首页> 外文会议>International Conference on Circuit, Power and Computing Technologies >Modified SA algorithm for wirelength minimization in VLSI circuits
【24h】

Modified SA algorithm for wirelength minimization in VLSI circuits

机译:VLSI电路中用于最小化线长的改进SA算法

获取原文

摘要

In modern VLSI circuits, number of parameters viz, placement of components, dead space (un occupied space in the layout), wire length has to be minimized. The defined problems are non deterministic and NP hard optimization problem. Hence probabilistic (stochastic) methods are adopted to solve these problems. The minimization problems are multi objective optimization problems (MOO). Hence, for the convenience of computation, these MOO problems are converted into Single objective optimization (SOO) problems. It is observed from the previous research works that the bio-inspired algorithms have worked very well in the minimizing process. Evolutionary Algorithms, Genetic Algorithms, Memetic Algorithms were notable in solving the non deterministic hard problems. Moreover, Simulated annealing algorithm developed by Kirpatrick et.al, proved itself to be worthy in the minimization process. The major aim of this research work is to modify or customize the Simulated Annealing Algorithm (SAA) based on the user defined values and test it with various bench marks. This research work may be used to minimize the wire length between the blocks in complex VLSI problems. The capability of the proposed algorithm may be more efficient because of the mechanism of reducing the uphill moves made during the initial stage of the algorithm, extended search at each temperature and the improved neighborhood procedure.
机译:在现代VLSI电路中,必须将参数数量,组件放置,死区(布局中的未占用空间),导线长度最小化。定义的问题是不确定性和NP硬优化问题。因此,采用概率(随机)方法来解决这些问题。最小化问题是多目标优化问题(MOO)。因此,为了方便计算,将这些MOO问题转换为单目标优化(SOO)问题。从先前的研究工作中可以看出,生物启发算法在最小化过程中效果很好。进化算法,遗传算法,模因算法在解决非确定性难题方面非常引人注目。此外,由Kirpatrick等人开发的模拟退火算法证明了自己在最小化过程中的价值。这项研究工作的主要目的是根据用户定义的值修改或自定义模拟退火算法(SAA),并使用各种基准进行测试。这项研究工作可用于在复杂的VLSI问题中最小化模块之间的导线长度。所提出的算法的能力可能更有效,因为该机制减少了在算法初始阶段进行的上坡运动,在每个温度下的扩展搜索以及改进的邻域过程。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号