BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20260308T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20251102T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260126T203400Z UID:FF106414-30A5-4E54-B53C-8A0815011F0B DTSTART;TZID=America/New_York:20260123T160000 DTEND;TZID=America/New_York:20260123T180000 DESCRIPTION:IEEE CAS Toronto is proud to invite you to a seminar by Prof. B ibhu Datta Sahoo of University of Buffalo.\n\nJoin us Friday\, 23 January 2026 at 4PM (ET) on University of Toronto St.George Campus. Refreshments w ill be served.\n\nAbstract:\n--------------------------------------------- ------------------\n\nQuantum Computing\, having the ability to exponentia lly enhance the raw computing power\, and Artificial Intelligence (AI)\, h aving the ability to impart unprecedented intelligence to connected device s through algorithms that learn\, are the two key technologies of the 21st century.Although novel devices can significantly advance the field of qua ntum computing\, conventional CMOS based analog and mixed signal circuits can enable quantum computing using classical op amp based circuits. AI alg orithms on the other hand are tolerant to errors in computation\, thereby enabling approximate and low-precision computing which has resulted in the resurrection of more than half-a-century old analog computing.\n\nThis ta lk would present various analog computing techniques that could enable AI algorithms or more specifically machine learning (ML) algorithms. The talk could also delve into mixed-sginal computing where it will dive into the details of in-memory computing as well as adoption of novel memory devices \, viz.\, memristors\, to enable energy efficient computing. The talk woul d also present some classical analog hardware for emulating quantum algori thms\, like Grover’s search algorithm\, for quantum computing systems.\n \nSpeaker(s): Dr. Bibhu Datta Sahoo\, \n\nRoom: 202\, Bldg: Galbraith Buil ding\, 35 St George St\, Toronto\, Toronto\, Ontario\, Canada LOCATION:Room: 202\, Bldg: Galbraith Building\, 35 St George St\, Toronto\, Toronto\, Ontario\, Canada ORGANIZER:jiupeng.zhang@mail.utoronto.ca SEQUENCE:28 SUMMARY:[IEEE CAS04/ED15/EP21/PHO36 CAS Distinguished Lecture] Analog and M ixed Signal Circuits and Systems for Emerging Applications URL;VALUE=URI:https://events.vtools.ieee.org/m/526671 X-ALT-DESC:Description: <br /><p class="MsoNormal"><span style="font-family : 'times new roman'\, times\, serif\; font-size: 14pt\;">IEEE CAS Toronto is proud to invite you to a seminar by Prof. Bibhu Datta Sahoo of Universi ty of Buffalo.</span></p>\n<p class="MsoNormal"><span style="font-family: 'times new roman'\, times\, serif\; font-size: 14pt\;">Join us <strong>Fri day\, 23 January 2026 at 4PM</strong> (ET) on University of Toronto St.Geo rge Campus. Refreshments will be served.&nbsp\;</span></p>\n<p class="MsoN ormal"><span lang="EN-US" style="font-family: 'times new roman'\, times\, serif\;">&nbsp\;</span></p>\n<p class="MsoNormal"><span style="font-family : 'times new roman'\, times\, serif\; font-size: 18pt\;"><strong><span lan g="EN-US" style="mso-ansi-language: EN-US\;">Abstract:</span></strong></sp an></p>\n<div class="MsoNormal" style="text-align: center\;" align="center "><hr align="center" size="2" width="100%"></div>\n<p class="MsoNormal" st yle="text-align: left\;"><span style="font-family: 'times new roman'\, tim es\, serif\; font-size: 14pt\;">Quantum Computing\, having the ability to exponentially enhance the raw computing power\, and Artificial Intelligenc e (AI)\, having the ability to impart unprecedented intelligence to connec ted devices through algorithms that learn\, are the two key technologies o f the 21st century.</span><span style="font-family: 'times new roman'\, ti mes\, serif\; font-size: 14pt\;">Although novel devices can significantly advance the field of quantum computing\, conventional CMOS based analog an d mixed signal circuits can enable quantum computing using classical op am p based circuits. AI algorithms on the other hand are tolerant to errors i n computation\, thereby enabling approximate and low-precision computing w hich has resulted in the resurrection of more than half-a-century old anal og computing.</span></p>\n<p><img style="float: right\;" src="https://even ts.vtools.ieee.org/vtools_ui/media/display/d2cd552f-1cab-4f8b-a0f4-e08f190 f0754" width="540" height="302"></p>\n<p>&nbsp\;</p>\n<p><span style="font -family: 'times new roman'\, times\, serif\; font-size: 14pt\;">This talk would present various analog computing techniques that could enable AI alg orithms or more specifically machine learning (ML) algorithms. The talk co uld also delve into mixed-sginal computing where it will dive into the det ails of in-memory computing as well as adoption of novel memory devices\, viz.\, memristors\, to enable energy efficient computing. The talk would a lso present some classical analog hardware for emulating quantum algorithm s\, like Grover&rsquo\;s search algorithm\, for quantum computing systems. </span></p> END:VEVENT END:VCALENDAR