Machine learning on FPGA for event selection. (1st June 2022)

• Record Type: Journal Article
• Authors: Furletov, S.; Barbosa, F.; Belfore, L.; Dickover, C.; Fanelli, C.; Furletova, Y.; Jokhovets, L.; Lawrence, D.; Romanov, D.
• Is Part Of: Journal of instrumentation. Volume 17:Number 6(2022)
• Publication Date: 2022-06-01
• Subjects: Data reduction methods -- Online farms and online filtering -- Pattern recognition, cluster finding, calibration and fitting methods -- Trigger concepts and systems (hardware and software); Scientific apparatus and instruments -- Periodicals; 502.84
• DOI: 10.1088/1748-0221/17/06/C06009
• Languages: English
• ISSNs: 1748-0221

Abstract

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.