Memristor-Based Edge Detection for Spike Encoded Pixels - Mannion, Daniel J.; Mehonic, Adnan; Ng, Wing H.; Kenyon, Anthony J. (Frontiers in Neuroscience, 2020)
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.