BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20240310T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20241103T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20240525T121828Z UID:59B034F0-925E-41A1-86AF-468D92803A29 DTSTART;TZID=America/New_York:20240521T180000 DTEND;TZID=America/New_York:20240521T191500 DESCRIPTION:[]\n\nQuadratic Unconstrained Binary Optimizing (QUBO) is a com binatorial optimization problem that has become essential to machine learn ing\, economics\, and healthcare applications. Therefore\, QUBO solvers ha ve seen a significant boost in their demand. These problems are computatio nally expensive\, complex to parallelize\, and require MIMD approaches. Is ing machines such as D-wave have proposed a quantum solution for solving t hese NP-hard optimization problems. Several data-dominant application spac es\, namely protein folding problems in Computational Biology\, genome seq uencing for COVID-19 and other pandemic diseases\, and traffic patterns in social media\, have benefitted from this problem mapping and one-shot sol ution. Although they can solve QUBO problems\, the exceptionally high oper ating cost due to the cryo-cooling for quantum Ising machines might not ju stify the accuracy they achieve. In this talk\, we will explore a magnetic QUBO-solver\, which could solve the problems more quickly and cost-effect ively at room temperature. Because the Hamiltonian of a system of coupled nanomagnets is quadratic\, a wide class of quadratic energy minimization c an be solved much more quickly by the relaxation of a grid of nanomagnets than by a conventional Boolean processor. Our research shows that magnet-b ased solutions are independent of problem size as the ground state of the magnets yield the optimization solution in parallel. This co-processor con sists of a programmable grid of magnetic cells that can generate any magne tic layout in a 2D plane and will be integrated with peripheral control si milar to STT-MRAM memory. This talk will focus on state-variable design\, problem mapping and reconfigurability.\n\nAttendee Feedback:\n"It was grea t to participate in the session. Please convey my thanks to the presenters ." -- Raghunath Indugu\n\nCo-sponsored by: Gordon Burkhead\n\nSpeaker(s): Sanjukta Bhanja\, \n\nAgenda: \n6:00 PM - Start of online/virtual event. L ocal chapter and Section updates\, introductions\, etc.\n6:05 PM - Start o f Distinguished Lecture\n6:55 PM - Formal End of Lecture\, Start of Q&A - Discussions\n7:15 PM - Formal end of event\, Vote of thanks to the Speaker ....\n\nVirtual: https://events.vtools.ieee.org/m/417892 LOCATION:Virtual: https://events.vtools.ieee.org/m/417892 ORGANIZER:sharan.kalwani@ieee.org SEQUENCE:35 SUMMARY:Unconventional Computing using Spintronics URL;VALUE=URI:https://events.vtools.ieee.org/m/417892 X-ALT-DESC:Description: <br /><p style="text-align: left\;"><img src="https ://en.wikipedia.org/wiki/File:Boron-arsenide-unit-cell-1963-CM-3D-balls.pn g" alt=""></p>\n<p style="text-align: justify\;">Quadratic Unconstrained B inary Optimizing (QUBO) is a combinatorial optimization problem that has b ecome essential to machine learning\, economics\, and healthcare applicati ons. Therefore\, QUBO solvers have seen a significant boost in their deman d. These problems are computationally expensive\, complex to parallelize\, and require MIMD approaches.&nbsp\; Ising machines such as D-wave have pr oposed a quantum solution for solving these NP-hard optimization problems. Several data-dominant application spaces\, namely protein folding problem s in Computational Biology\, genome sequencing for COVID-19 and other pand emic diseases\, and traffic patterns in social media\, have benefitted fro m this problem mapping and one-shot solution. Although they can solve QUBO problems\, the exceptionally high operating cost due to the cryo-cooling for quantum Ising machines might not justify the accuracy they achieve. In this talk\, we will explore a magnetic QUBO-solver\, which could solve th e problems more quickly and cost-effectively at room temperature.&nbsp\; B ecause the Hamiltonian of a system of coupled nanomagnets is quadratic\, a wide class of quadratic energy minimization can be solved much more quick ly by the relaxation of a grid of nanomagnets than by a conventional Boole an processor. Our research shows that magnet-based solutions are independe nt of problem size as the ground state of the magnets yield the optimizati on solution in parallel. This co-processor consists of a programmable grid of magnetic cells that can generate any magnetic layout in a 2D plane and will be integrated with peripheral control similar to STT-MRAM memory. Th is talk will focus on state-variable design\, problem mapping and reconfig urability.</p>\n<p style="text-align: justify\;">Attendee Feedback:<br><em >"It was great to participate in the session. Please convey my thanks to t he presenters." </em>-- Raghunath Indugu</p><br /><br />Agenda: <br /><p>6 :00 PM - Start of online/virtual event. Local chapter and Section updates\ , introductions\, etc.<br>6:05 PM - Start of Distinguished Lecture<br>6:55 PM - Formal End of Lecture\, Start of Q&amp\;A - Discussions<br>7:15 PM - Formal end of event\, Vote of thanks to the Speaker....</p> END:VEVENT END:VCALENDAR