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A course based on FINN with hands on Lectures, Examples and Labs to go from 0 to a full custom Quantized Neural Network running on your very own FPGA !

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Python AI to FPGA : Course Material

This repo contains all the course's material form my teaching activities (do not use outside the course, conform to french law intellectual property).

Lectures given at : IDEC & Sungkyunkwan University.

Note

This course is 9 hours long and this repo is meant for teaching eveything people need to know to deploy end-to-end solutions. It was not meant to be done alone. Attending the lectures is indeed far more efficient but students can reach out to me if needed.

Lectures

Examples are meant to be watched during the lectures to understand basic concepts, you can also follow along.

If these concepts are not acquired / understood, I strongly encourage you to look into deeper material. (the notebooks contains clues on where to look for such material in the "Learn More" sections)

Labs

Meant to be done from scratch, I highly recommend you do them by yourself; by following along during the labs or at home.

Each Lab has its specificities so each folder contain a readme.md file to provide details & context to the student.

Lab prerequisites :

  • Linux system is prefered but not mandatory
  • Python, Pytorch installed on your system
  • Docker environement setup for FINN and Brevitas (see below)
  • Xilinx tools Vivado, Vitis & Vitis HLS (2023 Version)
  • A zynq board for inference

Setup your docker environement for the lab in advance :

You can setup you docker environement by cloning finn and running :

bash run_docker.sh notebook

This will setup notebook dev environement. Here is the official tutorial to follow to also setup the environement vars.

/tmp/finn_dev_username will be a common folder where you can examine compiled outputs.

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A course based on FINN with hands on Lectures, Examples and Labs to go from 0 to a full custom Quantized Neural Network running on your very own FPGA !

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