Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

A memristor-based analogue reservoir computing system for real-time and power-efficient signal processing

Nature Electronics volume 5, pages 672–681 (2022)Cite this article

Subjects

Abstract

Reservoir computing offers a powerful neuromorphic computing architecture for spatiotemporal signal processing. To boost the power efficiency of the hardware implementations of reservoir computing systems, analogue devices and components—including spintronic oscillators, photonic modules, nanowire networks and memristors—have been used to partially replace the elements of fully digital systems. However, the development of fully analogue reservoir computing systems remains limited. Here we report a fully analogue reservoir computing system that uses dynamic memristors for the reservoir layer and non-volatile memristors for the readout layer. The system can efficiently process spatiotemporal signals in real time with three orders of magnitude lower power consumption than digital hardware. We illustrate the capabilities of the system using temporal arrhythmia detection and spatiotemporal dynamic gesture recognition tasks, achieving accuracies of 96.6% and 97.9%, respectively. Our memristor-based fully analogue reservoir computing system could be of use in edge computing applications that require extremely low power and hardware cost.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Different types of RC system.
Fig. 2: Device characteristics of memristors and architecture of DM-RC hardware system.
Fig. 3: Hyperparameter adjustment for improving the reservoir performance.
Fig. 4: Demonstration of arrhythmia detection with the DM-RC system.
Fig. 5: Demonstration of dynamic gesture recognition with the DM-RC system.

Similar content being viewed by others

Data availability

Data that support the findings of this study are available from the corresponding authors upon reasonable request. Source data are provided with this paper.

Code availability

The code that supports the DM-RC system simulations in this study is available via GitHub at https://github.com/Tsinghua-LEMON-Lab/Reservoir-computing. Other codes that support the findings of this study are available from the corresponding authors upon reasonable request.

References

  1. Zidan, M. A., Strachan, J. P. & Lu, W. D. The future of electronics based on memristive systems. Nat. Electron. 1, 22–29 (2018).

    Article  Google Scholar 

  2. Zhang, W. et al. Neuro-inspired computing chips. Nat. Electron. 3, 371–382 (2020).

    Article  Google Scholar 

  3. Chua, L. Memristor—the missing circuit element. IEEE Trans. Circuit Theory 18, 507–519 (1971).

    Article  Google Scholar 

  4. Strukov, D. B., Snider, G. S., Stewart, D. R. & Williams, R. S. The missing memristor found. Nature 453, 80–83 (2008).

    Article  Google Scholar 

  5. Cai, F. et al. Power-efficient combinatorial optimization using intrinsic noise in memristor Hopfield neural networks. Nat. Electron. 3, 409–418 (2020).

    Article  Google Scholar 

  6. Li, X. et al. Power-efficient neural network with artificial dendrites. Nat. Nanotechnol. 15, 776–782 (2020).

    Article  Google Scholar 

  7. Kumar, S., Williams, R. S. & Wang, Z. Third-order nanocircuit elements for neuromorphic engineering. Nature 585, 518–523 (2020).

    Article  Google Scholar 

  8. Yao, P. et al. Face classification using electronic synapses. Nat. Commun. 8, 15199 (2017).

    Article  Google Scholar 

  9. Yao, P. et al. Fully hardware-implemented memristor convolutional neural network. Nature 577, 641–646 (2020).

    Article  Google Scholar 

  10. Dalgaty, T. et al. In situ learning using intrinsic memristor variability via Markov chain Monte Carlo sampling. Nat. Electron. 4, 151–161 (2021).

    Article  Google Scholar 

  11. Cai, F. et al. A fully integrated reprogrammable memristor–CMOS system for efficient multiply–accumulate operations. Nat. Electron. 2, 290–299 (2019).

    Article  Google Scholar 

  12. Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9, 1735–1780 (1997).

    Article  Google Scholar 

  13. Hopfield, J. J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl Acad. Sci. USA 79, 2554–2558 (1982).

    Article  MathSciNet  Google Scholar 

  14. Salmela, L. et al. Predicting ultrafast nonlinear dynamics in fibre optics with a recurrent neural network. Nat. Mach. Intell. 3, 344–354 (2021).

