Carole-Jean Wu is currently a Research Scientist and Technical Lead Manager at Meta AI / FAIR. She is a founding member and a Vice President of MLCommons – a non-profit organization that aims to accelerate machine learning innovations 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. Her work spans across datacenter infrastructures and edge systems, such as developing energy- and memory-efficient systems and microarchitectures, 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 and responsible AI technologies.
Dr. Wu's work has been recognized with several awards, including IEEE Micro Top Picks and ACM / IEEE Best Paper Awards. In addition, her work has been featured at the MLPerf Inference v0.5 Launch and Results, MaskRCNN2Go for MLPerf, and from Understanding Computing's Carbon Footprint to Designing Low-Carbon Computers of Tech @ Meta, and Bloomberg Green.
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. She was the Program Co-Chair of the Conference on Machine Learning and Systems (MLSys 2022), the Program Chair of the IEEE International Symposium on Workload Characterization (IISWC 2018), and the Editor for the IEEE MICRO Special Issue on Environmentally Sustainable Computing. She received her M.A. and Ph.D. degrees in Electrical Engineering from Princeton University and the B.Sc. degree in Electrical and Computer Engineering from Cornell University.
[Google Scholar] [dblp]
My work sits in the intersection of computer architecture and machine learning with the following emphasis:
System design and optimization for deep learning
Learning-based approaches for system design and optimization
High-performance and energy-efficient heterogeneous CPU+GPU systems
Performance quality modeling and energy efficiency optimization for mobile systems
Memory system optimization
Sustainable computing via carbon-efficient system design and management, energy harvesting and temperature-aware management for portable electronics
Check out my work on Socio-Technological Challenges and Opportunities: Paths Forward, Sustainable AI: Environmental Implications, Challenges and Opportunities, and thoughts on inclusive approaches to technological innovations: Think Globally, Design Deliberately: Taking an Inclusive Approach to Innovation.
Honors and Awards
2023 IEEE Micro Top Picks for ACT: Designing Sustainable Computer Systems with an Architectural Carbon Modeling Tool
2022 ACM Conference on Recommender Systems Best Paper Award Finalist for Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity
2022 IEEE Micro Top Picks for Chasing Carbon: The Elusive Environmental Footprint of Computing
2021 IEEE Micro Top Picks for The Vision Behind MLPerf: Understanding AI Inference Performance
2021 IEEE Micro Top Picks Honorable Mention for DeepRecSys: A System for Optimizing End-to-end At-scale Neural Recommendation Inference
2020 Distinction of Honorable Mention of the CRA Anita Borg Early Career Award
2019 Facebook AI Infrastructure Mentorship Award
2019 ACM/IEEE ICSE Genetic Improvement on Software Best Paper Award for Genetic Improvement for GPU Code
2018 IEEE ITHERM Best Paper Award for Designing a Temperature Model to Understand the Thermal Challenges of Portable Computing Platforms
2017 NSF CAREER Award
2017 IEEE Young Engineer of the Year Award
2015 IEEE Best of Computer Architecture Letters for Architectural Thermal Energy Harvesting Opportunities for Sustainable Computing
2013 SFAz Bisgrove CAREER Award
2011 Intel PhD Fellowship
2011 IEEE ISPASS Best Paper Nomination for Characterization and Dynamic Mitigation of Intra-Application Cache Interference
2009 Princeton Excellence in Leadership Award
2006 Princeton PhD Fellowship
Tectonic-Shift: A Composite Storage Fabric for Large-Scale ML Training [paper]
To appear at USENIX ATC 2023
Mark Zhao, Satadru Pan, Niket Agarwal, Zhaoduo Wen, David Xu, Anand Natarajan, Pavan Kumar, Shiva Shankar, Ritesh Tijoriwala, Karan Asher, Hao Wu, Aarti Basant, Daniel Ford, Delia David, Nezih Yigitbasi, Pratap Singh, Carole-Jean Wu, Christos Kozyrakis.
RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure [paper]
To appear at MLSys-2023
Mark Zhao, Dhruv Choudhary, Devashish Tyagi, Ajay Somani, Max Kaplan, Sung-Han Lin, Sarunya Pumma, Jongsoo Park, Aarti Basant, Niket Agarwal, Carole-Jean Wu, Christos Kozyrakis.
High Capacity Learning for Recommendation and Ranking via Practical Federated Ensemble Learning [paper]
Meisam Hejazinia, Dzmitry Huba, Ilias Leontiadis, Kiwan Maeng, Mani Malek, Luca Melis, Ilya Mironov, Milad Nasr, Kaikai Wang, Carole-Jean Wu.
Understanding Scaling Laws for Recommendation Models [paper]
Newsha Ardalani, Carole-Jean Wu, Zeliang Chen, Bhargav Bhushanam, Adnan Aziz.
FlexShard: Flexible Sharding for Industry-Scale Sequence Recommendation Models [paper]
Geet Sethi, Pallab Bhattacharya, Dhruv Choudhary, Carole-Jean Wu, Christos Kozyrakis.
Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference [paper]
Haiyang Huang, Newsha Ardalani, Anna Sun, Liu Ke, Hsien-Hsin S. Lee, Anjali Sridhar, Shruti Bhosale, Carole-Jean Wu, Benjamin Lee.
GreenScale: Carbon-Aware Systems for Edge Computing [paper]
Young Geun Kim, Udit Gupta, Andrew McCrabb, Yonglak Son, Valeria Bertacco, David Brooks, Carole-Jean Wu.
Mariam Elgamal, Doug Carmean, Elnaz Ansari, Okay Zed, Ramesh Peri, Srilatha Manne, Udit Gupta, Gu-Yeon Wei, David Brooks, Gage Hills, Carole-Jean Wu.
Green Federated Learning [paper]
Ashkan Yousefpour, Shen Guo, Ashish Shenoy, Sayan Ghosh, Pierre Stock, Kiwan Maeng, Schalk-Willem Kruger, Mike Rabbat, Carole-Jean Wu, Ilya Mironov.
READ: Recurrent Adaptation of Large Transformers [paper]
Sid Wang, John Nguyen, Ke Li, Carole-Jean Wu.
[ASPLOS-2023] Carbon Explorer: A Holistic Approach for Designing Carbon Aware Datacenters
Bilge Acun, Benjamin C. Lee, Fiodar Kazhamiaka, Kiwan Maeng, Manoj Chakkaravarthy, Udit Gupta, David Brooks, Carole-Jean Wu. [code]
[ASPLOS-2023] MP-Rec: Hardware-Software Co-Design to Enable Multi-Path Recommendation
Samuel Hsia, Udit Gupta, Bilge Acun, Newsha Ardalani, Pan Zhong, Gu-Yeon Wei, David Brooks, Carole-Jean Wu.
[NeurIPS-2022] Infinite Recommendation Networks: A Data-Centric Approach
Noveen Sachdeva, Mehak Preet Dhaliwal, Carole-Jean Wu, Julian McAuley. [code: Infinite AE; Data-Distill]
Kiwan Maeng, Haiyu Lu, Luca Melis, John Nguyen, Mike Rabbat, Carole-Jean Wu. [code]
Best Paper Award Finalist
Udit Gupta, Mariam Elgamal, Gage Hills, Gu-Yeon Wei, Hsien-Hsin S. Lee, David Brooks, Carole-Jean Wu. [code]
IEEE Micro Top Picks
M. Zhao, N. Agarwal, A. Basant, B. Gedik, S. Pan, M. Ozdal, R. Komuravelli, J. Pan, T. Bao, H. Lu, S. Narayanan, J. Langman, K. Wilfong, H. Rastogi, C.-J. Wu, C. Kozyrakis, P. Pol.
