Wang Dongliang, Nie Yikun, Hu Gaolei, Tsang Hon Ki, Huang Chaoran
Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.
Nat Commun. 2024 Dec 30;15(1):10841. doi: 10.1038/s41467-024-55172-3.
Reservoir computing (RC) is a powerful machine learning algorithm for information processing. Despite numerous optical implementations, its speed and scalability remain limited by the need to establish recurrent connections and achieve efficient optical nonlinearities. This work proposes a streamlined photonic RC design based on a new paradigm, called next-generation RC, which overcomes these limitations. Our design leads to a compact silicon photonic computing engine with an experimentally demonstrated processing speed of over 60 GHz. Experimental results demonstrate state-of-the-art performance in prediction, emulation, and classification tasks across various machine learning applications. Compared to traditional RC systems, our silicon photonic RC engine offers several key advantages, including no speed limitations, a compact footprint, and a high tolerance to fabrication errors. This work lays the foundation for ultrafast on-chip photonic RC, representing significant progress toward developing next-generation high-speed photonic computing and signal processing.
储层计算(RC)是一种用于信息处理的强大机器学习算法。尽管有众多光学实现方式,但其速度和可扩展性仍受限于建立循环连接和实现高效光学非线性的需求。这项工作基于一种名为下一代RC的新范式提出了一种简化的光子RC设计,该设计克服了这些限制。我们的设计导致了一个紧凑的硅光子计算引擎,其实验证明的处理速度超过60 GHz。实验结果表明,在各种机器学习应用的预测、仿真和分类任务中,该引擎具有最先进的性能。与传统RC系统相比,我们的硅光子RC引擎具有几个关键优势,包括无速度限制、占地面积小以及对制造误差的高容忍度。这项工作为超快速片上光子RC奠定了基础,代表了在开发下一代高速光子计算和信号处理方面取得的重大进展。