Date：2025/4/21 16:00-17:10
Location: R105, CSIE
Speakers: Prof. Hussam Amrouch
Host:楊佳玲教授
Abstract:
Edge computing for AI has emerged as a pivotal strategy for ensuring securityand privacy, particularly in applications where personal data and sensitive biomarkers demand rigorous protection. Moreover, it profoundly helps reduce the carbon footprint associated with running AI algorithms on power-hungry GPUs. This talk explores a transformative approach that transcends traditional cloud infrastructures by executing AI algorithms directly at the end-user. We address the fundamental challenge of limited computing resources at the edge by introducing brain-inspired computational algorithms—specifically, hyperdimensional computing—that are inherently more energy efficient than classical deep learning methods. By leveraging innovative in-memory computing AI accelerators and custom RISC-V processor architectures augmented with specialized AI instructions, our work embodies a algorithm-technology co-optimization. Experimental silicon measurements and results from our AI processor chip, fabricated in a 22 nm technology node, demonstrate both inference and training capabilities operating within a mW power envelope, thereby opening new doors for secure, efficient, and sustainable edge AI. Furthermore, we demonstrate how emerging ferroelectric nonevolatile memories can open new doors to significantly increase the efficiency.
Biography:
Hussam Amrouch is Professor heading the Chair of AI Processor Design within the Technical University of Munich (TUM). He is, additionally, the head of Brain-inspired Computing at the Munich Institute of Robotics. Further, he is the head of the Semiconductor Test and Reliability department at the University of Stuttgart, Germany. He is also the Academic Director of TUM Venture Labs. He is founder and director of the Munich Advanced Technology Center for High-Tech AI Chips. He received his Ph.D. degree with the highest distinction(summa cum laude) from KIT in 2015. He has around 300 publications(including over 120 articles in many top journals including Nature Communications) in multidisciplinary research areas covering semiconductor device physics, circuit design and computer architecture. His research interest is AI acceleration, emerging technologies, in-memory computing with a special focus on reliability, and cryogenic circuits for quantum computing. His research in AI chips and reliability have been funded by the German Research Foundation (DFG), Bavarian ministry of economy, Bavarian ministry of science, Advantest Corporation, and the U.S. Office of Naval Research.
