Computing polynomials with positive coefficients using stochastic logic by double-NAND expansion

Sayed Ahmad Salehi, Yin Liu, Marc D. Riedel, Keshab K. Parhi

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

7 Scopus citations

Abstract

This paper proposes a novel method, referred to as double-NAND expansion, to implement polynomials with all positive coefficients using unipolar stochastic logic. The proposed double-NAND expansion leads to implementations of polynomials using no more than 2n NAND gates where n represents the degree of the polynomial. The proposed implementations are compared with those based on multiplexers, Bernstein polynomial method, finite state machine method and factorization. The paper also considers implementations of several functions expressed as polynomials using truncated Mclaurin series based on the proposed approach. The experimental results show that the proposed method outperforms the prior methods in terms of accuracy, hardware complexity, and critical path.

Original languageEnglish (US)
Title of host publicationGLSVLSI 2017 - Proceedings of the Great Lakes Symposium on VLSI 2017
PublisherAssociation for Computing Machinery
Pages471-474
Number of pages4
ISBN (Electronic)9781450349727
DOIs
StatePublished - May 10 2017
Event27th Great Lakes Symposium on VLSI, GLSVLSI 2017 - Banff, Canada
Duration: May 10 2017May 12 2017

Publication series

NameProceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI
VolumePart F127756

Other

Other27th Great Lakes Symposium on VLSI, GLSVLSI 2017
CountryCanada
CityBanff
Period5/10/175/12/17

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Keywords

  • Double-NAND expansion
  • Stochastic logic

Cite this

Salehi, S. A., Liu, Y., Riedel, M. D., & Parhi, K. K. (2017). Computing polynomials with positive coefficients using stochastic logic by double-NAND expansion. In GLSVLSI 2017 - Proceedings of the Great Lakes Symposium on VLSI 2017 (pp. 471-474). (Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI; Vol. Part F127756). Association for Computing Machinery. https://doi.org/10.1145/3060403.3060410