Carole-Jean Wu
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
52
Venues
18
Active years
2011–2026
Best venue rank
A*
Where they publish
Papers
52 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2026 | DATE | Exploring Heterogeneity-Aware Optimizations for Resource Efficient Edge Recommendation. | Yerin Lee, Gyudong Kim, Eunjin Lee, Jeff Zhang, Young-Ho Gong, Young Geun Kim, Carole-Jean Wu |
| 2026 | ISCA | KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta. | Gang Liao, Hongsen Qin, Ying Wang, Alicia Golden, Michael Kuchnik, Yavuz Yetim, Ruichao Xiao, Jia Jiunn Ang, Chunli Fu, Yihan He, Samuel Hsia, Zewei Jiang, Roman Levenstein, Dianshi Li, Liyuan Li, Ajit Mathews, Varna Puvvada, Feng Shi, Nathan Yan, Xiayu Yu, Uladzimir Pashkevich, Matt Steiner, Carole-Jean Wu, Gaoxiang Liu |
| 2026 | ICS | Scaling AI Computing Sustainably: A Journey Towards Sustainable AI. | Carole-Jean Wu |
| 2026 | ISPASS | The xPU-athalon: Quantifying the Competition of AI Acceleration. | Alicia Golden, Carole-Jean Wu, Gu-Yeon Wei, David Brooks |
| 2025 | EMNLP | Demystifying Synthetic Data in LLM Pre-training: A Systematic Study of Scaling Laws, Benefits, and Pitfalls. | Feiyang Kang, Newsha Ardalani, Michael Kuchnik, Youssef Emad, Mostafa Elhoushi, Shubhabrata Sengupta, Shang-wen Li, Ramya Raghavendra, Ruoxi Jia, Carole-Jean Wu |
| 2025 | HPCA | CORDOBA: Carbon-Efficient Optimization Framework for Computing Systems. | Mariam Elgamal, Doug Carmean, Elnaz Ansari, Okay Zed, Ramesh Peri, Srilatha Manne, Udit Gupta, Gu-Yeon Wei, David Brooks, Gage Hills, Carole-Jean Wu |
| 2025 | HPCA | Revisiting Reliability in Large-Scale Machine Learning Research Clusters. | Apostolos Kokolis, Michael Kuchnik, John Hoffman, Adithya Kumar, Parth Malani, Faye Ma, Zachary DeVito, Shubho Sengupta, Kalyan Saladi, Carole-Jean Wu |
| 2024 | ACL | LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding. | Mostafa Elhoushi, Akshat Shrivastava, Diana Liskovich, Basil Hosmer, Bram Wasti, Liangzhen Lai, Anas Mahmoud, Bilge Acun, Saurabh Agarwal, Ahmed Roman, Ahmed A. Aly, Beidi Chen, Carole-Jean Wu |
| 2024 | ICML | CHAI: Clustered Head Attention for Efficient LLM Inference. | Saurabh Agarwal, Bilge Acun, Basil Hosmer, Mostafa Elhoushi, Yejin Lee, Shivaram Venkataraman, Dimitris Papailiopoulos, Carole-Jean Wu |
| 2024 | ISCA | MAD-Max Beyond Single-Node: Enabling Large Machine Learning Model Acceleration on Distributed Systems. | Samuel Hsia, Alicia Golden, Bilge Acun, Newsha Ardalani, Zachary DeVito, Gu-Yeon Wei, David Brooks, Carole-Jean Wu |
| 2024 | ISPASS | 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 |
| 2023 | ASPLOS | Carbon Explorer: A Holistic Framework for Designing Carbon Aware Datacenters. | Bilge Acun, Benjamin C. Lee, Fiodar Kazhamiaka, Kiwan Maeng, Udit Gupta, Manoj Chakkaravarthy, David Brooks, Carole-Jean Wu |
| 2023 | ASPLOS | 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 |
| 2023 | USENIX | 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 P., Ritesh Tijoriwala, Karan Asher, Hao Wu, Aarti Basant, Daniel Ford, Delia David, Nezih Yigitbasi, Pratap Singh, Carole-Jean Wu |
| 2022 | ASPLOS | RecShard: statistical feature-based memory optimization for industry-scale neural recommendation. | Geet Sethi, Bilge Acun, Niket Agarwal, Christos Kozyrakis, Caroline Trippel, Carole-Jean Wu |
| 2022 | DAC | A joint management middleware to improve training performance of deep recommendation systems with SSDs. | Chun-Feng Wu, Carole-Jean Wu, Gu-Yeon Wei, David Brooks |
| 2022 | HPCA | Hercules: Heterogeneity-Aware Inference Serving for At-Scale Personalized Recommendation. | Liu Ke, Udit Gupta, Mark Hempstead, Carole-Jean Wu, Hsien-Hsin S. Lee, Xuan Zhang |
