障碍物内有限路由长度的x结构Steiner树算法

Liyuan Zhang, Yifei Huang, Weibin Chen, Wenzhong Guo, Genggeng Liu
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引用次数: 1

摘要

斯坦纳最小树构造是超大规模集成电路(VLSI)物理设计的关键步骤。在此基础上,进一步考虑了具有较好长度优化特性的x结构,并在一定程度上允许导线在信号失真之前穿过障碍物,形成了一种新的障碍内有限路由长度的x结构斯坦纳最小树(XSMT-LRLO)问题。为此,提出了基于离散粒子群优化算法的XSMT-LRLO (XSMT-LRLO- dpso)。首先,为了显著减少评估次数,提出了一种基于查找表的预处理策略。其次,采用适应进化迭代过程的边点对编码方法,对XSMT-LRLO-DPSO进行有效编码;然后,针对离散问题XSMT-LRLO问题,提出了一种基于变异操作和交叉操作的离散更新策略。最后,引入调整和细化策略,分别提高算法的越障能力和波长优化能力。实验结果表明,该算法充分利用了障碍物内的路由资源,有效地节省了路由资源。与同类算法相比,该算法具有最强的带宽优化能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
X-architecture Steiner Tree Algorithm with Limited Routing Length inside Obstacle
Steiner minimal tree construction is a key step in the physical design of Very Large Scale Integration (VLSI). Further considering X-architecture with better wirelength optimization and allowing wires to pass through obstacles to a certain extent before signal distortion, a novel X-architecture Steiner Minimal Tree with Limited Routing Length inside Obstacle (XSMT-LRLO) problem is formed. Therefore, the XSMT-LRLO based on Discrete Particle Swarm Optimization algorithm (XSMT-LRLO-DPSO) is proposed. Firstly, in order to significantly reduce the times of evaluations, a preprocessing strategy based on a lookup table is proposed. Secondly, XSMT-LRLO-DPSO is effectively en-coded by adopting the edge-point pairs encoding method adapted to an evolutionary iterative process. Then, aiming at the XSMT-LRLO problem, which is a discrete problem, a discrete update strategy based on mutation operation and crossover operation is proposed. Finally, adjustment and refinement strategies are introduced to respectively improve the obstacles bypassing ability and wirelength optimization ability of the proposed algorithm. The experimental results show that the proposed algorithm makes full use of the routing resources within the obstacles, and effectively saves routing resources. Compared with similar algorithms, the proposed algorithm has the strongest wirelength optimization ability.
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