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Article Dans Une Revue IEEE Transactions on Nuclear Science Année : 2022

Assessment of Tiny Machine-Learning Computing Systems under Neutron-Induced Radiation Effects

Résumé

This paper compares and assesses the effectiveness of three prominent machine learning (ML) models for tiny ML computing systems in tolerating neutron-induced soft errors. Results of 14-MeV and thermal neutron radiation tests suggest that the three case-study ML algorithms implemented – without any mitigation technique integrated – retain a certain intrinsic level of effectiveness in tolerating neutron effects, although all of them have been functionally interrupted in some occasions, requiring hardware resets. Notably, the implemented case-study ML algorithm “random forest” has performed no misclassification during the different radiation testing campaigns.

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Dates et versions

hal-03676824 , version 1 (24-05-2022)

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Paternité - Pas d'utilisation commerciale

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Rodrigo Possamai Bastos, Matheus Garay Trindade, Rafael Garibotti, Jonas Gava, Ricardo Reis, et al.. Assessment of Tiny Machine-Learning Computing Systems under Neutron-Induced Radiation Effects. IEEE Transactions on Nuclear Science, 2022, pp.1-1. ⟨10.1109/TNS.2022.3176485⟩. ⟨hal-03676824⟩
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