Abstract
The past 50 years has seen a dramatic increase in the amount of compute capability per person, in particular, those enabled by AI. It is essential that AI, the twenty-first century’s most important technology, be developed with sustainability in mind. I will highlight key efficiency optimization opportunities for cutting-edge AI technologies. To scale AI sustainably, we must also go beyond efficiency. I will talk about optimization opportunities across the life cycle of computing infrastructures, from hardware manufacturing to datacenter operations and end-of-life processing for the hardware, capturing both the operational and manufacturing carbon footprint of AI computing. Based on the industry experience and lessons learned, I will share key challenges and opportunities on the horizon for at-scale optimization. This talk will conclude with important development and research directions to advance computing sustainably.
Biography
Carole-Jean Wu is a Director of AI Research at Meta, leading 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 everyone. Dr. Wu’s expertise sits at the intersection of computer architecture and machine learning with a focus on performance, energy efficiency and sustainability. Dr. Wu is the recipient of the 2025 ACM SIGARCH Maurice Wilkes Award. Her work has been recognized with several IEEE Micro Top Picks and ACM/IEEE Best Paper Awards. In particular, her work in sustainability has influenced adoption in data center infrastructures at scale. She is in the Hall of Fame of ISCA, HPCA, IISWC, and serves on the study committee of the National Academies. Prior to Meta/Facebook, she was a tenured professor at ASU. Dr. Wu earned her M.A. and Ph.D. from Princeton and B.Sc. from Cornell.