L
Lin Qiao
Researcher at Facebook
Publications - Â 6
Citations - Â 566
Lin Qiao is an academic researcher from Facebook. The author has contributed to research in topics: Deep learning & Load balancing (computing). The author has an hindex of 4, co-authored 5 publications receiving 357 citations.
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Machine Learning at Facebook: Understanding Inference at the Edge
Carole-Jean Wu,David Brooks,Kevin Chen,Douglas Chen,Sy Choudhury,Marat Dukhan,Kim 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,Sungjoo Yoo,Peizhao Zhang +26 more
TL;DR: This paper takes a datadriven approach to present the opportunities and design challenges faced by Facebook in order to enable machine learning inference locally on smartphones and other edge platforms.
Posted Content
Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications
Jongsoo Park,Maxim Naumov,Protonu Basu,Summer Deng,Aravind Kalaiah,Daya Shanker 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 +27 more
TL;DR: Detailed characterizations of deep learning models used in many Facebook social network services are provided and the need for better co-design of algorithms, numerics and computing platforms to address the challenges of workloads often run in data centers is highlighted.
Posted Content
Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models
Dheevatsa Mudigere,Hao Yuchen,Jianyu Huang,Zhihao Jia,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,Dimitry Melts,Krishna Dhulipala,K. R. Kishore,Tyler Graf,Assaf Eisenman,Kiran Kumar Matam,Adi Gangidi,Guoqiang Jerry Chen,Manoj Krishnan,Avinash Nayak,Krishnakumar Nair,Bharath Muthiah,Mahmoud khorashadi,Pallab Bhattacharya,Petr Lapukhov,Maxim Naumov,Ajit Mathews,Lin Qiao,Mikhail Smelyanskiy,Bill Jia,Vijay Rao +52 more
TL;DR: ZionEX as mentioned in this paper is a high-performance scalable software stack based on PyTorch and pair it with the new evolution of Zion platform, namely ZionEX, which can attain 40X speedup in terms of time to solution over previous systems.
Posted Content
High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models.
Dheevatsa Mudigere,Hao Yuchen,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,Dimitry Melts,Krishna Dhulipala,K. R. Kishore,Tyler Graf,Assaf Eisenman,Kiran Kumar Matam,Adi Gangidi,Guoqiang Jerry Chen,Manoj Krishnan,Avinash Nayak,Krishnakumar Nair,Bharath Muthiah,Mahmoud khorashadi,Pallab Bhattacharya,Petr Lapukhov,Maxim Naumov,Lin Qiao,Mikhail Smelyanskiy,Bill Jia,Vijay Rao +50 more
TL;DR: ZionEX as discussed by the authors is a high-performance scalable software stack based on PyTorch and pair it with the new evolution of Zion platform, namely ZionEX, which can attain 40X speedup in terms of time to solution over previous systems.
Posted Content
Hybrid Composition with IdleBlock: More Efficient Networks for Image Recognition.
TL;DR: A new building block, IdleBlock, is proposed, which naturally prunes connections within the block, and introducing hybrid composition with Idle block, which suggests a new simpler and more efficient direction for network design and neural architecture search.