Efficient Learning-Driven Gate Sizing for Large-Scale Circuits

This work proposes a learning-driven physically-aware gate sizing framework to optimize timing performance on large-scale circuits efficiently by combining analytical and machine learning methods. The approach overcomes challenges in gate sizing through accurate timing modeling and effective gradient generation.