Computing radial basis function support vector machine using DNA via fractional coding

Xingyi Liu, Keshab K Parhi

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

Abstract

This paper describes a novel approach to synthesize molecular reactions to compute a radial basis function (RBF) support vector machine (SVM) kernel. The approach is based on fractional coding where a variable is represented by two molecules. The synergy between fractional coding in molecular computing and stochastic logic implementations in electronic computing is key to translating known stochastic logic circuits to molecular computing. Although inspired by prior stochastic logic implementation of the RBF-SVM kernel, the proposed molecular reactions require non-obvious modifications. This paper introduces a new explicit bipolar-to-unipolar molecular converter for intermediate format conversion. Two designs are presented; one is based on the explicit and the other is based on implicit conversion from prior stochastic logic. When 5 support vectors are used, it is shown that the DNA RBF-SVM realized using the explicit format conversion has orders of magnitude less regression error than that based on implicit conversion.

Original languageEnglish (US)
Title of host publicationProceedings of the 56th Annual Design Automation Conference 2019, DAC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781450367257
DOIs
StatePublished - Jun 2 2019
Event56th Annual Design Automation Conference, DAC 2019 - Las Vegas, United States
Duration: Jun 2 2019Jun 6 2019

Publication series

NameProceedings - Design Automation Conference
ISSN (Print)0738-100X

Conference

Conference56th Annual Design Automation Conference, DAC 2019
CountryUnited States
CityLas Vegas
Period6/2/196/6/19

Fingerprint

Radial Functions
Support vector machines
Basis Functions
Support Vector Machine
DNA
Fractional
Coding
Molecular Computing
Logic
Computing
Logic circuits
kernel
Support Vector
Synergy
Converter
Molecules
Regression
Electronics

Keywords

  • DNA computing
  • Fractional Coding
  • Molecular Computing
  • Radial Basis Function
  • Stochastic Logic
  • Support Vector Machine

Cite this

Liu, X., & Parhi, K. K. (2019). Computing radial basis function support vector machine using DNA via fractional coding. In Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019 [a143] (Proceedings - Design Automation Conference). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1145/3316781.3317791

Computing radial basis function support vector machine using DNA via fractional coding. / Liu, Xingyi; Parhi, Keshab K.

Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. a143 (Proceedings - Design Automation Conference).

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

Liu, X & Parhi, KK 2019, Computing radial basis function support vector machine using DNA via fractional coding. in Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019., a143, Proceedings - Design Automation Conference, Institute of Electrical and Electronics Engineers Inc., 56th Annual Design Automation Conference, DAC 2019, Las Vegas, United States, 6/2/19. https://doi.org/10.1145/3316781.3317791
Liu X, Parhi KK. Computing radial basis function support vector machine using DNA via fractional coding. In Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. a143. (Proceedings - Design Automation Conference). https://doi.org/10.1145/3316781.3317791
Liu, Xingyi ; Parhi, Keshab K. / Computing radial basis function support vector machine using DNA via fractional coding. Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. (Proceedings - Design Automation Conference).
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