Memristor Crossbar Array Based ACO For Image Edge Detection

• Published in: Neural processing letters Vol. 51; no. 2; pp. 1891 - 1905
• Main Authors: Yu, Yongbin, Deng, Quanxin, Ren, Liyong, Tashi, Nyima
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2020; Springer Nature B.V
• Subjects: Algorithms; Ant colony optimization; Arrays; Artificial Intelligence; Circuit design; Complex Systems; Computational Intelligence; Computer Science; Edge detection; Figure of merit; Heuristic; Memristors; Neural networks; Nonlinear control; Optimization; Pheromones; Swarm intelligence; Traveling salesman problem; Ant colony optimization; Memristor; Crossbar array; Image edge detection
• ISSN: 1370-4621; 1573-773X
• DOI: 10.1007/s11063-019-10179-6

Memristor provides an available way to design and deploy swarm intelligence. As a typical swarm intelligence algorithm, ant colony optimization is implemented by the memristor crossbar array to make image edge detection in this paper. Firstly, a non-linear voltage-controlled memristor model with a relaxation term is proposed. Then, an improved ant colony optimization with padding strategy is designed. Thirdly, a memristor crossbar array with external control circuits is designed to implement ant colony optimization for image edge detection, which offers high device density and parallel computing. In the course of ant colony optimization based image edge detection deployed by memristor crossbar array, the threshold to generating edges can be directly chosen as the mean of the final conductance matrix. On the one hand, experiment results show that more delicate edges can be detected by proposed method compared to holistically-nested edge detection based on neural networks. On the other hand, Figure of merit of proposed method is better than that of Sobel operator.