PFS - Reliability Assessment of Neural Networks in GPUs
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Document typeConference report
Defense date2022-05
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Abstract
Currently, Deep learning and especially
Convolutional Neural Networks (CNNs) have become a
fundamental computational approach applied in a wide range
of domains, including some safety-critical applications (e.g.,
automotive, robotics, and healthcare equipment). Therefore, the
reliability evaluation of those computational systems is
mandatory. The reliability evaluation of CNNs is performed by
fault injection campaigns at different levels of abstraction, from
the application level down to the hardware level. Many works
have focused their effort on evaluating the reliability of neural
networks in the presence of transient faults. However, the effects
of permanent faults have been investigated at the application
level, only, e.g., targeting the parameters of the network. This
paper presents the ongoing work on the reliability evaluation of
CNNs targeting permanent faults in GPU devices, considering
different fault injections levels. Our preliminary results show
that the fault injections performed at the application level
generate more optimistic results than considering an
architectural level fault injection.
CitationGuerrero-Balaguera, J.-D. Reliability Assessment of Neural Networks in GPUs. A: 27th IEEE European Test Symposium (ETS). 2022,
Publisher versionhttps://ieeexplore.ieee.org/xpl/conhome/9810327/proceeding
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