On the Impact of Energy-Accuracy Tradeoff in a Digital Cellular Neural Network for Image Processing

Abstract

This paper studies the opportunities of energy-accuracy tradeoff in cellular neural network (CNN). Algorithmic characteristics of CNN is coupled with hardware-induced error distribution of a digital CNN cell to evaluate energy-accuracy tradeoff for simple image processing tasks as well as a complex application. The analysis shows that errors modulate the cell dynamics and propagate through the network degrading the output quality and increasing the convergence time. The error propagation is determined by the task being performed by the CNN, specifically, the strength of the feedback template. Controlling precision is observed to be a more effective approach for energy-accuracy tradeoff in CNN than voltage over scaling.