| 2022 | HPCA | SecNDP: Secure Near-Data Processing with Untrusted Memory. | Wenjie Xiong, Liu Ke, Dimitrije Jankov, Michael Kounavis, Xiaochen Wang, Eric Northup, Jie Amy Yang, Bilge Acun, Carole-Jean Wu, Ping Tak Peter Tang, G. Edward Suh, Xuan Zhang, Hsien-Hsin S. Lee |
| 2022 | ISCA | ACT: designing sustainable computer systems with an architectural carbon modeling tool. | Udit Gupta, Mariam Elgamal, Gage Hills, Gu-Yeon Wei, Hsien-Hsin S. Lee, David Brooks, Carole-Jean Wu |
| 2022 | ISCA | Understanding data storage and ingestion for large-scale deep recommendation model training: industrial product. | Mark Zhao, Niket Agarwal, Aarti Basant, Bugra Gedik, Satadru Pan, Mustafa Ozdal, Rakesh Komuravelli, Jerry Pan, Tianshu Bao, Haowei Lu, Sundaram Narayanan, Jack Langman, Kevin Wilfong, Harsha Rastogi, Carole-Jean Wu, Christos Kozyrakis, Parik Pol |
| 2022 | RecSys | Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity. | Kiwan Maeng, Haiyu Lu, Luca Melis, John Nguyen, Mike Rabbat, Carole-Jean Wu |
| 2022 | WSDM | On Sampling Collaborative Filtering Datasets. | Noveen Sachdeva, Carole-Jean Wu, Julian J. McAuley |
| 2021 | ASPLOS | RecSSD: near data processing for solid state drive based recommendation inference. | Mark Wilkening, Udit Gupta, Samuel Hsia, Caroline Trippel, Carole-Jean Wu, David Brooks, Gu-Yeon Wei |
| 2021 | HPCA | Understanding Training Efficiency of Deep Learning Recommendation Models at Scale. | Bilge Acun, Matthew Murphy, Xiaodong Wang, Jade Nie, Carole-Jean Wu, Kim M. Hazelwood |
| 2021 | HPCA | Chasing Carbon: The Elusive Environmental Footprint of Computing. | Udit Gupta, Young Geun Kim, Sylvia Lee, Jordan Tse, Hsien-Hsin S. Lee, Gu-Yeon Wei, David Brooks, Carole-Jean Wu |
| 2021 | ISPASS | Understanding Capacity-Driven Scale-Out Neural Recommendation Inference. | Michael Lui, Yavuz Yetim, zgr zkan, Zhuoran Zhao, Shin-Yeh Tsai, Carole-Jean Wu, Mark Hempstead |
| 2021 | MICRO | RecPipe: Co-designing Models and Hardware to Jointly Optimize Recommendation Quality and Performance. | Udit Gupta, Samuel Hsia, Jeff Zhang, Mark Wilkening, Javin Pombra, Hsien-Hsin Sean Lee, Gu-Yeon Wei, Carole-Jean Wu, David Brooks |
| 2021 | MICRO | AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning. | Young Geun Kim, Carole-Jean Wu |
| 2021 | SmartComp | Energy-Efficient Mapping for a Network of DNN Models at the Edge. | Mehdi Ghasemi, Soroush Heidari, Young Geun Kim, Aaron Lamb, Carole-Jean Wu, Sarma B. K. Vrudhula |
| 2020 | DATE | Emerging Neural Workloads and Their Impact on Hardware. | David Brooks, Martin M. Frank, Tayfun Gokmen, Udit Gupta, Xiaobo Sharon Hu, Shubham Jain, Ann Franchesca Laguna, Michael T. Niemier, Ian O'Connor, Anand Raghunathan, Ashish Ranjan, Dayane Reis, Jacob R. Stevens, Carole-Jean Wu, Xunzhao Yin |
| 2020 | GECCO | GEVO-ML: a proposal for optimizing ML code with evolutionary computation. | Jhe-Yu Liou, Xiaodong Wang, Stephanie Forrest, Carole-Jean Wu |
| 2020 | HPCA | The Architectural Implications of Facebook's DNN-Based Personalized Recommendation. | Udit Gupta, Carole-Jean Wu, Xiaodong Wang, Maxim Naumov, Brandon Reagen, David Brooks, Bradford Cottel, Kim M. Hazelwood, Mark Hempstead, Bill Jia, Hsien-Hsin S. Lee, Andrey Malevich, Dheevatsa Mudigere, Mikhail Smelyanskiy, Liang Xiong, Xuan Zhang |
| 2020 | ISCA | DeepRecSys: A System for Optimizing End-To-End At-Scale Neural Recommendation Inference. | Udit Gupta, Samuel Hsia, Vikram Saraph, Xiaodong Wang, Brandon Reagen, Gu-Yeon Wei, Hsien-Hsin S. Lee, David Brooks, Carole-Jean Wu |
| 2020 | ISCA | RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing. | Liu Ke, Udit Gupta, Benjamin Youngjae Cho, David Brooks, Vikas Chandra, Utku Diril, Amin Firoozshahian, Kim M. Hazelwood, Bill Jia, Hsien-Hsin S. Lee, Meng Li, Bert Maher, Dheevatsa Mudigere, Maxim Naumov, Martin Schatz, Mikhail Smelyanskiy, Xiaodong Wang, Brandon Reagen, Carole-Jean Wu, Mark Hempstead, Xuan Zhang |
