Name
Abstract | ||
|---|---|---|
Statistical fault injection is a widely used methodology to early evaluation of soft error reliability of microprocessor based systems. Due to the increasing complexity of the software and hardware stack, the simulation of faults on modern processors is becoming a computationally demanding task even for ISA-equivalent models and virtualization tools. This paper proposes and explores the use of a supervised machine learning technique, Deep Feedforward Neural Network, to design a predictor which drastically reduces the computing time of fault injection campaigns. In addition, a novel approach is presented to increase the training data from a limited set of benchmarks. Thanks to this approach, the predictor can be modeled with an extensive data set comprising not only of millions of fault injections but also thousands of different benchmarks. Experiments show promising results for estimating the applications fault tolerance when they run on state of the art ARM processor. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/LATS49555.2020.9093688 | 2020 IEEE Latin-American Test Symposium (LATS) |
Keywords | DocType | ISSN |
Fault injection tool,reliability,microprocessor,radiation effects,deep learning | Conference | 2373-0862 |
ISBN | Citations | PageRank |
978-1-7281-8732-7 | 0 | 0.34 |
References | Authors | |
2 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Ruiz Falcó | 1 | 0 | 0.34 |
| Alejandro Serrano-Cases | 2 | 3 | 2.47 |
| Antonio Martínez-Álvarez | 3 | 118 | 16.22 |
| Sergio Cuenca-Asensi | 4 | 64 | 13.97 |