C.-J. Wu, R. Raghavendra, U. Gupta, B. Acun, N. Ardalani, K. Maeng, F. A. Behram, J. Huang, C. Bai, M. Gschwind, A. Gupta, M. Ott, A. Melnikov, S. Candido, D. Brooks, G. Chauhan, B. Lee, H.-S. S. Lee, B. Akyildiz, M. Balandat, J. Spisak, R. Jain, M. Rabbat, K. Hazelwood.
D. Huba, J. Nguyen, K. Malik, R. Zhu, M. Rabbat, A. Yousefpour, C.-J. Wu, G. Zhan, P. Ustinov, H. Srinivas, K. Wang, A. Shoumikhin, J. Min, M. Malek.
Geet Sethi, Bilge Acun, Niket Agarwal, Christos Kozyrakis, Caroline Trippel, Carole-Jean Wu.
[WSDM-2022] On Sampling Collaborative Filtering Datasets
Noveen Sachdeva, Carole-Jean Wu, and Julian McAuley. [code]
Young Geun Kim and Carole-Jean Wu.
U. Gupta, S. Hsia, J. Zhang, M. Wilkening, J. Pombra, H.-S. Lee, G. Wei, C.-J. Wu, and D. Brooks.
Chunxing Yin, Bilge Acun, Xing Liu, and Carole-Jean Wu. [code]
MLSys Outstanding Paper Award
K. Maeng, S. Bharuka, I. Gao, M. Jeffrey, V. Saraph, B.-Y. Su, C. Trippel, J. Yang, M. Rabbat, B. Lucia, and C.-J. Wu.
M. Wilkening, U. Gupta, S. Hsia, C. Trippel, C.-J. Wu, D. Brooks, G.-Y. Wei.
U. Gupta, Y. Kim, S. Lee, J. Tse, H.-H. Lee, G. Wei, D. Brooks, and C.-J. Wu.
IEEE Micro Top Picks
Bilge Acun, Matthew Murphy, Xiaodong Wang, Jade Nie, Carole-Jean Wu, and Kim Hazelwood.
Young Geun Kim and Carole-Jean Wu.
U. Gupta, S. Hsia, V. Saraph, X. Wang, B. Reagen, G.-Y. Wei, H.-S. Lee, D. Brooks, and C.-J. Wu. [code]
IEEE Micro Top Picks Honorable Mention
L. Ke, U. Gupta, B. Cho, D. Brooks, V. Chandra, U. Diril, A. Firoozshahian, K. Hazelwood, B. Jia, H.-S. Lee, M. Li, B. Maher, D. Mudigere, M. Naumov, M. Schatz, M. Smelyanskiy, X. Wang, B. Reagen, C.-J. Wu, M. Hempstead, X. Zhang.
[ISCA-2020] MLPerf Inference Benchmark
V. Reddi, C. Cheng, D. Kanter, P. Mattson, G. Schmuelling, C.-J. Wu, B. Anderson, M. Breughe, M. Charlebois, W. Chou, R. Chukka, C. Coleman, S. Davis, P. Deng, G. Diamos, J. Duke, D. Fick, J. Gardner, I. Hubara, S. Idgunji, T. Jablin, J. Jiao, T. St. John, P. Kanwar, D. Lee, J. Liao, A. Lokhmotov, F. Massa, P. Meng, P. Micikevicius, C. Osborne, G. Pekhimenko, A. Rajan, D. Sequeira, A. Sirasao, F. Sun, H. Tang, M. Thomson, F. Wei, E. Wu, L. Xu, K. Yamada, B. Yu, G. Yuan, A. Zhong, P. Zhang, Y. Zhou. [code]
IEEE Micro Top Picks -- The Vision Behind MLPerf: Understanding AI Inference Performance
[MLSys-2020] MLPerf Training Benchmark
P. Mattson, C. Cheng, C. Coleman, G. Diamos, P. Micikevicius, D. Patterson, H. Tang, G.-Y. Wei, P. Ballis, V. Bittorf, D. Brooks, D. Chen, D. Dutta, U. Gupta, K. Hazelwood, A. Hock, X. Huang, B. Jia, D. Kang, N. Kumar, J. Liao, G. Ma, D. Narayanan, T. Oguntebi, G. Pekhimenko, L. Pentecost, V. Reddi, T. Robie, T. St. John, C.-J. Wu, L. Xu, C. Young, M. Zaharia. [code]
U. Gupta, C.-J. Wu, X. Wang, M. Naumov, B. Reagen, D. Brooks, B. Cottel, K. Hazelwood, M. Hempstead, B. Jia, H.-H. Lee, A. Malevich, D. Mudigere, M. Smelyanskiy, L. Xiong, X. Zhang.
