Utility-Based Control of Hierarchical Design

这笔赠款为控制多层次设计优化的方法的开发和表征提供了资金。此方法旨在应用于在一系列阶段或抽象级别中执行的设计问题，其中每个阶段或抽象级别的结果将问题设置为下一级别。例如，在微处理器设计中，一个级别可能将微处理器表示为一组布尔逻辑公式，另一个级别可能会将这些公式转换为晶体管的接线图，而另一个级别可能会将接线图转换为芯片上的导线和掺杂区域的特定布局。该方法假设我们为每个级别都有一个优化器，并且每个优化器使用随机方法，例如模拟退火法、遗传算法或多起点爬山。该方法协调和控制这些优化器的使用，以高效地探索备选设计的空间，该方法基于它在将该备选方案用作产生期望的最终设计的基础上对每个当前备选方案所做的估计，其中效用是所得到的最终设计的价值减去寻找它的设计努力的成本。目前，该方法已经在一个任务域上进行了初步的可行性测试。这项研究的主要目标是更好地测试这种方法的普遍性，并将其扩展到尽可能广泛的一类问题和成本模型。如果成功，由这笔资金资助的研究将产生这种方法的一个版本，适用于广泛的设计优化任务，以及它对什么类型的任务最有用的特征，如何最好地将它应用于给定的任务，以及它可以被期望执行得多好。这应该会在许多设计领域改进设计时间和设计质量之间的权衡。

This grant provides funding for the development and characterization of a method for controlling multi-level design optimization. This method is intended to apply to design problems that are carried out in a series of stages or abstraction levels, where the result of each level sets the problem for the next level. For instance, in microprocessor design one level might represent the microprocessor as a set of boolean logic formulas, another might convert these formulas into wiring diagrams of transistors, and another might convert the wiring diagram into a specific layout of wires and doping regions on the chip. The method assumes that we have an optimizer for each level and that each optimizer uses stochastic methods, such as simulated annealing, genetic algorithms, or multi-start hill climbing. The method coordinates and controls the use of these optimizers to explore the space of alternative designs in an efficient way, based on an estimate it makes for each current alternative at each level of what the `utility` would be of using that alternative as the basis for generating the desired final design, where utility is the value of the resulting final design minus the cost in design effort to find it. Currently this method has had an initial feasibility test on one task domain. The primary goals of this research are to better test the generality of this approach and to extend it to as broad as possible a class of problems and models of cost. If successful, the research funded by this grant will result in a version of this method that is applicable to a broad spectrum of design optimization tasks, along with a characterization of what kinds of tasks it is most useful for, of how best to apply it to a given task, and of how well it can be expected to perform. This should lead to improved tradeoffs between design time and design quality in many design domains.