Ultrafast silicon photonic reservoir computing engine delivering over 200 TOPS

Dongliang Wang, Yikun Nie, Gaolei Hu, Hon Ki Tsang and Chaoran Huang ()
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Dongliang Wang: The Chinese University of Hong Kong
Yikun Nie: The Chinese University of Hong Kong
Gaolei Hu: The Chinese University of Hong Kong
Hon Ki Tsang: The Chinese University of Hong Kong
Chaoran Huang: The Chinese University of Hong Kong

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: Abstract 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.

Date: 2024
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DOI: 10.1038/s41467-024-55172-3

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