@inproceedings{de3f9a8db4794a989db1b6d065f418bd,
title = "A fully flexible circuit implementation of clique-based neural networks in 65-nm CMOS",
abstract = "Clique-based neural networks implement low-complexity functions working with a reduced connectivity between neurons. Thus, they address very specific applications operating with a very low energy budget. This paper proposes a flexible and iterative neural architecture able to implement multiple types of clique-based neural networks of up to 3968 neurons. The circuit has been integrated in a ST 65-nm CMOS ASIC and validated in the context of ECG classification. The network core reacts in 83ns to a stimulation and occupies a 0.21mm2 silicon area.",
author = "Benoit Larras and Paul Chollet and Cyril Lahuec and Fabrice Seguin and Matthieu Arzel",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 ; Conference date: 27-05-2018 Through 30-05-2018",
year = "2018",
month = apr,
day = "26",
doi = "10.1109/ISCAS.2018.8350954",
language = "English",
series = "Proceedings - IEEE International Symposium on Circuits and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings",
}