BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Chicago BEGIN:DAYLIGHT DTSTART:20240310T030000 TZOFFSETFROM:-0600 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:CDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20241103T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0600 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:CST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20241104T184249Z UID:53D9A4CC-BCFF-40CA-8E88-D505906243A4 DTSTART;TZID=America/Chicago:20241101T150000 DTEND;TZID=America/Chicago:20241101T160000 DESCRIPTION:Efficient Computing for AI and Robotics: From Hardware Accelera tors to Algorithm Design\n\nThe compute demands of AI and robotics continu e to rise due to the rapidly growing volume of data to be processed\; the increasingly complex algorithms for higher quality of results\; and the de mands for energy efficiency and real-time performance. In this talk\, we w ill discuss the design of efficient tailored hardware accelerators and the co-design of algorithms and hardware that reduce the energy consumption w hile delivering swift real-time and robust performance for applications in cluding deep neural networks\, data analytics with sparse tensor algebra\, and autonomous navigation. Throughout the talk\, we will highlight import ant design principles\, methodologies\, and tools that can facilitate an e ffective design process and various forms of co-design that can broaden th e design space.\n\nVivienne Sze is a Professor in the Electrical Engineeri ng and Computer Science Department at MIT. She works on computing systems that enable energy-efficient machine learning\, computer vision\, and vide o compression/processing for a wide range of applications\, including auto nomous navigation\, digital health\, and the internet of things. Her work has been recognized by various awards\, including faculty awards from Goog le\, Facebook\, and Qualcomm\, the Symposium on VLSI Circuits Best Student Paper Award\, the IEEE Custom Integrated Circuits Conference Outstanding Invited Paper Award\, the IEEE Micro Top Picks Award and the International Symposium on Performance Analysis of Systems and Software Best Paper Awar d. As a member of the Joint Collaborative Team on Video Coding\, she recei ved the Primetime Engineering Emmy Award for the development of the High-E fficiency Video Coding video compression standard. She is a co-editor of H igh Efficiency Video Coding (HEVC): Algorithms and Architectures (Springer \, 2014) and co-author of Efficient Processing of Deep Neural Networks (Sy nthesis Lectures on Computer Architecture\, Morgan Claypool\, 2020). For m ore information about Prof. Sze’s research\, please visit [http://sze.mi t.edu](http://sze.mit.edu/).\n\nCo-sponsored by: University of Texas at Au stin\n\nSpeaker(s): Vivienne Sze\, \n\nRoom: 1.518\, Bldg: EER\, 2501 Spee dway Dr. \, Austin\, Texas\, United States\, 78712 \, Virtual: https://eve nts.vtools.ieee.org/m/440366 LOCATION:Room: 1.518\, Bldg: EER\, 2501 Speedway Dr. \, Austin\, Texas\, Un ited States\, 78712 \, Virtual: https://events.vtools.ieee.org/m/440366 ORGANIZER:stefano.pietri@nxp.com SEQUENCE:33 SUMMARY:Practical Aspects of Machine Learning Circuits and Systems: Efficie nt Computing for AI and Robotics URL;VALUE=URI:https://events.vtools.ieee.org/m/440366 X-ALT-DESC:Description: <br /><p><span style="font-size: 12.0pt\; font-fami ly: 'Aptos'\,sans-serif\; mso-ascii-theme-font: minor-latin\; mso-fareast- font-family: Aptos\; mso-fareast-theme-font: minor-latin\; mso-hansi-theme -font: minor-latin\; mso-bidi-font-family: 'Times New Roman'\; mso-bidi-th eme-font: minor-bidi\; mso-ansi-language: EN-US\; mso-fareast-language: EN -US\; mso-bidi-language: AR-SA\;">Efficient Computing for AI and Robotics: From Hardware Accelerators to Algorithm Design<br><br>The compute demands of AI and robotics continue to rise due to the rapidly growing volume of data to be processed\; the increasingly complex algorithms for higher qual ity of results\; and the demands for energy efficiency and real-time perfo rmance. In this talk\, we will discuss the design of efficient tailored ha rdware accelerators and the co-design of algorithms and hardware that redu ce the energy consumption while delivering swift real-time and robust perf ormance for applications including deep neural networks\, data analytics w ith sparse tensor algebra\, and autonomous navigation.&nbsp\; Throughout t he talk\, we will highlight important design principles\, methodologies\, and tools that can facilitate an effective design process and various form s of co-design that can broaden the design space.</span></p>\n<p><span sty le="font-size: 12.0pt\; font-family: 'Aptos'\,sans-serif\; mso-ascii-theme -font: minor-latin\; mso-fareast-font-family: Aptos\; mso-fareast-theme-fo nt: minor-latin\; mso-hansi-theme-font: minor-latin\; mso-bidi-font-family : 'Times New Roman'\; mso-bidi-theme-font: minor-bidi\; mso-ansi-language: EN-US\; mso-fareast-language: EN-US\; mso-bidi-language: AR-SA\;">Vivienn e Sze is a Professor in the Electrical Engineering and Computer Science De partment at MIT. She works on computing systems that enable energy-efficie nt machine learning\, computer vision\, and video compression/processing f or a wide range of applications\, including autonomous navigation\, digita l health\, and the internet of things. Her work has been recognized by var ious awards\, including faculty awards from Google\, Facebook\, and Qualco mm\, the Symposium on VLSI Circuits Best Student Paper Award\, the IEEE Cu stom Integrated Circuits Conference Outstanding Invited Paper Award\, the IEEE Micro Top Picks Award and the International Symposium on Performance Analysis of Systems and Software Best Paper Award. As a member of the Join t Collaborative Team on Video Coding\, she received the Primetime Engineer ing Emmy Award for the development of the High-Efficiency Video Coding vid eo compression standard.&nbsp\; She is a co-editor of High Efficiency Vide o Coding (HEVC): Algorithms and Architectures (Springer\, 2014) and co-aut hor of Efficient Processing of Deep Neural Networks (Synthesis Lectures on Computer Architecture\, Morgan Claypool\, 2020). For more information abo ut Prof. Sze&rsquo\;s research\, please visit <a href="http://sze.mit.edu/ " target="_blank" rel="noopener">http://sze.mit.edu</a>.&nbsp\;&nbsp\;</sp an></p> END:VEVENT END:VCALENDAR