Towards Low-Cost High-Accuracy Stochastic Computing Architecture for Univariate Functions: Design and Design Space Exploration
Kuncai Zhong1,a, Zexi Li1,b and Weikang Qian1,2
1University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, China
akczhong@sjtu.edu.cn
blzx12138@sjtu.edu.cn
2MoE Key Laboratory of Artificial Intelligence, Shanghai Jiao Tong University, China
qianwk@sjtu.edu.cn
ABSTRACT
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 areaconsuming 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.
