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

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

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...

Full description

Bibliographic Details
Main Authors: Yutaro Iiyama, Gianluca Cerminara, Abhijay Gupta, Jan Kieseler, Vladimir Loncar, Maurizio Pierini, Shah Rukh Qasim, Marcel Rieger, Sioni Summers, Gerrit Van Onsem, Kinga Anna Wozniak, Jennifer Ngadiuba, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Dylan Rankin, Sergo Jindariani, Mia Liu, Kevin Pedro, Nhan Tran, Edward Kreinar, Zhenbin Wu
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-01-01
Series:Frontiers in Big Data
Subjects:
deep learning
field-programmable gate arrays
fast inference
graph network
imaging calorimeter
Online Access:https://www.frontiersin.org/articles/10.3389/fdata.2020.598927/full

Internet

https://www.frontiersin.org/articles/10.3389/fdata.2020.598927/full

Similar Items