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Seminar at LAAS

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This week, Juan Carlos Ruiz has been teaching the entitled seminar “Statistical Fault Injection: When is it enough in robustness assessment?”

“Simulation-based fault injection is commonly used to assess the robustness of hardware components modelled using Hardware Description Languages (HDL). The current complexity of modern circuits usually makes not feasible the consideration during experimentation of all possible combinations of fault models, targets, and times. By assuming a confidence interval and error margin, statistical fault injection exploits the principle of statistical sampling to reduce the number of experiments while keeping the results representative of the whole population of fault injections. Since the percentage of injected faults leading to failure is a priori unknown, such number of experiments is usually determined by selecting the value maximizing the sample size. This presentation argues that this conservative assumption leads to a worst-case scenario that can be improved. It proposes an new iterative approach to progressively adjust the number of experiments by estimating the percentage of those leading to failure and the error of such estimation. This proposal provides new means to decide when to stop a fault injection campaign and to estimate the error existing in the results finally reported.“

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