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Work-in-progress: Prediction based convolution neural network acceleration
KTH, School of Information and Communication Technology (ICT), Electronics.
KTH, School of Information and Communication Technology (ICT), Electronics.
2017 (English)In: Proceedings of the 2017 International Conference on Compilers, Architectures and Synthesis for Embedded Systems Companion, CASES 2017, Association for Computing Machinery (ACM), 2017, article id 3125523Conference paper, Published paper (Refereed)
Abstract [en]

Although intra-layer parallelism is commonly used to expedite CNN execution, it is difficult to achieve inter-layer parallelism because of data dependence between layers. In the paper, we propose a two-phase prediction and correction mechanism to break the data dependence between CNN layers so as to enable inter-layer parallelism. Our technique achieves one more order of magnitude (from the order of 10 to the order of 100) CNN acceleration compared to other three state-of-the-art GPU based CNN acceleration mechanisms.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2017. article id 3125523
National Category
Embedded Systems
Identifiers
URN: urn:nbn:se:kth:diva-219650DOI: 10.1145/3125501.3125523ISI: 000426915700002Scopus ID: 2-s2.0-85035321158ISBN: 9781450351843 (print)OAI: oai:DiVA.org:kth-219650DiVA, id: diva2:1164883
Conference
2017 International Conference on Compilers, Architectures and Synthesis for Embedded Systems, CASES 2017, Seoul, South Korea, 15 October 2017 through 20 October 2017
Note

QC 20171212

Available from: 2017-12-12 Created: 2017-12-12 Last updated: 2019-02-05Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf