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 WangYanghan Wang, Bram Wasti, Yiming Wu, Ran Xian, Sungjoo Yoo, Peizhao Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

73 Scopus citations

Abstract

At Facebook, machine learning provides a wide range of capabilities that drive many aspects of user experience including ranking posts, content understanding, object detection and tracking for augmented and virtual reality, speech and text translations. While machine learning models are currently trained on customized datacenter infrastructure, Facebook is working to bring machine learning inference to the edge. By doing so, user experience is improved with reduced latency (inference time) and becomes less dependent on network connectivity. Furthermore, this also enables many more applications of deep learning with important features only made available at the edge. 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.

Original languageEnglish (US)
Title of host publicationProceedings - 25th IEEE International Symposium on High Performance Computer Architecture, HPCA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages331-344
Number of pages14
ISBN (Electronic)9781728114446
DOIs
StatePublished - Mar 26 2019
Event25th IEEE International Symposium on High Performance Computer Architecture, HPCA 2019 - Washington, United States
Duration: Feb 16 2019Feb 20 2019

Publication series

NameProceedings - 25th IEEE International Symposium on High Performance Computer Architecture, HPCA 2019

Conference

Conference25th IEEE International Symposium on High Performance Computer Architecture, HPCA 2019
CountryUnited States
CityWashington
Period2/16/192/20/19

Keywords

  • Edge Inference
  • Machine learning

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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