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Customized simulated annealing based decision algorithms for combinatorial optimization in VLSI floorplanning problem

机译:针对VLSI布局规划问题的组合优化的定制模拟退火决策算法

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摘要

Modern very large scale integration technology is based on fixed-outline floorplan constraints, mostly with an objective of minimizing area and wirelength between the modules. The aim of this work is to minimize the unused area, that is, dead space in the floorplan, in addition to these objectives. In this work, a Simulated Annealing Algorithm (SAA) based heuristic, namely Simulated Spheroidizing Annealing Algorithm (SSAA) has been developed and improvements in the proposed heuristic algorithm is also suggested to improve its performance. Exploration capability of the proposed algorithm is due to the mechanism of reducing the uphill moves made during the initial stage of the algorithm, extended search at each temperature and the improved neighborhood search procedure. The proposed algorithm has been tested using two kinds of benchmarks: Microelectronics Center of North Carolina (MCNC) and Gigascale Systems Research Center (GSRC). The performance of the proposed algorithm is compared with that of other stochastic algorithms reported in the literature and is found to be efficient in producing floorplans with very minimal dead space. The proposed SSAA algorithm is also found more efficient for problems of larger sizes.
机译:现代超大规模集成技术基于固定轮廓的平面图约束,主要目的是最小化模块之间的面积和线长。这项工作的目的是除了这些目标之外,还要最小化未使用的区域,即平面图中的死空间。在这项工作中,开发了一种基于模拟退火算法(SAA)的启发式算法,即模拟球化退火算法(SSAA),并提出了对该提议的启发式算法进行改进以提高其性能的建议。提出的算法的探索能力是由于减少了算法初始阶段的上坡运动,在每个温度下的扩展搜索以及改进的邻域搜索程序的机制。该算法已通过两种基准测试:北卡罗来纳州微电子中心(MCNC)和千兆系统研究中心(GSRC)。将该算法的性能与文献中报道的其他随机算法的性能进行了比较,发现该算法可有效地生产出具有极小死角的平面图。还发现提出的SSAA算法对于较大的问题更有效。

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