Dynamic adaptive discrete particle swarm optimization algorithm based method on low-power mapping in network-on-chip
Compared to 2D NoC, 3D NoC has better integrated density and system performance, which was a reliable method to solve the problem about low-power mapping. On the basis of the traditional particle swarm optimization algo-rithm (PSOA), a dynamic adaptive discrete particle swarm optimization algorithm...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2016-11-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2016215/ |
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| Summary: | Compared to 2D NoC, 3D NoC has better integrated density and system performance, which was a reliable method to solve the problem about low-power mapping. On the basis of the traditional particle swarm optimization algo-rithm (PSOA), a dynamic adaptive discrete particle swarm optimization algorithm (DADPSOA) was proposed . Parame-ter in this algorithm was adjusted dynamically based on the degree of early convergence and the charge of individual adap-tive value to approach the optimal solution. At the same time, the reasonable structure of the particles was made aiming at reducing the time complexity of this algorithm. Experimental results show that comparing with the random mapping, genetic algorithm (GA), PSOA and dynamic ant colony algorithm (DACA), DADPSOA can save the execution time, reduce the communication power consumption of mapping results. The power consumption of the task graph is reduced. |
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| ISSN: | 1000-436X |