Name
Abstract | ||
|---|---|---|
Statistical static timing analysis (SSTA) has emerged as an essential tool for nanoscale designs. Monte Carlo methods are universally employed to validate the accuracy of the approximations made in all SSTA tools, but Monte Carlo itself is never employed as a strategy for practical SSTA. It is widely believed to be "too slow" -- despite an uncomfortable lack of rigorous studies to support this belief. We offer the first large-scale study to refute this belief. We synthesize recent results from fast quasi-Monte Carlo (QMC) deterministic sampling and efficient Karhunen-Loéve expansion (KLE) models of spatial correlation to show that Monte Carlo SSTA need not be slow. Indeed, we show for the ISCAS89 circuits, a few hundred, well-chosen sample points can achieve errors within 5%, with no assumptions on gate models, wire models, or the core STA engine, with runtimes less than 90 s.
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Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICCAD.2008.4681573 | ICCAD |
Keywords | Field | DocType |
core sta engine,efficient karhunen-lo,fast monte carlo,ssta tool,deterministic sampling,statistical static timing analysis,monte carlo ssta,practical ssta,monte carlo method,iscas89 circuit,monte carlo,spatial correlation,kernel,sampling methods,quasi monte carlo,correlation,convergence,monte carlo methods,logic gates | Convergence (routing),Kernel (linear algebra),Monte Carlo method,Spatial correlation,Statistical static timing analysis,Computer science,Electronic engineering,Kinetic Monte Carlo,Sampling (statistics),Monte Carlo molecular modeling | Conference |
ISBN | Citations | PageRank |
978-1-4244-2820-5 | 30 | 1.49 |
References | Authors | |
17 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amith Singhee | 1 | 347 | 22.94 |
| Sonia Singhal | 2 | 34 | 2.31 |
| Rob A. Rutenbar | 3 | 2283 | 280.48 |