Cloud computing accelerates design space exploration in logic synthesis, and parameter tuning in physical design. However, deploying EDA jobs on the cloud requires EDA teams to deeply understand the characteristics of their jobs in cloud environments. Unfortunately, there has been little to no public information on these characteristics. Thus, in this paper, we formulate the problem of migrating EDA jobs to the cloud. First, we characterize the performance of four main EDA applications, namely: synthesis, placement, routing and static timing analysis. We show that different EDA jobs require different machine configurations. Second, using observations from our characterization, we propose a novel model based on Graph Convolutional Networks to predict the total runtime of a given application on different machine configurations. Our model achieves a prediction accuracy of 87%. Third, we develop a new formulation for optimizing cloud deployments in order to reduce deployment costs while meeting deadline constraints. We present a pseudo-polynomial optimal solution using a multi-choice knapsack mapping that reduces costs by 35.29%.

中文翻译：

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表征和优化云的EDA流程

云计算可加快逻辑综合中的设计空间探索和物理设计中的参数调整。但是，在云上部署EDA作业需要EDA团队深入了解其在云环境中的工作特征。不幸的是，关于这些特征的公共信息很少甚至没有。因此，在本文中，我们提出了将EDA作业迁移到云的问题。首先，我们描述了四个主要EDA应用程序的性能，即：综合，布局，布线和静态时序分析。我们证明了不同的EDA作业需要不同的机器配置。其次，利用我们表征中的观察结果，我们提出了一种基于图卷积网络的新颖模型，以预测给定应用程序在不同机器配置下的总运行时间。我们的模型实现了87％的预测准确性。第三，我们开发了一种用于优化云部署的新公式，以在满足截止期限约束的同时降低部署成本。我们提出了使用多项选择背包映射的伪多项式最优解，可将成本降低35.29％。