default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDCarole-Jean Wu
Person information
- affiliation: Facebook AI Research
- affiliation: Arizona State University, AZ, USA
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[c67]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:
LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding. ACL (1) 2024: 12622-12642- [c66]Mubashara Akhtar
, Omar Benjelloun, Costanza Conforti, Pieter Gijsbers, Joan Giner-Miguelez, Nitisha Jain, Michael Kuchnik, Quentin Lhoest, Pierre Marcenac, Manil Maskey, Peter Mattson, Luis Oala, Pierre Ruyssen, Rajat Shinde, Elena Simperl, Goeffry Thomas, Slava Tykhonov, Joaquin Vanschoren, Jos van der Velde, Steffen Vogler, Carole-Jean Wu:
Croissant: A Metadata Format for ML-Ready Datasets. DEEM@SIGMOD 2024: 1-6 - [c65]Saurabh Agarwal, Bilge Acun, Basil Hosmer, Mostafa Elhoushi, Yejin Lee, Shivaram Venkataraman, Dimitris Papailiopoulos, Carole-Jean Wu:
CHAI: Clustered Head Attention for Efficient LLM Inference. ICML 2024 - [c64]Samuel Hsia, Alicia Golden, Bilge Acun, Newsha Ardalani, Zachary DeVito, Gu-Yeon Wei, David Brooks, Carole-Jean Wu:
MAD-Max Beyond Single-Node: Enabling Large Machine Learning Model Acceleration on Distributed Systems. ISCA 2024: 818-833 - [c63]Alicia Golden, Samuel Hsia, Fei Sun, Bilge Acun, Basil Hosmer, Yejin Lee, Zachary DeVito, Jeff Johnson, Gu-Yeon Wei, David Brooks, Carole-Jean Wu:
Generative AI Beyond LLMs: System Implications of Multi-Modal Generation. ISPASS 2024: 257-267 - [c62]Gyudong Kim, Mehdi Ghasemi, Soroush Heidari, Seungryong Kim, Young Geun Kim, Sarma B. K. Vrudhula, Carole-Jean Wu:
HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learning. MLSys 2024 - [i59]Gyudong Kim, Mehdi Ghasemi, Soroush Heidari, Seungryong Kim, Young Geun Kim, Sarma B. K. Vrudhula, Carole-Jean Wu:
HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learning. CoRR abs/2403.04207 (2024) - [i58]Saurabh Agarwal, Bilge Acun, Basil Hosmer, Mostafa Elhoushi, Yejin Lee, Shivaram Venkataraman, Dimitris Papailiopoulos, Carole-Jean Wu:
CHAI: Clustered Head Attention for Efficient LLM Inference. CoRR abs/2403.08058 (2024) - [i57]Mubashara Akhtar, Omar Benjelloun, Costanza Conforti, Joan Giner-Miguelez, Nitisha Jain, Michael Kuchnik, Quentin Lhoest, Pierre Marcenac, Manil Maskey, Peter Mattson, Luis Oala, Pierre Ruyssen, Rajat Shinde, Elena Simperl, Goeffry Thomas, Slava Tykhonov, Joaquin Vanschoren, Steffen Vogler, Carole-Jean Wu:
Croissant: A Metadata Format for ML-Ready Datasets. CoRR abs/2403.19546 (2024) - [i56]Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed, Victor Akinwande, Namir Al-Nuaimi, Najla Alfaraj, Elie Alhajjar, Lora Aroyo, Trupti Bavalatti, Borhane Blili-Hamelin, Kurt D. Bollacker, Rishi Bomassani, Marisa Ferrara Boston, Siméon Campos, Kal Chakra, Canyu Chen, Cody Coleman, Zacharie Delpierre Coudert, Leon Derczynski, Debojyoti Dutta, Ian Eisenberg, James Ezick, Heather Frase, Brian Fuller, Ram