A Low-Power Analog Integrated Euclidean Distance Radial Basis Function Classifier
This study introduces a low-power analog integrated Euclidean distance radial basis function classifier. The high-level architecture is composed of several Manhattan distance circuits in connection with a current comparator circuit. Notably, each implementation was designed with modularity and scala...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/13/5/921 |
| _version_ | 1797264676663328768 |
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| author | Vassilis Alimisis Christos Dimas Paul P. Sotiriadis |
| author_facet | Vassilis Alimisis Christos Dimas Paul P. Sotiriadis |
| author_sort | Vassilis Alimisis |
| collection | DOAJ |
| description | This study introduces a low-power analog integrated Euclidean distance radial basis function classifier. The high-level architecture is composed of several Manhattan distance circuits in connection with a current comparator circuit. Notably, each implementation was designed with modularity and scalability in mind, effectively accommodating variations in the classification parameters. The proposed classifier’s operational principles are meticulously detailed, tailored for low-power, low-voltage, and fully tunable implementations, specifically targeting biomedical applications. This design methodology materialized within a 90 nm CMOS process, utilizing the Cadence IC Suite for the comprehensive management of both the schematic and layout design aspects. During the verification phase, post-layout simulation results were meticulously cross-referenced with software-based classifier implementations. Also, a comparison study with related analog classifiers is provided. Through the simulation results and comparative study, the design architecture’s accuracy and sensitivity were effectively validated and confirmed. |
| first_indexed | 2024-04-25T00:32:41Z |
| format | Article |
| id | doaj.art-e046a94bd67d4770a7dd9551498292c4 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-04-25T00:32:41Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-e046a94bd67d4770a7dd9551498292c42024-03-12T16:42:36ZengMDPI AGElectronics2079-92922024-02-0113592110.3390/electronics13050921A Low-Power Analog Integrated Euclidean Distance Radial Basis Function ClassifierVassilis Alimisis0Christos Dimas1Paul P. Sotiriadis2Department of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, GreeceDepartment of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, GreeceDepartment of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, GreeceThis study introduces a low-power analog integrated Euclidean distance radial basis function classifier. The high-level architecture is composed of several Manhattan distance circuits in connection with a current comparator circuit. Notably, each implementation was designed with modularity and scalability in mind, effectively accommodating variations in the classification parameters. The proposed classifier’s operational principles are meticulously detailed, tailored for low-power, low-voltage, and fully tunable implementations, specifically targeting biomedical applications. This design methodology materialized within a 90 nm CMOS process, utilizing the Cadence IC Suite for the comprehensive management of both the schematic and layout design aspects. During the verification phase, post-layout simulation results were meticulously cross-referenced with software-based classifier implementations. Also, a comparison study with related analog classifiers is provided. Through the simulation results and comparative study, the design architecture’s accuracy and sensitivity were effectively validated and confirmed.https://www.mdpi.com/2079-9292/13/5/921analog VLSIlow-power designcardiovascular diseasemachine learninganalog classifiers |
| spellingShingle | Vassilis Alimisis Christos Dimas Paul P. Sotiriadis A Low-Power Analog Integrated Euclidean Distance Radial Basis Function Classifier Electronics analog VLSI low-power design cardiovascular disease machine learning analog classifiers |
| title | A Low-Power Analog Integrated Euclidean Distance Radial Basis Function Classifier |
| title_full | A Low-Power Analog Integrated Euclidean Distance Radial Basis Function Classifier |
| title_fullStr | A Low-Power Analog Integrated Euclidean Distance Radial Basis Function Classifier |
| title_full_unstemmed | A Low-Power Analog Integrated Euclidean Distance Radial Basis Function Classifier |
| title_short | A Low-Power Analog Integrated Euclidean Distance Radial Basis Function Classifier |
| title_sort | low power analog integrated euclidean distance radial basis function classifier |
| topic | analog VLSI low-power design cardiovascular disease machine learning analog classifiers |
| url | https://www.mdpi.com/2079-9292/13/5/921 |
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