Deterministic methods for stochastic computing using low-discrepancy sequences

M. Hassan Najafi, David J. Lilja, Marc Riedel

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

47 Scopus citations


Recently, deterministic approaches to stochastic computing (SC) have been proposed. These compute with the same constructs as stochastic computing but operate on deterministic bit streams. These approaches reduce the area, greatly reduce the latency (by an exponential factor), and produce completely accurate results. However, these methods do not scale well. Also, they lack the property of progressive precision enjoyed by SC. As a result, these deterministic approaches are not competitive for applications where some degree of inaccuracy can be tolerated. In this work we introduce two fast-converging, scalable deterministic approaches to SC based on low-discrepancy sequences. The results are completely accurate when running the operations for the required number of cycles. However, the computation can be truncated early if some inaccuracy is acceptable. Experimental results show that the proposed approaches significantly improve both the processing time and area-delay product compared to prior approaches.

Original languageEnglish (US)
Title of host publication2018 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2018 - Digest of Technical Papers
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781450359504
StatePublished - Nov 5 2018
Event37th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2018 - San Diego, United States
Duration: Nov 5 2018Nov 8 2018

Publication series

NameIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
ISSN (Print)1092-3152


Other37th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2018
Country/TerritoryUnited States
CitySan Diego

Bibliographical note

Funding Information:
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.

Publisher Copyright:
© 2018 ACM.

Copyright 2019 Elsevier B.V., All rights reserved.


  • deterministic computing
  • fast converging process
  • low-discrepancy sequences
  • scalable design
  • stochastic computing


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