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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, 3125523Conference 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. 3125523
National Category
Embedded Systems
Identifiers
URN: urn:nbn:se:kth:diva-219650DOI: 10.1145/3125501.3125523Scopus ID: 2-s2.0-85035321158ISBN: 9781450351843 OAI: oai:DiVA.org:kth-219650DiVA: 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: 2017-12-12Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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  • de-DE
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  • nn-NB
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  • Other locale
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Output format
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  • text
  • asciidoc
  • rtf