Quantized neural networks with new stochastic multipliers

Bingzhe Li, M. Hassan Najafi, Bo Yuan, David J. Lilja

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

3 Scopus citations

Abstract

With increased interests of neural networks, hardware implementations of neural networks have been investigated. Researchers pursue low hardware cost by using different technologies such as stochastic computing and quantization. For example, the quantization is able to reduce total number of trained weights and results in low hardware cost. Stochastic computing aims to lower hardware costs substantially by using simple gates instead of complex arithmetic operations. In this paper, we propose a new stochastic multiplier with shifted unary code adders (SUC-Adder) for quantized neural networks. The new design uses the characteristic of quantized weights and tremendously reduces the hardware cost of neural networks. Experimental results indicate that our stochastic design achieves about 10x energy reduction compared to its counterpart binary implementation while maintaining slightly higher recognition error rates than the binary implementation.

Original languageEnglish (US)
Title of host publication2018 19th International Symposium on Quality Electronic Design, ISQED 2018
PublisherIEEE Computer Society
Pages376-382
Number of pages7
ISBN (Electronic)9781538612149
DOIs
StatePublished - May 9 2018
Event19th International Symposium on Quality Electronic Design, ISQED 2018 - Santa Clara, United States
Duration: Mar 13 2018Mar 14 2018

Publication series

NameProceedings - International Symposium on Quality Electronic Design, ISQED
Volume2018-March
ISSN (Print)1948-3287
ISSN (Electronic)1948-3295

Other

Other19th International Symposium on Quality Electronic Design, ISQED 2018
CountryUnited States
CitySanta Clara
Period3/13/183/14/18

Bibliographical note

Funding Information:
This work was supported in part by National Science Foundation grant no. CCF-1408123. 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.

Keywords

  • Multiplier
  • Neural networks
  • Quantization
  • Stochastic computing

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