Year: 2023
Type: article
Abstract: In the past few years, using Machine and Deep Learning techniques has become more and more viable, thanks to the availability of tools which allow people without specific knowledge in the realm of data science and complex networks to build AIs for a variety of research fields in the context of High Energy Physics, new algorithms based on ML are being tested for event selection in trigger operations, end-user physics analysis, and more. Time critical applications can benefit from implementing algorithms on low-latency hardware like specifically designed ASICs and programmable micro-electronics devices known as FPGAs. In order to facilitate the translation of ML models to fit in the usual workflow for programming FPGAs, a variety of tools have been developed. One example is the HLS4ML toolkit, developed by the HEP community.This paper presents and discusses the activity started at the Physics and Astronomy department of University of Bologna and INFN-Bologna devoted to preliminary studies for the trigger systems of the CMS experiment at the CERN LHC accelerator. A broader-purpose open-source project from Xilinx (a major FPGA producer) called PYNQ is being tested combined with the HLS4ML toolkit.Even though a rich documentation can be found on how to use hls4ml, a comprehensive description of the entire workflow from Python to FPGA is still hard to find. This work tries to fill this gap, presenting hardware and software set-up, together with performance tests.
Author: Lorusso, Marco
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7010461022