Name
Abstract | ||
|---|---|---|
This paper presents a circuit/system level synthesis and optimization approach based on a learning scheme using support vectors machines (SVMs) and evolutionary strategies applied to the design of analog and mixed-signal ICs. This approach combines the best qualities of these two techniques, a robust classification and regression method and a powerful global optimization. The SVM is used to dynamically model performance space and identify the feasible design space regions while at the same time the evolutionary techniques are looking for the global optimum. Finally, the proposed optimization-based approach is demonstrated for the design of some analog circuits using HSPICE as the evaluation engine. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICECS.2006.379831 | ICECS |
Keywords | Field | DocType |
analogue integrated circuits,circuit CAD,genetic algorithms,integrated circuit design,regression analysis,support vector machines,GA-SVM optimization,HSPICE,analog IC design automation,genetic algorithms,regression method,robust classification,support vectors machines | Analogue electronics,Computer science,Control engineering,Automation,Electronic engineering,Artificial intelligence,Genetic algorithm,Design space,Kernel (linear algebra),Global optimization,Support vector machine,Integrated circuit design,Machine learning | Conference |
ISBN | Citations | PageRank |
1-4244-0395-2 | 2 | 0.62 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manuel F. M. Barros | 1 | 26 | 3.21 |
| Jorge Guilherme | 2 | 14 | 6.02 |
| Nuno Cavaco Horta | 3 | 310 | 49.65 |