TY  - JOUR
AU  - Furletov, S.
AU  - Barbosa, F.
AU  - Belfore, L.
AU  - Dickover, C.
AU  - Fanelli, C.
AU  - Furletova, Y.
AU  - Jokhovets, L.
AU  - Lawrence, D.
AU  - Romanov, D.
TI  - Machine learning on FPGA for event selection
JO  - Journal of Instrumentation
VL  - 17
IS  - 06
SN  - 1748-0221
CY  - London
PB  - Inst. of Physics
M1  - FZJ-2022-03849
SP  - C06009
PY  - 2022
N1  - Post-print leider nicht verfügbar
AB  - 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000823617800002
DO  - DOI:10.1088/1748-0221/17/06/C06009
UR  - https://juser.fz-juelich.de/record/910466
ER  -