IFDA: Intermittent Fault Diagnosis Algorithm for Augmented Cubes Under the PMC Model
Source abstract
Abstract
Fault diagnosis technology is a crucial technique for ensuring the reliability of multiprocessor systems. Many previous studies have paid close attention to the permanent faults of systems while ignoring the rise of intermittent faults. Meanwhile, there is a lack of a rapid diagnostic algorithm tailored for intermittent faults. In this paper, we propose multiple theorems to evaluate the intermittent fault diagnosability of different topologies under the PMC model. Through these theorems, we demonstrate that the intermittent fault diagnosability of an n-dimensional augmented cube (AQn) is (2n−2) when n is greater than or equal to 4. Furthermore, we present a fast intermittent fault diagnosis algorithm, which is named as IFDA, to identify the processors with intermittent fault in the networks. Finally, we evaluate the performance of the algorithm in terms of the parameters Accuracy and Precision. The simulation experimental results show that the algorithm IFDA has good performance and efficiency.
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Research context
OpenAlex topics: Manufacturing Process and Optimization · Engineering and Test Systems · Industrial Vision Systems and Defect Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer Science and Technology, Hainan University, Haikou 570228, China
School of Software, South China Normal University, Foshan 528225, China
Funding
Hainan Provincial Natural Science Foundation of China · Innovation Platform for Academicians of Hainan Province · Joint Fund for Basic and Applied Basic Research in Guangdong Province
References (28)
Show source-provided references
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