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) |
|---|---|
| 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 |
|---|---|
| ISSN (Print) | 0738-100X |
Conference
| Conference | 56th Annual Design Automation Conference, DAC 2019 |
|---|---|
| Country | United States |
| City | Las Vegas |
| Period | 6/2/19 → 6/6/19 |
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Keywords
- DNA computing
- Fractional Coding
- Molecular Computing
- Radial Basis Function
- Stochastic Logic
- Support Vector Machine
Cite this
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 proceeding › Conference contribution
}
TY - GEN
T1 - Computing radial basis function support vector machine using DNA via fractional coding
AU - Liu, Xingyi
AU - Parhi, Keshab K.
PY - 2019/6/2
Y1 - 2019/6/2
N2 - 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.
AB - 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.
KW - DNA computing
KW - Fractional Coding
KW - Molecular Computing
KW - Radial Basis Function
KW - Stochastic Logic
KW - Support Vector Machine
UR - http://www.scopus.com/inward/record.url?scp=85067814550&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85067814550&partnerID=8YFLogxK
U2 - 10.1145/3316781.3317791
DO - 10.1145/3316781.3317791
M3 - Conference contribution
AN - SCOPUS:85067814550
T3 - Proceedings - Design Automation Conference
BT - Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
ER -