Spintronics based Stochastic Computing for Efficient Bayesian Inference System

TL;DR

This paper introduces a spintronics-based stochastic computing system that enhances Bayesian inference efficiency, reducing power consumption and increasing speed through novel device utilization.

Contribution

It presents a new Bayesian inference system leveraging spintronics devices for stochastic bitstream generation, improving efficiency over traditional methods.

Findings

Significant reduction in power consumption.

Faster inference speeds demonstrated.

Effective application to data fusion and belief networks.

Abstract

Bayesian inference is an effective approach for solving statistical learning problems especially with uncertainty and incompleteness. However, inference efficiencies are physically limited by the bottlenecks of conventional computing platforms. In this paper, an emerging Bayesian inference system is proposed by exploiting spintronics based stochastic computing. A stochastic bitstream generator is realized as the kernel components by leveraging the inherent randomness of spintronics devices. The proposed system is evaluated by typical applications of data fusion and Bayesian belief networks. Simulation results indicate that the proposed approach could achieve significant improvement on inference efficiencies in terms of power consumption and inference speed.