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High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models


Apr 13, 2021
Dheevatsa Mudigere, Yuchen Hao, Jianyu Huang, Andrew Tulloch, Srinivas Sridharan, Xing Liu, Mustafa Ozdal, Jade Nie, Jongsoo Park, Liang Luo, Jie Amy Yang, Leon Gao, Dmytro Ivchenko, Aarti Basant, Yuxi Hu, Jiyan Yang, Ehsan K. Ardestani, Xiaodong Wang, Rakesh Komuravelli, Ching-Hsiang Chu, Serhat Yilmaz, Huayu Li, Jiyuan Qian, Zhuobo Feng, Yinbin Ma, Junjie Yang, Ellie Wen, Hong Li, Lin Yang, Chonglin Sun, Whitney Zhao, Krishna Dhulipala, KR Kishore, Tyler Graf, Assaf Eisenman, Kiran Kumar Matam, Adi Gangidi, Pallab Bhattacharya, Guoqiang Jerry Chen, Manoj Krishnan, Krishnakumar Nair, Petr Lapukhov, Maxim Naumov, Lin Qiao, Mikhail Smelyanskiy, Bill Jia, Vijay Rao


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FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference


Jan 13, 2021
Daya Khudia, Jianyu Huang, Protonu Basu, Summer Deng, Haixin Liu, Jongsoo Park, Mikhail Smelyanskiy


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Mixed-Precision Embedding Using a Cache


Oct 23, 2020
Jie Amy Yang, Jianyu Huang, Jongsoo Park, Ping Tak Peter Tang, Andrew Tulloch


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Adaptive Dense-to-Sparse Paradigm for Pruning Online Recommendation System with Non-Stationary Data


Oct 21, 2020
Mao Ye, Dhruv Choudhary, Jiecao Yu, Ellie Wen, Zeliang Chen, Jiyan Yang, Jongsoo Park, Qiang Liu, Arun Kejariwal


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Post-Training 4-bit Quantization on Embedding Tables


Nov 05, 2019
Hui Guan, Andrey Malevich, Jiyan Yang, Jongsoo Park, Hector Yuen

* Accepted in [email protected]'19 (http://learningsys.org/neurips19/

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A Study of BFLOAT16 for Deep Learning Training


Jun 13, 2019
Dhiraj Kalamkar, Dheevatsa Mudigere, Naveen Mellempudi, Dipankar Das, Kunal Banerjee, Sasikanth Avancha, Dharma Teja Vooturi, Nataraj Jammalamadaka, Jianyu Huang, Hector Yuen, Jiyan Yang, Jongsoo Park, Alexander Heinecke, Evangelos Georganas, Sudarshan Srinivasan, Abhisek Kundu, Misha Smelyanskiy, Bharat Kaul, Pradeep Dubey


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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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Spatial-Winograd Pruning Enabling Sparse Winograd Convolution


Jan 08, 2019
Jiecao Yu, Jongsoo Park, Maxim Naumov


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Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications


Nov 29, 2018
Jongsoo Park, Maxim Naumov, Protonu Basu, Summer Deng, Aravind Kalaiah, Daya Khudia, James Law, Parth Malani, Andrey Malevich, Satish Nadathur, Juan Pino, Martin Schatz, Alexander Sidorov, Viswanath Sivakumar, Andrew Tulloch, Xiaodong Wang, Yiming Wu, Hector Yuen, Utku Diril, Dmytro Dzhulgakov, Kim Hazelwood, Bill Jia, Yangqing Jia, Lin Qiao, Vijay Rao, Nadav Rotem, Sungjoo Yoo, Mikhail Smelyanskiy


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On Periodic Functions as Regularizers for Quantization of Neural Networks


Nov 24, 2018
Maxim Naumov, Utku Diril, Jongsoo Park, Benjamin Ray, Jedrzej Jablonski, Andrew Tulloch

* 11 pages, 7 figures 

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Enabling Sparse Winograd Convolution by Native Pruning


Oct 13, 2017
Sheng Li, Jongsoo Park, Ping Tak Peter Tang

* 10 pages, 2 figures 

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Faster CNNs with Direct Sparse Convolutions and Guided Pruning


Jul 28, 2017
Jongsoo Park, Sheng Li, Wei Wen, Ping Tak Peter Tang, Hai Li, Yiran Chen, Pradeep Dubey

* 12 pages, 5 figures 

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