Name
Abstract | ||
|---|---|---|
The multi-objective genetic algorithm is an effective solution to the complex problem of hardware software codesign. An extended genetic algorithm (EGA) has been developed that implements a novel selection method with function scaling, adaptive crossover and mutation. This EGA is applied in a codesign optimization stage for dataflow oriented applications and synthesis on Field-Programmable Gate Arrays (FPGAs). its effectiveness is illustrated on the problem of codesign of a self-tuning regulator considering area and performance. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/FPT.2004.1393282 | 2004 IEEE INTERNATIONAL CONFERENCE ON FIELD-PROGRAMMABLE TECHNOLOGY, PROCEEDINGS |
Keywords | Field | DocType |
genetic algorithms,genetic algorithm,field programmable gate array,field programmable gate arrays,integrated circuit design | Regulator,Digital signal processing,Crossover,Computer science,Parallel computing,Field-programmable gate array,Integrated circuit design,Dataflow,Scaling,Genetic algorithm | Conference |
Citations | PageRank | References |
4 | 0.47 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthew J. W. Savage | 1 | 4 | 0.47 |
| Zoran Salcic | 2 | 553 | 82.51 |
| George Coghill | 3 | 42 | 5.30 |
| Grant Covic | 4 | 52 | 5.61 |