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@ARTICLE{Furletov:910466,
author = {Furletov, S. and Barbosa, F. and Belfore, L. and Dickover,
C. and Fanelli, C. and Furletova, Y. and Jokhovets, L. and
Lawrence, D. and Romanov, D.},
title = {{M}achine learning on {FPGA} for event selection},
journal = {Journal of Instrumentation},
volume = {17},
number = {06},
issn = {1748-0221},
address = {London},
publisher = {Inst. of Physics},
reportid = {FZJ-2022-03849},
pages = {C06009},
year = {2022},
note = {Post-print leider nicht verfügbar},
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.},
cin = {ZEA-2},
ddc = {610},
cid = {I:(DE-Juel1)ZEA-2-20090406},
pnm = {622 - Detector Technologies and Systems (POF4-622)},
pid = {G:(DE-HGF)POF4-622},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000823617800002},
doi = {10.1088/1748-0221/17/06/C06009},
url = {https://juser.fz-juelich.de/record/910466},
}