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

优化启发式的预测和可预测性是收敛的、自上而下的系统综合的基础。不知道启发式在各种类型的相关实例上的行为会使准确的建模变得困难，因此难以预测。这个项目正在探索一些基本的想法，这些想法可以提高对自上而下的物理设计中启发式算法行为的可预测性的理解。该项目首先开发了一种新的方法和标准，用于描述给定（迭代的、组合的）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、实现和评估的需求。该项目还探讨了基于对启发式及其上下文的理解，开发给定启发式输出预测器的原则。最后，在多阶段优化（“设计流程”）的背景下，该项目解决了可以从设计状态的下游参数中有效优化的抽象目标的方法。

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.