Low-power mapping algorithm for three-dimensional network-on-chip based on diversity-controlled quantum-behaved particle swarm optimization
Three-dimensional network on chip (3D NoC) is developed based on three-dimensional integrated circuit, system on chip and two-dimensional network on chip. The 3D NoC is mainly used to solve the problems such as communication bottleneck of highly integrated chips. Mapping of 3D NoC is a key problem i...
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Online Access: | https://doi.org/10.1177/1748301816649070 |
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doaj-30a6fcf77c794c9eb806fee00a5baa4e2020-11-25T03:03:21ZengSAGE PublishingJournal of Algorithms & Computational Technology1748-30181748-30262016-09-011010.1177/1748301816649070Low-power mapping algorithm for three-dimensional network-on-chip based on diversity-controlled quantum-behaved particle swarm optimizationCui HuangDakun ZhangGuozhi SongThree-dimensional network on chip (3D NoC) is developed based on three-dimensional integrated circuit, system on chip and two-dimensional network on chip. The 3D NoC is mainly used to solve the problems such as communication bottleneck of highly integrated chips. Mapping of 3D NoC is a key problem in the research area of 3D NoC. The authors proposed a low-power mapping algorithm based on quantum-behaved particle swarm optimization. Simulation results show that the proposed algorithm can significantly reduce power consumption, but with large-scale application characteristic graph, the efficiency of power optimization cannot be improved very much. To solve this problem, a low-power mapping algorithm for 3D NoC based on diversity-controlled quantum-behaved particle swarm optimization is proposed in this paper. Simulation results show that for large-scale application characteristic graph, this algorithm is able to maintain a stable power optimization efficiency (4.08–8.04%) and converges much faster.https://doi.org/10.1177/1748301816649070 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Cui Huang Dakun Zhang Guozhi Song |
spellingShingle |
Cui Huang Dakun Zhang Guozhi Song Low-power mapping algorithm for three-dimensional network-on-chip based on diversity-controlled quantum-behaved particle swarm optimization Journal of Algorithms & Computational Technology |
author_facet |
Cui Huang Dakun Zhang Guozhi Song |
author_sort |
Cui Huang |
title |
Low-power mapping algorithm for three-dimensional network-on-chip based on diversity-controlled quantum-behaved particle swarm optimization |
title_short |
Low-power mapping algorithm for three-dimensional network-on-chip based on diversity-controlled quantum-behaved particle swarm optimization |
title_full |
Low-power mapping algorithm for three-dimensional network-on-chip based on diversity-controlled quantum-behaved particle swarm optimization |
title_fullStr |
Low-power mapping algorithm for three-dimensional network-on-chip based on diversity-controlled quantum-behaved particle swarm optimization |
title_full_unstemmed |
Low-power mapping algorithm for three-dimensional network-on-chip based on diversity-controlled quantum-behaved particle swarm optimization |
title_sort |
low-power mapping algorithm for three-dimensional network-on-chip based on diversity-controlled quantum-behaved particle swarm optimization |
publisher |
SAGE Publishing |
series |
Journal of Algorithms & Computational Technology |
issn |
1748-3018 1748-3026 |
publishDate |
2016-09-01 |
description |
Three-dimensional network on chip (3D NoC) is developed based on three-dimensional integrated circuit, system on chip and two-dimensional network on chip. The 3D NoC is mainly used to solve the problems such as communication bottleneck of highly integrated chips. Mapping of 3D NoC is a key problem in the research area of 3D NoC. The authors proposed a low-power mapping algorithm based on quantum-behaved particle swarm optimization. Simulation results show that the proposed algorithm can significantly reduce power consumption, but with large-scale application characteristic graph, the efficiency of power optimization cannot be improved very much. To solve this problem, a low-power mapping algorithm for 3D NoC based on diversity-controlled quantum-behaved particle swarm optimization is proposed in this paper. Simulation results show that for large-scale application characteristic graph, this algorithm is able to maintain a stable power optimization efficiency (4.08–8.04%) and converges much faster. |
url |
https://doi.org/10.1177/1748301816649070 |
work_keys_str_mv |
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_version_ |
1724686167007297536 |