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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20211207T055400Z
LOCATION:Second Floor Atrium
DTSTART;TZID=America/Chicago:20211118T083000
DTEND;TZID=America/Chicago:20211118T170000
UID:submissions.supercomputing.org_SC21_sess243_spostu108@linklings.com
SUMMARY:Optimizing Deep Learning Material Interface Reconstruction
DESCRIPTION:ACM Student Research Competition: Graduate Poster, ACM Student
  Research Competition: Undergraduate Poster, Posters\n\nOptimizing Deep Le
 arning Material Interface Reconstruction\n\nYamamoto\n\nCurrent material i
 nterface reconstruction methods provide inaccurate reconstructions; a neur
 al network provides more accurate reconstructions. The initial model archi
 tecture was too large to provide the required throughput. Reducing the siz
 e of the model shows a smaller model could be used and provide similar acc
 uracy. The throughput of the reduced model reaches the target throughput a
 nd increases throughput by 15x on NVIDIA A100 GPUs. Further work is being 
 done porting the full model to SambaNova systems.\n\nTag: In-Person Only\n
 \nRegistration Category: Tech Program Reg Pass, Exhibit Hall Only
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