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Experimental demonstration of reservoir computing on a silicon photonics chip

Author

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  • 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
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    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. 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.
    3. 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).
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.

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