Ultrafast silicon photonic reservoir computing engine delivering over 200 TOPS
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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-12-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-55172-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841559264746799104 |
|---|---|
| author | Dongliang Wang Yikun Nie Gaolei Hu Hon Ki Tsang Chaoran Huang |
| author_facet | Dongliang Wang Yikun Nie Gaolei Hu Hon Ki Tsang Chaoran Huang |
| author_sort | Dongliang Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-167e22ff04404bb69afbf9440686ca35 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-167e22ff04404bb69afbf9440686ca352025-01-05T12:35:51ZengNature PortfolioNature Communications2041-17232024-12-0115111110.1038/s41467-024-55172-3Ultrafast silicon photonic reservoir computing engine delivering over 200 TOPSDongliang Wang0Yikun Nie1Gaolei Hu2Hon Ki Tsang3Chaoran Huang4Department of Electronic Engineering, The Chinese University of Hong KongDepartment of Electronic Engineering, The Chinese University of Hong KongDepartment of Electronic Engineering, The Chinese University of Hong KongDepartment of Electronic Engineering, The Chinese University of Hong KongDepartment of Electronic Engineering, The Chinese University of Hong KongAbstract 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.https://doi.org/10.1038/s41467-024-55172-3 |
| spellingShingle | Dongliang Wang Yikun Nie Gaolei Hu Hon Ki Tsang Chaoran Huang Ultrafast silicon photonic reservoir computing engine delivering over 200 TOPS Nature Communications |
| title | Ultrafast silicon photonic reservoir computing engine delivering over 200 TOPS |
| title_full | Ultrafast silicon photonic reservoir computing engine delivering over 200 TOPS |
| title_fullStr | Ultrafast silicon photonic reservoir computing engine delivering over 200 TOPS |
| title_full_unstemmed | Ultrafast silicon photonic reservoir computing engine delivering over 200 TOPS |
| title_short | Ultrafast silicon photonic reservoir computing engine delivering over 200 TOPS |
| title_sort | ultrafast silicon photonic reservoir computing engine delivering over 200 tops |
| url | https://doi.org/10.1038/s41467-024-55172-3 |
| work_keys_str_mv | AT dongliangwang ultrafastsiliconphotonicreservoircomputingenginedeliveringover200tops AT yikunnie ultrafastsiliconphotonicreservoircomputingenginedeliveringover200tops AT gaoleihu ultrafastsiliconphotonicreservoircomputingenginedeliveringover200tops AT honkitsang ultrafastsiliconphotonicreservoircomputingenginedeliveringover200tops AT chaoranhuang ultrafastsiliconphotonicreservoircomputingenginedeliveringover200tops |