Efficient Statistical Model Checking of Hardware Circuits With Multiple Failure Regions

Kumar, Jayanand Asok; Ahmadyan, Seyed Nematollah; Vasudevan, Shobha

doi:10.1109/tcad.2014.2299957

Cited by 6 publications

(1 citation statement)

References 24 publications

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“…However, this method cannot obtain rare attribute samples. For the low-probability attributes of hardware circuits with multiple failure regions, Kumar et al [41] assumed that the system failure distribution is a Gaussian mixture model, thus proposed to use the variational Bayes method to learn an optimal importance sampling distribution from the Gaussian mixture model. However, the optimal importance sampling distribution is not a distribution family from the system path space.…”

Section: Related Workmentioning

confidence: 99%

Towards a Statistical Model Checking Method for Safety-Critical Cyber-Physical System Verification

Xie

Tan

Fang

et al. 2021

Security and Communication Networks

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Safety-Critical Cyber-Physical System (SCCPS) refers to the system that if the system fails or its key functions fail, it will cause casualties, property damage, environmental damage, and other catastrophic consequences. Therefore, it is vital to verify the safety of safety critical systems. In the community, the SCCPS safety verification mainly relies on the statistical model checking methodology, but for SCCPS with extremely high safety requirements, the statistical model checking method is difficult/infeasible to sample the extremely small probability event since the probability of the system violating the safety is very low (rare property). In response to this problem, we propose a new method of statistical model checking for high-safety SCCPS. Firstly, with the CTMC-approximated SCCPS path probability space model, it leverages the maximum likelihood estimation method to learn the parameters of CTMC. Then, the embedded DTMC can be derived from CTMC, and a cross-entropy optimization model based on DTMC can be constructed. Finally, we propose an algorithm of iteratively learning the optimal importance sampling distribution on the discrete path space and an algorithm to check the statistical model of verifying the rare attribute. Eventually, experimental results show that the method proposed in this paper can effectively verify the rare attributes of SCCPS. Under the same sample size, comparing with the heuristic importance sampling methods, the estimated value of this method can be better distributed around the mean value, and the related standard deviation and relative error are reduced by more than an order of magnitude.

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Section: Related Workmentioning

confidence: 99%