Date：2025/4/29 11:15
Location: R111, CSIE
Speakers: Prof. Younghyun Kim, Purdue University
Host: Prof. Chia-Lin Yang
Abstract:
Energy efficiency is one of the most crucial design constraints of modern computing systems, which dictates performance, lifetime, form factor, and cost. For decades, energy efficiency improvements have largely been driven by Moore’s law until the 2000s. For the last decade, however, technology scaling has slowed down, and with the prediction of the “end of Moore’s law” in the near future, technology scaling-driven energy efficiency improvement is coming to an end. "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.
Biography:
Younghyun Kim is an Associate Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University. Before joining Purdue in 2024, he was with the University of Wisconsin-Madison from 2016 to 2023. He was a Postdoctoral Research Assistant at Purdue University from 2013 to 2016. He received his Ph.D. degree in Electrical Engineering and Computer Science in 2013 and B.S. degree (highest honor) in Computer Science and Engineering in 2007, both from Seoul National University. His Ph.D. dissertation won the EDAA Outstanding Dissertation Award (2013). He is a recipient of the NSF CAREER Award (2019), Meta Research Faculty Research Award (2021), and other awards for innovative publications, designs, and demonstrations. His research interests include energy-quality scalable computing, machine learning at the edge, cyber-physical systems, and security and privacy for embedded computing systems.