BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:America/Chicago
X-LIC-LOCATION:America/Chicago
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20260522T150116Z
LOCATION:C2/3/4 Ballroom
DTSTART;TZID=America/Chicago:20181113T083000
DTEND;TZID=America/Chicago:20181113T170000
UID:submissions.supercomputing.org_SC18_sess322_post172@linklings.com
SUMMARY:MLModelScope: Evaluate and Measure Machine Learning Models within 
 AI Pipelines
DESCRIPTION:Abdul Dakkak, Cheng Li, and Wen-mei Hwu (University of Illinoi
 s) and Jinjun Xiong (IBM)\n\nThe current landscape of Machine Learning (ML
 ) and Deep Learning (DL) is rife with non-uniform frameworks, models, and 
 system stacks but lacks standard tools to facilitate the evaluation and me
 asurement of models. Due to the absence of such tools, the current practic
 e for evaluating and comparing the benefits of proposed AI innovations (be
  it hardware or software) on end-to-end AI pipelines is both arduous and e
 rror prone — stifling the adoption of the innovations.  We propose MLModel
 Scope— a hardware/software agnostic platform to facilitate the evaluation,
  measurement, and introspection of ML models within AI pipelines. MLModelS
 cope aids application developers in discovering and experimenting with mod
 els, data scientists developers in replicating and evaluating for publishi
 ng models, and system architects in understanding the performance of AI wo
 rkloads.\n\nRegistration Category: Tech Program Reg Pass, Exhibits Reg Pas
 s\n\n
END:VEVENT
END:VCALENDAR