Placement Optimization via PPA-Directed Graph Clustering

In this paper, we present the first Power, Performance, and Area (PPA)-directed, end-to-end placement optimization framework that provides cell clustering constraints as placement guidance to advance commercial placers. Specifically, we formulate PPA metrics as Machine Learning (ML) loss functions, and use graph clustering techniques to optimize them by improving clustering assignments. Experimental results on 5 GPU/CPU designs in a 5nm technology not only show that our framework immediately improves the PPA metrics at the placement stage, but also demonstrate that the improvements last firmly to the post-route stage, where we observe improvements of 89% in total negative slack (TNS), 26% in effective frequency, 2.4% in wirelength, and 1.4% in clock power.