IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v5y2014i1d10.1038_ncomms4541.html
   My bibliography  Save this article

Experimental demonstration of reservoir computing on a silicon photonics chip

Author

Listed:
  • Kristof Vandoorne

    (Photonics Research Group, Ghent University-Interuniversity Microelectronics Center
    Center for Nano-and Biophotonics (NB-Photonics), Ghent University)

  • Pauline Mechet

    (Photonics Research Group, Ghent University-Interuniversity Microelectronics Center
    Center for Nano-and Biophotonics (NB-Photonics), Ghent University)

  • Thomas Van Vaerenbergh

    (Photonics Research Group, Ghent University-Interuniversity Microelectronics Center
    Center for Nano-and Biophotonics (NB-Photonics), Ghent University)

  • Martin Fiers

    (Photonics Research Group, Ghent University-Interuniversity Microelectronics Center
    Center for Nano-and Biophotonics (NB-Photonics), Ghent University)

  • Geert Morthier

    (Photonics Research Group, Ghent University-Interuniversity Microelectronics Center
    Center for Nano-and Biophotonics (NB-Photonics), Ghent University)

  • David Verstraeten

    (Computer Systems Laboratory, Ghent University)

  • Benjamin Schrauwen

    (Computer Systems Laboratory, Ghent University)

  • Joni Dambre

    (Computer Systems Laboratory, Ghent University)

  • Peter Bienstman

    (Photonics Research Group, Ghent University-Interuniversity Microelectronics Center
    Center for Nano-and Biophotonics (NB-Photonics), Ghent University)

Abstract

In today’s age, companies employ machine learning to extract information from large quantities of data. One of those techniques, reservoir computing (RC), is a decade old and has achieved state-of-the-art performance for processing sequential data. Dedicated hardware realizations of RC could enable speed gains and power savings. Here we propose the first integrated passive silicon photonics reservoir. We demonstrate experimentally and through simulations that, thanks to the RC paradigm, this generic chip can be used to perform arbitrary Boolean logic operations with memory as well as 5-bit header recognition up to 12.5 Gbit s−1, without power consumption in the reservoir. It can also perform isolated spoken digit recognition. Our realization exploits optical phase for computing. It is scalable to larger networks and much higher bitrates, up to speeds >100 Gbit s−1. These results pave the way for the application of integrated photonic RC for a wide range of applications.

Suggested Citation

  • Kristof Vandoorne & Pauline Mechet & Thomas Van Vaerenbergh & Martin Fiers & Geert Morthier & David Verstraeten & Benjamin Schrauwen & Joni Dambre & Peter Bienstman, 2014. "Experimental demonstration of reservoir computing on a silicon photonics chip," Nature Communications, Nature, vol. 5(1), pages 1-6, May.
  • Handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms4541
    DOI: 10.1038/ncomms4541
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/ncomms4541
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/ncomms4541?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yang, J. & Primo, E. & Aleja, D. & Criado, R. & Boccaletti, S. & Alfaro-Bittner, K., 2022. "Implementing and morphing Boolean gates with adaptive synchronization: The case of spiking neurons," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    2. Jangsaeng Kim & Eun Chan Park & Wonjun Shin & Ryun-Han Koo & Chang-Hyeon Han & He Young Kang & Tae Gyu Yang & Youngin Goh & Kilho Lee & Daewon Ha & Suraj S. Cheema & Jae Kyeong Jeong & Daewoong Kwon, 2024. "Analog reservoir computing via ferroelectric mixed phase boundary transistors," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    3. Ali Momeni & Romain Fleury, 2022. "Electromagnetic wave-based extreme deep learning with nonlinear time-Floquet entanglement," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    4. Minati, Ludovico & Mancinelli, Mattia & Frasca, Mattia & Bettotti, Paolo & Pavesi, Lorenzo, 2021. "An analog electronic emulator of non-linear dynamics in optical microring resonators," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    5. Laura E. Suárez & Agoston Mihalik & Filip Milisav & Kenji Marshall & Mingze Li & Petra E. Vértes & Guillaume Lajoie & Bratislav Misic, 2024. "Connectome-based reservoir computing with the conn2res toolbox," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    6. Lukas Körber & Christopher Heins & Tobias Hula & Joo-Von Kim & Sonia Thlang & Helmut Schultheiss & Jürgen Fassbender & Katrin Schultheiss, 2023. "Pattern recognition in reciprocal space with a magnon-scattering reservoir," Nature Communications, Nature, vol. 14(1), pages 1-7, December.
    7. Chengkuan Gao & Prabhav Gaur & Dhaifallah Almutairi & Shimon Rubin & Yeshaiahu Fainman, 2023. "Optofluidic memory and self-induced nonlinear optical phase change for reservoir computing in silicon photonics," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    8. Yang Shi & Junyu Ren & Guanyu Chen & Wei Liu & Chuqi Jin & Xiangyu Guo & Yu Yu & Xinliang Zhang, 2022. "Nonlinear germanium-silicon photodiode for activation and monitoring in photonic neuromorphic networks," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    9. Guangwei Cong & Noritsugu Yamamoto & Takashi Inoue & Yuriko Maegami & Morifumi Ohno & Shota Kita & Shu Namiki & Koji Yamada, 2022. "On-chip bacterial foraging training in silicon photonic circuits for projection-enabled nonlinear classification," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    10. Yiming Sun & Tao Lin & Na Lei & Xing Chen & Wang Kang & Zhiyuan Zhao & Dahai Wei & Chao Chen & Simin Pang & Linglong Hu & Liu Yang & Enxuan Dong & Li Zhao & Lei Liu & Zhe Yuan & Aladin Ullrich & Chris, 2023. "Experimental demonstration of a skyrmion-enhanced strain-mediated physical reservoir computing system," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    11. Min Yan & Can Huang & Peter Bienstman & Peter Tino & Wei Lin & Jie Sun, 2024. "Emerging opportunities and challenges for the future of reservoir computing," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    12. Zhuohui Liu & Qinghua Zhang & Donggang Xie & Mingzhen Zhang & Xinyan Li & Hai Zhong & Ge Li & Meng He & Dashan Shang & Can Wang & Lin Gu & Guozhen Yang & Kuijuan Jin & Chen Ge, 2023. "Interface-type tunable oxygen ion dynamics for physical reservoir computing," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms4541. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.