26 citations · 53 across the 7 of their papers we have counts for
Showing 2020Show all
2 papers · 1 filter
physics.comp-ph2020★ 23 cited
FPGAs-as-a-Service Toolkit (FaaST)
Dylan Sheldon Rankin, Jeffrey Krupa, Philip Harris +13
Computing needs for high energy physics are already intensive and are expected to increase drastically in the coming years. In this context, heterogeneous computing, specifically a…
physics.comp-ph2020★ 26 cited
GPU-accelerated machine learning inference as a service for computing in neutrino experiments
Michael Wang, Tingjun Yang, Maria Acosta Flechas +7
Machine learning algorithms are becoming increasingly prevalent and performant in the reconstruction of events in accelerator-based neutrino experiments. These sophisticated algori…