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

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 en...

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Vydáno v:IEEE transactions on nuclear science Ročník 72; číslo 8; s. 2821 - 2829
Hlavní autoři: Gava, Jonas, Sherjil, Areeb, Laurini, Luiz H., Atukpor, Emmanuel, Bastos, Rodrigo Possamai, Moraes, Fernando, Reis, Ricardo, Ost, Luciano
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.08.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Application programming interface
Artificial neural networks
Convolutional neural networks
Energy efficiency
Error correction codes
Field programmable gate arrays
Industrial applications
Industrial Internet of Things
Microprocessors
MobileNet
Multicore processing
multicore processors
multithreaded convolutional neural network (CNN) models
Neural networks
Neutron radiation
Neutrons
Radiation
Radiation effects
Reliability
Reliability analysis
Resilience
Safety critical
Single instruction multiple data
soft error reliability
Soft errors
Thermal neutrons
ISSN:0018-9499, 1558-1578
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Popis
Shrnutí: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 <inline-formula> <tex-math notation="LaTeX">2.67\times </tex-math></inline-formula> 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 <inline-formula> <tex-math notation="LaTeX">5\times </tex-math></inline-formula>. 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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0018-9499
1558-1578
DOI:10.1109/TNS.2025.3585859