IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-022-35506-9.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-35506-9
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-022-35506-9?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
    ---><---

    References listed on IDEAS

    as
    1. Lin Chang & Weiqiang Xie & Haowen Shu & Qi-Fan Yang & Boqiang Shen & Andreas Boes & Jon D. Peters & Warren Jin & Chao Xiang & Songtao Liu & Gregory Moille & Su-Peng Yu & Xingjun Wang & Kartik Srinivas, 2020. "Ultra-efficient frequency comb generation in AlGaAs-on-insulator microresonators," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
    2. J. Feldmann & N. Youngblood & M. Karpov & H. Gehring & X. Li & M. Stappers & M. Gallo & X. Fu & A. Lukashchuk & A. S. Raja & J. Liu & C. D. Wright & A. Sebastian & T. J. Kippenberg & W. H. P. Pernice , 2021. "Publisher Correction: Parallel convolutional processing using an integrated photonic tensor core," Nature, Nature, vol. 591(7849), pages 13-13, March.
    3. Zi Wang & Tiantian Li & Anishkumar Soman & Dun Mao & Thomas Kananen & Tingyi Gu, 2019. "On-chip wavefront shaping with dielectric metasurface," Nature Communications, Nature, vol. 10(1), pages 1-7, December.
    4. H. Zhang & M. Gu & X. D. Jiang & J. Thompson & H. Cai & S. Paesani & R. Santagati & A. Laing & Y. Zhang & M. H. Yung & Y. Z. Shi & F. K. Muhammad & G. Q. Lo & X. S. Luo & B. Dong & D. L. Kwong & L. C., 2021. "An optical neural chip for implementing complex-valued neural network," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    5. 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.
    6. J. Feldmann & N. Youngblood & M. Karpov & H. Gehring & X. Li & M. Stappers & M. Gallo & X. Fu & A. Lukashchuk & A. S. Raja & J. Liu & C. D. Wright & A. Sebastian & T. J. Kippenberg & W. H. P. Pernice , 2021. "Parallel convolutional processing using an integrated photonic tensor core," Nature, Nature, vol. 589(7840), pages 52-58, January.
    7. Amir H. Atabaki & Sajjad Moazeni & Fabio Pavanello & Hayk Gevorgyan & Jelena Notaros & Luca Alloatti & Mark T. Wade & Chen Sun & Seth A. Kruger & Huaiyu Meng & Kenaish Al Qubaisi & Imbert Wang & Bohan, 2018. "Publisher Correction: Integrating photonics with silicon nanoelectronics for the next generation of systems on a chip," Nature, Nature, vol. 560(7716), pages 4-4, August.
    8. Amir H. Atabaki & Sajjad Moazeni & Fabio Pavanello & Hayk Gevorgyan & Jelena Notaros & Luca Alloatti & Mark T. Wade & Chen Sun & Seth A. Kruger & Huaiyu Meng & Kenaish Al Qubaisi & Imbert Wang & Bohan, 2018. "Integrating photonics with silicon nanoelectronics for the next generation of systems on a chip," Nature, Nature, vol. 556(7701), pages 349-354, April.
    9. Boqiang Shen & Lin Chang & Junqiu Liu & Heming Wang & Qi-Fan Yang & Chao Xiang & Rui Ning Wang & Jijun He & Tianyi Liu & Weiqiang Xie & Joel Guo & David Kinghorn & Lue Wu & Qing-Xin Ji & Tobias J. Kip, 2020. "Integrated turnkey soliton microcombs," Nature, Nature, vol. 582(7812), pages 365-369, June.
    10. Haowen Shu & Lin Chang & Yuansheng Tao & Bitao Shen & Weiqiang Xie & Ming Jin & Andrew Netherton & Zihan Tao & Xuguang Zhang & Ruixuan Chen & Bowen Bai & Jun Qin & Shaohua Yu & Xingjun Wang & John E. , 2022. "Microcomb-driven silicon photonic systems," Nature, Nature, vol. 605(7910), pages 457-463, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gao, Renjing & Zhou, Guangli & Wang, Qi, 2024. "Real-time three-level energy management strategy for series hybrid wheel loaders based on WG-MPC," Energy, Elsevier, vol. 295(C).
    2. Chen-Guang Wang & Wuyue Xu & Chong Li & Lili Shi & Junliang Jiang & Tingting Guo & Wen-Cheng Yue & Tianyu Li & Ping Zhang & Yang-Yang Lyu & Jiazheng Pan & Xiuhao Deng & Ying Dong & Xuecou Tu & Sining , 2024. "Integrated and DC-powered superconducting microcomb," Nature Communications, Nature, vol. 15(1), pages 1-7, December.
    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.
    4. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    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.
    3. Chen-Guang Wang & Wuyue Xu & Chong Li & Lili Shi & Junliang Jiang & Tingting Guo & Wen-Cheng Yue & Tianyu Li & Ping Zhang & Yang-Yang Lyu & Jiazheng Pan & Xiuhao Deng & Ying Dong & Xuecou Tu & Sining , 2024. "Integrated and DC-powered superconducting microcomb," Nature Communications, Nature, vol. 15(1), pages 1-7, December.
    4. 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.
    5. Xuguang Zhang & Zixuan Zhou & Yijun Guo & Minxue Zhuang & Warren Jin & Bitao Shen & Yujun Chen & Jiahui Huang & Zihan Tao & Ming Jin & Ruixuan Chen & Zhangfeng Ge & Zhou Fang & Ning Zhang & Yadong Liu, 2024. "High-coherence parallelization in integrated photonics," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    6. Xiangyan Meng & Guojie Zhang & Nuannuan Shi & Guangyi Li & José Azaña & José Capmany & Jianping Yao & Yichen Shen & Wei Li & Ninghua Zhu & Ming Li, 2023. "Compact optical convolution processing unit based on multimode interference," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    7. 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.
    8. Han Zhao & Bingzhao Li & Huan Li & Mo Li, 2022. "Enabling scalable optical computing in synthetic frequency dimension using integrated cavity acousto-optics," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    9. 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.
    10. Zhongjin Lin & Bhavin J. Shastri & Shangxuan Yu & Jingxiang Song & Yuntao Zhu & Arman Safarnejadian & Wangning Cai & Yanmei Lin & Wei Ke & Mustafa Hammood & Tianye Wang & Mengyue Xu & Zibo Zheng & Moh, 2024. "120 GOPS Photonic tensor core in thin-film lithium niobate for inference and in situ training," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    11. 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.
    12. 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.
    13. H. H. Zhu & J. Zou & H. Zhang & Y. Z. Shi & S. B. Luo & N. Wang & H. Cai & L. X. Wan & B. Wang & X. D. Jiang & J. Thompson & X. S. Luo & X. H. Zhou & L. M. Xiao & W. Huang & L. Patrick & M. Gu & L. C., 2022. "Space-efficient optical computing with an integrated chip diffractive neural network," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    14. Chenduan Chen & Zhan Yang & Tao Wang & Yalun Wang & Kai Gao & Jiajia Wu & Jun Wang & Jianrong Qiu & Dezhi Tan, 2024. "Ultra-broadband all-optical nonlinear activation function enabled by MoTe2/optical waveguide integrated devices," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    15. 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.
    16. 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.
    17. 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.
    18. Miltiadis Moralis-Pegios & George Giamougiannis & Apostolos Tsakyridis & David Lazovsky & Nikos Pleros, 2024. "Perfect linear optics using silicon photonics," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    19. 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.
    20. 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.

    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:14:y:2023:i:1:d:10.1038_s41467-022-35506-9. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.