Name
Abstract | ||
|---|---|---|
As being one of the most crucial steps in the design of embedded systems, hardware/software partitioning has received more concern than ever. The performance of a system design will strongly depend on the efficiency of the partitioning. In this paper, we construct a communication graph for embedded system and describe the delay-related constraints and the cost-related objective based on the graph structure. Then, we propose a heuristic based on genetic algorithm and simulated annealing to solve the problem near optimally. We note that the genetic algorithm has a strong global search capability, while the simulated annealing algorithm will fail in a local optimal solution easily. Hence, we can incorporate simulated annealing algorithm in genetic algorithm. The combined algorithm will provide more accurate near-optimal solution with faster speed. Experiment results show that the proposed algorithm produce more accurate partitions than the original genetic algorithm. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1155/2013/138037 | JOURNAL OF APPLIED MATHEMATICS |
Field | DocType | Volume |
Simulated annealing,Mathematical optimization,Algorithm design,Search algorithm,Adaptive simulated annealing,FSA-Red Algorithm,Suurballe's algorithm,Population-based incremental learning,Mathematics,Genetic algorithm | Journal | 2013 |
Issue | ISSN | Citations |
null | 1110-757X | 8 |
PageRank | References | Authors |
0.46 | 24 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xi-Bin Zhao | 1 | 290 | 30.98 |
| Hehua Zhang | 2 | 109 | 12.65 |
| Yu Jiang | 3 | 346 | 56.49 |
| Songzheng Song | 4 | 78 | 7.62 |
| Xun Jiao | 5 | 74 | 10.27 |
| Ming Gu | 6 | 14 | 3.93 |