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A Reliability Study on CNNs for Critical Embedded Systems


Deep learning systems such as Convolutional Neural Networks (CNNs) have shown remarkable efficiency in dealing with a variety of complex real life problems. To accelerate the execution of these heavy algorithms, a plethora of software implementations and hardware accelerators have been proposed. In a context of shrinking devices dimensions, reliability issues of CNN-hosting systems are under-explored. In this paper, we experimentally evaluate the inherent fault tolerance of CNNs by injecting errors within network modules, namely processing elements and memories. Our experiments demonstrate a non uniform sensitivity between different parts of the system. While CNNs are relatively resilient to errors occurring in processing elements, transient faults hitting memories lead to catastrophic degradation of accuracy.
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hal-03430208 , version 1 (16-11-2021)



Mohamed Ayoub Neggaz, Pablo Ribalta, Ihsen Alouani, Smail Niar. A Reliability Study on CNNs for Critical Embedded Systems. 2018 IEEE 36th International Conference on Computer Design (ICCD), Oct 2018, Orlando, United States. pp.476-479, ⟨10.1109/ICCD.2018.00077⟩. ⟨hal-03430208⟩
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