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Cloud computing accelerates design space exploration in logic synthesis, andparameter tuning in physical design. However, deploying EDA jobs on the cloudrequires EDA teams to deeply understand the characteristics of their jobs incloud environments. Unfortunately, there has been little to no publicinformation on these characteristics. Thus, in this paper, we formulate theproblem of migrating EDA jobs to the cloud. First, we characterize theperformance of four main EDA applications, namely: synthesis, placement,routing and static timing analysis. We show that different EDA jobs requiredifferent machine configurations. Second, using observations from ourcharacterization, we propose a novel model based on Graph ConvolutionalNetworks to predict the total runtime of a given application on differentmachine configurations. Our model achieves a prediction accuracy of 87%. Third,we develop a new formulation for optimizing cloud deployments in order toreduce deployment costs while meeting deadline constraints. We present apseudo-polynomial optimal solution using a multi-choice knapsack mapping thatreduces costs by 35.29%.

Characterizing and Optimizing EDA Flows for the Cloud