Exploiting approximate adder circuits for power-efficient Gaussian and Gradient filters for Canny edge detector algorithm

Abstract

This paper proposes the exploration of approximate adders for the implementation of power-efficient Gaussian and Gradient filters for Image Processing. The Gaussian filter is a convolution operator which is used to blur images and to remove noise. On the other hand, the Gradient of an image measures how it is changing. Both blocks can be designed in hardware using only shifts and additions. In this work we exploit a set of approximate adders in order to implement energy-efficient filters. The tree of adders of Gaussian and Gradient filters are implemented using one RCA-based approximate adder, as well as an Error-Tolerant Adder ETAI. The approximate architectures are compared to the best precise implementation of the filters. As the Gaussian and Gradient blocks are part of the Canny edge detector algorithm, we have implemented the adder trees of the filters aiming this application. Our main results show that for an efficient power realization of this algorithm, the best strategy consists in the implementation of the Gaussian filter with ETA I adder, and the Gradient filter with the RCA-based adder.