Computing complex functions using factorization in unipolar stochastic logic

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5 Scopus citations

Abstract

This paper addresses computing complex functions using unipolar stochastic logic. Stochastic computing requires simple logic gates and is inherently fault-tolerant. Thus, these structures are well suited for nanoscale CMOS technologies. Implementations of complex functions cost extremely low hardware complexity compared to traditional two's complement implementation. In this paper an approach based on polynomial factorization is proposed to compute functions in unipolar stochastic logic. In this approach, functions are expressed using polynomials, which are derived from Taylor expansion or Lagrange interpolation. Polynomials are implemented in stochastic logic by using factorization. Experimental results in terms of accuracy and hardware complexity are presented to compare the proposed designs of complex functions with previous implementations using Bernstein polynomials.

Original languageEnglish (US)
Title of host publicationGLSVLSI 2016 - Proceedings of the 2016 ACM Great Lakes Symposium on VLSI
PublisherAssociation for Computing Machinery
Pages109-112
Number of pages4
ISBN (Electronic)9781450342742
DOIs
StatePublished - May 18 2016
Event26th ACM Great Lakes Symposium on VLSI, GLSVLSI 2016 - Boston, United States
Duration: May 18 2016May 20 2016

Publication series

NameProceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI
Volume18-20-May-2016

Other

Other26th ACM Great Lakes Symposium on VLSI, GLSVLSI 2016
CountryUnited States
CityBoston
Period5/18/165/20/16

Keywords

  • Complex functions
  • Polynomial factorization
  • Stochastic division
  • Stochastic logic
  • Stochastic subtraction
  • Unipolar representation

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