时序驱动布局中收敛净加权方案的严格框架

T. Chan, J. Cong, Eric Radke
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引用次数: 14

摘要

我们提出了一个严格的框架,定义了一类净加权方案,其中连续对加权目标执行无约束最小化。我们证明,在无约束最小化中,只要满足一定的目标,这些净加权方案就保证收敛于原时间约束布局问题的最优解。这些是提供条件的第一个结果,在这些条件下,净加权方案将收敛到时间最优放置。然后,我们确定了几个满足给定收敛性质的加权方案并实现了它们,并取得了有希望的结果:在没有增加计算时间的情况下,对给出的加权方案进行修改,结果持续改善了原始延迟,平均提高了4%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A rigorous framework for convergent net weighting schemes in timing-driven placement
We present a rigorous framework that defines a class of net weighting schemes in which unconstrained minimization is successively performed on a weighted objective. We show that, provided certain goals are met in the unconstrained minimization, these net weighting schemes are guaranteed to converge to the optimal solution of the original timing-constrained placement problem. These are the first results that provide conditions under which a net weighting scheme will converge to a timing optimal placement. We then identify several weighting schemes that satisfy the given convergence properties and implement them, with promising results: a modification of the weighting scheme given in results in consistently improved delay over the original, 4% on average, without increase in computation time.
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CiteScore
4.60
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