A Robust Multilayer X-Architecture Global Routing System Based on Particle Swarm Optimization

被引:0
|
作者
Liu, Genggeng [1 ]
Zhu, Yuhan [1 ]
Zhuang, Zhen [1 ]
Pei, Zhenyu [1 ]
Gan, Min [1 ]
Huang, Xing [2 ]
Guo, Wenzhong [1 ]
机构
[1] Fuzhou Univ, Coll Comp & Data Sci, Fuzhou 350002, Peoples R China
[2] Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China
来源
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2024年 / 54卷 / 09期
基金
中国国家自然科学基金;
关键词
Global routing; integer linear programming (ILP); multilayer routing; particle swarm optimization (PSO); very large scale integration (VLSI); X-architecture; ROUTER; ALGORITHM; BOXROUTER; EVOLUTION; PARADIGM; RANKING;
D O I
10.1109/TSMC.2024.3407960
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Global routing is an extremely important stage of very large scale integration (VLSI) physical design. With the rise of nano-scale integrated circuit design, the multilayer global routing problem has attracted considerable research interest during the past few years. In this article, a multilayer X-architecture global routing (ML-XGR) system based on particle swarm optimization (PSO), called FZU-Router, is proposed to solve the ML-XGR problem for the first time. FZU-Router contains a multilayer X-architecture integer linear programming (MX-ILP) model and a multilayer X-architecture PSO (MX-PSO) algorithm, which are presented to formulate and solve the ML-XGR problem, respectively. Moreover, four effective strategies are designed to enhance the efficiency of FZU-Router: 1) a strategy for generating new routing modes is proposed to strengthen the robustness of encoding strategy of MX-PSO; 2) a strategy for combining MX-PSO with maze routing is proposed to improve the routability; 3) a strategy for reducing the channel capacity is proposed to make better use of optimization ability of MX-PSO; and 4) a strategy for dynamic resource assignment is proposed to make better use of routing resources and shorten the running time. Experimental results on multiple benchmarks confirm that the proposed FZU-Router leads to fewer total overflow and shorter total wirelength compared with the state-of-the-art routers.
引用
收藏
页码:5627 / 5640
页数:14
相关论文
共 50 条
  • [41] Particle Swarm Optimization for the Vehicle Routing Problem with Stochastic Demands
    Marinakis, Yannis
    Iordanidou, Georgia-Roumbini
    Marinaki, Magdalene
    APPLIED SOFT COMPUTING, 2013, 13 (04) : 1693 - 1704
  • [42] Discrete Particle Swarm Optimization for Multiple Destination Routing Problems
    Zhan, Zhi-hui
    Zhang, Jun
    APPLICATIONS OF EVOLUTIONARY COMPUTING, PROCEEDINGS, 2009, 5484 : 117 - 122
  • [43] Particle Swarm Optimization for Capacitated Location-Routing Problem
    Peng, Z.
    Manier, H.
    Manier, M. -A.
    IFAC PAPERSONLINE, 2017, 50 (01): : 14668 - 14673
  • [44] Optimizing Routing Path Selection Method Particle Swarm Optimization
    Guo, Kai
    Lv, Yang
    INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2020, 34 (12)
  • [45] Hierarchical Cluster-Based Multispecies Particle-Swarm Optimization for Fuzzy-System Optimization
    Juang, Chia-Feng
    Hsiao, Che-Meng
    Hsu, Chia-Hung
    IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2010, 18 (01) : 14 - 26
  • [46] A Particle Swarm Optimization Algorithm for the Open Vehicle Routing Problem
    Zhen, Tong
    Zhu, Yuhua
    Zhang, Qiuwen
    2009 INTERNATIONAL CONFERENCE ON ENVIRONMENTAL SCIENCE AND INFORMATION APPLICATION TECHNOLOGY, VOL II, PROCEEDINGS, 2009, : 560 - +
  • [47] Evaluating of the particle swarm optimization in a periodic vehicle routing problem
    Norouzi, Narges
    Sadegh-Amalnick, Mohsen
    Alinaghiyan, Mehdi
    MEASUREMENT, 2015, 62 : 162 - 169
  • [48] Solving Vehicle Routing Problem with Simultaneous Pickups and Deliveries Based on A Two-Layer Particle Swarm Optimization
    Chen, Ruey-Maw
    Fang, Po-Jen
    2019 20TH IEEE/ACIS INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING AND PARALLEL/DISTRIBUTED COMPUTING (SNPD), 2019, : 212 - 216
  • [49] X-architecture Steiner minimal tree algorithm based on multi-strategy optimization discrete differential evolution
    Liu, Genggeng
    Yang, Liliang
    Xu, Saijuan
    Li, Zuoyong
    Chen, Yeh-Cheng
    Chen, Chi-Hua
    PEERJ COMPUTER SCIENCE, 2021, 7 : 1 - 28
  • [50] Biogeography-based learning particle swarm optimization
    Chen, Xu
    Tianfield, Huaglory
    Mei, Congli
    Du, Wenli
    Liu, Guohai
    SOFT COMPUTING, 2017, 21 (24) : 7519 - 7541