Muon trigger using deep neural networks accelerated by FPGAs

Seulgi Kim, Jason Lee, Inkyu Park, Youngwan Son, Ian James Watson, Seungjin Yang

Research output: Contribution to journalConference articlepeer-review


Accuracy and latency are crucial to the trigger system in high luminosity particle physics experiments. We investigate the usage of deep neural networks (DNN) to improve the accuracy of the muon track segment reconstruction process at the trigger level. Track segments, made by hits within a detector module, are the initial partial reconstructed objects which are the typical building blocks for muon triggers. Currently, these segments are coarsely reconstructed on FPGAs to keep the latency manageable. DNNs are ideal for these types of pattern recognition problems, and so we examine the potential for DNN based track segment reconstruction to be accelerated by dedicated FPGAs to improve both processing speed and accuracy for the trigger system.

Original languageEnglish
Article number712
JournalProceedings of Science
StatePublished - 15 Apr 2021
Event40th International Conference on High Energy Physics, ICHEP 2020 - Virtual, Prague, Czech Republic
Duration: 28 Jul 20206 Aug 2020


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