], grows output to V * V * n_filters floats, and writes output[pixel * C + channel].

Parameters

input	Source buffer with packed channel-first planes.
output	Destination buffer grown as needed.
V	Input plane width and height.
n_filters	Number of channel planes.

Returns: Number of floats written, or 0 on null input/allocation failure.

◆ noodle_fs_init() [1/5]

bool noodle_fs_init ( )

Initialize the selected filesystem backend with default settings.

Returns: true when the selected backend initializes successfully.

◆ noodle_fs_init() [2/5]

bool noodle_fs_init	(	uint8_t	clk_pin,
		uint8_t	cmd_pin,
		uint8_t	d0_pin )

Initialize SD_MMC with explicit 1-bit pins, or default-init other backends.

Parameters

clk_pin	SD_MMC clock pin.
cmd_pin	SD_MMC command pin.
d0_pin	SD_MMC D0 pin.

Returns: true when the selected backend initializes successfully.

◆ noodle_fs_init() [3/5]

bool noodle_fs_init	(	uint8_t	clk_pin,
		uint8_t	cmd_pin,
		uint8_t	d0_pin,
		uint8_t	d1_pin,
		uint8_t	d2_pin,
		uint8_t	d3_pin )

Initialize SD_MMC with explicit 4-bit pins, or default-init other backends.

Parameters

clk_pin	SD_MMC clock pin.
cmd_pin	SD_MMC command pin.
d0_pin	SD_MMC D0 pin.
d1_pin	SD_MMC D1 pin.
d2_pin	SD_MMC D2 pin.
d3_pin	SD_MMC D3 pin.

Returns: true when the selected backend initializes successfully.

◆ noodle_fs_init() [4/5]

bool noodle_fs_init ( uint8_t cs_pin )

Initialize the selected filesystem backend with an SPI chip-select pin.

The chip-select pin is used by the SdFat backend. Other real backends ignore it and use their default initializer.

Parameters

cs_pin SPI chip-select pin for SdFat.

Returns: true when the selected backend initializes successfully.

◆ noodle_fs_init() [5/5]

bool noodle_fs_init	(	uint8_t	cs_pin,
		SPIClass &	spi,
		uint8_t	sck_mhz )

Initialize SdFat with an explicit SPI bus and clock speed.

Use this overload when the SD card is connected to a non-default SPI bus or custom SPI pins that have already been configured on spi.

Parameters

cs_pin	SPI chip-select pin for the SD card.
spi	SPI bus instance used by SdFat.
sck_mhz	Requested SD SPI clock in MHz.

Returns: true when SdFat initializes successfully.

◆ noodle_gap()

uint16_t noodle_gap	(	NoodleBuffer *	inout,
		uint16_t	C,
		uint16_t	W )

Apply global average pooling in place on packed channel-first maps.

Reduces [C][W][W] to [C] by writing each channel mean into the first C positions of inout.

Parameters

inout	Buffer containing packed [C][W][W] data.
C	Number of channels.
W	Plane width and height.

Returns: C, or 0 when inout has no data.

◆ noodle_gmp()

uint16_t noodle_gmp	(	NoodleBuffer *	inout,
		uint16_t	C,
		uint16_t	W )

Apply global max pooling in place on packed channel-first data.

Reduces each channel to one maximum value in the first C positions of inout. The current helper scans W values per channel.

Parameters

inout	Buffer containing packed channel data.
C	Number of channels.
W	Number of values scanned per channel.

Returns: C, or 0 when inout has no data.

◆ noodle_grid_from_file() [1/3]

void noodle_grid_from_file	(	const char *	fn,
		byte *	buffer,
		uint16_t	K )

Read a K x K grid into a byte buffer.

Parameters

fn	Input file.
buffer	Destination buffer with room for K * K byte values.
K	Grid width and height.

◆ noodle_grid_from_file() [2/3]

void noodle_grid_from_file	(	const char *	fn,
		float *	buffer,
		uint16_t	K )

Read a K x K grid into a float buffer.

Parameters

fn	Input file.
buffer	Destination buffer with room for K * K float values.
K	Grid width and height.

◆ noodle_grid_from_file() [3/3]

void noodle_grid_from_file	(	const char *	fn,
		int8_t *	buffer,
		uint16_t	K )

Read a K x K grid into an int8 buffer.

Parameters

fn	Input file.
buffer	Destination buffer with room for K * K int8 values.
K	Grid width and height.

