Computing RBF kernel for SVM classification using stochastic logic

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6 Scopus citations

Abstract

This paper presents novel architectures for radial basis function (RBF) kernel computation for support vector machine (SVM) classifier using stochastic logic. Stochastic computing systems involve low hardware complexity and are inherently faulttolerant. Two types of architectures are presented. These include: an implementation with input and output both in bipolar format and an implementation with bipolar input and unipolar output. The computation of RBF kernel is comprised of the squared Euclidean distance and the exponential function. In the first implementation, two components are implemented in bipolar format and the exponential function is designed based on the finite state machine (FSM) method. The second implementation computes the squared Euclidean distance with bipolar input and unipolar output. The exponential function is implemented in unipolar format based on the Maclaurin expansion. The accuracies of two architectures are compared using support vectors from classification of electroencephalogram (EEG) signals for seizure prediction. From simulation results, it is shown that the computational error of the second stochastic implementation with format conversion is 24.90% less than that of the first implementation in bipolar format.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Workshop on Signal Processing Systems, SiPS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages327-332
Number of pages6
ISBN (Electronic)9781509033614
DOIs
StatePublished - Dec 9 2016
Event2016 IEEE International Workshop on Signal Processing Systems, SiPS 2016 - Dallas, United States
Duration: Oct 26 2016Oct 28 2016

Publication series

NameIEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation
ISSN (Print)1520-6130

Other

Other2016 IEEE International Workshop on Signal Processing Systems, SiPS 2016
CountryUnited States
CityDallas
Period10/26/1610/28/16

Keywords

  • EEG signals
  • Machine learning
  • Radial basis function kernel
  • Stochastic logic
  • Support vector machine

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  • Cite this

    Liu, Y., & Parhi, K. K. (2016). Computing RBF kernel for SVM classification using stochastic logic. In Proceedings - IEEE International Workshop on Signal Processing Systems, SiPS 2016 (pp. 327-332). [7780118] (IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SiPS.2016.64