IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v4y2013i1d10.1038_ncomms2368.html
   My bibliography  Save this article

Parallel photonic information processing at gigabyte per second data rates using transient states

Author

Listed:
  • Daniel Brunner

    (Instituto de Física Interdisciplinar y Sistemas Complejos, IFISC (UIB-CSIC))

  • Miguel C. Soriano

    (Instituto de Física Interdisciplinar y Sistemas Complejos, IFISC (UIB-CSIC))

  • Claudio R. Mirasso

    (Instituto de Física Interdisciplinar y Sistemas Complejos, IFISC (UIB-CSIC))

  • Ingo Fischer

    (Instituto de Física Interdisciplinar y Sistemas Complejos, IFISC (UIB-CSIC))

Abstract

The increasing demands on information processing require novel computational concepts and true parallelism. Nevertheless, hardware realizations of unconventional computing approaches never exceeded a marginal existence. While the application of optics in super-computing receives reawakened interest, new concepts, partly neuro-inspired, are being considered and developed. Here we experimentally demonstrate the potential of a simple photonic architecture to process information at unprecedented data rates, implementing a learning-based approach. A semiconductor laser subject to delayed self-feedback and optical data injection is employed to solve computationally hard tasks. We demonstrate simultaneous spoken digit and speaker recognition and chaotic time-series prediction at data rates beyond 1 Gbyte/s. We identify all digits with very low classification errors and perform chaotic time-series prediction with 10% error. Our approach bridges the areas of photonic information processing, cognitive and information science.

Suggested Citation

  • Daniel Brunner & Miguel C. Soriano & Claudio R. Mirasso & Ingo Fischer, 2013. "Parallel photonic information processing at gigabyte per second data rates using transient states," Nature Communications, Nature, vol. 4(1), pages 1-7, June.
    • Handle: RePEc:nat:natcom:v:4:y:2013:i:1:d:10.1038_ncomms2368
    DOI: 10.1038/ncomms2368
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/ncomms2368
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/ncomms2368?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. 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).
    2. Zhuochao Wang & Guangwei Hu & Xinwei Wang & Xumin Ding & Kuang Zhang & Haoyu Li & Shah Nawaz Burokur & Qun Wu & Jian Liu & Jiubin Tan & Cheng-Wei Qiu, 2022. "Single-layer spatial analog meta-processor for imaging processing," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    3. Takatomo Mihana & Yuta Terashima & Makoto Naruse & Song-Ju Kim & Atsushi Uchida, 2018. "Memory Effect on Adaptive Decision Making with a Chaotic Semiconductor Laser," Complexity, Hindawi, vol. 2018, pages 1-8, April.
    4. Xiangpeng Liang & Yanan Zhong & Jianshi Tang & Zhengwu Liu & Peng Yao & Keyang Sun & Qingtian Zhang & Bin Gao & Hadi Heidari & He Qian & Huaqiang Wu, 2022. "Rotating neurons for all-analog implementation of cyclic reservoir computing," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    5. 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.

    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:4:y:2013:i:1:d:10.1038_ncomms2368. 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.