BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT DTSTART:20240310T030000 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:PDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20241103T010000 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:PST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20250923T212043Z UID:097B10FE-9591-4D3B-8FCA-8C7070E18416 DTSTART;TZID=America/Los_Angeles:20240314T183000 DTEND;TZID=America/Los_Angeles:20240314T200000 DESCRIPTION:ABSTACT\n\nProbabilistic computing with probabilistic bits (p-b its) has emerged as a promising candidate in physics-inspired computers\, offering an energy-efficient approach to probabilistic algorithms and appl ications\n\nIn this talk\, I will discuss how magnetic p-bits can be combi ned with conventional CMOS to create hybrid probabilistic-classical comput ers for various applications such as solving the Boolean satisfiability pr oblem\, energy-based generative machine learning models like deep Boltzman n machines\, and quantum simulation for investigating many-body quantum sy stems. I will demonstrate how physics-inspired probabilistic computing can lead to graphics-processing-unit-like success stories for a sustainable f uture in computing.\n\nFor a more detailed description see [this lecture ’s detailed description.](https://ieeemagnetics.org/presentation/probabi listic-computing-p-bits-optimization-machine-learning-and-quantum-simulati on)\n\nSPEAKER\n\nKerem Çamsarı received the Ph.D. in Electrical and Com puter Engineering from Purdue University in 2015\, where he continued as a postdoctoral researcher before becoming Assistant Professor at the Depart ment of Electrical and Computer Engineering at the University of Californi a Santa Barbara in 2020. His doctoral work established a modular approach to connect a growing set of emerging materials and phenomena to circuits a nd systems\, a framework adopted by others. In later work\, he used this a pproach to establish the concept of p-bits and p-circuits as a bridge betw een classical and quantum circuits to design efficient\, domain-specific h ardware accelerators for the “beyond-Moore” era of electronics. He is a founding member of the Technical Committee on Quantum\, Neuromorphic\, a nd Unconventional Computing within the IEEE Nanotechnology Council where h e currently leads the Unconventional Computing section. For his work on pr obabilistic computing\, he has received the IEEE Magnetics Society Early C areer Award\, a Bell Labs Prize\, an Office of Naval Research Young Invest igator Award\, and a National Science Foundation CAREER award. He is a sen ior member of the IEEE.\n\nSpeaker(s): Kerem Camsari\n\nAgenda: \n6:30 PM - 7:00 PM Networking at Quadrant\n\n7:00 PM - 8:00 PM Lecture with questio ns at end\n\n1120 Ringwood Ct.\, San Jose\, California\, United States\, 9 5131\, Virtual: https://events.vtools.ieee.org/m/408332 LOCATION:1120 Ringwood Ct.\, San Jose\, California\, United States\, 95131\ , Virtual: https://events.vtools.ieee.org/m/408332 ORGANIZER:yue.hu@seagate.com SEQUENCE:32 SUMMARY:Probabilistic Computing With p-Bits: Optimization\, Machine Learnin g and Quantum Simulation URL;VALUE=URI:https://events.vtools.ieee.org/m/408332 X-ALT-DESC:Description: <br /><p><strong><u>ABSTACT</u></strong></p>\n<p>&n bsp\;</p>\n<p>Probabilistic computing with probabilistic bits (p-bits) has emerged as a promising candidate in physics-inspired computers\, offering an energy-efficient approach to probabilistic algorithms and applications </p>\n<p>In this talk\, I will discuss how magnetic p-bits can be combined with conventional CMOS to create hybrid probabilistic-classical computers for various applications such as solving the Boolean satisfiability probl em\, energy-based generative machine learning models like deep Boltzmann m achines\, and quantum simulation for investigating many-body quantum syste ms. I will demonstrate how physics-inspired probabilistic computing can le ad to graphics-processing-unit-like success stories for a sustainable futu re in computing.</p>\n<p>For a more detailed description see <a href="http s://ieeemagnetics.org/presentation/probabilistic-computing-p-bits-optimiza tion-machine-learning-and-quantum-simulation">this lecture&rsquo\;s detail ed description.</a></p>\n<p><strong><u>SPEAKER</u></strong></p>\n<p>Kerem &Ccedil\;amsarı received the Ph.D. in Electrical and Computer Engineering from Purdue University in 2015\, where he continued as a postdoctoral res earcher before becoming Assistant Professor at the Department of Electrica l and Computer Engineering at the University of California Santa Barbara i n 2020. His doctoral work established a modular approach to connect a grow ing set of emerging materials and phenomena to circuits and systems\, a fr amework adopted by others. In later work\, he used this approach to establ ish the concept of p-bits and p-circuits as a bridge between classical and quantum circuits to design efficient\, domain-specific hardware accelerat ors for the &ldquo\;beyond-Moore&rdquo\; era of electronics. He is a found ing member of the Technical Committee on Quantum\, Neuromorphic\, and Unco nventional Computing within the IEEE Nanotechnology Council where he curre ntly leads the Unconventional Computing section. For his work on probabili stic computing\, he has received the IEEE Magnetics Society Early Career A ward\, a Bell Labs Prize\, an Office of Naval Research Young Investigator Award\, and a National Science Foundation CAREER award. He is a senior mem ber of the IEEE.</p><br /><br />Agenda: <br /><p>6:30 PM - 7:00 PM Network ing at Quadrant</p>\n<p>7:00 PM - 8:00 PM Lecture with questions at end</p > END:VEVENT END:VCALENDAR