BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
X-LIC-LOCATION:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20211208T164413Z
LOCATION:Level 2 - Lobby
DTSTART;TZID=America/Los_Angeles:20211208T180000
DTEND;TZID=America/Los_Angeles:20211208T190000
UID:dac_DAC 2021_sess243_WIP179@linklings.com
SUMMARY:A Robust DNN Accelerator with Data-path Fault Detection and Mitiga
 tion
DESCRIPTION:Networking Reception, Work-in-Progress Poster\n\nA Robust DNN 
 Accelerator with Data-path Fault Detection and Mitigation\n\nBurel\n\nDeep
  Neural Networks are increasingly used in safety critical autonomous syste
 ms. We present a DNN accelerator architecture which provides fault detecti
 on and fault tolerance. Our approach is based on an Output Stationary syst
 olic architecture augmented with fault detection based on fast on-line fun
 ctional test of the Processing Elements (PEs). The hardware cost is under 
 8% and with Squeezenet, the loss in accuracy with a single faulty PE is un
 der 3%, compared to 33% without mitigation. Dropout during training, witho
 ut a priori knowledge of the faults, further improves fault tolerance.\n\n
 Virtual Program: Presented In-Person
END:VEVENT
END:VCALENDAR