Toward Predictors and Predictability: Closing the Loop-Down Physical Design

走向预测器和可预测性：关闭循环物理设计

• 批准号: 0330867
• 负责人: Andrew Kahng
• 金额: $ 17.72万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-11-01 至 2006-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0330867&HistoricalAwards=false
• 关键词: Toward; Predictors; Predictability; Closing; Loop

Prediction and predictability of optimization heuristics are at the foundation of convergent, top-down system synthesis. Not knowing how a heuristic behaves on various types of relevant instances makes accurate modeling, and hence predictability, difficult. This project is exploring basic ideas that can lead to improved understanding of predictability of heuristic algorithm behavior within top-down physical design. The project first develops a new methodology and criteria for characterizing the operation of given (iterative, combinatorial) heuristic functions, and how the application context and use model define requirements for the design, implementation and evaluation of the heuristic. The project also explores principles for developing predictors of a given heuristic's output, based on understanding of the heuristic and its context. Finally, in the context of a multi-stage optimization ("design flow"), the project addresses means of abstracting objectives that can be effectively optimized from downstream parameters of the design state.

预测和可预测性的优化物流是收敛的，自上而下的系统综合的基础。不知道启发式算法在各种类型的相关实例上的行为会使准确建模变得困难，因此难以预测。这个项目是探索基本的想法，可以导致自顶向下的物理设计中的启发式算法行为的可预测性，以提高理解。该项目首先开发了一种新的方法和标准，用于表征给定（迭代，组合）启发式函数的操作，以及应用程序上下文和使用模型如何定义启发式的设计，实施和评估要求。该项目还探讨了开发一个给定的启发式的输出预测的原则，基于对启发式及其上下文的理解。最后，在多阶段优化（“设计流程”）的背景下，该项目解决了从设计状态的下游参数中提取可以有效优化的目标的方法。