    Article  Google Scholar 

  15. Wright, L. G. et al. Deep physical neural networks trained with backpropagation. Nature 601, 549–555 (2022).

    Article  Google Scholar 

  16. Appeltant, L. et al. Information processing using a single dynamical node as complex system. Nat. Commun. 2, 468 (2011).

    Article  Google Scholar 

  17. Shastri, B. J. et al. Photonics for artificial intelligence and neuromorphic computing. Nat. Photon. 15, 102–114 (2021).

    Article  Google Scholar 

  18. Jaeger, H. The ‘echo state’ approach to analysing and training recurrent neural networks—with an erratum note. Bonn., Ger.: Ger. Natl Res. Cent. Inf. Technol. GMD Tech. Rep. 148, 13 (2001).

    Google Scholar 

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

    Article  Google Scholar 

  20. Gauthier, D. J., Bollt, E., Griffith, A. & Barbosa, W. A. S. Next generation reservoir computing. Nat. Commun. 12, 5564 (2021).

    Article  Google Scholar 

  21. Alomar, M. L. et al. Digital implementation of a single dynamical node reservoir computer. IEEE Trans. Circuits Syst., II, Exp. Briefs 62, 977–981 (2015).

    Article  Google Scholar 

  22. Wang, Q. et al. Energy efficient parallel neuromorphic architectures with approximate arithmetic on FPGA. Neurocomputing 221, 146–158 (2017).

    Article  Google Scholar 

  23. Kleyko, D., Frady, E. P., Kheffache, M. & Osipov, E. Integer echo state networks: efficient reservoir computing for digital hardware. IEEE Trans. Neural Netw. Learn. Syst. 33, 1688–1701 (2020).

  24. Torrejon, J. et al. Neuromorphic computing with nanoscale spintronic oscillators. Nature 547, 428–431 (2017).

    Article  Google Scholar 

  25. Riou, M. et al. Neuromorphic computing through time-multiplexing with a spin-torque nano-oscillator. In 2017 IEEE International Electron Devices Meeting (IEDM) 36.3.1–36.3.4 (IEEE, 2017).

  26. 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 

  27. Martinenghi, R. et al. Photonic nonlinear transient computing with multiple-delay wavelength dynamics. Phys. Rev. Lett. 108, 244101 (2012).

    Article  Google Scholar 

  28. Vandoorne, K. et al. Experimental demonstration of reservoir computing on a silicon photonics chip. Nat. Commun. 5, 3541 (2014).

    Article  Google Scholar 

  29. Antonik, P., Marsal, N., Brunner, D. & Rontani, D. Human action recognition with a large-scale brain-inspired photonic computer. Nat. Mach. Intell. 1, 530–537 (2019).

    Article  Google Scholar 

  30. Hochstetter, J. et al. Avalanches and edge-of-chaos learning in neuromorphic nanowire networks. Nat. Commun. 12, 4008 (2021).

    Article  Google Scholar 

  31. Fu, K. et al. Reservoir computing with neuromemristive nanowire networks. In 2020 International Joint Conference on Neural Networks (IJCNN) 1–8 (IEEE, 2020).

  32. Milano, G. et al. In materia reservoir computing with a fully memristive architecture based on self-organizing nanowire networks. Nat. Mater. 21, 195–202 (2022).

    Article  Google Scholar 

  33. Zhong, Y. et al. Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing. Nat. Commun. 12, 408 (2021).

    Article  Google Scholar 

  34. Moon, J. et al. Temporal data classification and forecasting using a memristor-based reservoir computing system. Nat. Electron. 2, 480–487 (2019).

    Article  Google Scholar 

  35. Yu, J. et al. Energy efficient and robust reservoir computing system using ultrathin (3.5 nm) ferroelectric tunneling junctions for temporal data learning. In 2021 Symposium on VLSI Technology 1–2 (IEEE, 2021).

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

    Article  Google Scholar 

  37. Gao, B. et al. Oxide-based analog synapse: physical modeling, experimental characterization, and optimization. In 2016 IEEE International Electron Devices Meeting (IEDM) 7.3.1–7.3.4 (IEEE, 2016).

