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FPGA-accelerated machine learning inference as a service for particle physics computing
Large-scale particle physics experiments face challenging demands for high-throughput computing resources both now and in the future. New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions w...
Autores principales: | Duarte, Javier, Harris, Philip, Hauck, Scott, Holzman, Burt, Hsu, Shih-Chieh, Jindariani, Sergo, Khan, Suffian, Kreis, Benjamin, Lee, Brian, Liu, Mia, Lončar, Vladimir, Ngadiuba, Jennifer, Pedro, Kevin, Perez, Brandon, Pierini, Maurizio, Rankin, Dylan, Tran, Nhan, Trahms, Matthew, Tsaris, Aristeidis, Versteeg, Colin, Way, Ted W., Werran, Dustin, Wu, Zhenbin |
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Lenguaje: | eng |
Publicado: |
2019
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.1007/s41781-019-0027-2 http://cds.cern.ch/record/2695229 |
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