Carole-Jean Wu is a Director of AI Research at Meta. 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, Dr. Wu was a professor at ASU. She earned her M.A. and Ph.D. degrees in Electrical Engineering from Princeton University and a B.Sc. degree in Electrical and Computer Engineering from Cornell University. 

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 by MLPerf Inference v0.5 Launch and Results, MaskRCNN2Go for MLPerf, by Tech @ Meta on Understanding Computing's Carbon Footprint and Designing Low-Carbon Computers, by Bloomberg Green, by the Atlantic, and by the HiPEAC blog --- to minimize computing’s carbon footprint, the first step is to quantify lifecycle emissions.

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 currently serves on the ACM SIGARCH/SIGMICRO CARES committee. 

[Google Scholar]  [dblp]


My work sits in the intersection of computer architecture and machine learning with the following emphasis:

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.

If you are interested in learning more about Designing Computer Systems for Sustainability, check out my course on Designing Computer Systems for Sustainability for HiPEAC's Summer School. The course includes 

Honors and Awards

2024 Carbon Explorer: A Holistic Approach for Designing Carbon Aware Datacenters selected for IEEE Micro Top Picks Honorable Mention 

2024 MP-Rec: Hardware-Software Co-Design to Enable Multi-Path Recommendation selected for IEEE Micro Top Picks Honorable Mention 

2023 2020 ISCA Paper Selected for Inclusion in ISCA@50 25-Year Retrospective: 1996-2020: MLPerf Inference Benchmark (Retrospective: MLPerf)

One of the 98 papers (out of 1077) selected as one of the most significant and exciting papers from the ACM/IEEE International Symposium on Computer Architecture from 1996 -- 2020.

2023 ACT: Designing Sustainable Computer Systems with an Architectural Carbon Modeling Tool selected for IEEE Micro Top Picks

2022 Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity selected for ACM Conference on Recommender Systems Best Paper Award Finalist

2022 Chasing Carbon: The Elusive Environmental Footprint of Computing selected for IEEE Micro Top Picks

2021 MLPerf Inference Benchmark selected for IEEE Micro Top Picks (Article: The Vision Behind MLPerf: Understanding AI Inference Performance)

2021 DeepRecSys: A System for Optimizing End-to-end At-scale Neural Recommendation Inference selected for IEEE Micro Top Picks Honorable Mention

2020 Distinction of Honorable Mention of the CRA Anita Borg Early Career Award

2019 Facebook AI Infrastructure Mentorship Award

2019 Genetic Improvement for GPU Code selected for ACM/IEEE ICSE Genetic Improvement on Software Best Paper Award

2018 Designing a Temperature Model to Understand the Thermal Challenges of Portable Computing Platforms selected for IEEE ITHERM Best Paper Award

2017 NSF CAREER Award

2017 IEEE Young Engineer of the Year Award

2015 Architectural Thermal Energy Harvesting Opportunities for Sustainable Computing selected for IEEE Best of Computer Architecture Letters

2013 SFAz Bisgrove CAREER Award

2011 Intel PhD Fellowship

2011 Characterization and Dynamic Mitigation of Intra-Application Cache Interference nominated for IEEE ISPASS Best Paper Nomination

2009 Princeton Excellence in Leadership Award 

2006 Princeton PhD Fellowship


[ISCA-2024] Exploring System-Aware Parallelization for Efficient Large-Scale Machine Learning

Samuel Hsia, Alicia Golden, Bilge Acun, Newsha Ardalani, Zachary DeVito, Gu-Yeon Wei, David Brooks, Carole-Jean Wu.

[MLSys-2024] HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learning

Gyudong Kim, Mehdi Ghasemi, Soroush Heidari, Seungryong Kim, Young Geun Kim, Sarma Vrudhula, Carole-Jean Wu.

[ISPASS-2024] Generative AI Beyond LLMs: System Implications of Multi-Modal Generation

Alicia Golden, Samuel Hsia, Fei Sun, Bilge Acun, Basil Hosmer, Yejin Lee, Zachary DeVito, Jeff Johnson, Gu-Yeon Wei, David Brooks, Carole-Jean Wu.

Selected Publications

[USENIX-ATC 2023]  Tectonic-Shift: A Composite Storage Fabric for Large-Scale ML Training

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. 

[MLSys-2023] RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure

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.

[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]

IEEE Micro Top Picks Honorable Mention 

[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.

IEEE Micro Top Picks Honorable Mention 

[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.

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.

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

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]

ACT: Architectural Carbon Modeling Tool

Designing low-carbon computers with an architectural carbon modeling tool (Tech @ Meta article)

Carbon Explorer: Designing Sustainable Datacenter Computing 

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

Undergraduate/MS/PhD Advisees and Post-Doctoral Researchers

Thesis: Automatic Program Optimization by Semantic Relaxation for Parallel Processing Accelerators

Thesis: Memory Subsystem Optimization Techniques for Modern High-Performance General-Purpose Processors

Thesis: Intelligent Scheduling and Memory Management Techniques for Modern GPU Architectures

Outstanding Computer Engineering PhD Graduate Student Award

Thesis: An Intelligent Framework for Energy-aware Mobile Computing Subject to Stochastic System Dynamics

Thesis: Memory Interference Characterization and Mitigation for Heterogeneous Smartphones

Thesis: A Study of Latent Heat of Vaporization in Aqueous Nanofluids

Thesis: Data Movement Energy Characterization of Emerging Smartphone Workloads for Mobile Platforms

Outstanding Computer Engineering MS Graduate Student Award

Thesis: StreamWorks: An Energy-efficient Embedded Co-processor for Stream Computing

Professional Service

Journal Editor 

Executive Committee

Steering Committee

Award Selection Committee

Technical Program Chair

Technical Program Committee 

Journal Editorial Board

CRA-Widening Participation (WP) Career Mentoring Workshop