Energy-Efficient Convolutional Neural Networks with Deterministic Bit-Stream Processing

S. Rasoul Faraji, M. Hassan Najafi, Bingzhe Li, David J. Lilja, Kia Bazargan

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

16 Scopus citations

Abstract

Stochastic computing (SC) has been used for lowcost and low power implementation of neural networks. Inherent inaccuracy and long latency of processing random bit-streams have made prior SC-based implementations inefficient compared to conventional fixed-point designs. Random or pseudo-random bitstreams often need to be processed for a very long time to produce acceptable results. This long latency leads to a significantly higher energy consumption than binary design counterparts. Low-discrepancy sequences have been recently used for fast-converging deterministic computation with stochastic constructs. In this work, we propose a low-cost, low-latency, and energy-efficient implementation of convolutional neural networks based on low-discrepancy deterministic bit-streams. Experimental results show a significant reduction in the energy consumption compared to previous random bitstream-based implementations and to the optimized fixed-point design with no quality degradation.

Original languageEnglish (US)
Title of host publicationProceedings of the 2019 Design, Automation and Test in Europe Conference and Exhibition, DATE 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1757-1762
Number of pages6
ISBN (Electronic)9783981926323
DOIs
StatePublished - May 14 2019
Event22nd Design, Automation and Test in Europe Conference and Exhibition, DATE 2019 - Florence, Italy
Duration: Mar 25 2019Mar 29 2019

Publication series

NameProceedings of the 2019 Design, Automation and Test in Europe Conference and Exhibition, DATE 2019

Conference

Conference22nd Design, Automation and Test in Europe Conference and Exhibition, DATE 2019
CountryItaly
CityFlorence
Period3/25/193/29/19

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work was supported in part by National Science Foundation grant no. CCF-1438286. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.

Funding Information:
This work was supported in part by National Science Foundation grant no. CCF-1438286.

Keywords

  • Convolutional neural networks
  • bitstream processing
  • energy-efficient design
  • low-cost design
  • stochastic computing

Fingerprint Dive into the research topics of 'Energy-Efficient Convolutional Neural Networks with Deterministic Bit-Stream Processing'. Together they form a unique fingerprint.

Cite this