  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.
