Name
Abstract | ||
|---|---|---|
This paper approaches statistical optimization by examining gate delay variation models and optimization objectives. Most previous work on statistical optimization has focused exclusively on the optimization algorithms without considering the effects of the variation models and objective functions. This work empirically derives a simple variation model that is then used to optimize for robustness. Optimal results from example circuits used to study the effect of the statistical objective function on parametric yield. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1145/1057661.1057736 | ACM Great Lakes Symposium on VLSI |
Keywords | Field | DocType |
statistical objective function,simple variation model,optimization algorithm,statistical gate sizing,gate delay variation model,variation model,optimization objective,work empirically,statistical optimization,objective function,previous work,robust optimization | Continuous optimization,Mathematical optimization,Stochastic optimization,Probabilistic-based design optimization,Computer science,Discrete optimization,Meta-optimization,Test functions for optimization,Multi-objective optimization,Random optimization | Conference |
ISBN | Citations | PageRank |
1-59593-057-4 | 3 | 0.44 |
References | Authors | |
7 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthew R. Guthaus | 1 | 155 | 19.14 |
| Natesan Venkateswaran | 2 | 84 | 7.70 |
| Vladimir Zolotov | 3 | 1367 | 109.07 |
| Dennis Sylvester | 4 | 5295 | 535.53 |
| Richard B. Brown | 5 | 473 | 64.00 |