Accelerating Recurrent Neural Networks for Gravitational Wave Experiments

This paper presents novel reconfigurable architectures for reducing the latency of recurrent neural networks (RNNs) that are used for detecting gravitational waves. Gravitational interferometers such as the LIGO detectors capture cosmic events such as black hole mergers which happen at unknown times...

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Detalles Bibliográficos
Autores principales: Que, Zhiqiang, Wang, Erwei, Marikar, Umar, Moreno, Eric, Ngadiuba, Jennifer, Javed, Hamza, Borzyszkowski, Bartłomiej, Aarrestad, Thea, Loncar, Vladimir, Summers, Sioni, Pierini, Maurizio, Cheung, Peter Y., Luk, Wayne
Lenguaje:eng
Publicado: 2021
Materias:
physics.ins-det
Detectors and Experimental Techniques
cs.AR
Computing and Computers
cs.LG
Acceso en línea:https://dx.doi.org/10.1109/ASAP52443.2021.00025
http://cds.cern.ch/record/2775808

Internet

https://dx.doi.org/10.1109/ASAP52443.2021.00025
http://cds.cern.ch/record/2775808

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