Design of Hopfield network for cryptographic application by spintronic memristors

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


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.


Associative behaviour Cryptography Hopfield neural network MATLAB Spintronic memristor bridge 



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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

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