Scaling AI Sustainably: Environmental Implications, Challenges and Opportunities
Carole-Jean Wu (Facebook AI Research)
Distinguished Lecture Series
Thursday, December 9, 2021, 3:30 pm
Abstract
The past decade has witnessed orders-of-magnitude increase in the amount of compute for AI. Modern natural language processing models are fueled with over trillion parameters while the memory needs of deep learning recommendation and ranking models have grown from hundreds of gigabytes to the terabyte scale. We will explore the environmental implications of the super-linear growth trend for AI from a holistic perspective, spanning data, algorithms, and system hardware. I will talk about the carbon footprint of AI computing by examining the model development cycle across industry-scale use cases and, at the same time, considering the life cycle of system hardware. The talk will capture the operational and manufacturing carbon footprint of AI computing. I will present an end-to-end analysis for what and how hardware-software design and at-scale optimization can help reduce the overall carbon footprint of AI. Based on the industry experience and lessons learned, I will share the key challenges across the many dimensions of AI. This talk will conclude with important development and research directions to advance the field of AI in an environmentally-responsible and sustainable manner.
Bio
Carole-Jean Wu is currently a Research Scientist and Manager at Facebook AI Research. Her research lies in the domain of computer architecture. Her work has included designing energy- and memory-efficient systems, building and optimizing systems for machine learning execution at-scale. 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 a Director. She received her M.A. and Ph.D. from Princeton and B.Sc. from Cornell.