Survey of Stochastic Computing

TLDR

Stochastic computing, introduced in the 1960s as a low‑cost, low‑complexity alternative that encodes data as digitized probabilities, was once deemed impractical due to long runtimes and low accuracy, but modern trends in circuit uncertainty now motivate a renewed focus on its probabilistic advantages. This review examines stochastic computing from a contemporary standpoint, arguing that its compact size, error resilience, and probabilistic nature can rival conventional methods in selected applications. The authors survey existing literature, outline stochastic number representations and circuit architectures, evaluate design trade‑offs, and illustrate potential applications while highlighting remaining practical challenges.

Abstract

Stochastic computing (SC) was proposed in the 1960s as a low-cost alternative to conventional binary computing. It is unique in that it represents and processes information in the form of digitized probabilities. SC employs very low-complexity arithmetic units which was a primary design concern in the past. Despite this advantage and also its inherent error tolerance, SC was seen as impractical because of very long computation times and relatively low accuracy. However, current technology trends tend to increase uncertainty in circuit behavior and imply a need to better understand, and perhaps exploit, probability in computation. This article surveys SC from a modern perspective where the small size, error resilience, and probabilistic features of SC may compete successfully with conventional methodologies in certain applications. First, we survey the literature and review the key concepts of stochastic number representation and circuit structure. We then describe the design of SC-based circuits and evaluate their advantages and disadvantages. Finally, we give examples of the potential applications of SC and discuss some practical problems that are yet to be solved.