Advertisement

Design of Hopfield network for cryptographic application by spintronic memristors

  • A. Ruhan Bevi
  • P. MonurajanEmail author
  • J. Manjula
Original Article
  • 53 Downloads

Abstract

Memory being one of the essential credential in today’s computer world seeks forward newer research interests in its types. Hopfield neural networks of artificial neural networks are one of its classes that can be modelled to form an associative memory. In this paper, we have shown the Hopfield neural network constructed with spintronic memristor bridges accounting to act as an associative memory unit. The memristors are nanoscaled, in terms of size, which possess synaptic behaviour in the artificial neuromorphic system. The associative behaviour is realised by the updation of synaptic weights of memristive Hopfield with single- and multiple-bit associativity which is simulated in MATLAB. The application of the hardware in the field of cryptography is also proposed.

Keywords

Associative behaviour Cryptography Hopfield neural network MATLAB Spintronic memristor bridge 

Notes

References

  1. 1.
    Chua L (1971) Memristor-the missing circuit element. IEEE Trans Circuit Theory 18(5):507–519CrossRefGoogle Scholar
  2. 2.
    Strukov DB, Snider GS, Stewart DR, Williams RS (2008) The missing memristor found. Nat Publ Group 453:80–83Google Scholar
  3. 3.
    Akgül A (2015) New reproducing kernel functions. Math Prob Eng 2015:158134.  https://doi.org/10.1155/2015/158134 MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Yingjie F, Huang X, Wang Z, Li Y (2018) Nonlinear dynamics and chaos in a simplified memristor-based fractional-order neural network with discontinuous memductance function. Nonlinear Dyn 93(2):611–627CrossRefGoogle Scholar
  5. 5.
    Mustafa Inc, Akgul Ali (2014) Approximate solutions for MHD squeezing fluid flow by a novel method. Boundary Value Prob 2014:18.  https://doi.org/10.1186/1687-2770-2014-18 MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Mustafa Inc, Akgul A (2014) Numerical solution of seventh-order boundary value problems by a Novel method. Abstract Appl Anal 2014:745287.  https://doi.org/10.1155/2014/745287 MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Akgul A, Kılıçman A (2013) Numerical solutions of the second-order one-dimensional telegraph equation based on reproducing Kernel Hilbert Space method. Abstract Appl Anal 2013:768963.  https://doi.org/10.1155/2013/768963 MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Mustafa Inc, Akgül A, Kılıçman A (2013) A Novel method for solving KdV equation based on reproducing Kernel Hilbert space method. Abstract Appl Anal 2013:578942.  https://doi.org/10.1155/2013/578942 MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Mustafa Inc., Akgül A, Kılıçman A (2013) A new application of the reproducing Kernel Hilbert Space method to solve MHD Jeffery–Hamel flows problem in nonparallel walls. Abstract Appl Anal 2013:239454.  https://doi.org/10.1155/2013/239454 MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Mustafa Inc., Akgül A, Kılıçman A (2012) Explicit solution of telegraph equation based on reproducing Kernel method. J Funct Spaces Appl 2012:984682.  https://doi.org/10.1155/2012/984682 MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Inc M, Kılıçman EK, Akgül A (2017) Solitary wave solutions for the Sawada–Kotera equation. J Adv Phys 6(2):288–293CrossRefGoogle Scholar
  12. 12.
    Hashemi MS, Inc M, Kilicman AA (2016) On solitons and invariant solutions of the Magneto-electro-elastic circular rod. Waves Random Complex Media 26(3):259–271MathSciNetCrossRefGoogle Scholar
  13. 13.
    Akgul A (2017) Group preserving scheme and reproducing kernel method for the Poisson–Boltzmann equation for semiconductor devices. Nonlinear Dyn 88(4):2817–2829MathSciNetCrossRefGoogle Scholar
  14. 14.
