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

Iiyama, Yutaro and Cerminara, Gianluca and Gupta, Abhijay and Kieseler, Jan and Loncar, Vladimir and Pierini, Maurizio and Qasim, Shah Rukh and Rieger, Marcel and Summers, Sioni and Van Onsem, Gerrit and Woźniak, Kinga Anna and Ngadiuba, Jennifer and Di Guglielmo, Giuseppe and Duarte, Javier and Harris, Philip and Rankin, Dylan and Jindariani, Sergo and Liu, Mia and Pedro, Kevin and Tran, Nhan and Kreinar, Edward and Wu, Zhenbin (2021) Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics. Frontiers in Big Data, 3 . Art. No. 598927. ISSN 2624-909X. PMCID PMC8006281. doi:10.3389/fdata.2020.598927. https://resolver.caltech.edu/CaltechAUTHORS:20210406-120416997

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Abstract

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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.3389/fdata.2020.598927DOIArticle
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8006281PubMed CentralArticle
https://arxiv.org/abs/2008.03601arXivDiscussion Paper
https://doi.org/10.5281/zenodo.3992780DOIDatasets
https://doi.org/10.5281/zenodo.3992780DOIDatasets
https://zenodo.org/record/3888910DOISimulation data set and the KERAS source code used for the case study
ORCID:
AuthorORCID
Pierini, Maurizio0000-0003-1939-4268
Ngadiuba, Jennifer0000-0002-0055-2935
Duarte, Javier0000-0002-5076-7096
Additional Information:© 2021 Iiyama, Cerminara, Gupta, Kieseler, Loncar, Pierini, Qasim, Rieger, Summers, Van Onsem, Wozniak, Ngadiuba, Di Guglielmo, Duarte, Harris, Rankin, Jindariani, Liu, Pedro, Tran, Kreinar and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Received: 25 August 2020; Accepted: 26 October 2020; Published: 12 January 2021. We acknowledge the Fast Machine Learning collective as an open community of multi-domain experts and collaborators. This community was important for the development of this project. Data Availability Statement: The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://doi.org/10.5281/zenodo.3992780, doi:10.5281/zenodo.3992780. Simulation data set and the KERAS source code used for the case study are available on the Zenodo platform (Iiyama, 2020). Author Contributions: All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication. Funding: MP, AG, KW, SS, VL and JN are supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant Agreement No. 772369). SJ, ML, KP, and NT are supported by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy (DOE), Office of Science, Office of High Energy Physics. PH is supported by a Massachusetts Institute of Technology University grant. ZW is supported by the National Science Foundation under Grants Nos. 1606321 and 115164. JD is supported by DOE Office of Science, Office of High Energy Physics Early Career Research program under Award No. DE-SC0021187. CERN has provided the open access publication fee for this paper. Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Funders:
Funding AgencyGrant Number
European Research Council (ERC)772369
Department of Energy (DOE)DE-AC02-07CH11359
Massachusetts Institute of Technology (MIT)UNSPECIFIED
NSFPHY-1606321
NSFPHY-115164
Department of Energy (DOE)DE-SC0021187
Subject Keywords:deep learning, field-programmable gate arrays, fast inference, graph network, imaging calorimeter
PubMed Central ID:PMC8006281
DOI:10.3389/fdata.2020.598927
Record Number:CaltechAUTHORS:20210406-120416997
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210406-120416997
Official Citation:Iiyama Y, Cerminara G, Gupta A, Kieseler J, Loncar V, Pierini M, Qasim SR, Rieger M, Summers S, Van Onsem G, Wozniak KA, Ngadiuba J, Di Guglielmo G, Duarte J, Harris P, Rankin D, Jindariani S, Liu M, Pedro K, Tran N, Kreinar E and Wu Z (2021) Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics. Front. Big Data 3:598927. doi: 10.3389/fdata.2020.598927
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108633
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:08 Apr 2021 22:39
Last Modified:08 Apr 2021 22:39

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