On the Robustness of Stochastic Bayesian Machines

被引:5
|
作者
Coelho, Alexandre [1 ]
Laurent, Raphael [2 ]
Solinas, Miguel [1 ]
Fraire, Juan [1 ]
Mazer, Emmanuel [3 ]
Zergainoh, Nacer-Eddine [1 ]
Karaoui, Said [4 ]
Velazco, Raoul [1 ]
机构
[1] Univ Grenoble Alpes, CNRS, Grenoble INP, TIMA, F-38000 Grenoble, France
[2] ProbaYes SAS, F-38330 Grenoble, France
[3] Univ Grenoble Alpes, CNRS LIG, F-38000 Grenoble, France
[4] Univ Sci & Technol Oran Mohamed Boudiaf, Oran 31000, Algeria
关键词
Bayesian calculations; fault injection; soft errors; stochastic computing; COMPUTATION;
D O I
10.1109/TNS.2017.2678204
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper revisits the stochastic computing paradigm as a way to implement architectures dedicated to probabilistic inference. In general, it is assumed the operation over stochastic bit streams is robust with respect to radiation transient events effects. Moreover, it can be expected that leveraging the stochastic computing paradigm to implement probabilistic computations such as Bayesian inference implemented in hardware could yield an increased resilience to radiation effects comparatively to deterministic procedures. However, the practical assessment of the robustness against radiation is mandatory before considering stochastic Bayesian machines (SBMs) in hazardous environments. Results of fault injection campaigns at register transfer level provide the first evidences of the intrinsic robustness of SBMs with respect to single event upsets and single event transients.
引用
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页码:2276 / 2283
页数:8
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