Sequential logic to transform probabilities

Naman Saraf, Kia Bazargan

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

3 Scopus citations

Abstract

Stochastic computing is an alternative approach to conventional real arithmetic. A stochastic computing module is a digital system that operates on random bit streams representing real numbers. The success of stochastic computing relies on the efficient generation of random bit streams encoding real values in the unit interval. We present the design of random bit stream generators based on finite state machines (FSMs) that emulate Reversible Markov chains. We develop a general synthesis method to designs FSMs for generating arbitrary probabilities with finite resolution. We show that our method uses fewer input random sources for the constant random bit streams needed in a computation compared to the previous work. We further show that the output random bit stream quality and convergence times of our FSMs are reasonable.

Original languageEnglish (US)
Title of host publication2013 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2013 - Digest of Technical Papers
Pages732-738
Number of pages7
DOIs
StatePublished - Dec 1 2013
Event2013 32nd IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2013 - San Jose, CA, United States
Duration: Nov 18 2013Nov 21 2013

Publication series

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

Other

Other2013 32nd IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2013
CountryUnited States
CitySan Jose, CA
Period11/18/1311/21/13

Keywords

  • Finite state machines
  • Random bit streams
  • Reversible Markov chains
  • Stochastic computing

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