| 2020 | ISCA | MLPerf Inference Benchmark. | Vijay Janapa Reddi, Christine Cheng, David Kanter, Peter Mattson, Guenther Schmuelling, Carole-Jean Wu, Brian Anderson, Maximilien Breughe, Mark Charlebois, William Chou, Ramesh Chukka, Cody Coleman, Sam Davis, Pan Deng, Greg Diamos, Jared Duke, Dave Fick, J. Scott Gardner, Itay Hubara, Sachin Idgunji, Thomas B. Jablin, Jeff Jiao, Tom St. John, Pankaj Kanwar, David Lee, Jeffery Liao, Anton Lokhmotov, Francisco Massa, Peng Meng, Paulius Micikevicius, Colin Osborne, Gennady Pekhimenko, Arun Tejusve Raghunath Rajan, Dilip Sequeira, Ashish Sirasao, Fei Sun, Hanlin Tang, Michael Thomson, Frank Wei, Ephrem Wu, Lingjie Xu, Koichi Yamada, Bing Yu, George Yuan, Aaron Zhong, Peizhao Zhang, Yuchen Zhou |
| 2020 | MICRO | AutoScale: Energy Efficiency Optimization for Stochastic Edge Inference Using Reinforcement Learning. | Young Geun Kim, Carole-Jean Wu |
| 2019 | HPCA | Understanding the Future of Energy Efficiency in Multi-Module GPUs. | Akhil Arunkumar, Evgeny Bolotin, David W. Nellans, Carole-Jean Wu |
| 2019 | HPCA | Machine Learning at Facebook: Understanding Inference at the Edge. | Carole-Jean Wu, David Brooks, Kevin Chen, Douglas Chen, Sy Choudhury, Marat Dukhan, Kim M. 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 |
| 2019 | ICSE | Genetic improvement of GPU code. | Jhe-Yu Liou, Stephanie Forrest, Carole-Jean Wu |
| 2018 | HPCA | LATTE-CC: Latency Tolerance Aware Adaptive Cache Compression Management for Energy Efficient GPUs. | Akhil Arunkumar, Shin-Ying Lee, Vignesh Soundararajan, Carole-Jean Wu |
| 2018 | ISPASS | DORA: Optimizing Smartphone Energy Efficiency and Web Browser Performance under Interference. | Davesh Shingari, Akhil Arunkumar, Benjamin Gaudette, Sarma B. K. Vrudhula, Carole-Jean Wu |
| 2017 | ISCA | MCM-GPU: Multi-Chip-Module GPUs for Continued Performance Scalability. | Akhil Arunkumar, Evgeny Bolotin, Benjamin Y. Cho, Ugljesa Milic, Eiman Ebrahimi, Oreste Villa, Aamer Jaleel, Carole-Jean Wu, David W. Nellans |
| 2016 | HPCA | Improving smartphone user experience by balancing performance and energy with probabilistic QoS guarantee. | Benjamin Gaudette, Carole-Jean Wu, Sarma B. K. Vrudhula |
| 2016 | ICCD | Ctrl-C: Instruction-Aware Control Loop Based Adaptive Cache Bypassing for GPUs. | Shin-Ying Lee, Carole-Jean Wu |
| 2015 | ISCA | CAWA: coordinated warp scheduling and cache prioritization for critical warp acceleration of GPGPU workloads. | Shin-Ying Lee, Akhil Arunkumar, Carole-Jean Wu |
| 2015 | ISPASS | A study of mobile device utilization. | Cao Gao, Anthony Gutierrez, Madhav Rajan, Ronald G. Dreslinski, Trevor N. Mudge, Carole-Jean Wu |
| 2014 | DAC | Quantitative Analysis of Control Flow Checking Mechanisms for Soft Errors. | Aviral Shrivastava, Abhishek Rhisheekesan, Reiley Jeyapaul, Carole-Jean Wu |
| 2014 | ICCD | ReMAP: Reuse and memory access cost aware eviction policy for last level cache management. | Akhil Arunkumar, Carole-Jean Wu |
| 2014 | ISPASS | Characterizing the latency hiding ability of GPUs. | Shin-Ying Lee, Carole-Jean Wu |
| 2011 | ISPASS | Characterization and dynamic mitigation of intra-application cache interference. | Carole-Jean Wu, Margaret Martonosi |
| 2011 | MICRO | SHiP: signature-based hit predictor for high performance caching. | Carole-Jean Wu, Aamer Jaleel, William Hasenplaugh, Margaret Martonosi, Simon C. Steely Jr., Joel S. Emer |
| 2011 | MICRO | PACMan: prefetch-aware cache management for high performance caching. | Carole-Jean Wu, Aamer Jaleel, Margaret Martonosi, Simon C. Steely Jr., Joel S. Emer |