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

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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Bibliographic Details
Main Authors: Elham E Khoda, Dylan Rankin, Rafael Teixeira de Lima, Philip Harris, Scott Hauck, Shih-Chieh Hsu, Michael Kagan, Vladimir Loncar, Chaitanya Paikara, Richa Rao, Sioni Summers, Caterina Vernieri, Aaron Wang
Format: Article
Language:English
Published: IOP Publishing 2023-01-01
Series:Machine Learning: Science and Technology
Subjects:
deep learning
recurrent neural network
LSTM
GRU
hls4ml
FPGA
Online Access:https://doi.org/10.1088/2632-2153/acc0d7

Internet

https://doi.org/10.1088/2632-2153/acc0d7

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