Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml

Recurrent neural networks have been shown to be effective architectures for many tasks in high energy physics, and thus have been widely adopted. Their use in low-latency environments has, however, been limited as a result of the difficulties of implementing recurrent architectures on field-programm...

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Detalles Bibliográficos
Autores principales: Khoda, Elham E., Rankin, Dylan, de Lima, Rafael Teixeira, Harris, Philip, Hauck, Scott, Hsu, Shih-Chieh, Kagan, Michael, Loncar, Vladimir, Paikara, Chaitanya, Rao, Richa, Summers, Sioni, Vernieri, Caterina, Wang, Aaron
Lenguaje:eng
Publicado: 2022
Materias:
stat.ML
Mathematical Physics and Mathematics
physics.ins-det
Detectors and Experimental Techniques
hep-ex
Particle Physics - Experiment
cs.LG
Computing and Computers
Acceso en línea:https://dx.doi.org/10.1088/2632-2153/acc0d7
http://cds.cern.ch/record/2816114

Internet

https://dx.doi.org/10.1088/2632-2153/acc0d7
http://cds.cern.ch/record/2816114

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