ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2016, Vol. 53 ›› Issue (4): 921-931.doi: 10.7544/issn1000-1239.2016.20150682

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Double Improved Genetic Algorithm and Low Power Task Mapping in 3D Networks-on-Chip

Zhang Dakun, Song Guozhi, Lin Huazhou, Ren Shuxia   

  1. (School of Computer Science & Software Engineering, Tianjin Polytechnic University, Tianjin 300387)
  • Online:2016-04-01

Abstract: With the rapid development of integrated circuit technology, the number of integrated components on a chip continues to increase. Efficient interconnection between the processing units on chip becomes a key issue. Therefore firstly system-on-chip (SoC) and then two-dimensional networks-on-chip (2D NoC) have been proposed to cope with this problem. But now even the 2D NoC has reached a bottleneck in many aspects, so the design of Three-Dimensional networks-on-chip (3D NoC) is inevitable. 3D NoC has attracted the attention of the researchers from both Academia and industry. One of the key issues of 3D NoC is low-power mapping. We have previously proposed a 3D NoC low-power mapping algorithm based on improved genetic algorithm with good results. But when the scale of the problem gets larger, the amount of calculation increases gradually and operation efficiency is reduced significantly. To solve this problem, this paper proposes a new 3D NoC task mapping algorithm with power optimization based on a double improved genetic algorithm, and the simulation experiments are conducted to validate the algorithm. The results show that under the conditions of a large population size, the 3D NoC task mapping algorithm cannot only reduce the power, but also reduce the running time significantly.

Key words: 3D network-on-chip (3D NoC), low-power mapping, improved genetic algorithm, double improved genetic algorithm, Greedy algorithm

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