Ultrafast silicon photonic reservoir computing engine delivering over 200 TOPS

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.

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Institutions

Publication Details

Journal
Nature Communications
Published
2024-12-30
DOI
https://doi.org/10.1038/s41467-024-55172-3
Citations
61
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
21.19

Funders

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article

Ultrafast silicon photonic reservoir computing engine delivering over 200 TOPS

Gaolei Hu, Hon Ki Tsang, Chaoran Huang, Dongliang Wang et al.
61 citations
Nature Communications
Neural Networks and Reservoir Computing
21.19
article

Ultrafast silicon photonic reservoir computing engine delivering over 200 TOPS

Gaolei Hu, Hon Ki Tsang, Chaoran Huang, Dongliang Wang, Yikun Nie
article en
61 citations

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.

Nature CommunicationsVol. 15(1)
Chinese University of Hong Kong (HK)
National Natural Science Foundation of China, Research Grants Council, University Grants Committee, Chinese University of Hong Kong, Innovation and Technology Fund, Shun Hing Institute of Advanced Engineering
Industry, innovation and infrastructure
Openalex Percentile: Top 0%
Neural Networks and Reservoir Computing
21.19
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Ultrafast silicon photonic reservoir computing engine delivering over 200 TOPS — Gaolei Hu, Hon Ki Tsang, et al. · Nature Communications (2024) | TGRS Research Map | TGRS