Gandikota, Agasthya Gangavarapu, Ananya Gangavarapu, James Gealy, Rajat Ghosh, James Goel, Usman Gohar, Subhra S. Goswami, Scott A. Hale, Wiebke Hutiri, Joseph Marvin Imperial, Surgan Jandial, Nick Judd, Felix Juefei-Xu, Foutse Khomh, Bhavya Kailkhura, Hannah Rose Kirk, Kevin Klyman, Chris Knotz, Michael Kuchnik, Shachi H. Kumar, Chris Lengerich, Bo Li, Zeyi Liao, Eileen Peters Long, Victor Lu, Yifan Mai, Priyanka Mary Mammen, Kelvin Manyeki, Sean McGregor, Virendra Mehta, Shafee Mohammed, Emanuel Moss, Lama Nachman, Dinesh Jinenhally Naganna, Amin Nikanjam, Besmira Nushi, Luis Oala, Iftach Orr, Alicia Parrish, Cigdem Patlak, William Pietri, Forough Poursabzi-Sangdeh, Eleonora Presani, Fabrizio Puletti, Paul Röttger, Saurav Sahay, Tim Santos, Nino Scherrer, Alice Schoenauer Sebag, Patrick Schramowski, Abolfazl Shahbazi, Vin Sharma, Xudong Shen, Vamsi Sistla, Leonard Tang, Davide Testuggine, Vithursan Thangarasa, Elizabeth Anne Watkins, Rebecca Weiss, Chris Welty, Tyler Wilbers, Adina Williams, Carole-Jean Wu, Poonam Yadav, Xianjun Yang, Yi Zeng, Wenhui Zhang, Fedor Zhdanov, Jiacheng Zhu, Percy Liang, Peter Mattson, Joaquin Vanschoren:
Introducing v0.5 of the AI Safety Benchmark from MLCommons. CoRR abs/2404.12241 (2024) - [i55]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:
LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding. CoRR abs/2404.16710 (2024) - [i54]Alicia Golden, Samuel Hsia, Fei Sun, Bilge Acun, Basil Hosmer, Yejin Lee, Zachary DeVito, Jeff Johnson, Gu-Yeon Wei, David Brooks, Carole-Jean Wu:
Is Flash Attention Stable? CoRR abs/2405.02803 (2024) - [i53]Bilge Acun, Brent Morgan, Henry Richardson, Nat Steinsultz, Carole-Jean Wu:
Unlocking the Potential of Renewable Energy Through Curtailment Prediction. CoRR abs/2405.18526 (2024) - [i52]Carole-Jean Wu, Bilge Acun, Ramya Raghavendra, Kim M. Hazelwood:
Beyond Efficiency: Scaling AI Sustainably. CoRR abs/2406.05303 (2024) - 2023
- [j19]Meisam Hejazinia, Dzmitry Huba, Ilias Leontiadis, Kiwan Maeng, Mani Malek, Luca Melis, Ilya Mironov, Milad Nasr, Kaikai Wang, Carole-Jean Wu:
Federated Ensemble Learning: Increasing the Capacity of Label Private Recommendation Systems. IEEE Data Eng. Bull. 46(1): 145-157 (2023) - [j18]Carole-Jean Wu:
Special Issue on Environmentally Sustainable Computing. IEEE Micro 43(1): 7-8 (2023) - [j17]Udit Gupta, Mariam Elgamal, Gage Hills, Gu-Yeon Wei, Hsien-Hsin S. Lee, David Brooks, Carole-Jean Wu:
Architectural CO2 Footprint Tool: Designing Sustainable Computer Systems With an Architectural Carbon Modeling Tool. IEEE Micro 43(4): 107-117 (2023) - [c61]Bilge Acun, Benjamin Lee, Fiodar Kazhamiaka, Kiwan Maeng, Udit Gupta, Manoj Chakkaravarthy, David Brooks, Carole-Jean Wu:
Carbon Explorer: A Holistic Framework for Designing Carbon Aware Datacenters. ASPLOS (2) 2023: 118-132 - [c60]Samuel Hsia, Udit Gupta, Bilge Acun, Newsha Ardalani, Pan Zhong, Gu-Yeon Wei, David Brooks, Carole-Jean Wu:
