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Survey on activation functions for optical neural networks

Océane Destras, Sébastien Le Beux, Felipe Göhring de Magalhães and Gabriela Nicolescu

Article (2024)

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Integrated photonics arises as a fast and energy-efficient technology for the implementation of artificial neural networks (ANNs). Indeed, with the growing interest in ANNs, photonics shows great promise to overcome current limitations of electronic-based implementation. For example, it has been shown that neural networks integrating optical matrix multiplications can potentially run two orders of magnitude faster than their electronic counterparts. However, the transposition in the optical domain of the activation functions, which is a key feature of ANNs, remains a challenge. There is no direct optical implementation of state-of-the-art activation functions. Currently, most designs require time-consuming and power-hungry electro-optical conversions. In this survey, we review both all-optical and opto-electronic activation functions proposed in the state-of-the-art. We present activation functions with their key characteristics, and we summarize challenges for their use in the context of all-optical neural networks. We then highlight research directions for the implementation of fully optical neural networks.

Uncontrolled Keywords

computing methodologies; neural networks; hardware; emerging optical and photonic technologies

Subjects: 2500 Electrical and electronic engineering > 2500 Electrical and electronic engineering
2700 Information technology > 2700 Information technology
Department: Department of Computer Engineering and Software Engineering
Research Center: Other
PolyPublie URL: https://publications.polymtl.ca/56690/
Journal Title: ACM Computing Surveys (vol. 56, no. 2)
Publisher: Association for Computing Machinery
DOI: 10.1145/3607533
Official URL: https://doi.org/10.1145/3607533
Date Deposited: 23 Jan 2024 17:13
Last Modified: 30 Jan 2024 03:27
Cite in APA 7: Destras, O., Le Beux, S., Göhring de Magalhães, F., & Nicolescu, G. (2024). Survey on activation functions for optical neural networks. ACM Computing Surveys, 56(2), 35 (30 pages). https://doi.org/10.1145/3607533


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