Ultrafast jet classification on FPGAs for the HL-LHC

Autor:	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.
Rok vydání:	2024
Předmět:	High Energy Physics - Experiment Computer Science - Machine Learning Physics - Instrumentation and Detectors
Druh dokumentu:	Working Paper
DOI:	10.1088/2632-2153/ad5f10
Popis:	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. Comment: 13 pages, 3 figures, 3 tables. Mach. Learn.: Sci. Technol (2024)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2402.01876 Zobrazit plný text záznamu View this record from Arxiv