Analysis of AVF Evaluation Methods for Microprocessor Reliability

Guo, Baolong; Zhou, Guochang; Yufang, Cao; Gao, Xiang; Yan, Yunyi

doi:10.14355/spr.2015.04.008

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…In order to evaluate the reliability of the program before and after adding the error detection mechanism for soft errors and to verify the effectiveness of the error detection mechanism [6][7][8], fault injection method [9,10], irradiation experiment method [11,12] and theoretical model analysis method [13][14][15][16][17][18] are widely used at present. The fault injection method simulates software errors randomly by hardware or software methods and observes the running results of the program under fault injection.…”

Section: Introductionmentioning

confidence: 99%

Reliability Analysis for Programs with Redundancy Computation for Soft Errors

Meng

Zhang

Xue

et al. 2023

J. Phys.: Conf. Ser.

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Soft error is one of the factors which may affect the reliability of computer programs. A common method to alleviate the impact of soft errors is redundancy computation, a classical data flow error detection mechanism. However, a program with redundancy computation may still have some vulnerable spots, which might be caused by the flaw during implementation or the instruction reordering given by compiler optimization. Finding the vulnerable spots of a program with redundancy computation is of great significance to evaluate the capability of the error detection mechanism. There are some conventional methods to analyze the reliability of a program under soft errors, such as the irradiation experiment, fault injection, and modeling analysis. However, the irradiation experiment is expensive, fault injection is very time-consuming, and the existing modeling analysis methods have not considered the error detection mechanism. This paper proposes a novel method of reliability analysis for programs with redundancy computation by analyzing the dynamic instruction sequence. Experimental results show that our approach has fairly high accuracy and a false negative rate of about 0.0545.

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