An Elitist Non-Dominated Sorting Based Genetic Algorithm for Simultaneous Area and Wirelength Minimization in VLSI Floorplanning

Pradeep Fernando, S. Katkoori
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引用次数: 25

Abstract

VLSI floor-planning in the gigascale era must deal with multiple objectives including wiring congestion, performance and reliability. Genetic algorithms lend themselves naturally to multi-objective optimization. In this paper, a multi-objective genetic algorithm is proposed for floorplanning that simultaneously minimizes area and total wirelength. The proposed genetic floorplanner is the first to use non-domination concepts to rank solutions. Two novel crossover operators are presented that build floorplans using good sub-floorplans. The efficiency of the proposed approach is illustrated by the 18% wirelength savings and 4.6% area savings obtained for the GSRC benchmarks and 26% wirelength savings for the MCNC benchmarks for a marginal 1.3% increase in area when compared to previous floorplanners that perform simultaneous area and wirelength minimization.
基于精英非支配排序的超大规模集成电路平面规划中面积和长度同时最小化的遗传算法
千兆级时代的VLSI地板规划必须处理布线拥塞、性能和可靠性等多个目标。遗传算法天生适合多目标优化。本文提出了一种多目标遗传算法,以实现面积和总长度的最小化。提出的遗传地板规划器是第一个使用非支配概念对解决方案进行排序的。提出了两种新的交叉算子,利用良好的子平面来构建平面。与之前的地板规划者相比,该方法的效率体现在GSRC基准测试中节省了18%的带宽和4.6%的面积,在MCNC基准测试中节省了26%的带宽,而面积仅增加了1.3%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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