Evolutionary approximation of edge detection circuits

Abstract

Approximate computing exploits the fact that many applications are inherently error resilient which means that some errors in their outputs can safely be exchanged for improving other parameters such as energy consumption or operation frequency. A new method based on evolutionary computing is proposed in this paper which enables to approximate edge detection circuits. Rather than evolving approximate edge detectors from scratch, key components of existing edge detector are replaced by their approximate versions obtained using Cartesian Genetic Programming (CGP). Various approximate edge detectors are then composed and their quality is evaluated using a database of images. The paper reports interesting edge detectors showing a good tradeoff between the quality of edge detection and implementation cost.