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On the Robustness of Stochastic Bayesian Machines.

Authors :
Coelho, Alexandre
Laurent, Raphael
Solinas, Miguel
Fraire, Juan
Mazer, Emmanuel
Zergainoh, Nacer-Eddine
Karaoui, Said
Velazco, Raoul
Source :
IEEE Transactions on Nuclear Science. Aug2017, Vol. 64 Issue 8 Part 1, p2276-2283. 8p.
Publication Year :
2017

Abstract

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. [ABSTRACT FROM PUBLISHER]

Details

Language :
English
ISSN :
00189499
Volume :
64
Issue :
8 Part 1
Database :
Academic Search Index
Journal :
IEEE Transactions on Nuclear Science
Publication Type :
Academic Journal
Accession number :
125531007
Full Text :
https://doi.org/10.1109/TNS.2017.2678204