DMazeRunner: Optimizing Convolutions on Dataflow Accelerators

Shail Dave, Aviral Shrivastava, Youngbin Kim, Sasikanth Avancha, Kyoungwoo Lee

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

Abstract

Convolution neural networks (CNNs) can be efficiently executed on dataflow accelerators. However, the vast space of executing convolutions on computational and memory resources of accelerators makes difficult for programmers to automatically and efficiently accelerate the convolutions and for architects to achieve efficient accelerator designs. We propose dMazeRunner framework, which allows users to optimize execution methods for accelerating convolution and matrix multiplication on a given architecture and to explore dataflow accelerator designs for efficiently executing CNN models. dMazeRunner determines efficient dataflows tailored for CNN layers and achieves efficient execution methods for CNN models within several seconds.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1544-1548
Number of pages5
ISBN (Electronic)9781509066315
DOIs
StatePublished - May 2020
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
Duration: May 4 2020May 8 2020

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2020-May
ISSN (Print)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
CountrySpain
CityBarcelona
Period5/4/205/8/20

Keywords

  • deep learning
  • design space exploration
  • energy-efficiency
  • Hardware accelerators
  • mapping

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Dave, S., Shrivastava, A., Kim, Y., Avancha, S., & Lee, K. (2020). DMazeRunner: Optimizing Convolutions on Dataflow Accelerators. In 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings (pp. 1544-1548). [9054275] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2020-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP40776.2020.9054275