Accelerating stochastic computation for binary classification applications

Abstract

Stochastic computation is a non-conventional computation paradigm, which uses digital circuits to operate on stochastic bit streams. Although it has advantages such as strong fault tolerance and low hardware cost, its drawback is its long computation time. In this work, we target at stochastic computation used in binary classification applications, such as image segmentation and pattern classification, and propose a novel accelerating module. We study how the design parameters affect the error rate and computation time. We further propose how to find the optimal design parameters. A case study on an image segmentation algorithm shows the effectiveness of our proposed solution.