Compact and Power-efficient Sobel Edge Detection with Fully Connected Cube-network-based Stochastic Computing

Abstract

Stochastic computing, an approximate computing method using bitstreams, has attracted attention as an alternative to deterministic computing. Stochastic computing circuits are known to perform complex calculations with high density through probability calculations. Herein, we describe the design of an accurate and compact arithmetic circuit based on stochastic computing. First, we propose a simple technique to change the output of a random number generator that is an integral part of stochastic computing for stochastic multipliers and adders. Compared with conventional designs, the results indicate that the proposed design reduces power and area and enhances the performance. This method uses a fully connected cube network and does not lose accuracy without overhead. Subsequently, when applying this design to image processing in the real world, a 63% area reduction and 95% power savings are achieved when compared to an accurate operator. Therefore, it is clear that the proposed design is optimized for energy-efficient hardware designs with high imprecision tolerance.