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
Assistant Professor

I am currently a tenure-track assistant professor jointly affiliated with Institute for Artificial Intelligence and School of Integrated Circuits in Peking University. My research interests focus on efficient and secure multi-modality AI acceleration algorithms and hardwares.

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