A monte carlo simulation flow for seu analysis of sequential circuits

Abstract

An efficient methodology for soft error analysis of sequential circuits based on Monte Carlo sampling is proposed. It uses nested sampling for faster statistical convergence: it samples only from the workload space and statically evaluates the conditional probability over the subspace of particle strike and circuit parameters. A novel check on the stationarity of machine state sequence to reduce the number of samples to convergence is introduced. The flow combines logic simulation for latch-level error propagation and stationarity diagnostic and an improved combinational error simulator with a new masking model based on signal controllability. Experiments show that nested sampling reduces the number of samples by up to 1500X and runtime by up to 25X compared to direct sampling. Stationarity checking allows reducing sampling number by 25%, on average. The new latching window model permits accuracy of within 1% from SPICE, compared to a 12% error with a prior model.

Publication
In ACM/IEEE Design Automation Conference (DAC)
Meng Li
Meng Li
Staff Research Scientist

I am currently a staff research scientist and tech lead in the Meta On-Device AI team with a focus on researching and productizing efficient AI algorithms and hardwares for next generation AR/VR devices. I received my Ph.D. degree in the Department of Electrical and Computer Engineering, University of Texas at Austin under the supervision of Prof. David Z. Pan and my bachelor degree in Peking University under the supervision of Prof. Ru Huang and Prof. Runsheng Wang. My research interests include efficient and secure AI algorithms and systems.

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