1.1 Deep Learning Hardware: Past, Present, and Future

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

Abstract

Historically, progress in neural networks and deep learning research has been greatly influenced by the available hardware and software tools. This paper identifies trends in deep learning research that will influence hardware architectures and software platforms of the future.

Original languageEnglish (US)
Title of host publication2019 IEEE International Solid-State Circuits Conference, ISSCC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages12-19
Number of pages8
ISBN (Electronic)9781538685310
DOIs
StatePublished - Mar 6 2019
Event2019 IEEE International Solid-State Circuits Conference, ISSCC 2019 - San Francisco, United States
Duration: Feb 17 2019Feb 21 2019

Publication series

NameDigest of Technical Papers - IEEE International Solid-State Circuits Conference
Volume2019-February
ISSN (Print)0193-6530

Conference

Conference2019 IEEE International Solid-State Circuits Conference, ISSCC 2019
CountryUnited States
CitySan Francisco
Period2/17/192/21/19

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ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering

Cite this

Lecun, Y. (2019). 1.1 Deep Learning Hardware: Past, Present, and Future. In 2019 IEEE International Solid-State Circuits Conference, ISSCC 2019 (pp. 12-19). [8662396] (Digest of Technical Papers - IEEE International Solid-State Circuits Conference; Vol. 2019-February). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISSCC.2019.8662396