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All-analog photoelectronic chip for high-speed vision tasks

Author

Listed:
  • Yitong Chen

    (Tsinghua University)

  • Maimaiti Nazhamaiti

    (Tsinghua University)

  • Han Xu

    (Tsinghua University)

  • Yao Meng

    (Tsinghua University)

  • Tiankuang Zhou

    (Tsinghua University
    Tsinghua University
    Tsinghua University)

  • Guangpu Li

    (Tsinghua University
    Tsinghua University)

  • Jingtao Fan

    (Tsinghua University)

  • Qi Wei

    (Tsinghua University)

  • Jiamin Wu

    (Tsinghua University
    Tsinghua University
    Tsinghua University)

  • Fei Qiao

    (Tsinghua University)

  • Lu Fang

    (Tsinghua University
    Tsinghua University
    Tsinghua University)

  • Qionghai Dai

    (Tsinghua University
    Tsinghua University
    Tsinghua University)

Abstract

Photonic computing enables faster and more energy-efficient processing of vision data1–5. However, experimental superiority of deployable systems remains a challenge because of complicated optical nonlinearities, considerable power consumption of analog-to-digital converters (ADCs) for downstream digital processing and vulnerability to noises and system errors1,6–8. Here we propose an all-analog chip combining electronic and light computing (ACCEL). It has a systemic energy efficiency of 74.8 peta-operations per second per watt and a computing speed of 4.6 peta-operations per second (more than 99% implemented by optics), corresponding to more than three and one order of magnitude higher than state-of-the-art computing processors, respectively. After applying diffractive optical computing as an optical encoder for feature extraction, the light-induced photocurrents are directly used for further calculation in an integrated analog computing chip without the requirement of analog-to-digital converters, leading to a low computing latency of 72 ns for each frame. With joint optimizations of optoelectronic computing and adaptive training, ACCEL achieves competitive classification accuracies of 85.5%, 82.0% and 92.6%, respectively, for Fashion-MNIST, 3-class ImageNet classification and time-lapse video recognition task experimentally, while showing superior system robustness in low-light conditions (0.14 fJ μm−2 each frame). ACCEL can be used across a broad range of applications such as wearable devices, autonomous driving and industrial inspections.

Suggested Citation

  • Yitong Chen & Maimaiti Nazhamaiti & Han Xu & Yao Meng & Tiankuang Zhou & Guangpu Li & Jingtao Fan & Qi Wei & Jiamin Wu & Fei Qiao & Lu Fang & Qionghai Dai, 2023. "All-analog photoelectronic chip for high-speed vision tasks," Nature, Nature, vol. 623(7985), pages 48-57, November.
  • Handle: RePEc:nat:nature:v:623:y:2023:i:7985:d:10.1038_s41586-023-06558-8
    DOI: 10.1038/s41586-023-06558-8
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    Cited by:

    1. Yinan Zhang & Shengting Zhu & Jinming Hu & Min Gu, 2024. "Femtosecond laser direct nanolithography of perovskite hydration for temporally programmable holograms," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    2. Zhenjia Chen & Zhenyuan Lin & Ji Yang & Cong Chen & Di Liu & Liuting Shan & Yuanyuan Hu & Tailiang Guo & Huipeng Chen, 2024. "Cross-layer transmission realized by light-emitting memristor for constructing ultra-deep neural network with transfer learning ability," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    3. Xinxin Gao & Ze Gu & Qian Ma & Bao Jie Chen & Kam-Man Shum & Wen Yi Cui & Jian Wei You & Tie Jun Cui & Chi Hou Chan, 2024. "Terahertz spoof plasmonic neural network for diffractive information recognition and processing," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    4. Xiao Wang & Brandon Redding & Nicholas Karl & Christopher Long & Zheyuan Zhu & James Skowronek & Shuo Pang & David Brady & Raktim Sarma, 2024. "Integrated photonic encoder for low power and high-speed image processing," Nature Communications, Nature, vol. 15(1), pages 1-13, December.

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