Generating multiple correlated probabilities for MUX-based stochastic computing architecture

Abstract

Stochastic computing is a paradigm that performs computation on stochastic bit streams using conventional digital circuits. A general design for stochastic computing is a MUX-based architecture, which needs multiple constant probabilities as inputs. Previous approaches generate these probabilities by separate combinational circuits. The resulting designs are not area-efficient. In this work, we use the fact that these constant probabilities to the MUX can have correlation and propose two novel algorithms that produce low-cost circuits for generating these probabilities. Experimental results showed that our method greatly reduces the cost of generating constant probabilities for the MUX-based stochastic computing architecture.