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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Singhee, A. Rutenbar, R.A. |
| Copyright Year | 2007 |
| Description | Author affiliation: Dept. of ECE, Carnegie Mellon Univ., Pittsburgh, PA (Singhee, A.; Rutenbar, R.A.) |
| 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 |
| Starting Page | 1 |
| Ending Page | 6 |
| File Size | 346885 |
| Page Count | 6 |
| File Format | |
| ISBN | 9783981080124 |
| DOI | 10.1109/DATE.2007.364490 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-04-16 |
| Publisher Place | France |
| Access Restriction | Subscribed |
| Rights Holder | ESCI |
| Subject Keyword | Monte Carlo methods Random access memory Measurement Statistical distributions Probability distribution Tail Statistics Circuit simulation Filters Data mining |
| Content Type | Text |
| Resource Type | Article |
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