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Machine Learning-driven, Energy-Optimized VLSI Systems and Hardware-Efficient Algorithms세미나 안내,5.12(weds),10AM/Prof. Hun Seok Kim


“Machine Learning-driven, Energy-Optimized VLSI Systems and Hardware-Efficient Algorithms”.


May 12 (Wed), 10:00 AM


Hun Seok Kim ( Assistant Professor ,Electrical Engineering Computer Science /University of Michigan , USA)



This talk presents holistic approaches to realize energy-optimized, machine-learning-enabled Internet-of-Things (ML-IoT) systems and VLSI circuits. The optimized system integration is a major challenge in intelligent IoT systems. A truly energy-optimal ML-IoT solution is attainable only by a cross-layer optimization that requires a full characterization of the complete end-to-end system. Addressing this critical technical challenge in emerging ML-IoT applications, a cross-layer interdisciplinary research that spans deep learning algorithms, wireless communication, digital signal processing, and VLSI hardware architecture is discussed in this talk. Presented research projects aim for ultra-low power and/or energy-aware wireless IoT systems enabled by novel hardware-friendly algorithms and cross-layer optimized VLSI systems.


Hun-Seok Kim is an assistant professor at the University of Michigan, Ann Arbor. Kim received his B.S. degree from the Seoul National University (South Korea) in 2001, and M.S. & Ph.D. degrees from the University of California, Los Angeles (UCLA), all in Electrical Engineering. His research focuses on system analysis, novel algorithms, and efficient VLSI architectures for low-power/high-performance wireless communication, signal processing, computer vision, and machine learning systems. Before joining the University of Michigan, Kim worked as a technical staff member at Texas Instruments (2010 2014). He is serving as an associate editor of IEEE Solid State Circuits Letters, IEEE Transactions on Mobile Computing, and IEEE Transactions on Green Communications & Networking. Kim is a recipient of the 2018 Defense Advanced Research Projects Agency (DARPA) Young Faculty Award (YFA) and the National Science Foundation (NSF) Faculty Early Career Development (CAREER) Award 2019.