Emulating the Effects of Radiation-Induced Soft-Errors for the Reliability Assessment of Neural Networks
Matanaluza, L., Ruospo, A., Söderström, D., Cazzaniga, C., Kastriotou, M., Sanchez, E., Bosio, A., & Dilillo, L. (2022). Emulating the Effects of Radiation-Induced Soft-Errors for the Reliability Assessment of Neural Networks. IEEE Transactions on Emerging Topics in Computing, 10(4), 1867-1882. https://doi.org/10.1109/TETC.2021.3116999
Published inIEEE Transactions on Emerging Topics in Computing
Convolutional Neural Networks (CNNs) are currently one of the most widely used predictive models in machine learning. Recent studies have demonstrated that hardware faults induced by radiation fields, including cosmic rays, may significantly impact the CNN inference leading to wrong predictions. Therefore, ensuring the reliability of CNNs is crucial, especially for safety-critical systems. In the literature, several works propose reliability assessments of CNNs mainly based on statistically injected faults. This work presents a software emulator capable of injecting real faults retrieved from radiation tests. Specifically, from the device characterisation of a DRAM memory, we extracted event rates and fault models. The software emulator can reproduce their incidence and access their effect on CNN applications with a reliability assessment precision close to the physical one. Radiation-based physical injections and emulator-based injections are performed on three CNNs (LeNet-5) exploiting different data representations. Their outcomes are compared, and the software results evidence that the emulator is able to reproduce the faulty behaviours observed during the radiation tests for the targeted CNNs. This approach leads to a more concise use of radiation experiments since the extracted fault models can be reused to explore different scenarios (e.g., impact on a different application). ...
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN Search the Publication Forum2376-4562
Publication in research information system
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Related funder(s)European Commission
The content of the publication reflects only the author’s view. The funder is not responsible for any use that may be made of the information it contains.
Additional information about fundingVAN ALLEN Foundation (Grant Number: Contract No. UM 181387); European Unions Horizon 2020 research innovation programme (Grant Number: Marie Skodowska-Curie grant agreement No 721624); Region Occitanie (Grant Number: Contract No. UM 181386); IDEX Lyon OdeLe
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