Ultrafast jet classification on FPGAs for the HL-LHC
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929410086535168 |
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| author | Odagiu, Patrick Que, Zhiqiang Duarte, Javier Haller, Johannes Kasieczka, Gregor Lobanov, Artur Loncar, Vladimir Luk, Wayne Ngadiuba, Jennifer Pierini, Maurizio Rincke, Philipp Seksaria, Arpita Summers, Sioni Sznajder, Andre Tapper, Alexander Aarrestad, Thea K. |
| author_facet | Odagiu, Patrick Que, Zhiqiang Duarte, Javier Haller, Johannes Kasieczka, Gregor Lobanov, Artur Loncar, Vladimir Luk, Wayne Ngadiuba, Jennifer Pierini, Maurizio Rincke, Philipp Seksaria, Arpita Summers, Sioni Sznajder, Andre Tapper, Alexander Aarrestad, Thea K. |
| contents | Three machine learning models are used to perform jet origin classification. These models are optimized for deployment on a field-programmable gate array device. In this context, we demonstrate how latency and resource consumption scale with the input size and choice of algorithm. Moreover, the models proposed here are designed to work on the type of data and under the foreseen conditions at the CERN LHC during its high-luminosity phase. Through quantization-aware training and efficient synthetization for a specific field programmable gate array, we show that $O(100)$ ns inference of complex architectures such as Deep Sets and Interaction Networks is feasible at a relatively low computational resource cost. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01876 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Ultrafast jet classification on FPGAs for the HL-LHC Odagiu, Patrick Que, Zhiqiang Duarte, Javier Haller, Johannes Kasieczka, Gregor Lobanov, Artur Loncar, Vladimir Luk, Wayne Ngadiuba, Jennifer Pierini, Maurizio Rincke, Philipp Seksaria, Arpita Summers, Sioni Sznajder, Andre Tapper, Alexander Aarrestad, Thea K. High Energy Physics - Experiment Machine Learning Instrumentation and Detectors Three machine learning models are used to perform jet origin classification. These models are optimized for deployment on a field-programmable gate array device. In this context, we demonstrate how latency and resource consumption scale with the input size and choice of algorithm. Moreover, the models proposed here are designed to work on the type of data and under the foreseen conditions at the CERN LHC during its high-luminosity phase. Through quantization-aware training and efficient synthetization for a specific field programmable gate array, we show that $O(100)$ ns inference of complex architectures such as Deep Sets and Interaction Networks is feasible at a relatively low computational resource cost. |
| title | Ultrafast jet classification on FPGAs for the HL-LHC |
| topic | High Energy Physics - Experiment Machine Learning Instrumentation and Detectors |
| url | https://arxiv.org/abs/2402.01876 |