    Boutarfa AA, Inc M (2017) New approach for the Fornberg–Whitham type equations. J Comput Appl Math 312(1):13–26MathSciNetCrossRefGoogle Scholar
  15. 15.
    Hu SG, Liu Y, Liu Z, Chen TP et al (2015) A memristive hopfield network for associative memory. Nat Publ Group.  https://doi.org/10.1038/protex.2015.070 CrossRefGoogle Scholar
  16. 16.
    Tarkov MS (2016) Hopfield network with interneuronal connections based on memristor bridges. Springer, Cham, pp 196–203Google Scholar
  17. 17.
    Wang Y, Chen H, Xi H Li, Dimitrov D (2009) Spintronic memristor through spin-torque-induced magnetization motion. IEEE Electron Device Lett 30(3):294–297CrossRefGoogle Scholar
  18. 18.
    Wang L, Wang X, Duan S, Li H (2015) A spintronic memristor bridge synapse circuit and the application in memrisitive cellular automata. Neurocomputing 167(Supplement C):346–351Google Scholar
  19. 19.
    Lequeux S, Sampaio J, Cros V, Yakushiji K, Fukushima A, Matsumoto R, Kubota H, Yuasa S, Grollier J (2016) A magnetic synapse: multilevel spin-torque memristor with perpendicular anisotropy. Sci Rep 6(1):31510.  https://doi.org/10.1038/srep31510 CrossRefGoogle Scholar
  20. 20.
    Grollier J, Querlioz D, Stiles MD (2016) Spintronic nanodevices for bioinspired computing. Proc IEEE 104(10):2024–2039CrossRefGoogle Scholar
  21. 21.
    Zhou Y et al (2019) Associative memory for image recovery with a high-performance memristor array. Adv Func Mater.  https://doi.org/10.1002/adfm.201900155 CrossRefGoogle Scholar
  22. 22.
    Hu SG, Liu Y, Liu Z et al (2015) Associative memory realized by a reconfigurable memristive Hopfield neural network. Nat Commun 6:7522CrossRefGoogle Scholar
  23. 23.
    Wang Z et al (2018) Fully memristive neural networks for pattern classification with unsupervised learning. Nat Electron 1(1):137–145CrossRefGoogle Scholar
  24. 24.
    Miao H et al (2018) Memristor-based analog computation and neural network classification with a dot product engine. Adv Mater.  https://doi.org/10.1002/adma.201705914 CrossRefGoogle Scholar
  25. 25.
    Vourkas I (2016) Memristor-based nanoelectronic computing circuits and architectures. Springer.  https://doi.org/10.1007/978-3-319-22647-7 CrossRefGoogle Scholar
  26. 26.
    Zhang Y, Wang X, Friedman EG (2018) Memristive-based circuit design for multilayer neural networks. IEEE Trans Circuits Syst 65(2):677–684CrossRefGoogle Scholar
  27. 27.
    Li C et al (2018) Efficient and self-adaptive in situ learning in multilayer memristor neural networks. Nat Commun 9(1):2385.  https://doi.org/10.1038/s41467-018-04484-2. CrossRefGoogle Scholar
  28. 28.
    ChuanChen LL, Haipeng P, Yang Y (2018) Adaptive synchronization of memristor-based BAM neural networks with mixed delays. Appl Math Comput 322(1):100–110.  https://doi.org/10.1016/j.amc.2017.11.037 MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Wang L, Yang C, Wen J, Gai S, Peng Y (2015) Overview of emerging memristor families from resistive memristor to spintronic memristor. J Mater Sci Mater Electron 26(7):4618–4628CrossRefGoogle Scholar
  30. 30.
    Sah MP, Yang C, Kim H et al (2012) A voltage mode memristor bridge synaptic circuit with memristor emulators. Sensors 12(3):3587–3604CrossRefGoogle Scholar
  31. 31.
    Jian-anFang and HuiyuanLi (2017) Master–slave exponential synchronization of delayed complex-valued memristor based neural networks via impulsive control. Neural Netw 93(1):165–175.  https://doi.org/10.1016/j.neunet.2017.05.008 CrossRefGoogle Scholar
  32. 32.
    Pershin YV, Ventra M (2010) Experimental demonstration of associative memory with memristive neural networks. Neural Netw 23(7):881–886.  https://doi.org/10.1016/j.neunet.2010.05.001 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringSRM Institute of Science and TechnologyKattankulathur, ChennaiIndia

Personalised recommendations