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Microcomb-based integrated photonic processing unit

Author

Listed:
  • Bowen Bai

    (Peking University)

  • Qipeng Yang

    (Peking University)

  • Haowen Shu

    (Peking University)

  • Lin Chang

    (Peking University
    University of California
    Peking University)

  • Fenghe Yang

    (Zhangjiang Laboratory)

  • Bitao Shen

    (Peking University)

  • Zihan Tao

    (Peking University)

  • Jing Wang

    (Shanghai Jiao Tong University)

  • Shaofu Xu

    (Shanghai Jiao Tong University)

  • Weiqiang Xie

    (University of California)

  • Weiwen Zou

    (Shanghai Jiao Tong University)

  • Weiwei Hu

    (Peking University)

  • John E. Bowers

    (University of California)

  • Xingjun Wang

    (Peking University
    Peking University
    Peking University Yangtze Delta Institute of Optoelectronics)

Abstract

The emergence of parallel convolution-operation technology has substantially powered the complexity and functionality of optical neural networks (ONN) by harnessing the dimension of optical wavelength. However, this advanced architecture faces remarkable challenges in high-level integration and on-chip operation. In this work, convolution based on time-wavelength plane stretching approach is implemented on a microcomb-driven chip-based photonic processing unit (PPU). To support the operation of this processing unit, we develop a dedicated control and operation protocol, leading to a record high weight precision of 9 bits. Moreover, the compact architecture and high data loading speed enable a preeminent photonic-core compute density of over 1 trillion of operations per second per square millimeter (TOPS mm−2). Two proof-of-concept experiments are demonstrated, including image edge detection and handwritten digit recognition, showing comparable processing capability compared to that of a digital computer. Due to the advanced performance and the great scalability, this parallel photonic processing unit can potentially revolutionize sophisticated artificial intelligence tasks including autonomous driving, video action recognition and image reconstruction.

Suggested Citation

  • Bowen Bai & Qipeng Yang & Haowen Shu & Lin Chang & Fenghe Yang & Bitao Shen & Zihan Tao & Jing Wang & Shaofu Xu & Weiqiang Xie & Weiwen Zou & Weiwei Hu & John E. Bowers & Xingjun Wang, 2023. "Microcomb-based integrated photonic processing unit," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-022-35506-9
    DOI: 10.1038/s41467-022-35506-9
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    References listed on IDEAS

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    3. Qi Han & Jun Wang & Shuangshuang Tian & Shen Hu & Xuefeng Wu & Rongxu Bai & Haibin Zhao & David W. Zhang & Qingqing Sun & Li Ji, 2024. "Inorganic perovskite-based active multifunctional integrated photonic devices," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
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