Towards Low-Cost High-Accuracy Stochastic Computing Architecture for Univariate Functions: Design and Design Space Exploration

Univariate functions are widely used. Several recent works propose to implement them by an unconventional computing paradigm, stochastic computing (SC). However, existing SC designs either have a high hardware cost due to the area-consuming randomizer or a low accuracy. In this work, we propose a low-cost high-accuracy SC architecture for univariate functions. It consists of only a single stochastic number generator and a minimum number of D flip-flops. We also apply three methods, random number source (RNS) negating, RNS scrambling, and input scrambling, to improve the accuracy of the architecture. To efficiently configure the architecture to achieve a high accuracy, we further propose a design space exploration algorithm. The experimental results show that compared to the conventional architecture, the area of the proposed architecture is reduced by up to 76%, while its accuracy is close to or sometimes even higher than that of the conventional architecture.