SDC-causing Error Detection and Mitigation Based on Failure rate Prediction without Fault Injection
Subject Areas :
Multimedia Processing, Communications Systems, Intelligent Systems
Moona Yakhchi
1
,
Mahdi Fazeli
2
,
Seyed Amir Asghari Toochai
3
1 - Phd Student, Department of Computer, Borujerd Branch, Islamic Azad University, Borujerd, Iran.
2 - Associate Professor, School of Information Technology, Halmstad University, Halmstad, Sweden.
3 - Assistant Professor, Electrical and Computer Engineering Department, Kharazmi University, Tehran, Iran.
Received: 2022-09-23
Accepted : 2023-03-10
Published : 2022-12-23
Keywords:
Silent Data Corruption,
Multi-Bit Fault,
Soft Errors,
Machin Learning,
Fault Injection,
Abstract :
Introduction: Reducing the size of processing components and increasing the probability of failure even in ordinary components maintaining reliability has become a serious challenge of today’s computer systems. The soft errors can lead to silent data corruption which seriously compromises the reliability of a system. The Silent data corruption is a fault that affects running software and leads to incorrect output. Detecting silent data corruption needed a profile of the instructions causing the silent data corruption to decide which instructions to be protected. Current approaches by machine learning algorithms predict the occurrence rate of silent data corruption for each instruction. While most of the existing algorithms suffer from inaccuracy. Most current detection techniques require sufficient data from fault injection for training, which is difficult to achieve due to high resource consumption, such as execution time and code size costs. However, as technology is downscaling toward Nano-scale sizes, multiple-bit soft errors are emerging as an important reliability challenge. Therefore, identifying and determining vulnerable points in the presence of fault has so important.Method: Traditional solutions based on redundancies are very expensive in terms of chip area, energy consumption, and performance. Consequently, providing low cost and efficient approaches to cope with SDCs has received researchers’ attention more than ever. Hence the lack of a high-precision method without fault injection becomes a research challenge. Utilizing fault injection methods in complex systems is costly; therefore, in identifying silent data corruptions, a method based on machine learning algorithm is used, which is not necessary to inject fault in all software. Multi-bit faults and silent data corruptions with instruction sources are also considered. For this goal, we have proposed the M5rule decision tree model to detect the silent data corruption error by calculating the importance of the instruction feature for the vulnerability. Then we have used the error detection method by copying the critical instructions with sort.Results: Finally, we evaluated our model on Mibench benchmarks with multiple test programs. The results show an overhead of 58 % with data silent corruption coverage rate of about 99%.Discussion: In order that we not only did the single-bit fault consider but also multiple-bit fault. In addition, fault had been injected into instruction and data. Consequently, the evaluation results show that our method achieves a better detection accuracy compared to other state-of-the-art methods.
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