BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Denver BEGIN:DAYLIGHT DTSTART:20240310T030000 TZOFFSETFROM:-0700 TZOFFSETTO:-0600 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:MDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20241103T010000 TZOFFSETFROM:-0600 TZOFFSETTO:-0700 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:MST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20241020T162050Z UID:BC2A90FC-A960-4C69-AA2C-CCCFB66CFB6A DTSTART;TZID=America/Denver:20241017T110000 DTEND;TZID=America/Denver:20241017T133000 DESCRIPTION:IEEE Distinguished Lecture\n\nThe slowing down of Moore’s Law growth has coincided with escalating computational demands from machine l earning and artificial intelligence. An emerging trend in computing involv es building physics-inspired computers that leverage the intrinsic propert ies of physical systems for specific domains of applications. Probabilisti c computing with probabilistic bits (p-bits) has emerged as a promising ca ndidate in this area\, offering an energy-efficient approach to probabilis tic algorithms and applications [1]-[4].\n\nSeveral implementations of p-b its\, ranging from standard complementary metal oxide semiconductor (CMOS) technology to nanodevices\, have been demonstrated. Among these\, the mos t promising p-bits appear to be based on stochastic magnetic tunnel juncti ons (sMTJs) [2]. Such sMTJs harness the natural randomness in low-barrier nanomagnets to create energy-efficient and fast fluctuations\, up to gigah ertz frequencies [4]. In this talk\, I will discuss how magnetic p-bits ca n be combined with conventional CMOS to create hybrid probabilistic-classi cal computers for various applications. I will provide recent examples of how p-bits are naturally applicable to combinatorial optimization\, such a s solving the Boolean satisfiability problem [3]\, energy-based generative machine learning models like deep Boltzmann machines\, and quantum simula tion for investigating many-body quantum systems. Through experimentally i nformed projections for scaled p-bit computers using sMTJs\, I will demons trate how physics-inspired probabilistic computing can lead to graphics-pr ocessing-unit-like success stories for a sustainable future in computing.\ n\n[1] S. Chowdhury\, A. Grimaldi\, N. A. Aadit\, S. Niazi\, M. Mohseni\, S. Kanai\, H. Ohno\, S. Fukami\, L. Theogarajan\, G. Finocchio\, S. Datta\ , K. Y. Camsari\, “A Full-Stack View of Probabilistic Computing with p-B its: Devices\, Architectures and Algorithms\,” IEEE J. Expl. Solid-State Comp. Dev. Cir. 9\, 1-11 (2023).\n\n[2] W. A. Borders\, A. Z. Pervaiz\, S . Fukami\, K. Y. Camsari\, H. Ohno\, S. Datta\, “Integer Factorization U sing Stochastic Magnetic Tunnel Junctions\,” Nature 573\, 390-393 (2019) .\n\n[3] N. A. Aadit\, A. Grimaldi\, M. Carpentieri\, L. Theogarajan\, J. M. Martinis\, G. Finocchio\, K. Y. Camsari\, “Massively Parallel Probabi listic Computing with Sparse Ising Machines\,” Nature Electronics 5\, 46 0–468 (2022).\n\n[4] N. S. Singh\, S. Niazi\, S. Chowdhury\, K. Selcuk\, H. Kaneko\, K. Kobayashi\, S. Kanai\, H. Ohno\, S. Fukami\, K. Y. Camsari \, “Hardware Demonstration of Feedforward Stochastic Neural Networks wit h Fast MTJ-Based p-Bits\,” IEEE Int. Electron Dev. Meeting (2023).\n\nSp eaker(s): Kerem Camsari\, \n\nRoom: 81-1A116\, Bldg: 81\, NIST 325 Broadwa y\, Boulder\, Colorado\, United States LOCATION:Room: 81-1A116\, Bldg: 81\, NIST 325 Broadway\, Boulder\, Colorado \, United States ORGANIZER:stephen.russek@nist.gov SEQUENCE:35 SUMMARY:IEEE Distinguished Lecture: Probabilistic Computing With p-Bits: Op timization\, Machine Learning and Quantum Simulation URL;VALUE=URI:https://events.vtools.ieee.org/m/433496 X-ALT-DESC:Description: <br /><p>IEEE Distinguished Lecture</p>\n<p>The slo wing down of Moore&rsquo\;s Law growth has coincided with escalating compu tational demands from machine learning and artificial intelligence. An eme rging trend in computing involves building physics-inspired computers that leverage the intrinsic properties of physical systems for specific domain s of applications. Probabilistic computing with probabilistic bits (p-bits ) has emerged as a promising candidate in this area\, offering an energy-e fficient approach to probabilistic algorithms and applications [1]-[4].</p >\n<p>Several implementations of p-bits\, ranging from standard complement ary metal oxide semiconductor (CMOS) technology to nanodevices\, have been demonstrated. Among these\, the most promising p-bits appear to be based on stochastic magnetic tunnel junctions (sMTJs) [2]. Such sMTJs harness th e natural randomness in low-barrier nanomagnets to create energy-efficient and fast fluctuations\, up to gigahertz frequencies [4]. In this talk\, I will discuss how magnetic p-bits can be combined with conventional CMOS t o create hybrid probabilistic-classical computers for various applications . I will provide recent examples of how p-bits are naturally applicable to combinatorial optimization\, such as solving the Boolean satisfiability p roblem [3]\, energy-based generative machine learning models like deep Bol tzmann machines\, and quantum simulation for investigating many-body quant um systems. Through experimentally informed projections for scaled p-bit c omputers using sMTJs\, I will demonstrate how physics-inspired probabilist ic computing can lead to graphics-processing-unit-like success stories for a sustainable future in computing.</p>\n<p>[1] S. Chowdhury\, A. Grimaldi \, N. A. Aadit\, S. Niazi\, M. Mohseni\, S. Kanai\, H. Ohno\, S. Fukami\, L. Theogarajan\, G. Finocchio\, S. Datta\, K. Y. Camsari\, &ldquo\;A Full- Stack View of Probabilistic Computing with p-Bits: Devices\, Architectures and Algorithms\,&rdquo\;&nbsp\;<em>IEEE J. Expl. Solid-State Comp. Dev. C ir.</em>&nbsp\;9\, 1-11 (2023).</p>\n<p>[2] W. A. Borders\, A. Z. Pervaiz\ , S. Fukami\, K. Y. Camsari\, H. Ohno\, S. Datta\, &ldquo\;Integer Factori zation Using Stochastic Magnetic Tunnel Junctions\,&rdquo\;&nbsp\;<em>Natu re&nbsp\;</em>573\, 390-393 (2019).</p>\n<p>[3] N. A. Aadit\, A. Grimaldi\ , M. Carpentieri\, L. Theogarajan\, J. M. Martinis\, G. Finocchio\, K. Y. Camsari\, &ldquo\;Massively Parallel Probabilistic Computing with Sparse I sing Machines\,&rdquo\;&nbsp\;<em>Nature Electronic</em>s 5\, 460&ndash\;4 68 (2022).</p>\n<p>[4] N. S. Singh\, S. Niazi\, S. Chowdhury\, K. Selcuk\, H. Kaneko\, K. Kobayashi\, S. Kanai\, H. Ohno\, S. Fukami\, K. Y. Camsari \, &ldquo\;Hardware Demonstration of Feedforward Stochastic Neural Network s with Fast MTJ-Based p-Bits\,&rdquo\;&nbsp\;<em>IEEE Int. Electron Dev. M eeting</em> (2023).</p> END:VEVENT END:VCALENDAR