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SUMMARY:[Systems talk]: "The Different Scales of Resource-Aware ML & How t
 o Tackle Them "
DTSTART:20231208T120000
DTEND:20231208T130000
DTSTAMP:20260506T144650Z
UID:427c1d96ce20a702cd84f773cedc063c4947ac58c3ee72f11f842dad
CATEGORIES:Conferences - Seminars
DESCRIPTION:Pinar Tözün\, Associate Professor\, IT University of Copenha
 gen.\nAbstract:\nToday\, machine learning (ML) runs at various scales of h
 ardware resources from the cloud and high-performance computing (HPC) cen
 ters to edge and Internet-of-Things (IoT) devices. To achieve resource-aw
 are machine learning\, we must understand the needs and challenges of ML a
 pplications at these different scales. In this talk\, we will first inves
 tigate ways of improving hardware utilization on modern and powerful CPU-G
 PU co-processors\, which serve as the commodity hardware for ML in the clo
 ud and HPC\, using workload collocation. Then\, we will investigate perfor
 mance and power trade-offs for ML-based image analysis in space using reso
 urce-constrained edge/IoT devices.\n\nBio:\nPinar Tözün\, is an Associat
 e Professor at IT University of Copenhagen. Before ITU\, she was a researc
 h staff member at IBM Almaden Research Center. Prior to joining IBM\, she 
 received her PhD from EPFL. Her thesis received the ACM SIGMOD Jim Gray Do
 ctoral Dissertation Award Honorable Mention in 2016. Her research focuses 
 on hardware-conscious machine learning\, performance characterization of d
 ata-intensive systems\, and scalability and efficiency of data-intensive s
 ystems on modern hardware.
LOCATION:BC 410 https://plan.epfl.ch/?room==BC%20410
STATUS:CONFIRMED
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