2017/11/03 by Xiaotao Jia, Jia, Xiaotao, Jianlei Yang +11
Computer Science · Engineering · #Advanced Memory and Neural Computing #Artificial Intelligence (cs.AI) #Emerging Technologies (cs.ET) #Error Correcting Code Techniques #FOS: Computer and information sciences #Hardware Architecture (cs.AR) #Neural Networks and Reservoir Computing
paper · pdf · doi:10.48550/arxiv.1711.01125
openalex publication_date 2017/11/03 · openalex created_date 2022/09/29 · openalex updated_date 2026/08/01
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.