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.
Akhil Arunkumar, Evgeny Bolotin, David Nellans, and Carole-Jean Wu.
[HPCA-2018] LATTE-CC: Latency Tolerance Aware Adaptive Cache Compression Management for Energy Efficient GPUs
Akhil Arunkumar, Shin-Ying Lee, Vignesh Soundararajan, and Carole-Jean Wu. [paper]
[ISCA-2017] MCM-GPU: Multi-Chip-Module GPUs for Continued Performance Scalability
Akhil Arunkumar, Evgeny Bolotin, Benjamin Cho, Ugljesa Milic, Eiman Ebrahimi, Oreste Villa, Aamer Jaleel, Carole-Jean Wu, and David Nellans. [paper]
[HPCA-2016] Improving Smartphone/Mobile User Experience by Balancing Performance and Energy with Probabilistic QoS Guarantee
Benjamin Gaudette, Carole-Jean Wu, and Sarma Vrudhula. [paper]
[ISCA-2015] CAWA: Coordinated Warp Scheduling and Cache Prioritization for Critical Warp Acceleration for GPGPU Workloads
Shin-Ying Lee, Akhil Arunkumar, and Carole-Jean Wu. [paper]
[PACT-2014] CAWS: Criticality-Aware Warp Scheduling for GPGPU Workloads
Shin-Ying Lee and Carole-Jean Wu. [paper]
[MICRO-2011] PACMan: Prefetch-Aware Cache Management for High Performance Caching
Carole-Jean Wu, Aamer Jaleel, Will Hasenplaugh, Margaret Martonosi, Simon Steely Jr., and Joel Emer. [paper]
[MICRO-2011] SHiP: Signature-Based Hit Predictor for High Performance Caching
Carole-Jean Wu, Aamer Jaleel, Margaret Martonosi, Simon Steely Jr., and Joel Emer. [paper]
Industry Initiatives and Open Source Software
Fair and useful benchmarks for measuring training and inference performance of machine learning hardware, software, and services
CVPR-LPCV [Slide Deck][Talk]
Embedded Vision Summit [Slide Deck][Talk]
CLEAR: Computing Landscapes for Environmental Accountability and Responsibility
PERSONAL: Personalized Recommendation Systems and Algorithms
AutoScale: Energy Efficiency Optimization of Stochastic Edge Inference Using Reinforcement Learning
GEVO: Genetic Improvement of GPU Code
DORA: Optimizing Smartphone Energy Efficiency and Web Browser Performance under Interference
MobileBench: Performance, Energy Characterizations and Architectural Implications of an Emerging Mobile Platform Benchmark Suite
Mentorship of Student and Post-Doctoral Researchers at FAIR
Mariam Elgamal (2022; PhD researcher from Harvard University)
Mark Zhao (2021 — 2022; PhD researcher from Stanford University)
Samuel Hsia (2021 — present; PhD researcher from Harvard University)
Geet Sethi (2021 PhD researcher from Stanford University)
Kiwan Maeng (2020 PhD researcher from CMU; 2021-22 post-doctoral researcher at FAIR)
Chunxing Yin [with Bilge Acun] (2020 PhD researcher from Georgia Tech)
Mike Lui (2019-20 PhD researcher from Drexel University)
Emma Yu Wang [with Xiaodong Wang] (2019 PhD researcher from Harvard University; now at Google Research)
Udit Gupta (2018 — 2022; PhD researcher from Harvard University)
Undergraduate/MS/PhD Advisees and Post-Doctoral Researchers
Jhe-Yu Liou (PhD candidate; 2015 — present)
Young-Geun Kim (Post-doctoral Researcher 2019 — 2020) [First employment: Soongsil University; now Assistant Professor at Korea University]
