Memristor-Based Edge Detection for Spike Encoded Pixels

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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 behaviours, the memristor has proved a versatile component. Here we demonstrate an analogue 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 analogue behaviour can prove a scalable solution to edge detection. We confirm its behaviour 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.
Funding Information
  • Engineering and Physical Sciences Research Council (EP/P013503/1)

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