STI Publications - View Publication Form #16832
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Publication Information
| Title | Machine learning on FPGA for event selection | ||||
| 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 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. | ||||
| Author(s) | Sergey Furletov, Fernando Barbosa, Cody Dickover, Yulia Furletova, David Lawrence, Dmitry Romanov, Lee Belfore, Cristiano Fanelli, Lioubov Jokhovets | ||||
| Publication Date | June 2022 | ||||
| Document Type | Journal Article | ||||
| Primary Institution | Thomas Jefferson National Accelerator Facility, Newport News | ||||
| Affiliation | Exp Nuclear Physics / Experimental Halls / Hall D | ||||
| Funding Source | Nuclear Physics (NP) | ||||
| Proprietary? | No | ||||
| This publication conveys | Technical Science Results | ||||
| Document Numbers |
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| Associated with an experiment | No | ||||
| Associated with EIC | Yes | ||||
| Supported by Jefferson Lab LDRD Funding | No |
Journal Article
| Journal Name | Journal of Instrumentation |
| Refereed | Yes |
| Volume | 17 |
| Issue | |
| Page(s) | C06009 |
Attachments/Datasets/DOI Link
| Document(s) |
AI4EIC_Jinst-2.pdf
(STI Document)
Machine learning on FPGA for event selection.pdf
(Accepted Manuscript)
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| DOI Link | |
| Dataset(s) | (none) |
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