Agile Simulation of Stochastic Computing Image Processing With Contingency Tables

The rapid computerized simulation of stochastic computing (SC) systems is a challenging problem. A method for agile simulation of SC image processing is proposed in this work. The input operands are processed with the aid of a correlation-controlled contingency table (CT) construct without using actual stochastic bit-streams. The proposed approach underlines the validity of CT simulation with 1) image compositing; 2) pattern detection; and 3) bilinear interpolation case studies. Using the corresponding error models, we emulate the state-of-the-art pseudo-random and quasi-random bit-streams. Experimental results show that the proposed approach achieves similar computation accuracy to the traditional SC simulation while performing runtime- and memory-efficient computations. The execution time reduces more than
$200\times $
for the image compositing task when emulating random bit-streams with CT. Pattern detection and bilinear interpolation further showed
$76\times $
and
$22\times $
lower memory usage, respectively, when employing CT.