Timing macro modeling with graph neural networks

Abstract

Due to rapidly growing design complexity, timing macro modeling has been widely adopted to enable hierarchical and parallel timing analysis. The main challenge of timing macro modeling is to identify timing variant pins for achieving high timing accuracy while keeping a compact model size. To tackle this challenge, prior work applied ad-hoc techniques and threshold setting. In this work, we present a novel timing macro modeling approach based on graph neural networks (GNNs). A timing sensitivity metric is proposed to precisely evaluate the influence of each pin on the timing accuracy. Based on the timing sensitivity data and the circuit topology, the GNN model can effectively learn and capture timing variant pins. Experimental results show that our GNN-based framework reduces 10% model sizes while preserving the same timing accuracy as the state-of-the-art. Furthermore, taking common path pessimism removal (CPPR) as an example, the generality and applicability of our framework on various timing analysis models and modes are also validated empirically.