MP-Rec: Hardware-Software Co-design to Enable Multi-path Recommendation. ASPLOS (3) 2023: 449-465 - [c59]Mariam Elgamal, Doug Carmean, Elnaz Ansari, Okay Zed, Ramesh Peri, Srilatha Manne, Udit Gupta, Gu-Yeon Wei, David Brooks, Gage Hills, Carole-Jean Wu:
Carbon-Efficient Design Optimization for Computing Systems. HotCarbon 2023: 16:1-16:7 - [c58]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:
RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure. MLSys 2023 - [c57]Mark Mazumder, Colby R. Banbury, Xiaozhe Yao, Bojan Karlas, William Gaviria Rojas, Sudnya Frederick Diamos, Greg Diamos, Lynn He, Alicia Parrish, Hannah Rose Kirk, Jessica Quaye, Charvi Rastogi, Douwe Kiela, David Jurado, David Kanter, Rafael Mosquera, Will Cukierski, Juan Ciro, Lora Aroyo, Bilge Acun, Lingjiao Chen, Mehul Raje, Max Bartolo, Evan Sabri Eyuboglu, Amirata Ghorbani, Emmett D. Goodman, Addison Howard, Oana Inel, Tariq Kane, Christine R. Kirkpatrick, D. Sculley, Tzu-Sheng Kuo, Jonas W. Mueller, Tristan Thrush, Joaquin Vanschoren, Margaret Warren, Adina Williams, Serena Yeung, Newsha Ardalani, Praveen K. Paritosh, Ce Zhang, James Y. Zou, Carole-Jean Wu, Cody Coleman, Andrew Y. Ng, Peter Mattson, Vijay Janapa Reddi:
DataPerf: Benchmarks for Data-Centric AI Development. NeurIPS 2023 - [c56]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:
Tectonic-Shift: A Composite Storage Fabric for Large-Scale ML Training. USENIX ATC 2023: 433-449 - [i51]Geet Sethi, Pallab Bhattacharya, Dhruv Choudhary, Carole-Jean Wu, Christos Kozyrakis:
FlexShard: Flexible Sharding for Industry-Scale Sequence Recommendation Models. CoRR abs/2301.02959 (2023) - [i50]Samuel Hsia, Udit Gupta, Bilge Acun, Newsha Ardalani, Pan Zhong, Gu-Yeon Wei, David Brooks, Carole-Jean Wu:
MP-Rec: Hardware-Software Co-Design to Enable Multi-Path Recommendation. CoRR abs/2302.10872 (2023) - [i49]Haiyang Huang, Newsha Ardalani, Anna Y. Sun, Liu Ke, Hsien-Hsin S. Lee, Anjali Sridhar, Shruti Bhosale, Carole-Jean Wu, Benjamin Lee:
Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference. CoRR abs/2303.06182 (2023) - [i48]Ashkan Yousefpour, Shen Guo, Ashish Shenoy, Sayan Ghosh, Pierre Stock, Kiwan Maeng, Schalk-Willem Krüger, Michael G. Rabbat, Carole-Jean Wu, Ilya Mironov:
Green Federated Learning. CoRR abs/2303.14604 (2023) - [i47]Young Geun Kim, Udit Gupta, Andrew McCrabb, Yonglak Son, Valeria Bertacco, David Brooks, Carole-Jean Wu:
GreenScale: Carbon-Aware Systems for Edge Computing. CoRR abs/2304.00404 (2023) - [i46]Mariam Elgamal, Doug Carmean, Elnaz Ansari, Okay Zed, Ramesh Peri, Srilatha Manne, Udit Gupta, Gu-Yeon Wei, David Brooks, Gage Hills, Carole-Jean Wu:
Design Space Exploration and Optimization for Carbon-Efficient Extended Reality Systems. CoRR abs/2305.01831 (2023) - [i45]Sid Wang, John Nguyen, Ke Li, Carole-Jean Wu:
READ: Recurrent Adaptation of Large Transformers. CoRR abs/2305.15348 (2023) - [i44]Samuel Hsia, Alicia Golden, Bilge Acun, Newsha Ardalani, Zachary DeVito, Gu-Yeon Wei, David Brooks, Carole-Jean Wu:
