All‐optical nonlinear activation function based on stimulated Brillouin scattering

Optical neural networks have demonstrated their potential to overcome the computational bottleneck of modern digital electronics. However, their development towards high-performing computing alternatives is hindered by one of the optical neural networks' key components: the activation function. Most of the reported activation functions rely on opto-electronic conversion, sacrificing the unique advantages of photonics, such as resource-efficient coherent and frequency-multiplexed information encoding. Here, we experimentally demonstrate a photonic nonlinear activation function based on stimulated Brillouin scattering. It is coherent and frequency selective and can be tuned all-optically to take LeakyReLU, Sigmoid, and Quadratic shape. Our design compensates for the insertion loss automatically by providing net gain as high as 20 dB, paving the way for deep optical neural networks.

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Publication Details

Journal
Nanophotonics
Published
2025-02-13
DOI
https://doi.org/10.1515/nanoph-2024-0513
Citations
12
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
29.77

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article

All‐optical nonlinear activation function based on stimulated Brillouin scattering

Grigorii Slinkov, Dirk Englund, Birgit Stiller, Steven Becker
12 citations
Nanophotonics
Neural Networks and Reservoir Computing
29.77
article

All‐optical nonlinear activation function based on stimulated Brillouin scattering

Grigorii Slinkov, Dirk Englund, Birgit Stiller, Steven Becker
article en
12 citations

Abstract

Optical neural networks have demonstrated their potential to overcome the computational bottleneck of modern digital electronics. However, their development towards high-performing computing alternatives is hindered by one of the optical neural networks' key components: the activation function. Most of the reported activation functions rely on opto-electronic conversion, sacrificing the unique advantages of photonics, such as resource-efficient coherent and frequency-multiplexed information encoding. Here, we experimentally demonstrate a photonic nonlinear activation function based on stimulated Brillouin scattering. It is coherent and frequency selective and can be tuned all-optically to take LeakyReLU, Sigmoid, and Quadratic shape. Our design compensates for the insertion loss automatically by providing net gain as high as 20 dB, paving the way for deep optical neural networks.

NanophotonicsVol. 14(16)
Leibniz University Hannover (DE), Friedrich-Alexander-Universität Erlangen-Nürnberg (DE), Max Planck Institute for the Science of Light (DE), Massachusetts Institute of Technology (US)
Deutsche Forschungsgemeinschaft, Max-Planck-Gesellschaft, Studienstiftung des Deutschen Volkes
Decent work and economic growth
Openalex Percentile: Top 0%
Neural Networks and Reservoir Computing
29.77
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