Compact and accurate stochastic circuits with shared random number sources

Abstract

Stochastic computing, which is an approximate computation with probabilities (called stochastic numbers), draws attention as an alternative method of deterministic computing. In this paper, we discuss a design of compact and accurate stochastic circuits. Stochastic circuits are known as a way to stochastically compute complex calculation at low hardware cost, while stochastic number generators (SNGs), which are used for converting deterministic numbers into stochastic numbers, account for a large fraction of the circuits. To reduce such SNGs in stochastic circuits, we propose a technique to share random number generators with several SNGs. This sharing method employs circular shift of the output of LFSRs to reduce the correlation between stochastic numbers. We also discuss the influence of input correlation around a multiplexer, which is a scaled adder for stochastic computing, so as to avoid over reducing the input correlation. Application of the proposed techniques to two stochastic image processing shows the reduction in the size of SNGs without greatly sacrificing accuracy.