Warning: Use of undefined constant ‘doing_it_wrong_trigger_error’ - assumed '‘doing_it_wrong_trigger_error’' (this will throw an Error in a future version of PHP) in /home/users/mest/lastsunday-htdocs/wp-includes/functions.php on line 10

Warning: Use of undefined constant ‘__return_false’ - assumed '‘__return_false’' (this will throw an Error in a future version of PHP) in /home/users/mest/lastsunday-htdocs/wp-includes/functions.php on line 10

Warning: Cannot modify header information - headers already sent by (output started at /home/users/mest/lastsunday-htdocs/wp-includes/functions.php:10) in /home/users/mest/lastsunday-htdocs/wp-content/plugins/events-manager/em-ical.php on line 70

Warning: Cannot modify header information - headers already sent by (output started at /home/users/mest/lastsunday-htdocs/wp-includes/functions.php:10) in /home/users/mest/lastsunday-htdocs/wp-content/plugins/events-manager/em-ical.php on line 71
BEGIN:VCALENDAR VERSION:2.0 PRODID:-//wp-events-plugin.com//6.6.3//EN TZID:America/New_York X-WR-TIMEZONE:America/New_York BEGIN:VEVENT UID:93@mestcenter.org DTSTART;TZID=America/New_York:20220406T120000 DTEND;TZID=America/New_York:20220406T130000 DTSTAMP:20240905T133327Z URL:https://mestcenter.org/training/fault-criticality-assessment-in-ai-acc elerators/ SUMMARY:Webinar: Fault Criticality Assessment in AI Accelerators DESCRIPTION:To watch the recorded webinar\, click on the recording.\nPlay V ideo\nSpeaker: \nDr. Krishnendu Chakrabarty\, John Cocke Distinguished Pr ofessor\, Chair of Department of Electrical and Computer Engineering Profe ssor of Computer Science Duke University\nAbstract:\nThe ubiquitous applic ation of deep neural networks (DNN) has led to a rise in demand for AI acc elerators. For example\, the Tensor Processing Unit from Google and its va riants are of considerable interest for DNN inferencing using AI accelerat ors.  DNN-specific functional criticality analysis identifies faults that cause measurable and significant deviations from acceptable requirements such as the inferencing accuracy. This talk will examine the problem of cl assifying structural faults in the processing elements (PEs) of systolic-a rray accelerators. The speaker will first analyze the impact of stuck-at f aults and present a two-tier machine-learning (ML) based method to assess the functional criticality of these faults. The problem of minimizing misc lassification will be addressed by utilizing generative adversarial networ ks (GANs). The two- tier ML/GAN-based criticality assessment method leads to less than 1% test escapes during functional criticality evaluation of s tructural faults. While supervised learning techniques can be used to accu rately estimate fault criticality\, it requires a considerable amount of g round truth for model training. The speaker will therefore present a neura l-twin framework for analyzing fault criticality with a negligible amount of ground-truth data. A recently proposed misclassification-driven trainin g algorithm will be used to sensitize and identify biases that are critica l to the functioning of the accelerator for a given application workload. The proposed framework achieves up to 100% accuracy in fault-criticality c lassification in 16-bit and 32-bit PEs by using the criticality knowledge of only 2% of the total faults in a PE.\n\nSpeaker Bio: \nKrishnendu Chak rabarty has been at Duke University since 1998. His current research is fo cused on: testing and design-for-testability of integrated circuits (espec ially 3D SOC)\; microfluidic biochips\; hardware security\; neuromorphic c omputing. His research projects in the past have also included digital pri nt and enterprise system optimization\, chip cooling using digital microfl uidics\, wireless sensor networks\, and real-time embedded systems. Resear ch support is provided by the National Science Foundation\, DARPA\, Semi conductor Research Corporation\, Air Force Research Labs\, Intel\, Synopsy s\, and IBM. Other sponsors in the past have included the Army Research Of fice\, National Institutes of Health\, Office of Naval Research\, Cisco\, and HP.\n\nProf. Chakrabarty is a recipient of the 1999 National Science Foundation Early Faculty (CAREER) Award\, the 2001 Office of Naval Researc h Young Investigator Award\, the Mercator Professor award from Deutsche Forschungsgemeinschaft\, Germany\, for 2000-2002\, and over a dozen best paper awards at top conferences. He is a recipient of Duke University's 2 008 Dean's Award for Excellence in Mentoring\, and a recipient of the 2010 Capers and Marion McDonald Award for Excellence in Mentoring and Advising \, Pratt School of Engineering\, Duke University. He was also awarded the Distinguished Alumnus Award by the Indian Institute of Technology\, Kharag pur\, in 2014. Prof. Chakrabarty has served as an ACM Distinguished Speake r\, a Distinguished Visitor of the IEEE Computer Society\, and a Distingui shed Lecturer of the IEEE Circuits and Systems Society. He is also a recip ient of the Humboldt Research Award (2013) and the Humboldt Research Fel lowship (2003)\, awarded by the Alexander von Humboldt Foundation\, Germa ny. He holds 18 US patents and has several pending US patents. He served a s Editor-in-Chief of IEEE Design &\; Test of Computers during 2010-20 12\, ACM Journal on Emerging Technologies in Computing Systems during 20 10-2015\, and IEEE Transactions on VLSI Systems during 2015-2018.\n\nPro f. Chakrabarty received the B. Tech. degree from the Indian Institute of T echnology\, Kharagpur\, India in 1990\, and the M.S.E. and Ph.D. degrees f rom the University of Michigan\, Ann Arbor in 1992 and 1995\, respectively \, all in Computer Science and Engineering . During 1990-95\, he was a r esearch assistant at the Advanced Computer Architecture Laboratory of the Department of Electrical Engineering and Computer Science\, University of Michigan. During 1995-1998\, he was an Assistant Professor of Electrical a nd Computer Engineering at Boston University.\n\n\n \;\n\n \n \nZoom I nformation:\nJoin Zoom Meeting\nhttps://ufl.zoom.us/j/904047693\n\nMeeting ID: 904 047 693\n\nOne tap mobile\n+16465588656\,\,904047693# US (New Yor k)\n+16699006833\,\,904047693# US (San Jose)\n\nDial by your location\n+1 646 558 8656 US (New York)\n+1 669 900 6833 US (San Jose)\nMeeting ID: 904 047 693\nFind your local number: https://ufl.zoom.us/u/ab5VOI6m6G\n\nJoin by SIP\n904047693@zoomcrc.com\n\nJoin by H.323\n162.255.37.11 (US West)\n 162.255.36.11 (US East)\n221.122.88.195 (China)\n115.114.131.7 (India Mumb ai)\n115.114.115.7 (India Hyderabad)\n213.19.144.110 (EMEA)\n103.122.166.5 5 (Australia)\n209.9.211.110 (Hong Kong)\n64.211.144.160 (Brazil)\n69.174. 57.160 (Canada)\n207.226.132.110 (Japan)\nMeeting ID: 904 047 693\n\nJoin by Skype for Business\nhttps://ufl.zoom.us/skype/904047693 ATTACH;FMTTYPE=image/jpeg:https://mestcenter.org/wp-content/uploads/2022/0 5/Krishnendu.jpg CATEGORIES:Home Page,Webinars END:VEVENT BEGIN:VTIMEZONE TZID:America/New_York X-LIC-LOCATION:America/New_York BEGIN:DAYLIGHT DTSTART:20220313T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT END:DAYLIGHT END:VTIMEZONE END:VCALENDAR