Article Dans Une Revue IEEE Transactions on Nuclear Science Année : 2025

Radiation-Induced Soft Error Assessment of a Multithreaded MobileNet CNN Model Running in a Multicore Edge Processor

Résumé

Convolutional neural networks (CNNs) have become a standard technology in numerous industrial Internet of Things (IoT) applications and sectors, such as automotive and aerospace. Recent advancements in hardware and software (e.g., application programming interface (API)/libraries) components have enabled the efficient execution of multithreaded CNN models on edge devices. As the complexity and adoption of CNNs in safety-critical systems continue to grow, ensuring their resilience becomes key and increasingly challenging. In this context, this work promotes two original contributions: 1) the proposal of a multithreaded implementation of MobileNet, which achieves a 2.67× speedup and an energy reduction of 16% with four worker threads, and 2) the first soft error reliability assessment of a multithreaded CNN model running in a multicore processor under high-energy and thermal neutron radiation flux. Results from the radiation campaigns, with more than 31k runs, suggest that multithreaded executions can increase the occurrence of critical faults by up to 5× . Results also show a greater number of events during the thermal neutron campaign, and some input images are significantly more robust against silent data corruption (SDC) events.

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Dates et versions

hal-05494674 , version 1 (05-02-2026)

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Jonas Gava, Areeb Sherjil, Luiz Henrique Laurini, Emmanuel Atukpor, Rodrigo Possamai Bastos, et al.. Radiation-Induced Soft Error Assessment of a Multithreaded MobileNet CNN Model Running in a Multicore Edge Processor. IEEE Transactions on Nuclear Science, 2025, 72 (8), pp.2821-2829. ⟨10.1109/TNS.2025.3585859⟩. ⟨hal-05494674⟩

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