Interconnect Length Prediction and Length Prediction Driven Placement

互连长度预测和长度预测驱动的布局

• 批准号: 0098227
• 负责人: Jeffrey Davis
• 金额: $ 17.15万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-08-01 至 2005-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0098227&HistoricalAwards=false
• 关键词: Interconnect; Length; Prediction; Driven; Placement

Complex ULSI designs will be plagued by a myriad of interconnects problems that could require many design iterations to meet timing, power, area, and noise specifications. To cope with this interconnect dilemma, the current transistor-centric VLSI design flow must evolve into an interconnect-centric design flow; however, for this design flow to develop it must accurately predict the impact of interconnects on the performance of logic megacells at the earliest stages of design. This research effort is focused on developing an accurate prediction of the length of each net in a given netlist during logic design. The novel aspect of this work is that a stochastic wire length distribution model, which has been developed in part by the principal investigator, is being used to aid in this prediction. In addition, prediction methodologies that give the designer the ability to choose from a set of possible wiring configurations is emphasized. An integral part of this prediction methodology is the incorporation of the wire length predictions into length prediction driven placement algorithms. The significance of this proposed research is that highly accurate wire length predictions could substantially enhance the accuracy of early timing analysis and give the designer greater flexibility in finding solutions to overcome impending interconnect limits.

复杂的ULSI设计将受到无数互连问题的困扰，这些问题可能需要多次设计迭代才能满足时序、功耗、面积和噪声规范。为了应对这种互连困境，当前以晶体管为中心的VLSI设计流程必须演变为以互连为中心的设计流程；然而，要发展这种设计流程，必须在设计的最早阶段准确地预测互连对逻辑巨单元性能的影响。这项研究致力于在逻辑设计过程中对给定网表中的每个网络的长度进行准确的预测。这项工作的新颖之处在于，部分由首席研究员开发的随机导线长度分布模型被用来帮助这一预测。此外，还强调了使设计者能够从一组可能的布线配置中进行选择的预测方法。这种预测方法的一个组成部分是将线长预测合并到长度预测驱动的布局算法中。这项拟议研究的意义在于，高精度的导线长度预测可以大大提高早期时序分析的准确性，并使设计者在寻找解决方案以克服迫在眉睫的互连限制方面具有更大的灵活性。