Statistical blockade: a novel method for very fast Monte Carlo simulation of rare circuit events, and its application
Amith Singhee, Rob A. Rutenbar
Abstract
Amith Singhee, Rob A. Rutenbar
Abstract
Circuit reliability under statistical process variation is an area of growing concern. For highly replicated circuits such as SRAMs and flip flops, a rare statistical event for one circuit may induce a not-so-rare system failure. Existing techniques perform poorly when tasked to generate both efficient sampling and sound statistics for these rare events. Statistical blockade is a novel Monte Carlo technique that allows us to efficiently filter - to block - unwanted samples insufficiently rare in the tail distributions we seek. The method synthesizes ideas from data mining and extreme value theory, and shows speed-ups of 10times - 100times over standard Monte Carlo
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Circuit reliability under statistical process variation is an area of growing concern. For highly replicated circuits such as SRAMs and flip flops, a rare statistical event for one circuit may induce a not-so-rare system failure. Existing techniques perform poorly when tasked to generate both efficient sampling and sound statistics for these rare events. Statistical blockade is a novel Monte Carlo technique that allows us to efficiently filter - to block - unwanted samples insufficiently rare in the tail distributions we seek. The method synthesizes ideas from data mining and extreme value theory, and shows speed-ups of 10times - 100times over standard Monte Carlo
Key concepts: Monte Carlo method, Rare events, Computer science, Block (permutation group theory), Algorithm, Reliability (semiconductor), Filter (signal processing), Sampling (signal processing)