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Exploring the Robustness of Magnetic Ring Arrays Reservoir Computing with Linear Field Calibration

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Unconventional Computation and Natural Computation (UCNC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14003))

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Abstract

One of the challenges for reservoir computing is the robustness of the implementation in the face of fabrication error. If a system is too sensitive to fabrication error, then each manufactured reservoir becomes a unique artefact with unique computational properties. Under most circumstances, this is undesirable as it makes reproduction of results, or useful systems, complicated. This paper uses simulation to examine the properties of nano-scale magnetic ring arrays as reservoir computers under parameters corresponding to a wide variety of physically derived parameters, and investigates the effectiveness of linear field calibration to minimise the difference in unexpected behaviour of the systems.

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Notes

  1. 1.

    This differs from controlled behaviours, which under normal circumstances it is desirable to have as many different types of behaviour as possible.

  2. 2.

    For brevity some details, such as penalty terms on selecting the partition to explore that ensure that all areas of the behaviour space are explored rather than focusing on a infinitesimally small but interesting area, are omitted.

  3. 3.

    These limits are somewhat arbitrary, but approximately reflect the limitations of the current physical implementation with regard to sustained magnetic fields.

References

  1. Bhovad, P., Li, S.: Physical reservoir computing with origami and its application to robotic crawling. Sci. Rep. 11(1), 1–18 (2021)

    Article  Google Scholar 

  2. Bordignon, G., et al.: Analysis of magnetoresistance in arrays of connected Nano-rings. IEEE Trans. Magn. 43(6), 2881–2883 (2007)

    Article  Google Scholar 

  3. Brosamler, G.A.: An almost everywhere central limit theorem. Math. Proc. Cambridge Philos. Soc. 104, 561–574 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  4. Büsing, L., Schrauwen, B., Legenstein, R.: Connectivity, dynamics, and memory in reservoir computing with binary and analog neurons. Neural Comput. 22(5), 1272–1311 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  5. Dale, M., et al.: Reservoir computing with thin-film ferromagnetic devices. arXiv preprint arXiv:2101.12700 (2021)

  6. Dale, M., Miller, J.F., Stepney, S., Trefzer, M.: A substrate-independent framework to characterise reservoir computers. Proceed. Royal Soc. A 475, 2226 (2019). https://doi.org/10.1098/rspa.2018.0723

  7. Dawidek, R.W., et al.: Dynamically driven emergence in a nanomagnetic system. Adv. Func. Mater. 31(15), 2008389 (2021)

    Article  Google Scholar 

  8. Franklin, A.: Calibration. Perspect. Sci. 5(1), 31–80 (1997)

    Article  Google Scholar 

  9. Griffin, D.: PyCHARC. https://github.com/dgdguk/pycharc/

  10. Harvey, I.: The microbial genetic algorithm. In: Kampis, G., Karsai, I., Szathmáry, E. (eds.) ECAL 2009. LNCS (LNAI), vol. 5778, pp. 126–133. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21314-4_16

    Chapter  Google Scholar 

  11. Jaeger, H.: Short term memory in echo state networks. GMD-report 152. In: GMD-German National Research Institute for Computer Science (2002). http://www.faculty.jacobs-university.de/hjaeger/pubs/STMEchoStatesTechRep pdf (2002)

  12. Jaeger, H., Haas, H.: Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304(5667), 78–80 (2004)

    Article  Google Scholar 

  13. Kendall, A., Badrinarayanan, V., Cipolla, R.: Bayesian segNet: model uncertainty in deep convolutional encoder-decoder architectures for scene understanding. In: Kim, T.-K., Stefanos Zafeiriou, G.B., Mikolajczyk, K. (eds.) Proceedings of the British Machine Vision Conference (BMVC), pp. 1-512. BMVA Press (2017). https://doi.org/10.5244/C.31.57

  14. Lehman, J., Stanley, K.O.: Exploiting open-endedness to solve problems through the search for novelty. In: ALife XI, Boston, MA, USA, pp. 329–336. MIT Press (2008)

    Google Scholar 

  15. Schrauwen, B., Verstraeten, D., Van Campenhout, J.: An overview of reservoir computing: theory, applications and implementations. In: Proceedings of the 15th European Symposium on Artificial Neural Networks, pp. 471–482 (2007)

    Google Scholar 

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

    Article  Google Scholar 

  17. Vidamour, I.T., et al.: Quantifying the computational capability of a nanomagnetic reservoir computing platform with emergent magnetisation dynamics. Nanotechnology 33(48), 485203 (2022). https://doi.org/10.1088/1361-6528/ac87b5

    Article  Google Scholar 

  18. Vidamour, I., et al.: Reservoir computing with emergent dynamics in a magnetic metamaterial (2022). https://doi.org/10.48550/ARXIV.2206.04446

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Acknowledgments

The authors wish to thank Chalres Vidamour for sharing insight into challenges of the fabrication process of the magnetic ring arrays used in prior work [17]. DG and SS acknowledge funding from the MARCH project, EPSRC grant numbers EP/V006029/1 and EP/V006339/1. IV acknowledges a DTA-funded PhD studentship from EPSRC.

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Authors and Affiliations

  1. University of York, York, UK

    David Griffin & Susan Stepney

  2. University of Sheffield, Sheffield, UK

    Ian Vidamour

Authors
  1. David Griffin
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  2. Susan Stepney
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  3. Ian Vidamour
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Corresponding author

Correspondence to David Griffin .

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Editors and Affiliations

  1. University of North Florida, Jacksonville, FL, USA

    Daniela Genova

  2. University of Turku, Turku, Finland

    Jarkko Kari

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Griffin, D., Stepney, S., Vidamour, I. (2023). Exploring the Robustness of Magnetic Ring Arrays Reservoir Computing with Linear Field Calibration. In: Genova, D., Kari, J. (eds) Unconventional Computation and Natural Computation. UCNC 2023. Lecture Notes in Computer Science, vol 14003. Springer, Cham. https://doi.org/10.1007/978-3-031-34034-5_7

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  • DOI: https://doi.org/10.1007/978-3-031-34034-5_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34033-8

  • Online ISBN: 978-3-031-34034-5

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