Carole-Jean Wu

Carole-Jean Wu is a Research Scientist at Facebook’s AI Research. Carole's research focuses in the area of Computer and System Architectures, in particular, on designing high-performance and energy-efficient computer systems through domain-specific architectures, heterogeneity-aware optimization, energy harvesting techniques, temperature and energy management for portable electronics, and memory subsystem design and optimization. More recently, Carole-Jean's research has pivoted into designing systems for machine learning execution at-scale, such as for personalized recommender systems and mobile deployment. She chairs the MLPerf Recommendation Benchmark Advisory Board and co-chairs MLPerf Inference, a multi-industry benchmarking consortium for machine learning. She completed her M.A. and Ph.D. degrees from Princeton University and received a B.Sc. degree from Cornell University. She is the recipient of the NSF 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, among a number of Best Paper awards.

Carole-Jean's Publications

May 06, 2020

RESEARCH

Machine Learning at Facebook: Understanding Inference at the Edge

At Facebook, machine learning provides a wide range of capabilities that drive many aspects of user experience including ranking posts, content understanding, object detection and tracking for augmented and virtual reality, speech and text...

Carole-Jean Wu, David Brooks, Kevin Chen, Douglas Chen, Sy Choudhury, Marat Dukhan, Kim Hazelwood, Eldad Isaac, Yangqing Jia, Bill Jia, Tommer Leyvand, Hao Lu, Yang Lu, Lin Qiao, Brandon Reagen, Joe Spisak, Fei Sun, Andrew Tulloch, Peter Vajda, Xiaodong Wang, Yanghan Wang, Bram Wasti, Yiming Wu, Ran Xian, Sungjoo Yoo, Peizhao Zhang,

May 06, 2020

May 06, 2020

RESEARCH

ML APPLICATIONS

The Architectural Implications of Facebook’s DNN-based Personalized Recommendation

The widespread application of deep learning has changed the landscape of computation in data centers. In particular, personalized recommendation for content ranking is now largely accomplished using deep neural networks. However, despite their...

Udit Gupta, Carole-Jean Wu, Xiaodong Wang, Maxim Naumov, Brandon Reagen, David Brooks, Bradford Cottel, Kim Hazelwood, Mark Hempstead, Bill Jia, Hsien-Hsin S. Lee, Andrey Malevich, Dheevatsa Mudigere, Mikhail Smelyanskiy, Liang Xiong, Xuan Zhang,

May 06, 2020