HLS4ML Tutorial Workshop

HLS4ML tutorial: Open-Source Design Automation of Machine Learning Devices

Presenter: Nhan Tran, FastML Organization

December 8th , 2020

Time: 9:00AM-12:PM ET

The hls4ml package translates trained neural network models into synthesizable FPGA firmware. The firmware library targets efficient, ultrafast inference for its original application in real-time processing in particle physics. However, the generality of the package makes it applicable to a wide range of scientific and industry areas in which real-time processing on-device is needed.

In this tutorial we will give hands on experience with the workflow, including:

• Demonstration of the easy to use, yet deep customisation options hls4ml provides, including tunable parallelism and quantization.

• Model pruning, observing the impact on the resource usage of the inference.

• Quantization-aware training, resulting in low precision weights and activations and enabling very lightweight inference without loss of model accuracy.

• Synthesizing the FPGA firmware and evaluating the relevant metrics.

Attendees should have basic familiarity with Python, machine learning concepts, and ideally hands on experience with ML frameworks. Knowledge of FPGAs is advantageous, but not essential.

Prerequisites: We will authenticate participants to our interactive tutorial notebooks using Github accounts. If you intend to take part in the tutorial, and do not already have a Github account, please sign up for one: https://github.com/

The tutorial material is here: https://github.com/fastmachinelearning/hls4ml-tutorial

(If the hyperlink does not work, please copy the link and paste it directly into the browser.)