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DPAHMA: a novel dual-population adaptive hybrid memetic algorithm for non-slicing VLSI floorplans

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Abstract

Floorplanning is a crucial part of very large-scale integration (VLSI) physical design flow. It primarily determines the position of the blocks on a chip by considering the area, the total wirelength, etc., in light of several real-world limitations such as delay, price, and chip performance. Adopting B*-tree representation, this paper proposes a novel dual-population adaptive hybrid memetic algorithm called DPAHMA to handle the VLSI floorplanning problem effectively by optimizing the chip area and the total wirelength. Three main ideas are presented in this paper, including new definitions of crossover and mutation operators based on B*-tree encoding that overcome the shortcomings of the existing method, such as overly complicated operations on binary trees and a lack of diversity; a dynamic self-adjusting objective function, namely WeightDS, which is able to find solutions more suitable for the user-specified weight; and a main-auxiliary population mechanism by which a candidate population is introduced to assist the normal population in the global search phase. To make full use of the information obtained by the local search method, the candidate population keeps its high-quality solutions. The individuals from the candidate population crossover with the individuals from the normal population before the end of each iteration to obtain higher-quality solutions as much as possible. Experimental results for MCNC and GSRC benchmarks show that DPAHMA obtains floorplans effectively with better area and total wirelength than those of the state-of-the-art floorplanners.

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Data availability

Experimental data and the source code are available upon request from the corresponding author zhangliming_jlu@163.com. For datasets, all MCNC benchmarks can be found in http://vlsicad.eecs.umich.edu/BK/MCNCbench/, and all GSRC benchmarks can be found in http://vlsicad.eecs.umich.edu/BK/GSRCbench/. Moreover, we transform the n100, n200, and n300 benchmarks into YAL format, and they are also available upon request from the corresponding author zhangliming_jlu@163.com.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. 62076108, 61872159 and 61672261, the education department of Jilin Province (JJKH20211106KJ, JJKH20211103KJ). The authors would like to thank Jianli Chen from Fuzhou University for providing the source code of AHMA and Qi Xu from University of Science and Technology of China for his valuable advice. Special thanks to Prof. Yaowen Chang from National University of Taiwan for the B*-tree package.

Funding

This work was supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. 62076108, 61872159 and 61672261.

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Luyu Jiang, Dantong Ouyang, Huisi Zhou, Naiyu Tian & Liming Zhang

  2. Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun, 130012, China

    Luyu Jiang, Dantong Ouyang, Huisi Zhou, Naiyu Tian & Liming Zhang

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Contributions

LJ contributes mostly on the ideas and the implementations and writes the paper, while DO and LZ contribute mostly on improvments of the writing quality and help correct the faultiness of this manuscript. HZ and NT contribute on recording the experimental data and collecting other information such as the figures of floorplanning.

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Correspondence to Liming Zhang.

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Jiang, L., Ouyang, D., Zhou, H. et al. DPAHMA: a novel dual-population adaptive hybrid memetic algorithm for non-slicing VLSI floorplans. J Supercomput 79, 15496–15534 (2023). https://doi.org/10.1007/s11227-023-05277-1

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