Muon Trigger using Deep Neural Networks accelerated by FPGAs Article Swipe
Y. Son
,
Seulgi Kim
,
J. S. H. Lee
,
Inkyu Park
,
I. J. Watson
,
S. Yang
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.22323/1.390.0712
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.22323/1.390.0712
Accuracy and latency are crucial to the trigger system in high luminosity particle physics experiments. We investigate the usage of deep neural networks (DNN) to improve the accuracy of the muon track segment reconstruction process at the trigger level. Track segments, made by hits within a detector module, are the initial partial reconstructed objects which are the typical building blocks for muon triggers. Currently, these segments are coarsely reconstructed on FPGAs to keep the latency manageable. DNNs are ideal for these types of pattern recognition problems, and so we examine the potential for DNN based track segment reconstruction to be accelerated by dedicated FPGAs to improve both processing speed and latency for the trigger system.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.22323/1.390.0712
- https://pos.sissa.it/390/712/pdf
- OA Status
- hybrid
- References
- 7
- Related Works
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- OpenAlex ID
- https://openalex.org/W3135969215
All OpenAlex metadata
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- DOI
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https://doi.org/10.22323/1.390.0712Digital Object Identifier
- Title
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Muon Trigger using Deep Neural Networks accelerated by FPGAsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-01-31Full publication date if available
- Authors
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Y. Son, Seulgi Kim, J. S. H. Lee, Inkyu Park, I. J. Watson, S. YangList of authors in order
- Landing page
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https://doi.org/10.22323/1.390.0712Publisher landing page
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https://pos.sissa.it/390/712/pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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Field-programmable gate array, Latency (audio), Computer science, Track (disk drive), Deep neural networks, Artificial neural network, Process (computing), Artificial intelligence, Muon, Computer hardware, Real-time computing, Physics, Particle physics, Telecommunications, Operating systemTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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| referenced_works | https://openalex.org/W2033815440, https://openalex.org/W1990869665, https://openalex.org/W3088573188, https://openalex.org/W4308909683, https://openalex.org/W2765877886, https://openalex.org/W2531409750, https://openalex.org/W3088476872 |
| referenced_works_count | 7 |
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| abstract_inverted_index.by | 42, 101 |
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| abstract_inverted_index.to | 5, 24, 71, 98, 104 |
| abstract_inverted_index.we | 88 |
| abstract_inverted_index.DNN | 93 |
| abstract_inverted_index.and | 1, 86, 109 |
| abstract_inverted_index.are | 3, 48, 55, 66, 77 |
| abstract_inverted_index.for | 60, 79, 92, 111 |
| abstract_inverted_index.the | 6, 17, 26, 29, 36, 49, 56, 73, 90, 112 |
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| abstract_inverted_index.reconstruction | 33, 97 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5043109251 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I124633538 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.6000000238418579 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.03933434 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |