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 language | English (US) |
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Title of host publication | Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781450367257 |
DOIs | |
State | Published - Jun 2 2019 |
Event | 56th Annual Design Automation Conference, DAC 2019 - Las Vegas, United States Duration: Jun 2 2019 → Jun 6 2019 |
Publication series
Name | Proceedings - Design Automation Conference |
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ISSN (Print) | 0738-100X |
Conference
Conference | 56th Annual Design Automation Conference, DAC 2019 |
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Country/Territory | United States |
City | Las Vegas |
Period | 6/2/19 → 6/6/19 |
Bibliographical note
Publisher Copyright:© 2019 Copyright held by the owner/author(s).
Keywords
- DNA computing
- Fractional Coding
- Molecular Computing
- Radial Basis Function
- Stochastic Logic
- Support Vector Machine