Efficient Implementation of Mahalanobis Distance on Ferroelectric FinFET Crossbar for Outlier Detection
The developments in the nascent field of artificial-intelligence-of-things (AIoT) relies heavily on the availability of high-quality multi-dimensional data. A huge amount of data is being collected in this era of big data, predominantly for AI/ML algorithms and emerging applications. Considering suc...
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IEEE
2024-01-01
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| Series: | IEEE Journal of the Electron Devices Society |
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| Online Access: | https://ieeexplore.ieee.org/document/10563982/ |
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| author | Musaib Rafiq Yogesh Singh Chauhan Shubham Sahay |
| author_facet | Musaib Rafiq Yogesh Singh Chauhan Shubham Sahay |
| author_sort | Musaib Rafiq |
| collection | DOAJ |
| description | The developments in the nascent field of artificial-intelligence-of-things (AIoT) relies heavily on the availability of high-quality multi-dimensional data. A huge amount of data is being collected in this era of big data, predominantly for AI/ML algorithms and emerging applications. Considering such voluminous quantities, the collected data may contain a substantial number of outliers which must be detected before utilizing them for data mining or computations. Therefore, outlier detection techniques such as Mahalanobis distance computation have gained significant popularity recently. Mahalanobis distance, the multivariate equivalent of the Euclidean distance, is used to detect the outliers in the correlated data accurately and finds widespread application in fault identification, data clustering, singleclass classification, information security, data mining, etc. However, traditional CMOS-based approaches to compute Mahalanobis distance are bulky and consume a huge amount of energy. Therefore, there is an urgent need for a compact and energy-efficient implementation of an outlier detection technique which may be deployed on AIoT primitives, including wireless sensor nodes for in-situ outlier detection and generation of high-quality data. To this end, in this paper, for the first time, we have proposed an efficient Ferroelectric FinFET-based implementation for detecting outliers in correlated multivariate data using Mahalanobis distance. The proposed implementation utilizes two crossbar arrays of ferroelectric FinFETs to calculate the Mahalanobis distance and detect outliers in the popular Wisconsin breast cancer dataset using a novel inverter-based threshold circuit. Our implementation exhibits an accuracy of 94.1% which is comparable to the software implementations while consuming a significantly low energy (27.2 pJ). |
| format | Article |
| id | doaj-art-ad1f9fe079cf4f3aac961cb5c8c262bb |
| institution | Kabale University |
| issn | 2168-6734 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
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| series | IEEE Journal of the Electron Devices Society |
| spelling | doaj-art-ad1f9fe079cf4f3aac961cb5c8c262bb2025-01-29T00:00:17ZengIEEEIEEE Journal of the Electron Devices Society2168-67342024-01-011251652410.1109/JEDS.2024.341644110563982Efficient Implementation of Mahalanobis Distance on Ferroelectric FinFET Crossbar for Outlier DetectionMusaib Rafiq0https://orcid.org/0000-0002-1715-8301Yogesh Singh Chauhan1https://orcid.org/0000-0002-3356-8917Shubham Sahay2https://orcid.org/0000-0001-9992-3240Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, IndiaDepartment of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, IndiaDepartment of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, IndiaThe developments in the nascent field of artificial-intelligence-of-things (AIoT) relies heavily on the availability of high-quality multi-dimensional data. A huge amount of data is being collected in this era of big data, predominantly for AI/ML algorithms and emerging applications. Considering such voluminous quantities, the collected data may contain a substantial number of outliers which must be detected before utilizing them for data mining or computations. Therefore, outlier detection techniques such as Mahalanobis distance computation have gained significant popularity recently. Mahalanobis distance, the multivariate equivalent of the Euclidean distance, is used to detect the outliers in the correlated data accurately and finds widespread application in fault identification, data clustering, singleclass classification, information security, data mining, etc. However, traditional CMOS-based approaches to compute Mahalanobis distance are bulky and consume a huge amount of energy. Therefore, there is an urgent need for a compact and energy-efficient implementation of an outlier detection technique which may be deployed on AIoT primitives, including wireless sensor nodes for in-situ outlier detection and generation of high-quality data. To this end, in this paper, for the first time, we have proposed an efficient Ferroelectric FinFET-based implementation for detecting outliers in correlated multivariate data using Mahalanobis distance. The proposed implementation utilizes two crossbar arrays of ferroelectric FinFETs to calculate the Mahalanobis distance and detect outliers in the popular Wisconsin breast cancer dataset using a novel inverter-based threshold circuit. Our implementation exhibits an accuracy of 94.1% which is comparable to the software implementations while consuming a significantly low energy (27.2 pJ).https://ieeexplore.ieee.org/document/10563982/Crossbar arraydeep learningferroelectric FETsMahalanobis distanceoutlier detection |
| spellingShingle | Musaib Rafiq Yogesh Singh Chauhan Shubham Sahay Efficient Implementation of Mahalanobis Distance on Ferroelectric FinFET Crossbar for Outlier Detection IEEE Journal of the Electron Devices Society Crossbar array deep learning ferroelectric FETs Mahalanobis distance outlier detection |
| title | Efficient Implementation of Mahalanobis Distance on Ferroelectric FinFET Crossbar for Outlier Detection |
| title_full | Efficient Implementation of Mahalanobis Distance on Ferroelectric FinFET Crossbar for Outlier Detection |
| title_fullStr | Efficient Implementation of Mahalanobis Distance on Ferroelectric FinFET Crossbar for Outlier Detection |
| title_full_unstemmed | Efficient Implementation of Mahalanobis Distance on Ferroelectric FinFET Crossbar for Outlier Detection |
| title_short | Efficient Implementation of Mahalanobis Distance on Ferroelectric FinFET Crossbar for Outlier Detection |
| title_sort | efficient implementation of mahalanobis distance on ferroelectric finfet crossbar for outlier detection |
| topic | Crossbar array deep learning ferroelectric FETs Mahalanobis distance outlier detection |
| url | https://ieeexplore.ieee.org/document/10563982/ |
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