超大规模集成电路设计中标准单元放置技术的性能评估与比较与改进

A. Bunglowala, B. M. Singhi
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引用次数: 8

摘要

由于精确优化方法的速度限制,启发式方法是解决非平凡大小的非确定性多项式困难(NP-hard)问题的首选方法。因此,本文建议研究最新的启发式技术,以解决超大规模集成电路设计周期物理设计阶段的标准单元放置问题。考虑的技术有模拟退火(SA)、Hopfield神经网络和遗传算法(GA)。除了对这些方法的单独研究外,我们还比较了它们在与标准细胞放置问题相关的溶液质量和计算速度方面的差异。最后,我们将提出一种使用模因算法(Memetic Algorithms, MA)来增强它们的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance Evaluation and Comparison and Improvement of Standard Cell Placement Techniques in VLSI Design
Heuristic approach is preferred as a solution to optimization of Non-Deterministic Polynomial hard (NP-hard) problems of sizes that are nontrivial because of speed limitations of exact optimization methods. This paper, therefore, proposes to investigate recent heuristic techniques for solving the standard cell placement problems at physical design stage of VLSI design cycle. The techniques considered are Simulated Annealing (SA), Hopfield Neural Network and Genetic Algorithm (GA). In addition to individual studies of the methods, we compare them in terms of solution quality and computing speed in connection with the standard cell placement problems. Finally we shall suggest a method to enhance them using Memetic Algorithms (MA).
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