Slinkov Grigorii, Becker Steven, Englund Dirk, Stiller Birgit
Max-Planck-Institute for the Science of Light, Staudtstr. 2, 91058 Erlangen, Germany.
Department of Physics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Staudtstr. 7, 91058 Erlangen, Germany.
Nanophotonics. 2025 Feb 14;14(16):2711-2722. doi: 10.1515/nanoph-2024-0513. eCollection 2025 Aug.
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.
光学神经网络已展现出克服现代数字电子学计算瓶颈的潜力。然而,它们向高性能计算替代方案的发展受到光学神经网络关键组件之一的阻碍:激活函数。大多数已报道的激活函数依赖光电转换,牺牲了光子学的独特优势,比如资源高效的相干和频率复用信息编码。在此,我们通过实验展示了一种基于受激布里渊散射的光子非线性激活函数。它具有相干性和频率选择性,并且可以全光调谐以呈现泄漏修正线性单元(LeakyReLU)、Sigmoid和二次函数形状。我们的设计通过提供高达20 dB的净增益自动补偿插入损耗,为深度光学神经网络铺平了道路。