Akhil Arunkumar (PhD 2018) [First employment: Samsung Austin R&D Center; now at AMD] [Memory Subsystem Optimization Techniques for Modern High-Performance General-Purpose Processors]
Viraj Wadhwa (High school intern from BASIS Chandler Primary, 2017-18. Now an undergraduate student at UT-Austin) [Improving Image Recognition with Tensor Flow API for Autonomous Driving]
Shin-Ying Lee (PhD 2017) [First employment: Samsung Austin R&D Center; AMD; now at Amazon] [Intelligent Scheduling and Memory Management Techniques For Modern GPU Architectures]
Outstanding Computer Engineering PhD Graduate Student Award
TJ Smith (Research Experience for Undergraduates (REU) from Princeton EE; 2017)
Katherine Hann (High school intern from Xavier College Preparatory High School, 2017. Now an undergraduate student at University of Pennsylvania) [Designing A Paired Robotic Car Indoor Navigation and Tracking System]
Rashmi Athavale (High school intern from Hamilton High School, 2017. Now an undergraduate student at Georgia Tech) [Designing A Paired Robotic Car Indoor Navigation and Tracking System]
Benjamin Gaudette (PhD 2017; co-advised with Prof. Sarma Vrudhula) [First employment: Benchmark Electronics; now at Intel] [An Intelligent Framework for Energy-aware Mobile Computing Subject to Stochastic System Dynamics]
Ying-Ju Yu (Post-doctoral Researcher, 2016-17) [First employment: Intel]
Kody Stribrny (BS 2017; co-advised with Prof. Sarma Vrudhula) [First employment: Amazon] [Honors Thesis: Mobile Waterway Monitor]
Davesh Shingari (MS 2016) [First employment: Marvell] [Memory Interference Characterization and Mitigation for Heterogeneous Smartphones]
Soochan Lee (PhD 2015; co-advised with Prof. Patrick E. Phelan) [First employment: LG Electronics] [A Study of Latent Heat of Vaporization in Aqueous Nanofluids]
Ryan Brazones (BS 2014) [First employment: Intel]
Dhinakaran Pandiyan (MS 2014) [First employment: Intel] [Data Movement Energy Characterization of Emerging Smartphone Workloads for Mobile Platforms]
Received the Outstanding Computer Engineering MS Graduate Student Award
Amrit Panda (PhD 2014; co-advised with Prof. Karam S. Chatha) [First employment: Qualcomm Research; now at Microsoft] [StreamWorks: An Energy-efficient Embedded Co-processor for Stream Computing]
IEEE Micro Magazine Special Issue on Environmentally Sustainable Computing, 2022-23.
IEEE Technical Committee on Computer Architecture (TCCA), 2017-18.
Conference Steering Committee
IEEE Intl. Symp. on Performance Analysis of Systems and Software (ISPASS), 2018-23.
IEEE Intl. Symp. on Workload Characterization (IISWC), 2018-23.
Award Selection Committee
IEEE TCCA Young Computer Architect Award, 2021-23.
Technical Program Chair
Conference on Machine Learning and Systems (MLSys), 2022.
IEEE Intl. Symp. on Workload Characterization (IISWC), 2018.
Technical Program Committee
HotCarbon 2022, 2023
HPCA 2020; 2014-17
IISWC 2019; 2013-17
MICRO 2014; 2016-17
Journal Editorial Board
IEEE Micro 2019-23
IEEE Computer Architecture Letters 2019-22
CRA-Widening Participation (WP) Career Mentoring Workshop
- Finding a Research Topic with Soha Hassoun, 2023.
- Strategies for Your Career with Amber Settle, 2020.