1. Evidences of Stochastic Bayesian Machines Robustness Against SEUs and SETs
- Author
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Raphaël Laurent, Alexandre Siqueira Guedes Coelho, Miguel Solinas, S. Karaoui, Raoul Velazco, Juan A. Fraire, Emmanuel Mazer, Nacer-Eddine Zergainoh, Techniques of Informatics and Microelectronics for integrated systems Architecture (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Probayes [Montbonnot], Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Université des sciences et de la Technologie d'Oran Mohamed Boudiaf [Oran] (USTO MB), Techniques de l'Informatique et de la Microélectronique pour l'Architecture des systèmes intégrés (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and Laboratoire d'Informatique de Grenoble (LIG )
- Subjects
010308 nuclear & particles physics ,Stochastic process ,Computer science ,business.industry ,Computation ,Bayesian probability ,Probabilistic logic ,02 engineering and technology ,Machine learning ,computer.software_genre ,01 natural sciences ,020202 computer hardware & architecture ,PACS 8542 ,Robustness (computer science) ,0103 physical sciences ,0202 electrical engineering, electronic engineering, information engineering ,Probability distribution ,Artificial intelligence ,[SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics ,business ,computer - Abstract
International audience; This work revisits the stochastic computationparadigm as a way to implement architectures dedicated toBayesian computation. It is assumed that Stochastic BayesianMachines (SMBs) are intrinsically tolerant to the effects ofradiation. However, practical assessment is mandatory beforeconsidering SBMs in hazardous environments. Results of faultinjectioncampaigns performed at the RTL level provide the firstevidences of SBMs robustness with respect to SEUs and SETs.
- Published
- 2016