@inproceedings{2078098be26f4350a4018ce58411fee6,
title = "Sleep stage classification with stochastic Bayesian inference",
abstract = "The design of electronic circuits that can realize Bayesian inference is an important goal for exploiting machine learning in a fast and efficient way. We recently developed a novel architecture based on stochastic computation with Muller C-elements that can realize a circuit level na{\"i}ve Bayes inference. This technique can be implemented using low power nanodevices exhibiting faults and device variations. Here we show how a more complex classification problem can be transformed into a simple circuit using this framework where an effective classification can be obtained with a minimal amount of information. This suggests that substantially smaller spatial footprints for portable devices could ultimately be achieved.",
keywords = "Bayesian inference, Biomedical data, Muller C-element, Sleep classification, Stochastic computing",
author = "Calvet, \{L. E.\} and Friedman, \{J. S.\} and D. Querlioz and P. Bessiere and J. Droulez",
note = "Publisher Copyright: {\textcopyright} 2016 ACM.; 2016 IEEE/ACM International Symposium on Nanoscale Architectures, NANOARCH 2016 ; Conference date: 18-07-2016 Through 20-07-2016",
year = "2016",
month = sep,
day = "14",
doi = "10.1145/2950067.2950085",
language = "English",
series = "Proceedings of the 2016 IEEE/ACM International Symposium on Nanoscale Architectures, NANOARCH 2016",
publisher = "Presses Polytechniques Et Universitaires Romandes",
pages = "117--122",
booktitle = "Proceedings of the 2016 IEEE/ACM International Symposium on Nanoscale Architectures, NANOARCH 2016",
}