Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Jia, Bill"'
Autor:
Anderson, Michael, Chen, Benny, Chen, Stephen, Deng, Summer, Fix, Jordan, Gschwind, Michael, Kalaiah, Aravind, Kim, Changkyu, Lee, Jaewon, Liang, Jason, Liu, Haixin, Lu, Yinghai, Montgomery, Jack, Moorthy, Arun, Nadathur, Satish, Naghshineh, Sam, Nayak, Avinash, Park, Jongsoo, Petersen, Chris, Schatz, Martin, Sundaram, Narayanan, Tang, Bangsheng, Tang, Peter, Yang, Amy, Yu, Jiecao, Yuen, Hector, Zhang, Ying, Anbudurai, Aravind, Balan, Vandana, Bojja, Harsha, Boyd, Joe, Breitbach, Matthew, Caldato, Claudio, Calvo, Anna, Catron, Garret, Chandwani, Sneh, Christeas, Panos, Cottel, Brad, Coutinho, Brian, Dalli, Arun, Dhanotia, Abhishek, Duncan, Oniel, Dzhabarov, Roman, Elmir, Simon, Fu, Chunli, Fu, Wenyin, Fulthorp, Michael, Gangidi, Adi, Gibson, Nick, Gordon, Sean, Hernandez, Beatriz Padilla, Ho, Daniel, Huang, Yu-Cheng, Johansson, Olof, Juluri, Shishir, Kanaujia, Shobhit, Kesarkar, Manali, Killinger, Jonathan, Kim, Ben, Kulkarni, Rohan, Lele, Meghan, Li, Huayu, Li, Huamin, Li, Yueming, Liu, Cynthia, Liu, Jerry, Maher, Bert, Mallipedi, Chandra, Mangla, Seema, Matam, Kiran Kumar, Mehta, Jubin, Mehta, Shobhit, Mitchell, Christopher, Muthiah, Bharath, Nagarkatte, Nitin, Narasimha, Ashwin, Nguyen, Bernard, Ortiz, Thiara, Padmanabha, Soumya, Pan, Deng, Poojary, Ashwin, Ye, Qi, Raginel, Olivier, Rajagopal, Dwarak, Rice, Tristan, Ross, Craig, Rotem, Nadav, Russ, Scott, Shah, Kushal, Shan, Baohua, Shen, Hao, Shetty, Pavan, Skandakumaran, Krish, Srinivasan, Kutta, Sumbaly, Roshan, Tauberg, Michael, Tzur, Mor, Verma, Sidharth, Wang, Hao, Wang, Man, Wei, Ben, Xia, Alex, Xu, Chenyu, Yang, Martin, Zhang, Kai, Zhang, Ruoxi, Zhao, Ming, Zhao, Whitney, Zhu, Rui, Mathews, Ajit, Qiao, Lin, Smelyanskiy, Misha, Jia, Bill, Rao, Vijay
In this paper, we provide a deep dive into the deployment of inference accelerators at Facebook. Many of our ML workloads have unique characteristics, such as sparse memory accesses, large model sizes, as well as high compute, memory and network band
Externí odkaz:
http://arxiv.org/abs/2107.04140
Autor:
Mudigere, Dheevatsa, Hao, Yuchen, Huang, Jianyu, Jia, Zhihao, Tulloch, Andrew, Sridharan, Srinivas, Liu, Xing, Ozdal, Mustafa, Nie, Jade, Park, Jongsoo, Luo, Liang, Yang, Jie Amy, Gao, Leon, Ivchenko, Dmytro, Basant, Aarti, Hu, Yuxi, Yang, Jiyan, Ardestani, Ehsan K., Wang, Xiaodong, Komuravelli, Rakesh, Chu, Ching-Hsiang, Yilmaz, Serhat, Li, Huayu, Qian, Jiyuan, Feng, Zhuobo, Ma, Yinbin, Yang, Junjie, Wen, Ellie, Li, Hong, Yang, Lin, Sun, Chonglin, Zhao, Whitney, Melts, Dimitry, Dhulipala, Krishna, Kishore, KR, Graf, Tyler, Eisenman, Assaf, Matam, Kiran Kumar, Gangidi, Adi, Chen, Guoqiang Jerry, Krishnan, Manoj, Nayak, Avinash, Nair, Krishnakumar, Muthiah, Bharath, khorashadi, Mahmoud, Bhattacharya, Pallab, Lapukhov, Petr, Naumov, Maxim, Mathews, Ajit, Qiao, Lin, Smelyanskiy, Mikhail, Jia, Bill, Rao, Vijay
Deep learning recommendation models (DLRMs) are used across many business-critical services at Facebook and are the single largest AI application in terms of infrastructure demand in its data-centers. In this paper we discuss the SW/HW co-designed so
Externí odkaz:
http://arxiv.org/abs/2104.05158
Autor:
Ke, Liu, Gupta, Udit, Wu, Carole-Jean, Cho, Benjamin Youngjae, Hempstead, Mark, Reagen, Brandon, Zhang, Xuan, Brooks, David, Chandra, Vikas, Diril, Utku, Firoozshahian, Amin, Hazelwood, Kim, Jia, Bill, Lee, Hsien-Hsin S., Li, Meng, Maher, Bert, Mudigere, Dheevatsa, Naumov, Maxim, Schatz, Martin, Smelyanskiy, Mikhail, Wang, Xiaodong
Personalized recommendation systems leverage deep learning models and account for the majority of data center AI cycles. Their performance is dominated by memory-bound sparse embedding operations with unique irregular memory access patterns that pose
Externí odkaz:
http://arxiv.org/abs/1912.12953
Autor:
Mattson, Peter, Cheng, Christine, Coleman, Cody, Diamos, Greg, Micikevicius, Paulius, Patterson, David, Tang, Hanlin, Wei, Gu-Yeon, Bailis, Peter, Bittorf, Victor, Brooks, David, Chen, Dehao, Dutta, Debojyoti, Gupta, Udit, Hazelwood, Kim, Hock, Andrew, Huang, Xinyuan, Ike, Atsushi, Jia, Bill, Kang, Daniel, Kanter, David, Kumar, Naveen, Liao, Jeffery, Ma, Guokai, Narayanan, Deepak, Oguntebi, Tayo, Pekhimenko, Gennady, Pentecost, Lillian, Reddi, Vijay Janapa, Robie, Taylor, John, Tom St., Tabaru, Tsuguchika, Wu, Carole-Jean, Xu, Lingjie, Yamazaki, Masafumi, Young, Cliff, Zaharia, Matei
Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML. But ML training presents three unique benchmarking challenges absent from o
Externí odkaz:
http://arxiv.org/abs/1910.01500
Autor:
Gupta, Udit, Wu, Carole-Jean, Wang, Xiaodong, Naumov, Maxim, Reagen, Brandon, Brooks, David, Cottel, Bradford, Hazelwood, Kim, Jia, Bill, Lee, Hsien-Hsin S., Malevich, Andrey, Mudigere, Dheevatsa, Smelyanskiy, Mikhail, Xiong, Liang, Zhang, Xuan
The widespread application of deep learning has changed the landscape of computation in the data center. In particular, personalized recommendation for content ranking is now largely accomplished leveraging deep neural networks. However, despite the
Externí odkaz:
http://arxiv.org/abs/1906.03109
Autor:
Naumov, Maxim, Mudigere, Dheevatsa, Shi, Hao-Jun Michael, Huang, Jianyu, Sundaraman, Narayanan, Park, Jongsoo, Wang, Xiaodong, Gupta, Udit, Wu, Carole-Jean, Azzolini, Alisson G., Dzhulgakov, Dmytro, Mallevich, Andrey, Cherniavskii, Ilia, Lu, Yinghai, Krishnamoorthi, Raghuraman, Yu, Ansha, Kondratenko, Volodymyr, Pereira, Stephanie, Chen, Xianjie, Chen, Wenlin, Rao, Vijay, Jia, Bill, Xiong, Liang, Smelyanskiy, Misha
With the advent of deep learning, neural network-based recommendation models have emerged as an important tool for tackling personalization and recommendation tasks. These networks differ significantly from other deep learning networks due to their n
Externí odkaz:
http://arxiv.org/abs/1906.00091
Autor:
Park, Jongsoo, Naumov, Maxim, Basu, Protonu, Deng, Summer, Kalaiah, Aravind, Khudia, Daya, Law, James, Malani, Parth, Malevich, Andrey, Nadathur, Satish, Pino, Juan, Schatz, Martin, Sidorov, Alexander, Sivakumar, Viswanath, Tulloch, Andrew, Wang, Xiaodong, Wu, Yiming, Yuen, Hector, Diril, Utku, Dzhulgakov, Dmytro, Hazelwood, Kim, Jia, Bill, Jia, Yangqing, Qiao, Lin, Rao, Vijay, Rotem, Nadav, Yoo, Sungjoo, Smelyanskiy, Mikhail
The application of deep learning techniques resulted in remarkable improvement of machine learning models. In this paper provides detailed characterizations of deep learning models used in many Facebook social network services. We present computation
Externí odkaz:
http://arxiv.org/abs/1811.09886
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