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Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale


May 26, 2021
Zhaoxia, Deng, Jongsoo Park, Ping Tak Peter Tang, Haixin Liu, Jie, Yang, Hector Yuen, Jianyu Huang, Daya Khudia, Xiaohan Wei, Ellie Wen, Dhruv Choudhary, Raghuraman Krishnamoorthi, Carole-Jean Wu, Satish Nadathur, Changkyu Kim, Maxim Naumov, Sam Naghshineh, Mikhail Smelyanskiy


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RecPipe: Co-designing Models and Hardware to Jointly Optimize Recommendation Quality and Performance


May 22, 2021
Udit Gupta, Samuel Hsia, Jeff Zhang, Mark Wilkening, Javin Pombra, Hsien-Hsin S. Lee, Gu-Yeon Wei, Carole-Jean Wu, David Brooks


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RecSSD: Near Data Processing for Solid State Drive Based Recommendation Inference


Jan 29, 2021
Mark Wilkening, Udit Gupta, Samuel Hsia, Caroline Trippel, Carole-Jean Wu, David Brooks, Gu-Yeon Wei


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TT-Rec: Tensor Train Compression for Deep Learning Recommendation Models


Jan 25, 2021
Chunxing Yin, Bilge Acun, Xing Liu, Carole-Jean Wu

* To appear in Conference on Machine Learning and Systems (MlSys 2021, https://mlsys.org/

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Understanding Capacity-Driven Scale-Out Neural Recommendation Inference


Nov 11, 2020
Michael Lui, Yavuz Yetim, Özgür Özkan, Zhuoran Zhao, Shin-Yeh Tsai, Carole-Jean Wu, Mark Hempstead

* 16 pages + references, 16 Figures. Additive revision to clarify distinction between this work and other DLRM-like models and add Acknowledgments 

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Understanding Training Efficiency of Deep Learning Recommendation Models at Scale


Nov 11, 2020
Bilge Acun, Matthew Murphy, Xiaodong Wang, Jade Nie, Carole-Jean Wu, Kim Hazelwood

* To appear in IEEE International Symposium on High-Performance Computer Architecture (HPCA 2021) 

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CPR: Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery


Nov 05, 2020
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


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AutoScale: Optimizing Energy Efficiency of End-to-End Edge Inference under Stochastic Variance


May 06, 2020
Young Geun Kim, Carole-Jean Wu


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GEVO: GPU Code Optimization using Evolutionary Computation


Apr 27, 2020
Jhe-Yu Liou, Xiaodong Wang, Stephanie Forrest, Carole-Jean Wu


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GEVO: GPU Code Optimization using EvolutionaryComputation


Apr 17, 2020
Jhe-Yu Liou, Xiaodong Wang, Stephanie Forrest, Carole-Jean Wu


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Developing a Recommendation Benchmark for MLPerf Training and Inference


Apr 14, 2020
Carole-Jean Wu, Robin Burke, Ed H. Chi, Joseph Konstan, Julian McAuley, Yves Raimond, Hao Zhang


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MLPerf Inference Benchmark


Nov 06, 2019
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


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MLPerf Training Benchmark


Oct 30, 2019
Peter Mattson, Christine Cheng, Cody Coleman, Greg Diamos, Paulius Micikevicius, David Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, David Brooks, Dehao Chen, Debojyoti Dutta, Udit Gupta, Kim Hazelwood, Andrew Hock, Xinyuan Huang, Bill Jia, Daniel Kang, David Kanter, Naveen Kumar, Jeffery Liao, Guokai Ma, Deepak Narayanan, Tayo Oguntebi, Gennady Pekhimenko, Lillian Pentecost, Vijay Janapa Reddi, Taylor Robie, Tom St. John, Carole-Jean Wu, Lingjie Xu, Cliff Young, Matei Zaharia


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Exploiting Parallelism Opportunities with Deep Learning Frameworks


Aug 13, 2019
Yu Emma Wang, Carole-Jean Wu, Xiaodong Wang, Kim Hazelwood, David Brooks


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The Architectural Implications of Facebook's DNN-based Personalized Recommendation


Jun 18, 2019
Udit Gupta, Xiaodong Wang, Maxim Naumov, Carole-Jean Wu, Brandon Reagen, David Brooks, Bradford Cottel, Kim Hazelwood, Bill Jia, Hsien-Hsin S. Lee, Andrey Malevich, Dheevatsa Mudigere, Mikhail Smelyanskiy, Liang Xiong, Xuan Zhang

* 11 pages 

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Deep Learning Recommendation Model for Personalization and Recommendation Systems


May 31, 2019
Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi, Jianyu Huang, Narayanan Sundaraman, Jongsoo Park, Xiaodong Wang, Udit Gupta, Carole-Jean Wu, Alisson G. Azzolini, Dmytro Dzhulgakov, Andrey Mallevich, Ilia Cherniavskii, Yinghai Lu, Raghuraman Krishnamoorthi, Ansha Yu, Volodymyr Kondratenko, Stephanie Pereira, Xianjie Chen, Wenlin Chen, Vijay Rao, Bill Jia, Liang Xiong, Misha Smelyanskiy

* 10 pages, 6 figures 

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