◆ noodle_grid_to_file() [1/2]

void noodle_grid_to_file	(	byte *	grid,
		const char *	fn,
		uint16_t	n )

Write an n x n byte grid to a file in row-major order.

Parameters

grid	Source grid with n * n values.
fn	Output file.
n	Grid width and height.

◆ noodle_grid_to_file() [2/2]

void noodle_grid_to_file	(	float *	grid,
		const char *	fn,
		uint16_t	n )

Write an n x n float grid to a file in row-major order.

Parameters

grid	Source grid with n * n values.
fn	Output file.
n	Grid width and height.

◆ noodle_logit()

uint16_t noodle_logit	(	NoodleBuffer *	input_output,
		uint16_t	n )

Apply logistic sigmoid in place on a NoodleBuffer.

Parameters

input_output	Vector buffer updated in place.
n	Number of vector elements.

Returns: n, or 0 when input_output has no data.

◆ noodle_pgm_float()

float noodle_pgm_float	(	const float *	p,
		uint32_t	idx )

inlinestatic

Read a float from normal memory or near AVR PROGMEM.

On AVR this uses pgm_read_float_near(). On other platforms it indexes normal memory directly, which lets the same PROGMEM-backed wrappers compile across targets.

Parameters

p	Base pointer to packed float values.
idx	Element index to read.

Returns: Float value at idx.

◆ noodle_read_byte()

byte noodle_read_byte ( NDL_File & f )

Read a byte using NOODLE_FILE_FORMAT.

Parameters

f	Open input file.

Returns: Parsed or decoded byte value.

◆ noodle_read_bytes_until()

size_t noodle_read_bytes_until	(	NDL_File &	file,
		char	terminator,
		char *	buffer,
		size_t	length )

Read bytes until a terminator or until the buffer is full.

The terminator is consumed but not stored. buffer is NUL-terminated when length is greater than zero.

Parameters

file	Open file handle.
terminator	Character that ends the read.
buffer	Destination character buffer.
length	Destination capacity, including the trailing NUL.

Returns: Number of characters written, excluding the trailing NUL.

◆ noodle_read_float()

float noodle_read_float ( NDL_File & f )

Read a float using NOODLE_FILE_FORMAT.

Parameters

f	Open input file.

Returns: Parsed or decoded float value.

◆ noodle_read_top_line()

void noodle_read_top_line	(	const char *	fn,
		char *	line,
		size_t	maxlen )

Read the first line from a text file.

The destination is NUL-terminated when maxlen is greater than zero. If the file cannot be opened, line is set to an empty string.

Parameters

fn	File to read.
line	Destination character buffer.
maxlen	Destination capacity, including the trailing NUL.

◆ noodle_relu()

uint16_t noodle_relu	(	NoodleBuffer *	input_output,
		uint16_t	n )

Apply ReLU in place on a NoodleBuffer.

Parameters

input_output	Vector buffer updated in place.
n	Number of vector elements.

Returns: n, or 0 when input_output has no data.

◆ noodle_reshape()

uint16_t noodle_reshape	(	NoodleBuffer *	src_hwc,
		NoodleBuffer *	dst_chw,
		uint16_t	W,
		uint16_t	C )

Convert HWC-like NoodleBuffer data to packed channel-first data.

The destination buffer grows automatically to W * W * C floats.

Parameters

src_hwc	Source buffer in src[pixel * C + channel] order.
dst_chw	Destination buffer grown as needed.
W	Output plane width and height.
C	Number of channel planes.

Returns: Number of floats written, or 0 on null input/allocation failure.

◆ noodle_setup_temp_buffers() [1/2]

void noodle_setup_temp_buffers	(	void *	b1,
		void *	b2 )

Install caller-owned internal scratch buffers.

This legacy hook is optional because Noodle now allocates scratch buffers on demand. When installed, these pointers are used as-is, never resized, and not freed by noodle_temp_buffers_free().

Parameters

b1	Input scratch buffer for file-backed input planes or sequences.
b2	Accumulation scratch buffer for one pre-pooling output map.

◆ noodle_setup_temp_buffers() [2/2]

void noodle_setup_temp_buffers ( void * b2 )

Install only the caller-owned accumulation scratch buffer.

Use this when a legacy raw-pointer path needs only temp buffer 2.

Parameters

b2	Accumulation scratch buffer for one pre-pooling output map.

◆ noodle_sigmoid()

uint16_t noodle_sigmoid	(	NoodleBuffer *	input_output,
		uint16_t	n )

Apply sigmoid in place on a NoodleBuffer.

