SUSTAINABILITY SEMINAR SERIES: 

ABSTRACT: 

The past 50 years has seen a dramatic increase in the amount of compute capability per person, in particular, those enabled by AI. Despite the positive societal benefits, AI technologies come with significant environmental implications. I will talk about the scaling trend and the operational carbon footprint of AI computing by examining the model development cycle, spanning data, algorithms, and system hardware. At the same time, we will consider the life cycle of system hardware from the perspective of hardware architectures and manufacturing technologies. To scale AI sustainably, we need to make AI and computing more broadly efficient and flexible. We must also go beyond efficiency and optimize across the life cycle of computing infrastructures, from hardware manufacturing to datacenter operation and end-of-life processing for the hardware. Based on the industry experience and lessons learned, my talk will conclude with important development and research directions to advance the field of computing sustainably..

SPEAKER BIO

Carole-Jean Wu is a Director of AI Research at Meta, where she leads the Systems and Machine Learning Research team. 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 as a Director, chaired the MLPerf Recommendation Benchmark Advisory Board, and co-chaired for MLPerf Inference. Prior to Meta/Facebook, Dr. Wu was a professor with tenure at ASU. She earned her M.A. and Ph.D. from Princeton University and B.Sc. from Cornell University. Dr. Wu’s expertise sits at the intersection of computer architecture and machine learning. Her work spans across datacenter infrastructures and edge systems with a focus on performance, energy efficiency and sustainability. She is passionate about pathfinding and tackling system challenges to enable efficient, scalable, and environmentally-sustainable AI technologies. Dr. Wu's work has been recognized with several awards, including IEEE Micro Top Picks and ACM / IEEE Best Paper Awards. She is the recipient of NSF CAREER Award, CRA-WP Anita Borg Early Career Award Distinction of Honorable Mention, IEEE Young Engineer of the Year Award, Science Foundation Arizona Bisgrove Early Career Scholarship, and Facebook AI Infrastructure Mentorship Award. She is in the Hall of Fame of ISCA, HPCA and IISWC. She currently serves on the ACM SIGARCH/SIGMICRO CARES committee, as well as the National Academies of Sciences, Engineering, Medicine workshop planning committee.