Machine learning (ML) has been applied to many EDA problems in recent years. We can classify these applications into three major categories: Predictor, Optimizer and Generator based on the role of ML played in these applications and the ML techniques used. Ideally one would like to adopt the Optimizer and Generator approaches to solve a hard EDA problem directly with ML, and we call these ML-alone approach. It is very challenging, however, to scale the ML-alone approach to solve real world EDA problems. We propose to integrate machine learning with conventional algorithms to solve EDA problems more efficiently. We will introduce several machine learning and algorithm integration methods and use routing problems as examples to illustrate the ideas.
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