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CATEGORIES:Seminar
DESCRIPTION:Exploring Autonomous Intelligence Beyond the Digital von Neuman
 n Abstraction\n\nAbstract: Artificial intelligence (AI) and machine learnin
 g (ML) technologies have fueled many burgeoning applications from on-device
  learning and personalized recommendations to self-driving cars and collabo
 rative robots. Despite these unprecedented advancements\, the holy grail of
  enabling fully autonomous machine intelligence remains far from our grasp.
  One key challenge is the lack of performant and efficient hardware impleme
 ntation\, especially on edge devices with stringent resource constraints.\n
 \nIn this talk\, I will present our recent efforts to tackle this challenge
  from a unique angle that explores system/design abstractions beyond the co
 nventional binary digital logic and von Neumann architecture. Specifically\
 , we leverage information processing ability innate in the analog/mixed-sig
 nal (AMS) domain and exploit co-located compute and memory organization. Ou
 r AMS-domain method not only leads to novel resistive RAM (RRAM) based anal
 og-to-digital converters that exceed the state-of-the-art performance\, but
  it also transforms the design of peripheral circuits in Processing-in-Memo
 ry (PIM) systems delivering much-improved computing performance and efficie
 ncy. Taking one step further than simply addressing the compute bottleneck\
 , we demonstrate how PIM architecture can accelerate sparse memory-heavy em
 bedding operations and propose a near-memory solution for large-scale perso
 nalized recommendation systems.\n\nBesides pushing the envelope of computin
 g both on the edge and in the cloud\, I will also briefly highlight the pot
 ential of this beyond-digital approach in diverse fields such as learning-a
 ssisted design automation\, analog/physical domain security\, and real-time
  adversarial cyberphysical systems. I will conclude the talk with a vision 
 for future autonomous intelligence powered by distinctive yet complementary
  computing paradigms.\n\nBio: Dr. Xuan ‘Silvia’ Zhang is an Associate Profe
 ssor in the Preston M. Green Department of Electrical and Systems Engineeri
 ng at Washington University in St. Louis. She received her B. Eng. degree i
 n Electrical Engineering from Tsinghua University in China\, and her MS and
  Ph.D. degrees in Electrical and Computer Engineering from Cornell Universi
 ty. She works across the fields of integrated circuits/VLSI design\, comput
 er architecture\, and electronic design automation. Her research interests 
 include novel circuits and architecture for efficient machine learning and 
 artificial intelligence\, adaptive power and resource management for autono
 mous systems\, and system security in analog and physical domains. Dr. Zhan
 g is an IEEE Circuits and Systems Society (CAS) Distinguished Lecturer for 
 2022-2023\, and is the recipient of NSF CAREER Award in 2020\, AsianHOST Be
 st Paper Award in 2020\, DATE Best Paper Award in 2019\, and ISLPED Design 
 Contest Award in 2013. Her work has also been nominated for Best Paper Awar
 ds at ASP-DAC 2021\, MLCAD 2020\, DATE 2019\, and DAC 2017.\n\nPlease conta
 ct Jessie at jjh348@cornell.edu for Zoom info.
DTEND:20220315T171500Z
DTSTAMP:20260410T143731Z
DTSTART:20220315T161500Z
LOCATION:Phillips Hall\, 233
SEQUENCE:0
SUMMARY:ECE/Cornell Tech Seminar - Silvia Zhang\, Washington University in 
 St. Louis
UID:tag:localist.com\,2008:EventInstance_39332499884275
URL:https://events.cornell.edu/event/ececornell_tech_seminar_-_silvia_zhang
 _washington_university_in_st_louis
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