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Limitations and opportunities for wire length prediction in gigascale integration.

机译:千兆级集成中线长预测的局限性和机会。

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摘要

Wires have become a major source of bottleneck in current VLSI designs, and wire length prediction is therefore essential to overcome these bottlenecks. Wire length prediction is broadly classified into two types: macroscopic prediction, which is the prediction of wire length distribution, and microscopic prediction, which is the prediction of individual wire lengths. The objective of this thesis is to develop a clear understanding of limitations to both macroscopic and microscopic a priori, post-placement, pre-routing wire length predictions, and thereby develop better wire length prediction models.; Investigations carried out to understand the limitations to macroscopic prediction reveal that, in a given design (i) the variability of the wire length distribution increases with length and (ii) the use of Rent's role with a constant Rent's exponent p, to calculate the terminal count of a given block size, limits the accuracy of the results from a macroscopic model. Therefore, a new model for the parameter p is developed to more accurately reflect the terminal count of a given block size in placement, and using this, a new more accurate macroscopic model is developed. In addition, a model to predict the variability is also incorporated into the macroscopic model.; Studies to understand limitations to microscopic prediction reveal that (i) only a fraction of the wires in a given design are predictable, and these are mostly from shorter nets with smaller degrees and (ii) the current microscopic prediction models are built based on the assumption that a single metric could be used to accurately predict the individual length of all the wires in a design. In this thesis, an alternative microscopic model is developed for the predicting the shorter wires based on a hypothesis that there are multiple metrics that influence the length of the wires. Three different metrics are developed and fitted into a heuristic classification tree framework to provide a unified and more accurate microscopic model.
机译:导线已成为当前VLSI设计中瓶颈的主要来源,因此,导线长度预测对于克服这些瓶颈至关重要。线长预测大致分为两种:宏观预测(即线长分布的预测)和微观预测(即单根线长的预测)。本文的目的是要对宏观和微观先验,后置,布线前的线长预测的局限性有一个清晰的认识,从而开发出更好的线长预测模型。为了解宏观预测的局限性而进行的研究表明,在给定的设计中(i)导线长度分布的可变性随长度而增加,并且(ii)使用具有恒定Rent指数p的Rent角色来计算终端给定块大小的计数限制了宏观模型结果的准确性。因此,开发了用于参数p的新模型以更准确地反映放置中给定块大小的终端计数,并且使用此模型,开发了新的更精确的宏观模型。另外,用于预测可变性的模型也被合并到宏观模型中。旨在理解微观预测局限性的研究表明:(i)在给定设计中只有一小部分金属线是可预测的,并且大多数来自较小度数的较短网,并且(ii)当前的微观预测模型是基于假设建立的可以使用单个度量标准来准确预测设计中所有导线的长度。在这篇论文中,基于一个假设,即存在多个影响导线长度的指标,开发了一种替代的微观模型来预测较短的导线。开发了三种不同的度量标准并将其装入启发式分类树框架中,以提供统一且更准确的微观模型。

著录项

  • 作者

    Anbalagan, Pranav.;

  • 作者单位

    Georgia Institute of Technology.;

  • 授予单位 Georgia Institute of Technology.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2008
  • 页码 146 p.
  • 总页数 146
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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