Memristive Fast-Canny Operation for Edge Detection

Memristor-based in- memory computing paradigm is a promising path for edge detection in image preprocessing on end devices that reduces the computational pressure on data centers. However, the implementation of the well-performing Canny operator for edge detection faces challenges in terms of comput...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on electron devices 2022-11, Vol.69 (11), p.6043-6048
Main Authors: Tian, Jing, Zhou, Hou-Ji, Bao, Han, Chen, Jia, Huang, Xiao-Di, Li, Jian-Cong, Yang, Ling, Li, Yi, Miao, Xiang-Shui
Format: Article
Language:English
Subjects:
Arrays
Associativity
Canny operator
Computing time
Convolution
Data centers
Edge detection
Hardware
Image edge detection
Image sensors
in-memory computing
memristor
Memristors
Voltage
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Memristor-based in- memory computing paradigm is a promising path for edge detection in image preprocessing on end devices that reduces the computational pressure on data centers. However, the implementation of the well-performing Canny operator for edge detection faces challenges in terms of computational time and area overhead when mapped to memristor arrays. In this work, we proposed an efficient memristive one-step implementation of a fast-Canny operator. Exploiting the associative property of multiplication, the conventional Canny operator consisting of Gaussian and Sobel operators is converted into a fast-Canny operator and mapped to an array of nine parallel memristors. Then, the output currents are the final pixels of the edge image. To verify the feasibility of the method, successful edge detection with high accuracy (OIS = 0.73) is achieved in device-aware simulation under device variation (
ISSN:0018-9383
1557-9646
DOI:10.1109/TED.2022.3204525