MiniDelay: Multi-Strategy Timing-Aware Layer Assignment for Advanced Technology Nodes

Zhang, Xinghai; Zhuang, Zhen; Liu, Genggeng; Huang, Xing; Liu, Wenhao; Guo, Wenzhong; Wang, Ting-Chi

doi:10.23919/date48585.2020.9116269

Cited by 10 publications

(1 citation statement)

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“…Wire length has a decisive influence on the chip performance. Based on this situation, non-Manhattan structure, which can make full use of the routing resources and shorten the wire length, has become the mainstream model of global routing ( Zhu et al, 2020 ; Zhuang et al, 2020 ; Zhang et al, 2020b ).…”

Section: Introductionmentioning

confidence: 99%

X-architecture Steiner minimal tree algorithm based on multi-strategy optimization discrete differential evolution

Liu

Yang

et al. 2021

PeerJ Computer Science

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Global routing is an important link in very large scale integration (VLSI) design. As the best model of global routing, X-architecture Steiner minimal tree (XSMT) has a good performance in wire length optimization. XSMT belongs to non-Manhattan structural model, and its construction process cannot be completed in polynomial time, so the generation of XSMT is an NP hard problem. In this paper, an X-architecture Steiner minimal tree algorithm based on multi-strategy optimization discrete differential evolution (XSMT-MoDDE) is proposed. Firstly, an effective encoding strategy, a fitness function of XSMT, and an initialization strategy of population are proposed to record the structure of XSMT, evaluate the cost of XSMT and obtain better initial particles, respectively. Secondly, elite selection and cloning strategy, multiple mutation strategies, and adaptive learning factor strategy are presented to improve the search process of discrete differential evolution algorithm. Thirdly, an effective refining strategy is proposed to further improve the quality of the final Steiner tree. Finally, the results of the comparative experiments prove that XSMT-MoDDE can get the shortest wire length so far, and achieve a better optimization degree in the larger-scale problem.

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Section: Introductionmentioning

confidence: 99%