Fully Memristive Spiking-Neuron Learning Framework and its Applications on Pattern Recognition and Edge Detection

TL;DR

This paper introduces a fully memristive spiking-neuron framework using only drift and diffusion memristors, simplifying the structure and improving speed and resource efficiency in pattern recognition and edge detection tasks.

Contribution

It presents a novel, fully memristive neuron model with simplified structure, eliminating the need for additional devices, and demonstrates its advantages in speed, resource usage, and signal quality.

Findings

Faster processing speed compared to other memristive neural networks.

Reduced hardware resource requirements due to simpler neuron structure.

Higher PSNR in edge detection owing to dynamic filtering of diffusion memristor.

Abstract

Fully memristive spiking-neuron learning framework, which uses drift and diffusion memristor models as axon and dendrite respectively, becomes a hot topic recently with the development of memristor devices. Normally, some other devices like resistor or capacitor are still necessary on recent works of fully memristive learning framework. However, theoretically, one neuron needs axon and dendrite only, which makes technique process simpler and learning framework more similar to biologic brain. In this paper, a fully memristive spiking-neuron learning framework is introduced, in which a neuron structure is just built of one drift and one diffusion memristive models. To verify it merits, a feedforward neural network for pattern recognition and a cellular neural network for edge detection are designed. Experiment results show that compared to other memristive neural networks, our framework's the processing speed is much faster and the hardware resource is saved in pattern recognition due to its simple structure. Further due to the dynamic filtering function of diffusion memristor model in our learning framework, its peak signal noise ratio (PSNR) is much higher than traditional algorithms in edge detection.