Carole-Jean Wu is currently a Research Scientist and Technical Lead Manager at Meta AI / FAIR. Her expertise sits at the intersection of computer architecture and machine learning. Her work spans across datacenter infrastructures and edge systems, including developing energy- and memory-efficient systems and microarchitectures, optimizing systems for machine learning execution at-scale, and designing learning-based approaches for system design, resource management and optimization. She is passionate about pathfinding and tackling system challenges to enable efficient, responsible AI computing. Carole-Jean chairs the MLPerf Recommendation Benchmark Advisory Board, co-chaired MLPerf Inference, and serves on the MLCommons Board as the Vice President and a Director.

Prior to Facebook/Meta, Carole-Jean was a tenured Associate Professor at ASU. She received her M.A. and Ph.D. from Princeton and B.Sc. from Cornell. 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. In addition, her research has been recognized with several awards, including IEEE Micro Top Picks and IEEE/ACM Best Paper Awards. Her work has been featured for the MLPerf Inference v0.5 Launch and Results, MaskRCNN2Go for MLPerf, and from Understanding Computing's Carbon Footprint to Designing Low-Carbon Computers.

[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

  • Energy harvesting and temperature-aware management for portable electronics

Check out our recent article 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

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


  1. 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.

  1. Infinite Recommendation Networks: A Data-Centric Approach [paper][code: infinite AE; data-distill]

Accepted to the Thirty-Sixth Conference on Neural Information Processing Systems (NeurIPS-2022)

Noveen Sachdeva, Mehak Preet Dhaliwal, Carole-Jean Wu, Julian McAuley.

  1. A Holistic Approach for Designing Carbon Aware Datacenters [paper][code]

Accepted to ASPLOS-2023

Bilge Acun, Benjamin C. Lee, Fiodar Kazhamiaka, Kiwan Maeng, Manoj Chakkaravarthy, Udit Gupta, David Brooks, Carole-Jean Wu.

  1. Understanding the Power of Evolutionary Computation for GPU Code Optimization [paper]

Accepted to IISWC-2022

Jhe-Yu Liou, Muaaz Awan, Steven Hofmeyr, Stephanie Forrest, Carole-Jean Wu.

  1. FedGPO: Characterizing and Designing for Efficient Federated Learning using Heterogeneity-Aware Global Parameter Optimization

Accepted to IISWC-2022

Young Geun Kim and Carole-Jean Wu.

  1. Understanding Scaling Laws for Recommendation Models [paper]

Newsha Ardalani, Carole-Jean Wu, Zeliang Chen, Bhargav Bhushanam, Adnan Aziz

Selected Publications

Kiwan Maeng, Haiyu Lu, Luca Melis, John Nguyen, Mike Rabbat, Carole-Jean Wu. [code]

Udit Gupta, Mariam Elgamal, Gage Hills, Gu-Yeon Wei, Hsien-Hsin S. Lee, David Brooks, Carole-Jean Wu. [code]

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]

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 — present; PhD researcher from Harvard University)

  • Mark Zhao (2021 — present; 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)

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 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]

  • 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]

  • Shin-Ying Lee (PhD 2017) [First employment: Samsung Austin R&D Center; now at AMD] [Intelligent Scheduling and Memory Management Techniques For Modern GPU Architectures]

Received the Outstanding Computer Engineering PhD Graduate Student Award

  • 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]

Professional Service

Executive Committee

  • 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

  • ISCA 2014-21

  • MLSys 2020-21

  • 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