  38. Li, C. et al. Analogue signal and image processing with large memristor crossbars. Nat. Electron. 1, 52–59 (2017).

    Article  Google Scholar 

  39. Li, C. et al. Efficient and self-adaptive in-situ learning in multilayer memristor neural networks. Nat. Commun. 9, 2385 (2018).

    Article  Google Scholar 

  40. Moody, G. B. & Mark, R. G. The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 20, 45–50 (2001).

    Article  Google Scholar 

  41. Lukosevicius, M. & Jaeger, H. Survey: reservoir computing approaches to recurrent neural network training. Comput. Sci. Rev. 3, 127–149 (2009).

    Article  Google Scholar 

  42. Wan, W. et al. 33.1 A 74 TMACS/W CMOS-RRAM neurosynaptic core with dynamically reconfigurable dataflow and in-situ transposable weights for probabilistic graphical models. In 2020 IEEE International Solid-State Circuits Conference—(ISSCC) 498–500 (IEEE, 2020).

  43. Brunner, D., Soriano, M. C., Mirasso, C. R. & Fischer, I. Parallel photonic information processing at gigabyte per second data rates using transient states. Nat. Commun. 4, 1364 (2013).

    Article  Google Scholar 

  44. Liang, X. et al. Rotating neurons for all-analog implementation of cyclic reservoir computing. Nat. Commun. 13, 1549 (2022).

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by China Key Research and Development Program (2021ZD0201205), National Natural Science Foundation of China (91964104, 61974081, 62025111, 92064001 and 62104126) and the XPLORER Prize.

Author information

Author notes

  1. These authors contributed equally: Yanan Zhong, Jianshi Tang, Xinyi Li.

Authors and Affiliations

  1. School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China

    Yanan Zhong, Jianshi Tang, Xinyi Li, Xiangpeng Liang, Zhengwu Liu, Yijun Li, Yue Xi, Peng Yao, Zhenqi Hao, Bin Gao, He Qian & Huaqiang Wu

  2. Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, China

    Yanan Zhong

  3. Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing, China

    Jianshi Tang, Bin Gao, He Qian & Huaqiang Wu

  4. Microelectronics Lab, James Watt School of Engineering, University of Glasgow, Glasgow, UK

    Xiangpeng Liang

Authors
  1. Yanan Zhong
    View author publications

    Search author on:PubMed Google Scholar

  2. Jianshi Tang
    View author publications

    Search author on:PubMed Google Scholar

  3. Xinyi Li
    View author publications

    Search author on:PubMed Google Scholar

  4. Xiangpeng Liang
    View author publications

    Search author on:PubMed Google Scholar

  5. Zhengwu Liu
    View author publications

    Search author on:PubMed Google Scholar

  6. Yijun Li
    View author publications

    Search author on:PubMed Google Scholar

  7. Yue Xi
    View author publications

    Search author on:PubMed Google Scholar

  8. Peng Yao
    View author publications

    Search author on:PubMed Google Scholar

  9. Zhenqi Hao
    View author publications

    Search author on:PubMed Google Scholar

  10. Bin Gao
    View author publications

    Search author on:PubMed Google Scholar

  11. He Qian
    View author publications

    Search author on:PubMed Google Scholar

  12. Huaqiang Wu
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Y.Z. and J.T. conceived and designed the experiments. Xinyi Li contributed to device preparation and material analysis. Y.Z. performed the experiments and data analysis. Y.Z. and J.T. wrote the paper. All the authors discussed the results and commented on the manuscript. J.T., H.W. and H.Q. supervised the project.

Corresponding authors

Correspondence to Jianshi Tang or Huaqiang Wu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Electronics thanks Xiaobing Yan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary Information

Supplementary Figs. 1–9 and Tables 1 and 2.

41928_2022_838_MOESM2_ESM.mp4

Real-time demonstration of the DM-RC system.

Source data

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhong, Y., Tang, J., Li, X. et al. A memristor-based analogue reservoir computing system for real-time and power-efficient signal processing. Nat Electron 5, 672–681 (2022). https://doi.org/10.1038/s41928-022-00838-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41928-022-00838-3

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing