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Wire Length Prediction in Constraint Driven Placement

机译:约束驱动放置中的线材长度预测

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Experiments show that lengths of individual wires are different for different placement algorithms. To achieve accurate wire length prediction, some knowledge of a placer's details is necessary. We postulate that wire length prediction should be coupled with placement flow to obtain accurate results. In this paper, we embed wire length prediction into our constraint-driven placer, developed in Fast Placer Implementation (FPI) framework. We predict individual wire lengths during the clustering step. The predicted wire lengths act as constraints for the simulated annealing refinement stage, which guides the placement towards a solution fulfilling the predictions. Experimental results show that our wire length prediction process yields accurate results without quality loss at a small cost of placement effort. This is the first time that constraints have been used to guide placement and thus increase the accuracy of wire length prediction.
机译:实验表明,不同放置算法的单个线的长度是不同的。为了实现准确的线材长度预测,需要一些知识的垫盘细节。我们假设线长预测应与放置流相结合以获得准确的结果。在本文中,我们将线长预测嵌入到我们的约束驱动的放置器中,在快速放置方式(FPI)框架中开发。我们在聚类步骤期间预测单个线长度。预测的线长度充当模拟退火改进阶段的约束,这引导了满足预测的解决方案的放置。实验结果表明,我们的线材长度预测过程产生了精确的结果,无需质量损失,以小费放置努力。这是第一次限制已经用于指导放置,从而提高线长预测的准确性。

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