Doomed Run Prediction in Physical Design by Exploiting Sequential Flow and Graph Learning

Modern designs are increasingly reliant on physical design (PD) tools to derive full technology scaling benefits of Moore's Law. Designers often perform power, performance, and area (PPA) exploration through parallel PD runs with different tool configurations. Efficient exploration of PPA is mission-critical for chip designers who are working with stringent time-to-market constraints and finite compute resources. Therefore, a framework that can accurately predict a “doomed run” (i.e., will not meet the PPA targets) at early phases of the PD flow can provide a significant productivity boost by enabling early termination of such runs. Multiple QoR metrics can be leveraged to classify successful or doomed PD runs. In this paper, we specifically focus on the aspect of timing, where our goal is to identify the PD runs that cannot achieve end-of-flow timing results by predicting the post-route total negative slack (TNS) values in early PD phases. To achieve our goal, we develop an end-to-end machine learning (ML) framework that performs TNS prediction by modeling PD implementation as a sequential flow. Particularly, our framework leverages graph neural networks (GNNs) to encode netlist graphs extracted from various PD phases, and utilize long short-term memory (LSTM) networks to perform sequential modeling based on the GNN-encoded features. Experimental results on seven industrial designs with 5:2 train/test split ratio demonstrate that our framework predicts post-route TNS values in high fidelity within 5.2% normalized root mean squared error (NRMSE) in early design stages (e.g., placement, CTS) on the two validation designs that are unseen during training.