MAD Max Beyond Single-Node: Enabling Large Machine Learning Model Acceleration on Distributed Systems. CoRR abs/2310.02784 (2023) - [i43]Jhe-Yu Liou, Stephanie Forrest, Carole-Jean Wu:
GEVO-ML: Optimizing Machine Learning Code with Evolutionary Computation. CoRR abs/2310.10211 (2023) - [i42]Jiali Xing, Bilge Acun, Aditya Sundarrajan, David Brooks, Manoj Chakkaravarthy, Nikky Avila, Carole-Jean Wu, Benjamin C. Lee:
Carbon Responder: Coordinating Demand Response for the Datacenter Fleet. CoRR abs/2311.08589 (2023) - [i41]Lingjiao Chen, Bilge Acun, Newsha Ardalani, Yifan Sun, Feiyang Kang, Hanrui Lyu, Yongchan Kwon, Ruoxi Jia, Carole-Jean Wu, Matei Zaharia, James Zou:
Data Acquisition: A New Frontier in Data-centric AI. CoRR abs/2311.13712 (2023) - [i40]Yu Yang, Aaditya K. Singh, Mostafa Elhoushi, Anas Mahmoud, Kushal Tirumala, Fabian Gloeckle, Baptiste Rozière, Carole-Jean Wu, Ari S. Morcos, Newsha Ardalani:
Decoding Data Quality via Synthetic Corruptions: Embedding-guided Pruning of Code Data. CoRR abs/2312.02418 (2023) - [i39]Alicia Golden, Samuel Hsia, Fei Sun, Bilge Acun, Basil Hosmer, Yejin Lee, Zachary DeVito, Jeff Johnson, Gu-Yeon Wei, David Brooks, Carole-Jean Wu:
Generative AI Beyond LLMs: System Implications of Multi-Modal Generation. CoRR abs/2312.14385 (2023) - 2022
- [j16]Udit Gupta, Young Geun Kim, Sylvia Lee, Jordan Tse, Hsien-Hsin S. Lee, Gu-Yeon Wei, David Brooks, Carole-Jean Wu:
Chasing Carbon: The Elusive Environmental Footprint of Computing. IEEE Micro 42(4): 37-47 (2022) - [j15]Soroush Heidari, Mehdi Ghasemi, Young Geun Kim, Carole-Jean Wu, Sarma B. K. Vrudhula:
CAMDNN: Content-Aware Mapping of a Network of Deep Neural Networks on Edge MPSoCs. IEEE Trans. Computers 71(12): 3191-3202 (2022) - [j14]Mehdi Ghasemi, Daler N. Rakhmatov, Carole-Jean Wu, Sarma B. K. Vrudhula:
EdgeWise: Energy-efficient CNN Computation on Edge Devices under Stochastic Communication Delays. ACM Trans. Embed. Comput. Syst. 21(5): 66:1-66:27 (2022) - [c55]Geet Sethi, Bilge Acun, Niket Agarwal, Christos Kozyrakis, Caroline Trippel, Carole-Jean Wu:
RecShard: statistical feature-based memory optimization for industry-scale neural recommendation. ASPLOS 2022: 344-358 - [c54]Chun-Feng Wu, Carole-Jean Wu, Gu-Yeon Wei, David Brooks:
A joint management middleware to improve training performance of deep recommendation systems with SSDs. DAC 2022: 157-162 - [c53]Liu Ke, Udit Gupta, Mark Hempstead, Carole-Jean Wu, Hsien-Hsin S. Lee, Xuan Zhang:
Hercules: Heterogeneity-Aware Inference Serving for At-Scale Personalized Recommendation. HPCA 2022: 141-154 - [c52]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:
SecNDP: Secure Near-Data Processing with Untrusted Memory. HPCA 2022: 244-258 - [c51]Young Geun Kim, Carole-Jean Wu:
FedGPO: Heterogeneity-Aware Global Parameter optimization for Efficient Federated Learning. IISWC 2022: 117-129 - [c50]Jhe-Yu Liou, Muaaz Awan, Steven A. Hofmeyr, Stephanie Forrest, Carole-Jean Wu:
Understanding the Power of Evolutionary Computation for GPU Code Optimization. IISWC 2022: 185-198 - [c49]Udit Gupta, Mariam Elgamal, Gage Hills, Gu-Yeon Wei, Hsien-Hsin S. Lee, David Brooks, Carole-Jean Wu:
ACT: designing sustainable computer systems with an architectural carbon modeling tool. ISCA 2022: 784-799 - [c48]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:
Understanding data storage and ingestion for large-scale deep recommendation model training: industrial product. ISCA 2022: 1042-1057 - [c47]Dzmitry Huba, John Nguyen, Kshitiz Malik, Ruiyu Zhu, Mike Rabbat, Ashkan Yousefpour, Carole-Jean Wu, Hongyuan Zhan, Pavel Ustinov, Harish Srinivas, Kaikai Wang, Anthony Shoumikhin, Jesik Min, Mani Malek:
PAPAYA: Practical, Private, and Scalable Federated Learning. MLSys 2022 - [c46]Carole-Jean Wu, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha Ardalani, Kiwan Maeng, Gloria Chang, Fiona Aga Behram, Jinshi Huang, Charles Bai, Michael Gschwind, Anurag Gupta, Myle Ott, Anastasia Melnikov, Salvatore Candido, David Brooks, Geeta Chauhan, Benjamin Lee, Hsien-Hsin S. Lee, Bugra Akyildiz, Maximilian Balandat, Joe Spisak, Ravi Jain, Mike Rabbat, Kim M. Hazelwood:
Sustainable AI: Environmental Implications, Challenges and Opportunities. MLSys 2022 - [c45]Noveen Sachdeva, Mehak Preet Dhaliwal, Carole-Jean Wu, Julian J. McAuley:
Infinite Recommendation Networks: A Data-Centric Approach. NeurIPS 2022 - [c44]Kiwan Maeng, Haiyu Lu, Luca Melis, John Nguyen, Mike Rabbat, Carole-Jean Wu:
Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity. RecSys 2022: 156-167 - [c43]Noveen Sachdeva, Carole-Jean Wu, Julian J. McAuley:
On Sampling Collaborative Filtering Datasets. WSDM 2022: 842-850 - [e1]Diana Marculescu, Yuejie Chi, Carole-Jean Wu:
Proceedings of the Fifth Conference on Machine Learning and Systems, MLSys 2022, Santa Clara, CA, USA, August 29 - September 1, 2022. mlsys.org 2022 [contents] - [i38]Noveen Sachdeva, Carole-Jean Wu, Julian J. McAuley:
On Sampling Collaborative Filtering Datasets. CoRR abs/2201.04768 (2022) - [i37]Bilge Acun, Benjamin Lee, Kiwan Maeng, Manoj Chakkaravarthy, Udit Gupta, David Brooks, Carole-Jean Wu:
A Holistic Approach for Designing Carbon Aware Datacenters. CoRR abs/2201.10036 (2022) - [i36]Geet Sethi, Bilge Acun, Niket Agarwal, Christos Kozyrakis, Caroline Trippel, Carole-Jean Wu:
RecShard: Statistical Feature-Based Memory Optimization for Industry-Scale Neural Recommendation. CoRR abs/2201.10095 (2022) - [i35]Liu Ke, Udit Gupta, Mark Hempstead, Carole-Jean Wu, Hsien-Hsin S. Lee, Xuan Zhang:
Hercules: Heterogeneity-Aware Inference Serving for At-Scale Personalized Recommendation. CoRR abs/2203.07424 (2022) - [i34]Noveen Sachdeva, Mehak Preet Dhaliwal, Carole-Jean Wu, Julian J. McAuley:
Infinite Recommendation Networks: A Data-Centric Approach. CoRR abs/2206.02626 (2022) - [i33]Kiwan Maeng, Haiyu Lu, Luca Melis, John Nguyen, Mike Rabbat, Carole-Jean Wu:
Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity. CoRR abs/2206.02633 (2022) - [i32]Meisam Hejazinia, Dzmitry Huba, Ilias Leontiadis, Kiwan Maeng, Mani Malek, Luca Melis, Ilya Mironov, Milad Nasr, Kaikai Wang, Carole-Jean Wu:
FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning. CoRR abs/2206.03852 (2022) - [i31]Mark Mazumder, Colby R. Banbury, Xiaozhe Yao, Bojan Karlas, William Gaviria Rojas, Sudnya Frederick Diamos, Greg Diamos, Lynn He, Douwe Kiela, David Jurado, David Kanter, Rafael Mosquera, Juan Ciro, Lora Aroyo, Bilge Acun, Sabri Eyuboglu, Amirata Ghorbani, Emmett D. Goodman, Tariq Kane, Christine R. Kirkpatrick, Tzu-Sheng Kuo, Jonas Mueller, Tristan Thrush, Joaquin Vanschoren, Margaret Warren, Adina Williams, Serena Yeung, Newsha Ardalani, Praveen K. Paritosh, Ce Zhang, James Zou, Carole-Jean Wu, Cody Coleman, Andrew Y. Ng, Peter Mattson, Vijay Janapa Reddi:
DataPerf: Benchmarks for Data-Centric AI Development. CoRR abs/2207.10062 (2022) - [i30]Newsha Ardalani, Carole-Jean Wu, Zeliang Chen, Bhargav Bhushanam, Adnan Aziz:
Understanding Scaling Laws for Recommendation Models. CoRR abs/2208.08489 (2022) - [i29]Jhe-Yu Liou, Muaaz Awan, Steven A. Hofmeyr, Stephanie Forrest, Carole-Jean Wu:
Understanding the Power of Evolutionary Computation for GPU Code Optimization. CoRR abs/2208.12350 (2022) - [i28]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:
RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure. CoRR abs/2211.05239 (2022) - [i27]Young Geun Kim, Carole-Jean Wu:
FedGPO: Heterogeneity-Aware Global Parameter Optimization for Efficient Federated Learning. CoRR abs/2211.16669 (2022) - 2021
- [j13]Vijay Janapa Reddi, Christine Cheng, David Kanter, Peter Mattson, Guenther Schmuelling, Carole-Jean Wu:
The Vision Behind MLPerf: Understanding AI Inference Performance. IEEE Micro 41(3): 10-18 (2021) - [j12]Zhaoxia Deng, Jongsoo Park, Ping Tak Peter Tang, Haixin Liu, Jie Yang, Hector Yuen, Jianyu Huang, Daya Shanker Khudia, Xiaohan Wei, Ellie Wen, Dhruv Choudhary, Raghuraman Krishnamoorthi, Carole-Jean Wu, Nadathur Satish, Changkyu Kim, Maxim Naumov, Sam Naghshineh, Mikhail Smelyanskiy:
Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale. IEEE Micro 41(5): 93-100 (2021) - [j11]Venkatesh Kodukula, Saad Katrawala, Britton Jones, Carole-Jean Wu, Robert LiKamWa:
Dynamic Temperature Management of Near-Sensor Processing for Energy-Efficient High-Fidelity Imaging. Sensors 21(3): 926 (2021) - [j10]Yu Emma Wang, Carole-Jean Wu, Xiaodong Wang, Kim M. Hazelwood, David Brooks:
Exploiting Parallelism Opportunities with Deep Learning Frameworks. ACM Trans. Archit. Code Optim. 18(1): 9:1-9:23 (2021) - [c42]Mark Wilkening, Udit Gupta, Samuel Hsia, Caroline Trippel, Carole-Jean Wu, David Brooks, Gu-Yeon Wei:
RecSSD: near data processing for solid state drive based recommendation inference. ASPLOS 2021: 717-729 - [c41]Bilge Acun, Matthew Murphy, Xiaodong Wang, Jade Nie, Carole-Jean Wu, Kim M. Hazelwood:
Understanding Training Efficiency of Deep Learning Recommendation Models at Scale. HPCA 2021: 802-814 - [c40]Udit Gupta, Young Geun Kim, Sylvia Lee, Jordan Tse, Hsien-Hsin S. Lee, Gu-Yeon Wei, David Brooks, Carole-Jean Wu:
Chasing Carbon: The Elusive Environmental Footprint of Computing. HPCA 2021: 854-867 - [c39]Michael Lui, Yavuz Yetim, Özgür Özkan, Zhuoran Zhao, Shin-Yeh Tsai, Carole-Jean Wu, Mark Hempstead:
Understanding Capacity-Driven Scale-Out Neural Recommendation Inference. ISPASS 2021: 162-171 - [c38]Young Geun Kim, Carole-Jean Wu:
AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning. MICRO 2021: 183-198 - [c37]Udit Gupta, Samuel Hsia, Jeff Zhang, Mark Wilkening, Javin Pombra, Hsien-Hsin Sean Lee, Gu-Yeon Wei, Carole-Jean Wu, David Brooks:
RecPipe: Co-designing Models and Hardware to Jointly Optimize Recommendation Quality and Performance. MICRO 2021: 870-884 - [c36]Kiwan Maeng, Shivam Bharuka, Isabel Gao, Mark C. Jeffrey, Vikram Saraph, Bor-Yiing Su, Caroline Trippel, Jiyan Yang, Mike Rabbat, Brandon Lucia, Carole-Jean Wu:
Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery. MLSys 2021 - [c35]Chunxing Yin, Bilge Acun, Carole-Jean Wu, Xing Liu:
TT-Rec: Tensor Train Compression for Deep Learning Recommendation Models. MLSys 2021 - [c34]Mehdi Ghasemi, Soroush Heidari, Young Geun Kim, Aaron Lamb, Carole-Jean Wu, Sarma B. K. Vrudhula:
Energy-Efficient Mapping for a Network of DNN Models at the Edge. SMARTCOMP 2021: 25-30 - [i26]Chunxing Yin, Bilge Acun, Xing Liu, Carole-Jean Wu:
TT-Rec: Tensor Train Compression for Deep Learning Recommendation Models. CoRR abs/2101.11714 (2021) - [i25]Mark Wilkening, Udit Gupta, Samuel Hsia, Caroline Trippel, Carole-Jean Wu, David Brooks, Gu-Yeon Wei:
RecSSD: Near Data Processing for Solid State Drive Based Recommendation Inference. CoRR abs/2102.00075 (2021) - [i24]Udit Gupta, Samuel Hsia, Jeff Jun Zhang, Mark Wilkening, Javin Pombra, Hsien-Hsin S. Lee, Gu-Yeon Wei, Carole-Jean Wu, David Brooks:
RecPipe: Co-designing Models and Hardware to Jointly Optimize Recommendation Quality and Performance. CoRR abs/2105.08820 (2021) - [i23]Zhaoxia Deng, Jongsoo Park, Ping Tak Peter Tang, Haixin Liu, Jie Yang, Hector Yuen, Jianyu Huang, Daya Shanker Khudia, Xiaohan Wei, Ellie Wen, Dhruv Choudhary, Raghuraman Krishnamoorthi, Carole-Jean Wu, Nadathur Satish, Changkyu Kim, Maxim Naumov, Sam Naghshineh, Mikhail Smelyanskiy:
Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale. CoRR abs/2105.12676 (2021) - [i22]Noveen Sachdeva, Carole-Jean Wu, Julian J. McAuley:
SVP-CF: Selection via Proxy for Collaborative Filtering Data. CoRR abs/2107.04984 (2021) - [i21]Young Geun Kim, Carole-Jean Wu:
AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning. CoRR abs/2107.08147 (2021) - [i20]Carole-Jean Wu, Srilatha Manne, Parthasarathy Ranganathan, Sarah Bird, Shane Greenstein:
Socio-Technological Challenges and Opportunities: Paths Forward. CoRR abs/2108.06738 (2021) - [i19]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:
Understanding and Co-designing the Data Ingestion Pipeline for Industry-Scale RecSys Training. CoRR abs/2108.09373 (2021) - [i18]Carole-Jean Wu, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha Ardalani, Kiwan Maeng, Gloria Chang, Fiona Aga Behram, James Huang, Charles Bai, Michael Gschwind, Anurag Gupta, Myle Ott, Anastasia Melnikov, Salvatore Candido, David Brooks, Geeta Chauhan, Benjamin Lee, Hsien-Hsin S. Lee, Bugra Akyildiz, Maximilian Balandat, Joe Spisak, Ravi Jain, Mike Rabbat, Kim M. Hazelwood:
Sustainable AI: Environmental Implications, Challenges and Opportunities. CoRR abs/2111.00364 (2021) - [i17]Dzmitry Huba, John Nguyen, Kshitiz Malik, Ruiyu Zhu, Mike Rabbat, Ashkan Yousefpour, Carole-Jean Wu, Hongyuan Zhan, Pavel Ustinov, Harish Srinivas, Kaikai Wang, Anthony Shoumikhin, Jesik Min, Mani Malek:
Papaya: Practical, Private, and Scalable Federated Learning. CoRR abs/2111.04877 (2021) - [i16]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:
SecNDP: Secure Near-Data Processing with Untrusted Memory. IACR Cryptol. ePrint Arch. 2021: 1642 (2021) - 2020
- [j9]Peter Mattson, Hanlin Tang, Gu-Yeon Wei, Carole-Jean Wu, Vijay Janapa Reddi, Christine Cheng, Cody Coleman, Greg Diamos, David Kanter, Paulius Micikevicius, David A. Patterson, Guenther Schmuelling:
MLPerf: An Industry Standard Benchmark Suite for Machine Learning Performance. IEEE Micro 40(2): 8-16 (2020) - [j8]Jhe-Yu Liou, Xiaodong Wang, Stephanie Forrest, Carole-Jean Wu:
GEVO: GPU Code Optimization Using Evolutionary Computation. ACM Trans. Archit. Code Optim. 17(4): 33:1-33:28 (2020) - [c33]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:
Emerging Neural Workloads and Their Impact on Hardware. DATE 2020: 1462-1471 - [c32]Jhe-Yu Liou, Xiaodong Wang, Stephanie Forrest, Carole-Jean Wu:
GEVO-ML: a proposal for optimizing ML code with evolutionary computation. GECCO Companion 2020: 1849-1856 - [c31]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:
The Architectural Implications of Facebook's DNN-Based Personalized Recommendation. HPCA 2020: 488-501 - [c30]Samuel Hsia, Udit Gupta, Mark Wilkening, Carole-Jean Wu, Gu-Yeon Wei, David Brooks:
Cross-Stack Workload Characterization of Deep Recommendation Systems. IISWC 2020: 157-168 - [c29]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:
MLPerf Inference Benchmark. ISCA 2020: 446-459 - [c28]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:
RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing. ISCA 2020: 790-803 - [c27]Udit Gupta, Samuel Hsia, Vikram Saraph, Xiaodong Wang, Brandon Reagen, Gu-Yeon Wei, Hsien-Hsin S. Lee, David Brooks, Carole-Jean Wu:
DeepRecSys: A System for Optimizing End-To-End At-Scale Neural Recommendation Inference. ISCA 2020: 982-995 - [c26]Young Geun Kim, Carole-Jean Wu:
AutoScale: Energy Efficiency Optimization for Stochastic Edge Inference Using Reinforcement Learning. MICRO 2020: 1082-1096 - [c25]