Machine learning classifiers using stochastic logic

Yin Liu, Hariharasudhan Venkataraman, Zisheng Zhang, Keshab K. Parhi

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

8 Scopus citations

Abstract

This paper presents novel architectures for machine learning based classifiers using stochastic logic. Two types of classifier architectures are presented. These include: linear support vector machine (SVM) and artificial neural network (ANN). Stochastic computing systems require fewer logic gates and are inherently fault-tolerant. Thus, these structures are well suited for nanoscale CMOS technologies. These architectures are validated using seizure prediction from electroencephalogram (EEG) as an application example. To improve the accuracy of proposed stochastic classifiers, a novel approach based on linear transformation of input data is proposed for EEG signal classification using linear SVM classifiers. Simulation results in terms of the classification accuracy are presented for the proposed stochastic computing and the traditional binary implementations based datasets from one patient. Compared to conventional binary implementation, the accuracy of the proposed stochastic ANN is improved by 5.89%. Synthesis results are also presented for EEG signal classification. Compared to the traditional binary linear SVM, the hardware complexity, power consumption and critical path of the stochastic implementation are reduced by 78%, 74% and 53%, respectively. The hardware complexity, power consumption and critical path of the stochastic ANN classifier are reduced by 92%, 88% and 47%, respectively, compared to the conventional binary implementation.

Original languageEnglish (US)
Title of host publicationProceedings of the 34th IEEE International Conference on Computer Design, ICCD 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages408-411
Number of pages4
ISBN (Electronic)9781509051427
DOIs
StatePublished - Nov 22 2016
Event34th IEEE International Conference on Computer Design, ICCD 2016 - Scottsdale, United States
Duration: Oct 2 2016Oct 5 2016

Publication series

NameProceedings of the 34th IEEE International Conference on Computer Design, ICCD 2016

Other

Other34th IEEE International Conference on Computer Design, ICCD 2016
CountryUnited States
CityScottsdale
Period10/2/1610/5/16

Keywords

  • Artificial Neural Network
  • EEG signals
  • Machine learning
  • Seizure diagnosis
  • Stochastic logic
  • Support Vector Machine
  • Synthesis

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