Speaker: Younghyun Kim
Date and Time: Tuesday, May 17, 2022 at 2:00 p.m.
Location: Zoom https://uci.zoom.us/j/98632011722
Approximate computing is a new paradigm to accomplish energy-efficient computing in this twilight of Moore’s law by relaxing the exactness requirement of computation results for intrinsically error-resilient applications, such as deep learning and signal processing, and producing results that are “just good enough.” It exploits that the output quality of such error-resilient applications is not fundamentally degraded even if the underlying computations are greatly approximated. This favorable energy-quality tradeoff opens up new opportunities to improve the energy efficiency of computing, and a large body of approximate computing methods for energy-efficient “data processing” have been proposed. In this talk, I will introduce approximate computing methods to accomplish “full-system energy-quality scalability.” It extends the scope of approximation from the processor to other system components including sensors, interconnects, etc., for energy-efficient “data generation” and “data transfer” to fully exploit the energy-quality tradeoffs across the entire system. I will also discuss how approximate computing can benefit the implementation of machine learning on ultra low-power embedded systems.
Prof. Younghyun Kim is an Assistant Professor in the Department of Electrical and Computer Engineering and an ECE Grainger Faculty Scholar at the University of Wisconsin-Madison, where leads the Wisconsin Embedded Systems and Computing (WISEST) Laboratory (https://wisest.ece.wisc.edu/)
Hosted By: Prof. Nikil Dutt