Memristor-Based Edge Detection for Spike Encoded Pixels

• Main Authors: Daniel J. Mannion, Adnan Mehonic, Wing H. Ng, Anthony J. Kenyon
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2020-01-01
• Series: Frontiers in Neuroscience
• Subjects: memristor; edge detection; computer vision; spiking neural networks; neuromorphic computing
• Online Access: https://www.frontiersin.org/article/10.3389/fnins.2019.01386/full

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.