@inproceedings{fa3001aa327841e0ab154a6eb7f549ab,
title = "39fJ Analog Artificial Neural Network for Breast Cancer Classification in 65nm CMOS",
abstract = "An analog artificial neural network (ANN) classifier using a common-source amplifier based nonlinear activation function is presented in this work. A shallow ANN is designed in 65nm CMOS to perform binary classification on breast cancer dataset and identify each patient data as either benign or malignant. Use of common-source amplifier structure simplifies the ANN and results in only 39fJ/classification at 0.8V power supply and core area of only 240μm2. The classifier is trained using Matlab and validated using Spectre simulations.",
keywords = "CMOS, WBCD, analog AI circuit, artificial neural network, classifier",
author = "Ruobing Hua and Arindam Sanyal",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 62nd IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2019 ; Conference date: 04-08-2019 Through 07-08-2019",
year = "2019",
month = aug,
doi = "10.1109/MWSCAS.2019.8885149",
language = "English (US)",
series = "Midwest Symposium on Circuits and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "436--439",
booktitle = "2019 IEEE 62nd International Midwest Symposium on Circuits and Systems, MWSCAS 2019",
}