Towards theoretical cost limit of stochastic number generators for stochastic computing

Meng Yang, Bingzhe Li, David J. Lilja, Bo Yuan, Weikang Qian

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

11 Scopus citations

Abstract

Stochastic number generator (SNG) is one important component of stochastic computing (SC). An SNG usually consists of a random number source (RNS) and a probability conversion circuit (PCC). The SNGs occupy a large portion of the total area and power of a stochastic circuit. Thus, it is critical to lower the area and power of the SNGs. The existing methods only focused on simplifying the RNSs inside the SNGs, such as sharing the RNSs and using emerging devices. However, how to reduce the area and power of PCCs is never studied. In this work, we explore this problem and propose a solution that can effectively reduce the area and power of PCCs. We also study the theoretical limit on the cost of SNG and find that our proposed design approaches the limit. The experimental results show that our design can gain up to 2x improvement in power-delay product over the traditional SNGs.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2018
PublisherIEEE Computer Society
Pages154-159
Number of pages6
ISBN (Print)9781538670996
DOIs
StatePublished - Aug 7 2018
Event17th IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2018 - Hong Kong, Hong Kong
Duration: Jul 9 2018Jul 11 2018

Publication series

NameProceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI
Volume2018-July
ISSN (Print)2159-3469
ISSN (Electronic)2159-3477

Other

Other17th IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2018
CountryHong Kong
CityHong Kong
Period7/9/187/11/18

Bibliographical note

Funding Information:
This work is supported in part by National Natural Science Foundation of China (NSFC) under grant no. 61472243 and 61204042 and in part by National Science Foundation (NSF) of U.S.A. under grant no. CCF-1408123 and 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 NSFC and NSF.

Publisher Copyright:
© 2018 IEEE.

Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.

Keywords

  • Probability Conversion Circuit
  • Stochastic number generator
  • Theoretical Limit

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