Dataset Information

Multimodal transistors as ReLU activation functions in physical neural network classifiers.

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.

SUBMITTER: Surekcigil Pesch I

PROVIDER: S-EPMC8758690 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Multimodal transistors as ReLU activation functions in physical neural network classifiers.

Surekcigil Pesch Isin I Bestelink Eva E de Sagazan Olivier O Mehonic Adnan A Sporea Radu A RA

Scientific reports 20220113 1

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 system ...[more]

PMID: 35027631

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Dataset Information

Multimodal transistors as ReLU activation functions in physical neural network classifiers.

Publications

Multimodal transistors as ReLU activation functions in physical neural network classifiers.

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