Prof. Younghyun Kim’s Seminar on ‘Time-Accuracy Scalable Compute for Energy-Efficient Machine Learning'
우리 연구실은 2026년 6월 15일, Purdue University 전자전기컴퓨터공학과의 Younghyun Kim 교수님을 모시고 “Time-Accuracy Scalable Compute for Energy-Efficient Machine Learning"을 주제로 한 세미나를 개최했습니다.
Younghyun Kim 교수님은 Purdue University Elmore Family School of Electrical and Computer Engineering의 부교수로, Networked and Embedded Intelligent Systems Lab을 이끌고 계시며 에너지-품질 확장형 컴퓨팅, 엣지/응용/물리 AI, 사이버 물리 시스템 등을 연구하고 계십니다.
이번 세미나에서는 전통적인 반도체 미세화를 넘어 에너지 효율적인 머신러닝을 실현하기 위한 방법론으로 시간-정확도 확장형 컴퓨팅(time-accuracy scalable computing)이 소개되었습니다. 근사 컴퓨팅(approximate computing)을 통해 정확도를 성능 및 에너지 효율과 맞교환함으로써, 동적 정확도 제어와 전 시스템 최적화가 머신러닝의 정확도를 유지하면서도 지연시간, 전력, 에너지를 크게 절감할 수 있음을 보여주었습니다.
귀중한 강연으로 깊은 통찰을 나누어 주신 Younghyun Kim 교수님께 진심으로 감사드립니다.
On June 15, 2026, our lab hosted a seminar featuring Professor Younghyun Kim from the School of Electrical and Computer Engineering at Purdue University, on the topic of “Time-Accuracy Scalable Compute for Energy-Efficient Machine Learning.” Younghyun Kim is an Associate Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University, where he leads the Networked and Embedded Intelligent Systems Lab. His research interests include energy-quality scalable computing, edge/applied/physical AI, cyber-physical systems, and security and privacy for embedded computing systems. In this seminar, Professor Younghyun Kim presented time-accuracy scalable computing as a path toward energy-efficient machine learning beyond traditional semiconductor scaling. He showed how approximate computing trades accuracy for improved performance and energy efficiency, demonstrating that dynamic accuracy control and full-system optimization can substantially reduce latency, power, and energy while maintaining acceptable machine-learning accuracy. We sincerely thank Professor Younghyun Kim for sharing his valuable insights through this enlightening lecture.
