CISE Seminar: Carole-Jean Wu, Meta AI
- Starts: 3:00 pm on Friday, March 17, 2023
- Ends: 4:00 pm on Friday, March 17, 2023
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 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. She is a founding member and a Vice President of MLCommons – a non-profit organization that aims to accelerate machine learning 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, She was an Associate Professor at ASU.
Dr. Wu’s 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. Her work has been recognized with several awards, including IEEE Micro Top Picks and ACM/IEEE Best Paper Awards. Dr. Wu 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, Facebook AI Infrastructure Mentorship Award, and HPCA and IISWC Hall of Fame.
Dr. Wu was the Program Co-Chair of the Conference on Machine Learning and Systems (MLSys), the Program Chair of the IEEE International Symposium on Workload Characterization (IISWC), and the Editor for the IEEE MICRO Special Issue on Environmentally Sustainable Computing. She received her M.A. and Ph.D. from Princeton and B.Sc. from Cornell.
Faculty Host: Ayse Coskun
Student Host: Zili Wang
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