Ever want to run a feedforward neural network on a microcontroller? Cool, me too.
This library is intended to provide a generic way to construct, train, and implement fully connected feedforward artificial neural networks. It makes use of the excellent libfixmath library to ensure reasonable performance on low end cores without the use of a floating point math coprocessor. Also of note: where appropriate, all fixed point math ops use their saturating variants, to ensure overflow never occurs.
To build libneural.a, simply type make. If you are cross compiling
(for example, targeting a microcontroller), you may provide a cross
compiler argument in the form make CROSS=arm-none-eabi- (note the
inclusion of the trailing dash).
Examples can be built via make example.
A basic 4 input, single output feedforward network with a single, 3 element hidden layer. Training parameters and layer type can be modified by changing the defintions at the top of the file.