Machine learning on FPGA for event selection

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) el...

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Bibliographic Details
Published in:Journal of instrumentation 2022-06, Vol.17 (6), p.C06009
Main Authors: Furletov, S., Barbosa, F., Belfore, L., Dickover, C., Fanelli, C., Furletova, Y., Jokhovets, L., Lawrence, D., Romanov, D.
Format: Article
Language:English
Subjects:
Algorithms
Data acquisition
Data processing
Data reduction methods
Machine learning
Online farms and online filtering
OTHER INSTRUMENTATION
Particle physics
Pattern recognition, cluster finding, calibration and fitting methods
Real time
Trigger concepts and systems (hardware and software)
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Summary: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.
ISSN:1748-0221
1748-0221
DOI:10.1088/1748-0221/17/06/C06009