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Open AccessArticle

A Fully-Integrated Analog Machine Learning Classifier for Breast Cancer Classification

1
Department of Electrical Engineering, University at Buffalo, Buffalo, NY 14260, USA
2
Department of Biomedical Informatics and the Department of Radiology, Emory University, Atlanta, GA 30322, USA
3
Georgia Tech, Atlanta, GA 30322, USA
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(3), 515; https://doi.org/10.3390/electronics9030515
Received: 11 February 2020 / Revised: 13 March 2020 / Accepted: 14 March 2020 / Published: 20 March 2020
We propose a fully integrated common-source amplifier based analog artificial neural network (ANN). The performance of the proposed ANN with a custom non-linear activation function is demonstrated on the breast cancer classification task. A hardware-software co-design methodology is adopted to ensure good matching between the software AI model and hardware prototype. A 65 nm prototype of the proposed ANN is fabricated and characterized. The prototype ANN achieves 97% classification accuracy when operating from a 1.1 V supply with an energy consumption of 160 fJ/classification. The prototype consumes 50 μ W power and occupies 0.003 mm 2 die area. View Full-Text
Keywords: machine learning; classification; analog-AI; neural network; intelligence-at-the-edge; CS amplifier; breast cancer detection machine learning; classification; analog-AI; neural network; intelligence-at-the-edge; CS amplifier; breast cancer detection
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MDPI and ACS Style

T. Chandrasekaran, S.; Hua, R.; Banerjee, I.; Sanyal, A. A Fully-Integrated Analog Machine Learning Classifier for Breast Cancer Classification. Electronics 2020, 9, 515.

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