Surekcigil Pesch, I;
Bestelink, E;
de Sagazan, O;
Mehonic, A;
Sporea, RA;
(2022)
Multimodal transistors as ReLU activation functions in physical neural network classifiers.
Scientific Reports
, 12
, Article 670. 10.1038/s41598-021-04614-9.
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Abstract
Artificial neural networks (ANNs) providing sophisticated, power-efficient classification are finding their way into thin-film electronics. Thin-film technologies require robust, layout-efficient devices with facile manufacturability. Here, we show how the multimodal transistor’s (MMT’s) transfer characteristic, with linear dependence in saturation, replicates the rectified linear unit (ReLU) activation function of convolutional ANNs (CNNs). Using MATLAB, we evaluate CNN performance using systematically distorted ReLU functions, then substitute measured and simulated MMT transfer characteristics as proxies for ReLU. High classification accuracy is maintained, despite large variations in geometrical and electrical parameters, as CNNs use the same activation functions for training and classification.
| Type: | Article |
|---|---|
| Title: | Multimodal transistors as ReLU activation functions in physical neural network classifiers |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/s41598-021-04614-9 |
| Publisher version: | https://doi.org/10.1038/s41598-021-04614-9 |
| Language: | English |
| Additional information: | © Te Author(s) 2022. Tis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. Te images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10141969 |
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