IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-35216-2.html
   My bibliography  Save this article

Physical deep learning with biologically inspired training method: gradient-free approach for physical hardware

Author

Listed:
  • Mitsumasa Nakajima

    (NTT Device Technology Labs.)

  • Katsuma Inoue

    (The University of Tokyo)

  • Kenji Tanaka

    (NTT Device Technology Labs.)

  • Yasuo Kuniyoshi

    (The University of Tokyo
    The University of Tokyo)

  • Toshikazu Hashimoto

    (NTT Device Technology Labs.)

  • Kohei Nakajima

    (The University of Tokyo
    The University of Tokyo)

Abstract

Ever-growing demand for artificial intelligence has motivated research on unconventional computation based on physical devices. While such computation devices mimic brain-inspired analog information processing, the learning procedures still rely on methods optimized for digital processing such as backpropagation, which is not suitable for physical implementation. Here, we present physical deep learning by extending a biologically inspired training algorithm called direct feedback alignment. Unlike the original algorithm, the proposed method is based on random projection with alternative nonlinear activation. Thus, we can train a physical neural network without knowledge about the physical system and its gradient. In addition, we can emulate the computation for this training on scalable physical hardware. We demonstrate the proof-of-concept using an optoelectronic recurrent neural network called deep reservoir computer. We confirmed the potential for accelerated computation with competitive performance on benchmarks. Our results provide practical solutions for the training and acceleration of neuromorphic computation.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-35216-2
    DOI: 10.1038/s41467-022-35216-2
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-35216-2
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-022-35216-2?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. Chao Du & Fuxi Cai & Mohammed A. Zidan & Wen Ma & Seung Hwan Lee & Wei D. Lu, 2017. "Reservoir computing using dynamic memristors for temporal information processing," Nature Communications, Nature, vol. 8(1), pages 1-10, December.
    2. Logan G. Wright & Tatsuhiro Onodera & Martin M. Stein & Tianyu Wang & Darren T. Schachter & Zoey Hu & Peter L. McMahon, 2022. "Deep physical neural networks trained with backpropagation," Nature, Nature, vol. 601(7894), pages 549-555, January.
    3. Tianyu Wang & Shi-Yuan Ma & Logan G. Wright & Tatsuhiro Onodera & Brian C. Richard & Peter L. McMahon, 2022. "An optical neural network using less than 1 photon per multiplication," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    4. Lukas Mennel & Joanna Symonowicz & Stefan Wachter & Dmitry K. Polyushkin & Aday J. Molina-Mendoza & Thomas Mueller, 2020. "Ultrafast machine vision with 2D material neural network image sensors," Nature, Nature, vol. 579(7797), pages 62-66, March.
    5. Jacob Torrejon & Mathieu Riou & Flavio Abreu Araujo & Sumito Tsunegi & Guru Khalsa & Damien Querlioz & Paolo Bortolotti & Vincent Cros & Kay Yakushiji & Akio Fukushima & Hitoshi Kubota & Shinji Yuasa , 2017. "Neuromorphic computing with nanoscale spintronic oscillators," Nature, Nature, vol. 547(7664), pages 428-431, July.
    6. Sören Boyn & Julie Grollier & Gwendal Lecerf & Bin Xu & Nicolas Locatelli & Stéphane Fusil & Stéphanie Girod & Cécile Carrétéro & Karin Garcia & Stéphane Xavier & Jean Tomas & Laurent Bellaiche & Manu, 2017. "Learning through ferroelectric domain dynamics in solid-state synapses," Nature Communications, Nature, vol. 8(1), pages 1-7, April.
    7. 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.
    8. Tao Chen & Jeroen van Gelder & Bram van de Ven & Sergey V. Amitonov & Bram de Wilde & Hans-Christian Ruiz Euler & Hajo Broersma & Peter A. Bobbert & Floris A. Zwanenburg & Wilfred G. van der Wiel, 2020. "Classification with a disordered dopant-atom network in silicon," Nature, Nature, vol. 577(7790), pages 341-345, January.
    9. Miguel Romera & Philippe Talatchian & Sumito Tsunegi & Flavio Abreu Araujo & Vincent Cros & Paolo Bortolotti & Juan Trastoy & Kay Yakushiji & Akio Fukushima & Hitoshi Kubota & Shinji Yuasa & Maxence E, 2018. "Vowel recognition with four coupled spin-torque nano-oscillators," Nature, Nature, vol. 563(7730), pages 230-234, November.
    10. 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.
    11. 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.
    12. Timothy P. Lillicrap & Daniel Cownden & Douglas B. Tweed & Colin J. Akerman, 2016. "Random synaptic feedback weights support error backpropagation for deep learning," Nature Communications, Nature, vol. 7(1), pages 1-10, December.
    13. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
    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. 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.

    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    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. Ke Yang & Yanghao Wang & Pek Jun Tiw & Chaoming Wang & Xiaolong Zou & Rui Yuan & Chang Liu & Ge Li & Chen Ge & Si Wu & Teng Zhang & Ru Huang & Yuchao Yang, 2024. "High-order sensory processing nanocircuit based on coupled VO2 oscillators," Nature Communications, Nature, vol. 15(1), pages 1-12, 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. 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.
    17. 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.
    18. 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.
    19. Yang He & Raymond Lopez-Rios & Usman A. Javid & Jingwei Ling & Mingxiao Li & Shixin Xue & Kerry Vahala & Qiang Lin, 2023. "High-speed tunable microwave-rate soliton microcomb," Nature Communications, Nature, vol. 14(1), pages 1-7, December.
    20. 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.

    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:13:y:2022:i:1:d:10.1038_s41467-022-35216-2. 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.