Abstract

Assessing the reliability of modern devices running CNN algorithms is a very difficult task. Actually, the complexity of the state-of-the-art devices makes exhaustive Fault Injection (FI) campaigns impractical and typically out of the computational capabilities. A possible solution consists of resorting to statistical FI campaigns that allow a reduction in the number of needed experiments by injecting only a carefully selected small part of it. Under specific hypothesis, statistical FIs guarantee an accurate picture of the problem, albeit selecting a reduced sample size. The main problems today are related to the choice of the sample size, the location of the faults, and the correct understanding of the statistical assumptions. The intent of this paper is twofold: first, we describe how to correctly specify statistical FIs for Convolutional Neural Networks; second, we propose a data analysis on the CNN parameters that drastically reduces the number of FIs needed to achieve statistically significant results without compromising the validity of the proposed method. The methodology is experimentally validated on two CNNs, ResNet-20 and MobileNetV2, and the results show that a statistical FI campaign on about 1.21% and 0.55% of the possible faults, provides very precise information of the CNN reliability. The statistical results have been confirmed by the exhaustive FI campaigns on the same cases of study.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.