Modules

Here is a list of all modules:

[detail level 123]

Convolutional Neural Network Example
Gated Recurrent Unit Example
▼Public	A collection of functions to perform basic operations for neural network layers. Functions with a _s8 suffix support TensorFlow Lite framework
Structure Types	Enums and Data Structures used in public API
Activation Functions	Perform activation layers, including ReLU (Rectified Linear Unit), sigmoid and tanh
Elementwise Functions	Elementwise add and multiplication functions
MinimumMaximum
Concatenation Functions
▼Convolution Functions	Collection of convolution, depthwise convolution functions and their variants
GetBufferSizeNNConv
▼Fully-connected Layer Functions	Collection of fully-connected and matrix multiplication functions
GetBufferSizeFC
LSTM Layer Functions
Pad Layer Functions:
▼Pooling Functions	Perform pooling functions, including max pooling and average pooling
GetBufferSizePooling
Reshape Functions
Softmax Functions
▼SVDF Functions
GetBufferSizeSVDF
Transpose Functions
▼Private	Internal Support functions. Not intended to be called direclty by a NMSIS-NN user
Structure Types	Data structure types used by private functions
Convolution	Support functions for Convolution and DW Convolution
LSTM	Support functions for LSTM
Fully Connected	Support functions for Fully Connected
Softmax	Support functions for Softmax
BasicMath
Basic Math Functions for Neural Network Computation	Basic Math Functions for Neural Network Computation
Copy
Fill
Nndata_convert
Data Conversion	Perform data type conversion in-between neural network operations