Skip to main content
Springer Nature Link
Log in

Processing-Response Dependence on the On-Chip Readout Positions in Spin-Wave Reservoir Computing

  • Conference paper
  • First Online:
Neural Information Processing (ICONIP 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 13110))

Included in the following conference series:

  • 1745 Accesses

Abstract

This paper reports and discusses the processing response dependence on a spin-wave reservoir chip, a natural computing device, to present one of the important steps to design a spin-wave reservoir computing hardware. As an example, we deal with a sinusoidal-square wave distinction task, where signals with a certain duration switch to each other at random. Observation of the transient response provides us with information useful for determining chip size and other parameters. Accumulation of this type of investigations will elucidate how we should design a spin-wave reservoir chip.

A. Hirose—This work was supported in part by the New Energy and Industrial Technology Development Organization (NEDO) under Project JPNP16007, and in part by Tohoku University RIEC Cooperative Research Project.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Bueno, J., et al.: Reinforcement learning in a large-scale photonic recurrent neural network. Optica 5(6), 756–760 (2018)

    Article  Google Scholar 

  2. Fernando, C., Sojakka, S.: Pattern recognition in a bucket. In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim, J.T. (eds.) ECAL 2003. LNCS (LNAI), vol. 2801, pp. 588–597. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39432-7_63

    Chapter  Google Scholar 

  3. Hirose, A.: Physical reservoir computing and complex-valued neural networks. In: International Conference on Neuromorphic Systems. Oak Ridge National Laboratory, Oak Ridge (online) (July 2020)

    Google Scholar 

  4. Hirose, A., Nakane, R., Tanaka, G.: Keynote speech: Information processing hardware, physical reservoir computing and complex-valued neural networks. In: Kimura, M. (ed.) IEEE International Meeting for Future of Electron Devices, Kansai 2019 (IMFEDK) Kyoto, pp. 19–24 (November 2019)

    Google Scholar 

  5. Hirose, A., et al.: Physical reservoir computing: possibility to resolve the inconsistency between neuro-AI principles and its hardware. Aust. J. Intell. Inf. Process. Syst. (AJIIPS) 16(4), 49–54 (2019)

    Google Scholar 

  6. Hirose, A., et al.: Proposal of carrier-wave reservoir computing. In: Cheng, L., Leung, A.C.S., Ozawa, S. (eds.) ICONIP 2018. LNCS, vol. 11301, pp. 616–624. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-04167-0_56

    Chapter  Google Scholar 

  7. Ichimura, T., Nakane, R., Tanaka, G., Hirose, A.: Spatial distribution of information effective for logic function learning in spin-wave reservoir computing chip utilizing spatiotemporal physical dynamics. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2020). https://doi.org/10.1109/IJCNN48605.2020.9207629

  8. Ichimura, T., Nakane, R., Tanaka, G., Hirose, A.: A numerical exploration of signal detector arrangement in a spin-wave reservoir computing device. IEEE Access 9, 72637–72646 (2021). https://doi.org/10.1109/ACCESS.2021.3079583

    Article  Google Scholar 

  9. Jaeger, H.: The “echo state” approach to analysing and training recurrent neural networks-with an erratum note. Bonn, Germany: German Natl. Res. Cent. Inf. Technol. GMD Tech. Rep. 148(34), 13 (2001)

    Google Scholar 

  10. Kanazawa, N., et al.: Demonstration of a robust magnonic spin wave interferometer. Sci. Rep. 6(1), 30268 (2016). https://doi.org/10.1038/srep30268

    Article  Google Scholar 

  11. Katayama, Y., Yamane, T., Nakano, D., Nakane, R., Tanaka, G.: Wave-based neuromorphic computing framework for brain-like energy efficiency and integration. IEEE Trans. Nanotechnol. 15(5), 762–769 (2016)

    Article  Google Scholar 

  12. Larger, L., Baylón-Fuentes, A., Martinenghi, R., Udaltsov, V.S., Chembo, Y.K., Jacquot, M.: High-speed photonic reservoir computing using a time-delay-based architecture: million words per second classification. Phys. Rev. X 7, 011015 (2017)

    Google Scholar 

  13. Maass, W., Natschläger, T., Markram, H.: Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput. 14(11), 2531–2560 (2002)

    Article  Google Scholar 

  14. Nakajima, K., Hauser, H., Li, T., Pfeifer, R.: Information processing via physical soft body. Sci. Rep. 5, 10487 (2015)

    Article  Google Scholar 

  15. Nakajima, K., Li, T., Hauser, H., Pfeifer, R.: Exploiting short-term memory in soft body dynamics as a computational resource. J. R. Soc. Interface 11, 20140437 (2018)

