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 system and hardware architectural design and parallelism 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 a Research Scientist and Tech Lead Manager at Meta AI. Her expertise sits at the intersection of computer architecture and machine learning with particular emphasis on developing energy- and memory-efficient systems, 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, responsible AI execution. Carole-Jean chairs the MLPerf Recommendation Benchmark Advisory Board, co-chaired MLPerf Inference, and serves on the MLCommons Board as the Vice President and a board director. Prior to Facebook/Meta, Carole-Jean was a tenured Associate Professor at Arizona State University. She received her M.A. and Ph.D. from Princeton and B.Sc. from Cornell. She is the recipient of the NSF CAREER Award,Distinction of Honorable Mention of the CRA Anita Borg Early Career Award, Facebook AI Infrastructure Mentorship Award, the IEEE Young Engineer of the Year Award, the Science Foundation Arizona Bisgrove Early Career Scholarship, and the Intel PhD Fellowship. In addition, her research has been recognized with several awards, including IEEE Micro Top Picks and IEEE/ACM Best Paper Awards. Her work has been featured for the MLPerf Inference v0.5 Launch and Results, MaskRCNN2Go for MLPerf, and from Understanding Computing's Carbon Footprint to Designing Low-Carbon Computers.