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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
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    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. Fangjun Hu & Saeed A. Khan & Nicholas T. Bronn & Gerasimos Angelatos & Graham E. Rowlands & Guilhem J. Ribeill & Hakan E. Türeci, 2024. "Overcoming the coherence time barrier in quantum machine learning on temporal data," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    6. 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.
    7. Wang, Tao & Zhou, Hanxu & Fang, Qing & Han, Yanan & Guo, Xingxing & Zhang, Yahui & Qian, Chao & Chen, Hongsheng & Barland, Stéphane & Xiang, Shuiying & Lippi, Gian Luca, 2024. "Reservoir computing-based advance warning of extreme events," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).

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