FANN-on-ARM: Optimized FANN Inference for ARM Cortex M-series
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
sample-data
LICENSE
README.md
fann.c
fann.h
fann_structs.h
generate.py

README.md

Copyright (c) 2018 ETH Zurich, Ferdinand von Hagen, Michele Magno, Lukas Cavigelli

FANN-on-ARM: Optimized FANN Inference for ARM Cortex M-series

This repository contains optimized code to perform inference of FANN-trained neural network on the ARM Cortex M-series platform.

Given a data file and pre-trained network in FANN's format, all necessary files to run and test the network on the microcontroller are generated.

Prerequisites

You should have data and a pre-trained network in the FANN format. To run the script, python2 needs to be installed. This code has been tested with the MSP432 platform.

Usage

First, you need to export your data in the FANN default format and train a neural network with FANN. How to do this is explained here. You should end up with two files, a .data file and a .net file. An example can be found in the sample-data folder.

Then, you can use the generate.py script to generate the files to run on the microcontroller, e.g.

python2 generate.py sample-data/myNetwork

Now all the *.h and *.c files can be copied to you project. They include all the data and code to run the network. To call it from your code, just include fann.h and call fann_type *fann_run(fann_type * input);, where fann_type is float or int depending on whether you started with a fixed-point model or not. Don't forget to include the files in your build scripts/makefile/project.

File Description

Constant files:

  • generate.py: the script generating the network and data-specific code files based an FANN-format data
  • fann_structs.h and fann.c: contain the implementation of the NN building blocks.
  • fann.h: the header file to be included in your code providing the fann_type *fann_run(fann_type * input); function declaration.
  • sample-data/{myNetwork.net, myNetwork.data}: sample data and network pre-trained with FANN.

Generated files:

  • fann_net.h: contains the trained parameters and the network structure.
  • fann_conf.h: contains some more meta information on the network; #layers, fixed-point parameters (if applicable), ...
  • test_data.h: contains the test input data and expected result

License and Attribution

Please refer to the LICENSE file for the licensing of our code. We rely on the interfaces, specifications, and some code of the FANN project.