Optoelectronic Demonstration of the Nonlinear Activation Module for Optical Neural Networks

In this letter, we experimentally demonstrated the viability of the optoelectronic nonlinear activation module in optical neural networks. By adopting the beam splitter, photo-diode, electronic processing unit and tunable attenuator, nonlinear activation function ReLU which is widely used in the state of the art neural networks can be implemented in an optoelectronic way. Compared with other optical and optoelectronic implementations of nonlinear activation function, this method has advantages in expanses and complexity. This method also has potential to be applied into multiple types of optical neural networks to further increase the prediction accuracy.

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Publication Details

Journal
IEEE Photonics Technology Letters
Published
2024-12-13
DOI
https://doi.org/10.1109/lpt.2024.3517159
Citations
2
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
0.69
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article

Optoelectronic Demonstration of the Nonlinear Activation Module for Optical Neural Networks

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2 citations
IEEE Photonics Technology Letters
Neural Networks and Reservoir Computing
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article

Optoelectronic Demonstration of the Nonlinear Activation Module for Optical Neural Networks

Zuxing Zhang, Boyu Hua, Yubin Zang, Fangzheng Zhang, Zhipeng Lin, Simin Li
article en
2 citations

Abstract

In this letter, we experimentally demonstrated the viability of the optoelectronic nonlinear activation module in optical neural networks. By adopting the beam splitter, photo-diode, electronic processing unit and tunable attenuator, nonlinear activation function ReLU which is widely used in the state of the art neural networks can be implemented in an optoelectronic way. Compared with other optical and optoelectronic implementations of nonlinear activation function, this method has advantages in expanses and complexity. This method also has potential to be applied into multiple types of optical neural networks to further increase the prediction accuracy.

IEEE Photonics Technology LettersVol. 37(2)
Nanjing University of Posts and Telecommunications (CN), Nanjing University of Aeronautics and Astronautics (CN)
Openalex Percentile: Top 23%
Neural Networks and Reservoir Computing
0.69
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