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University of Hawaii

Electrical Engineering

Machine Learning in Resource-Constrained Hardware

Date: 2019-10-31           Add to Google Calendar
Time: 11:00am - 12:00pm
Location: Holmes Hall 389
Speaker: Boris Murmann, Professor of Electrical Engineering, Stanford University

Abstract
Over the past decade, machine learning algorithms have been deployed in many cloud-centric applications. However, as the application space continues to grow, various algorithms are now being embedded “closer to the sensor,” eliminating the latency, privacy and energy penalties associated with cloud access. In this talk, I will review mixed-signal circuit techniques that can improve the efficiency of moderate-complexity, low-power inference algorithms. Specific examples include: (1) analog feature extraction for image and audio processing, (2) mixed-signal compute circuits for convolutional neural networks, and (3) in-memory compute approaches using emerging memory technologies such as RRAM.

Bio
Dr. Boris Murmann is a Professor of Electrical Engineering at Stanford University. He joined Stanford in 2004 after completing his Ph.D. degree in electrical engineering at the University of California, Berkeley in 2003. Since 2004, he has worked as a consultant with numerous Silicon Valley companies. Dr. Murmann’s research interests are in mixed-signal integrated circuit design with special emphasis on sensor interfaces, data converters and custom circuits for embedded machine learning. He is a Fellow of the IEEE.



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