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11 TOPS photonic convolutional accelerator for optical neural networks

Author

Listed:
  • Xingyuan Xu

    (Swinburne University of Technology
    Monash University)

  • Mengxi Tan

    (Swinburne University of Technology)

  • Bill Corcoran

    (Monash University)

  • Jiayang Wu

    (Swinburne University of Technology)

  • Andreas Boes

    (RMIT University)

  • Thach G. Nguyen

    (RMIT University)

  • Sai T. Chu

    (City University of Hong Kong)

  • Brent E. Little

    (Chinese Academy of Sciences)

  • Damien G. Hicks

    (Swinburne University of Technology
    Walter & Eliza Hall Institute of Medical Research)

  • Roberto Morandotti

    (Matériaux et Télécommunications
    University of Electronic Science and Technology of China)

  • Arnan Mitchell

    (RMIT University)

  • David J. Moss

    (Swinburne University of Technology)

Abstract

Convolutional neural networks, inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to provide greatly reduced parametric complexity and to enhance the accuracy of prediction. They are of great interest for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis1–7. Optical neural networks offer the promise of dramatically accelerating computing speed using the broad optical bandwidths available. Here we demonstrate a universal optical vector convolutional accelerator operating at more than ten TOPS (trillions (1012) of operations per second, or tera-ops per second), generating convolutions of images with 250,000 pixels—sufficiently large for facial image recognition. We use the same hardware to sequentially form an optical convolutional neural network with ten output neurons, achieving successful recognition of handwritten digit images at 88 per cent accuracy. Our results are based on simultaneously interleaving temporal, wavelength and spatial dimensions enabled by an integrated microcomb source. This approach is scalable and trainable to much more complex networks for demanding applications such as autonomous vehicles and real-time video recognition.

Suggested Citation

  • Xingyuan Xu & Mengxi Tan & Bill Corcoran & Jiayang Wu & Andreas Boes & Thach G. Nguyen & Sai T. Chu & Brent E. Little & Damien G. Hicks & Roberto Morandotti & Arnan Mitchell & David J. Moss, 2021. "11 TOPS photonic convolutional accelerator for optical neural networks," Nature, Nature, vol. 589(7840), pages 44-51, January.
  • Handle: RePEc:nat:nature:v:589:y:2021:i:7840:d:10.1038_s41586-020-03063-0
    DOI: 10.1038/s41586-020-03063-0
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    Cited by:

    1. 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.
    2. Wen Zhou & Bowei Dong & Nikolaos Farmakidis & Xuan Li & Nathan Youngblood & Kairan Huang & Yuhan He & C. David Wright & Wolfram H. P. Pernice & Harish Bhaskaran, 2023. "In-memory photonic dot-product engine with electrically programmable weight banks," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
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    9. Ming Deng & Michele Cotrufo & Jian Wang & Jianji Dong & Zhichao Ruan & Andrea Alù & Lin Chen, 2024. "Broadband angular spectrum differentiation using dielectric metasurfaces," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    10. Liuting Shan & Qizhen Chen & Rengjian Yu & Changsong Gao & Lujian Liu & Tailiang Guo & Huipeng Chen, 2023. "A sensory memory processing system with multi-wavelength synaptic-polychromatic light emission for multi-modal information recognition," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    11. Cansu Demirkiran & Lakshmi Nair & Darius Bunandar & Ajay Joshi, 2024. "A blueprint for precise and fault-tolerant analog neural networks," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    12. G. Mourgias-Alexandris & M. Moralis-Pegios & A. Tsakyridis & S. Simos & G. Dabos & A. Totovic & N. Passalis & M. Kirtas & T. Rutirawut & F. Y. Gardes & A. Tefas & N. Pleros, 2022. "Noise-resilient and high-speed deep learning with coherent silicon photonics," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    13. Xuan-Kun Li & Jian-Xu Ma & Xiang-Yu Li & Jun-Jie Hu & Chuan-Yang Ding & Feng-Kai Han & Xiao-Min Guo & Xi Tan & Xian-Min Jin, 2024. "High-efficiency reinforcement learning with hybrid architecture photonic integrated circuit," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    14. Xiaoyun Yuan & Yong Wang & Zhihao Xu & Tiankuang Zhou & Lu Fang, 2023. "Training large-scale optoelectronic neural networks with dual-neuron optical-artificial learning," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    15. Yiwei Li & Ning An & Zheyi Lu & Yuchen Wang & Bing Chang & Teng Tan & Xuhan Guo & Xizhen Xu & Jun He & Handing Xia & Zhaohui Wu & Yikai Su & Yuan Liu & Yunjiang Rao & Giancarlo Soavi & Baicheng Yao, 2022. "Nonlinear co-generation of graphene plasmons for optoelectronic logic operations," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    16. Saket Kaushal & A. Aadhi & Anthony Roberge & Roberto Morandotti & Raman Kashyap & José Azaña, 2023. "All-fibre phase filters with 1-GHz resolution for high-speed passive optical logic processing," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    17. 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.
    18. Jingwei Ling & Zhengdong Gao & Shixin Xue & Qili Hu & Mingxiao Li & Kaibo Zhang & Usman A. Javid & Raymond Lopez-Rios & Jeremy Staffa & Qiang Lin, 2024. "Electrically empowered microcomb laser," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    19. Shaofu Xu & Jing Wang & Sicheng Yi & Weiwen Zou, 2022. "High-order tensor flow processing using integrated photonic circuits," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    20. Mitsumasa Nakajima & Katsuma Inoue & Kenji Tanaka & Yasuo Kuniyoshi & Toshikazu Hashimoto & Kohei Nakajima, 2022. "Physical deep learning with biologically inspired training method: gradient-free approach for physical hardware," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    21. Wenting Wang & Ping-Keng Lu & Abhinav Kumar Vinod & Deniz Turan & James F. McMillan & Hao Liu & Mingbin Yu & Dim-Lee Kwong & Mona Jarrahi & Chee Wei Wong, 2022. "Coherent terahertz radiation with 2.8-octave tunability through chip-scale photomixed microresonator optical parametric oscillation," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    22. Liangliang Min & Haoxuan Sun & Linqi Guo & Meng Wang & Fengren Cao & Jun Zhong & Liang Li, 2024. "Frequency-selective perovskite photodetector for anti-interference optical communications," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    23. Bitao Shen & Haowen Shu & Weiqiang Xie & Ruixuan Chen & Zhi Liu & Zhangfeng Ge & Xuguang Zhang & Yimeng Wang & Yunhao Zhang & Buwen Cheng & Shaohua Yu & Lin Chang & Xingjun Wang, 2023. "Harnessing microcomb-based parallel chaos for random number generation and optical decision making," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    24. Junwei Cheng & Chaoran Huang & Jialong Zhang & Bo Wu & Wenkai Zhang & Xinyu Liu & Jiahui Zhang & Yiyi Tang & Hailong Zhou & Qiming Zhang & Min Gu & Jianji Dong & Xinliang Zhang, 2024. "Multimodal deep learning using on-chip diffractive optics with in situ training capability," Nature Communications, Nature, vol. 15(1), pages 1-10, December.

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