Image Edge Detection based on Swarm Intelligence using Memristive Networks

TL;DR

This paper presents a hardware-efficient method for image edge detection using swarm intelligence implemented with memristive networks, achieving significant energy savings and reduced area compared to traditional CMOS solutions.

Contribution

It introduces a novel memristive network-based hardware implementation of ant colony algorithm for edge detection, demonstrating improved energy efficiency and compactness.

Findings

28% energy improvement over CMOS hardware

Up to 5x reduction in area

Effective parameter tuning of memristors enhances performance

Abstract

Recent advancements in the development of memristive devices has opened new opportunities for hardware implementation of non-Boolean computing. To this end, the suitability of memristive devices for swarm intelligence algorithms has enabled researchers to solve a maze in hardware. In this paper, we utilize swarm intelligence of memristive networks to perform image edge detection. First, we propose a hardware-friendly algorithm for image edge detection based on ant colony. Second, we implement the image edge detection algorithm using memristive networks. Furthermore, we explain the impact of various parameters of the memristors on the efficacy of the implementation. Our results show 28% improvement in the energy compared to a low power CMOS hardware implementation based on stochastic circuits. Furthermore, our design occupies up to 5x less area.