Stochastic Spiking Behavior in Neuromorphic Networks Enables True Random Number Generation.

Journal: ACS applied materials & interfaces
Published Date: Nov 1, 2021

Abstract

There is currently a great deal of interest in the use of nanoscale devices to emulate the behaviors of neurons and synapses and to facilitate brain-inspired computation. Here, it is shown that percolating networks of nanoparticles exhibit stochastic spiking behavior that is strikingly similar to that observed in biological neurons. The spiking rate can be controlled by the input stimulus, similar to "rate coding" in biology, and the distributions of times between events are log-normal, providing insights into the atomic-scale spiking mechanism. The stochasticity of the spiking behavior is then used for true random number generation, and the high quality of the generated random bit-streams is demonstrated, opening up promising routes toward integration of neuromorphic computing with secure information processing.

Authors

  • Susant K Acharya
    The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, Te Kura Matu̅, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand.
  • Edoardo Galli
    The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, Te Kura Matu̅, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand.
  • Joshua B Mallinson
    The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, Te Kura Matu̅, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand.
  • Saurabh K Bose
    The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, Te Kura Matu̅, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand.
  • Ford Wagner
    The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, Te Kura Matu, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand.
  • Zachary E Heywood
    Electrical and Computer Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand.
  • Philip J Bones
    Electrical and Computer Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand.
  • Matthew D Arnold
    School of Mathematical and Physical Sciences, University of Technology Sydney, Sydney, Australia.
  • Simon A Brown
    The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, Te Kura Matu̅, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand.

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