High-accuracy mean circuits design by manipulating correlation for stochastic computing

Stochastic computing (SC) encodes numerical values into probabilistic binary bitstreams to enable complex arithmetic operations to be transformed into simple bit operations. Different operations can be performed through the same SC element by adjusting the correlation between bitstreams. In this work, two stochastic mean circuits (MCs) are proposed by manipulating positively and negatively correlated bitstreams, respectively named PCMC and NCMC, to improve computing accuracy, lower area, and power consumption. Furthermore, a general structure for MCs, denoted as GMC, is also proposed to generalize their design approach. Compared with the existing multiplexer (MUX)-based stochastic MCs, the proposed circuits have approximately 50%, 59%, 61%, and 64% reduction in area, and 100x, 10x, 151x, and 12 x improvement in MSE, for 2-, 3-, 4-, and 5-input situations, respectively. With applications to edge detection and mean filtering, the proposed designs are superior to the existing counterparts in terms of accuracy and hardware cost.