Speaker: Carole-Jean Wu
Location: Soda 430-438, Woz Lounge
Date: February 3, 2023
Time: 12-1pm PST
Title: Scaling AI Computing Sustainably
The past 50 years has seen a dramatic increase in the amount of compute per person, in particular, those enabled by AI. Modern natural language processing models are fueled with over trillion parameters while the memory needs of neural recommendation and ranking models have grown from hundreds of gigabytes to the terabyte scale. I will highlight recent advancement on important deep learning models and present hardware-software optimization opportunities across the machine learning system stack.
AI technologies come with significant environmental implications. I will talk about the carbon footprint of AI computing by examining the model development cycle, spanning data, algorithms, and system hardware, and, at the same time, considering the life cycle of system hardware from the perspective of hardware architectures and manufacturing technologies. The talk will capture the operational and manufacturing carbon footprint of AI computing. Based on the industry experience and lessons learned, I will share key challenges across the many dimensions of AI and what and how at-scale optimization can help reduce the overall carbon footprint of AI and computing. This talk will conclude with important development and research directions to advance the field of computing in an environmentally-responsible and sustainable manner.
Carole-Jean Wu is currently a Research Scientist at Meta. She is a founding member and a Vice President of MLCommons – a non-profit organization that aims to accelerate machine learning innovations for the benefits of all. Dr. Wu also serves on the MLCommons Board of a Director, chaired the MLPerf Recommendation Benchmark Advisory Board, and co-chaired for MLPerf Inference. Prior to Facebook/Meta, Carole-Jean was an Associate Professor at ASU. Dr. Wu’s expertise sits at the intersection of computer architecture and machine learning. Her work spans across datacenter infrastructures and edge systems, such as developing energy- and memory-efficient systems and microarchitectures, optimizing systems for machine learning execution at-scale, and designing learning-based approaches for system design and optimization. She is passionate about pathfinding and tackling system challenges to enable efficient and responsible AI technologies. Her work has been recognized with several awards, including IEEE Micro Top Picks and ACM / IEEE Best Paper Awards. In addition, her work has been featured at the MLPerf Inference v0.5 Launch and Results, MaskRCNN2Go for MLPerf, Tech @ Meta, and Bloomberg Green. Dr. Wu is the recipient of NSF CAREER Award, IEEE Young Engineer of the Year Award, Science Foundation Arizona Bisgrove Early Career Scholarship, Facebook AI Infrastructure Mentorship Award, and HPCA and IISWC Hall of Fame. She was the Program Co-Chair of the Conference on Machine Learning and Systems (MLSys) and Program Chair of the IEEE International Symposium on Workload Characterization (IISWC). She received her M.A. and Ph.D. degrees in Electrical Engineering from Princeton University and the B.Sc. degree in Electrical and Computer Engineering from Cornell University.