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Investigating the Impact of Radiation-Induced Soft Errors on the Reliability of Approximate Computing Systems

Abstract : Approximate Computing (AxC) is a well-known paradigm able to reduce the computational and power overheads of a multitude of applications, at the cost of a decreased accuracy. Convolutional Neural Networks (CNNs) have proven to be particularly suited for AxC because of their inherent resilience to errors. However, the implementation of AxC techniques may affect the intrinsic resilience of the application to errors induced by Single Events in a harsh environment. This work introduces an experimental study of the impact of neutron irradiation on approximate computing techniques applied on the data representation of a CNN.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-03025736
Contributor : Luigi Dilillo <>
Submitted on : Thursday, November 26, 2020 - 1:48:07 PM
Last modification on : Monday, July 5, 2021 - 3:44:05 PM

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Lucas Matana Luza, Daniel Soderstrom, Georgios Tsiligiannis, Helmut Puchner, Carlo Cazzaniga, et al.. Investigating the Impact of Radiation-Induced Soft Errors on the Reliability of Approximate Computing Systems. IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT), Oct 2020, Frascati, Italy. pp.1-6, ⟨10.1109/DFT50435.2020.9250865⟩. ⟨lirmm-03025736⟩

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