Memristor-Based Edge Detection for Spike Encoded Pixels.

Journal: Frontiers in neuroscience
Published Date: Jan 17, 2020

Abstract

Memristors have many uses in machine learning and neuromorphic hardware. From memory elements in dot product engines to replicating both synapse and neuron wall behaviors, the memristor has proved a versatile component. Here we demonstrate an analog mode of operation observed in our silicon oxide memristors and apply this to the problem of edge detection. We demonstrate how a potential divider exploiting this analog behavior can prove a scalable solution to edge detection. We confirm its behavior experimentally and simulate its performance on a standard testbench. We show good performance comparable to existing memristor based work with a benchmark score of 0.465 on the BSDS500 dataset, while simultaneously maintaining a lower component count.

Authors

  • Daniel J Mannion
    Department of Electronic and Electrical Engineering, University College London, London, United Kingdom.
  • Adnan Mehonic
    Department of Electronic and Electrical Engineering, University College London, London, United Kingdom.
  • Wing H Ng
    Department of Electronic and Electrical Engineering, University College London, London, United Kingdom.
  • Anthony J Kenyon
    Department of Electronic and Electrical Engineering, University College London, London, United Kingdom.

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