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 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 (<50%) and image noise (
$\sigma $
= 6%). Additionally, the implementation of the fast-Canny operator on memristor arrays can reduce the processing time by half and save the area of buffer compared to the prior two-convolution Canny operation. Our work suggests that the memristive fast-Canny operator could be a promising and efficient hardware solution for edge detection at the network edge.