@inproceedings{17d05be3338a475db92c1131f1d12510,
title = "Spintronics based stochastic computing for efficient Bayesian inference system",
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.",
keywords = "Bayesian Inference, Magnetic Tunnel Junction, Spintronics, Stochastic Computing",
author = "Xiaotao Jia and Jianlei Yang and Zhaohao Wang and Yiran Chen and Li, \{Hai Helen\} and Weisheng Zhao",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 23rd Asia and South Pacific Design Automation Conference, ASP-DAC 2018 ; Conference date: 22-01-2018 Through 25-01-2018",
year = "2018",
month = feb,
day = "20",
doi = "10.1109/ASPDAC.2018.8297385",
language = "英语",
series = "Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "580--585",
booktitle = "ASP-DAC 2018 - 23rd Asia and South Pacific Design Automation Conference, Proceedings",
address = "美国",
}