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DTSTAMP:20190719T085744Z
LOCATION:HG E 3
DTSTART;TZID=Europe/Stockholm:20190613T121500
DTEND;TZID=Europe/Stockholm:20190613T124500
UID:submissions.pasc-conference.org_PASC19_sess121_msa246@linklings.com
SUMMARY:FPGA-Accelerated Deep Learning Inference for Particle Physics Appl
 ications
DESCRIPTION:Minisymposium\nComputer Science and Applied Mathematics, Physi
 cs\n\nFPGA-Accelerated Deep Learning Inference for Particle Physics Applic
 ations\n\nNgadiuba\n\nDeep learning algorithms are widely used in various 
 fields, e.g., computer vision, speech recognition, or natural language pro
 cessing, and are becoming ubiquitous across particle physics. As these mod
 els are getting larger and larger, the computational complexity poses seve
 re demands in terms of resources required for high-throughput computing. I
 n addition to CPU and GPU, the use of dedicated hardware solutions is beco
 ming essential and emerging to provide advantages over pure software solut
 ions. More specifically, in the high performance computing sector more and
  more Field Programmable Gate Arrays (FPGAs) compute accelerators are bein
 g used to improve the computing performance and reduce the power consumpti
 on (e.g. in the Microsoft Catapult project, Bing search engine and Amazon 
 EC2 F1 Instances). This talk will present an overview of the ongoing R&amp
 ;D to accelerate deep learning inference on FPGAs to solve the increasing 
 computing challenges in particle physics.
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