AUTHOR=Iiyama Yutaro , Cerminara Gianluca , Gupta Abhijay , Kieseler Jan , Loncar Vladimir , Pierini Maurizio , Qasim Shah Rukh , Rieger Marcel , Summers Sioni , Van Onsem Gerrit , Wozniak Kinga Anna , Ngadiuba Jennifer , Di Guglielmo Giuseppe , Duarte Javier , Harris Philip , Rankin Dylan , Jindariani Sergo , Liu Mia , Pedro Kevin , Tran Nhan , Kreinar Edward , Wu Zhenbin 

TITLE=Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics

JOURNAL=Frontiers in Big Data

VOLUME=3

YEAR=2021

URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2020.598927

DOI=10.3389/fdata.2020.598927

ISSN=2624-909X

ABSTRACT=<p>Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering. An important domain for the application of these networks is the FGPA-based first layer of real-time data filtering at the CERN Large Hadron Collider, which has strict latency and resource constraints. We discuss how to design distance-weighted graph networks that can be executed with a latency of less than one μs on an FPGA. To do so, we consider a representative task associated to particle reconstruction and identification in a next-generation calorimeter operating at a particle collider. We use a graph network architecture developed for such purposes, and apply additional simplifications to match the computing constraints of Level-1 trigger systems, including weight quantization. Using the hls4ml library, we convert the compressed models into firmware to be implemented on an FPGA. Performance of the synthesized models is presented both in terms of inference accuracy and resource usage.</p>