    Article  Google Scholar 

  16. Nakane, R.: On-chip reservoir computing device utilizing spin waves. J. IEICE 102(2), 140–146 (2019). (in Japanese)

    Google Scholar 

  17. Nakane, R., Tanaka, G., Hirose, A.: Demonstration of spin-wave-based reservoir computing for next-generation machine-learning devices. In: International Conference on Magnetism (ICM) 2018 San Francisco, pp. 26–27 (July 2018)

    Google Scholar 

  18. Nakane, R., Tanaka, G., Hirose, A.: Reservoir computing with spin waves excited in a garnet film. IEEE Access 6, 4462–4469 (2018)

    Article  Google Scholar 

  19. Nakane, R., Tanaka, G., Hirose, A.: Numerical analysis on wave dynamics in a spin-wave reservoir for machine learning. In: IEEE/INNS International Joint Conference on Neural Networks (IJCNN) 2019 Budapest, N-20170 (2019). https://doi.org/10.1109/IJCNN.2019.8852280

  20. Nomura, H., et al.: Reservoir computing with dipole-coupled nanomagnets. Japan. J. Appl. Phys. 58(7), 070901 (2019). https://doi.org/10.7567/1347-4065/ab2406

    Article  Google Scholar 

  21. Paquot, Y., et al.: Optoelectronic reservoir computing. Sci. Rep. 2, 287 (2012)

    Article  Google Scholar 

  22. Prychynenko, D., et al.: Magnetic skyrmion as a nonlinear resistive element: a potential building block for reservoir computing. Phys. Rev. Appl. 9, 014034 (2018)

    Article  Google Scholar 

  23. Tanaka, G., et al.: Recent advances in physical reservoir computing: a review. Neural Netw. 115, 100–123 (2019)

    Article  Google Scholar 

  24. Torrejon, J., et al.: Neuromorphic computing with nanoscale spintronic oscillators. Nature 547(7664), 428 (2017)

    Article  Google Scholar 

  25. Tsunegi, S., et al.: Physical reservoir computing based on spin torque oscillator with forced synchronization. Appl. Phys. Lett. 114(16), 164101 (2020)

    Article  Google Scholar 

  26. Vansteenkiste, A., Leliaert, J., Dvornik, M., Helsen, M., Garcia-Sanchez, F., Van Waeyenberge, B.: The design and verification of mumax3. AIP Adv. 4(10), 107133 (2014)

    Article  Google Scholar 

  27. Verstraeten, D., Schrauwen, B., D’Haene, M., Stroobandt, D.: An experimental unification of reservoir computing methods. Neural Netw. 20(3), 391–403 (2007). https://doi.org/10.1016/j.neunet.2007.04.003, http://www.sciencedirect.com/science/article/pii/S089360800700038X, echo State Networks and Liquid State Machines

  28. Yamane, T., et al.: Simulation study of physical reservoir computing by nonlinear deterministic time series analysis. In: International Conference on Neural Information Processing (ICONIP) 2017 Guangzhou, pp. 639–647 (2017)

    Google Scholar 

  29. Yi, Y., et al.: FPGA based spike-time dependent encoder and reservoir design in neuromorphic computing processors. Microprocess. Microsyst. 46, 175–183 (2016)

    Article  Google Scholar 

Download references

Acknowledgment

The authors thank Dr. T.Yamane, Dr. J.B.Heroux, Dr. H.Numata, and D.Nakano of IBM–Research Tokyo and Dr. G.Tanaka of the University of Tokyo for their helpful discussion.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo, 153-8656, Japan

    Takehiro Ichimura, Ryosho Nakane & Akira Hirose

Authors
  1. Takehiro Ichimura
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ryosho Nakane
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Akira Hirose
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Akira Hirose .

Editor information

Editors and Affiliations

  1. Sampoerna University, Jakarta, Indonesia

    Teddy Mantoro

  2. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

  3. Sampoerna University, Jakarta, Indonesia

    Media Anugerah Ayu

  4. Murdoch University, Murdoch, WA, Australia

    Kok Wai Wong

  5. Universitas Indonesia, Depok, Indonesia

    Achmad Nizar Hidayanto

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ichimura, T., Nakane, R., Hirose, A. (2021). Processing-Response Dependence on the On-Chip Readout Positions in Spin-Wave Reservoir Computing. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13110. Springer, Cham. https://doi.org/10.1007/978-3-030-92238-2_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-92238-2_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-92237-5

  • Online ISBN: 978-3-030-92238-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics