ML-Based AIG Timing Prediction to Enhance Logic Optimization

Traditional logic optimization relies on proxy metrics to approximate post-mapping performance and area, which may not correlate well with post-mapping delay and area. This paper explore a ground-truth-based optimization flow that directly incorporates the post-mapping delay and area during optimization using decision tree-based machine learning models. Results show high prediction accuracy and generalization to unseen designs,