Journal Article

FZJ-2022-03849

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Machine learning on FPGA for event selection

Furletov, S. (Corresponding author) ; Barbosa, F. ; Belfore, L. ; Dickover, C. ; Fanelli, C. ; Furletova, Y. ; Jokhovets, L.FZJ* ; Lawrence, D. ; Romanov, D.

2022
Inst. of Physics London

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Please use a persistent id in citations: doi:10.1088/1748-0221/17/06/C06009

Abstract: Real-time data processing is a frontier field in experimental particle physics. The application of FPGAs at the trigger level is used by many current and planned experiments (CMS, LHCb, Belle2, PANDA). Usually they use conventional processing algorithms. LHCb has implemented Machine Learning (ML) elements for real-time data processing with a triggered readout system that runs most of the ML algorithms on a computer farm. The work described in this article aims to test the ML-FPGA algorithms for streaming data acquisition. There are many experiments working in this area and they have a lot in common, but there are many specific solutions for detector and accelerator parameters that are worth exploring further. This report describes the purpose of the work and progress in evaluating the ML-FPGA application.

Note: Post-print leider nicht verfügbar

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Contributing Institute(s):

1. Zentralinstitut für Elektronik (ZEA-2)

Research Program(s):

1. 622 - Detector Technologies and Systems (POF4-622) (POF4-622)

Appears in the scientific report 2022

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Database coverage:
; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; Essential Science Indicators ; IF < 5 ; JCR ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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