Design of a Stochastic Computing Architecture for the Phansalkar Algorithm

Yongqiang Zhang, Jiao Qin, Jie Han, Guangjun Xie

Abstract: Binarization plays a key role in image processing. Its performance directly affects the success of subsequent character segmentation and recognition. The Phansalkar algorithm performs excellent in processing heavily degraded or poor-quality images. However, this algorithm incurs significant hardware costs. In this article, efficient stochastic computing (SC) functions and an architecture are proposed for the Phansalkar algorithm. Highly accurate stochastic elements are designed for this architecture, including a stochastic mean circuit (SMC), a stochastic unipolar subtractor (USUB), a stochastic square root circuit (SQRT), and a stochastic exponential circuit (SEXP). Simulation results show that the SC architecture using 64-bit streams for the Phansalkar algorithm provides sufficient accuracy. Physical implementation indicates the effectiveness of the proposed architecture in lowering hardware costs for this algorithm compared with the binary counterpart.