Parameters

input_output	Vector buffer updated in place.
n	Number of vector elements.

Returns: n, or 0 when input_output has no data.

◆ noodle_sigmoidf()

float noodle_sigmoidf ( float x )

Compute sigmoid for one scalar.

Parameters

x	Scalar input.

Returns: Logistic sigmoid of x.

◆ noodle_soft_max()

uint16_t noodle_soft_max	(	NoodleBuffer *	input_output,
		uint16_t	n )

Apply numerically stabilized softmax in place on a NoodleBuffer.

Parameters

input_output	Vector buffer updated in place.
n	Number of vector elements.

Returns: n, or 0 when input_output has no data.

◆ noodle_temp_buffers_free()

void noodle_temp_buffers_free ( void )

Release automatically allocated internal scratch buffers.

External buffers installed with noodle_setup_temp_buffers() are detached but not freed.

◆ noodle_write_byte()

void noodle_write_byte	(	NDL_File &	f,
		byte	d )

Write a byte using NOODLE_FILE_FORMAT.

Parameters

f	Open output file.
d	Value to write.

◆ noodle_write_float()

void noodle_write_float	(	NDL_File &	f,
		float	d )

Write a float using NOODLE_FILE_FORMAT.

Parameters

f	Open output file.
d	Value to write.

Files

Classes

Typedefs

Enumerations

Functions

File-backed layers

NoodleBuffer RAM-to-RAM layers

Detailed Description

Typedef Documentation

◆ CBFPtr

Enumeration Type Documentation

◆ Activation

Function Documentation

◆ noodle_array_from_file()

◆ noodle_array_to_file()

◆ noodle_bn() [1/2]

◆ noodle_bn() [2/2]

◆ noodle_bn1d() [1/2]

◆ noodle_bn1d() [2/2]

◆ noodle_bn1d_relu() [1/2]

◆ noodle_bn1d_relu() [2/2]

◆ noodle_bn2d() [1/2]

◆ noodle_bn2d() [2/2]

◆ noodle_bn2d_relu() [1/2]

◆ noodle_bn2d_relu() [2/2]

◆ noodle_bn_relu() [1/2]

◆ noodle_bn_relu() [2/2]

◆ noodle_buffer_capacity()

◆ noodle_buffer_capacity_bytes()

◆ noodle_buffer_free()

◆ noodle_buffer_init()

◆ noodle_buffer_require()

◆ noodle_conv1d() [1/5]

◆ noodle_conv1d() [2/5]

◆ noodle_conv1d() [3/5]

◆ noodle_conv1d() [4/5]

◆ noodle_conv1d() [5/5]

◆ noodle_conv_byte()

◆ noodle_conv_float() [1/6]

◆ noodle_conv_float() [2/6]

◆ noodle_conv_float() [3/6]

◆ noodle_conv_float() [4/6]

◆ noodle_conv_float() [5/6]

◆ noodle_conv_float() [6/6]

◆ noodle_conv_transpose_float()

◆ noodle_create_buffer()

◆ noodle_delete_buffer()

◆ noodle_delete_file()

◆ noodle_dwconv_float() [1/5]

◆ noodle_dwconv_float() [2/5]

◆ noodle_dwconv_float() [3/5]

◆ noodle_dwconv_float() [4/5]

◆ noodle_dwconv_float() [5/5]

◆ noodle_fcn() [1/9]

◆ noodle_fcn() [2/9]

◆ noodle_fcn() [3/9]

◆ noodle_fcn() [4/9]

◆ noodle_fcn() [5/9]

◆ noodle_fcn() [6/9]

◆ noodle_fcn() [7/9]

◆ noodle_fcn() [8/9]

◆ noodle_fcn() [9/9]

◆ noodle_fcn_progmem()

◆ noodle_find_max()

◆ noodle_flat() [1/2]

◆ noodle_flat() [2/2]

◆ noodle_fs_init() [1/5]

◆ noodle_fs_init() [2/5]

◆ noodle_fs_init() [3/5]

◆ noodle_fs_init() [4/5]

◆ noodle_fs_init() [5/5]

◆ noodle_gap()

◆ noodle_gmp()

◆ noodle_grid_from_file() [1/3]

◆ noodle_grid_from_file() [2/3]

◆ noodle_grid_from_file() [3/3]

◆ noodle_grid_to_file() [1/2]

◆ noodle_grid_to_file() [2/2]

◆ noodle_logit()

◆ noodle_pgm_float()