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Optimizing Graph Neural Networks for Jet Tagging in Particle Physics on FPGAs
This work proposes a novel reconfigurable architecture for reducing the latency of JEDI-net, a Graph Neural Network (GNN) based algorithm for jet tagging in particle physics, which achieves state-of-the-art accuracy. Accelerating JEDI-net is challenging since it requires low latency to deploy the ne...
| Autores principales: | , , , , , |
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| Lenguaje: | eng |
| Publicado: |
2022
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| Materias: | |
| Acceso en línea: | https://dx.doi.org/10.1109/FPL57034.2022.00057 http://cds.cern.ch/record/2861082 |