Graph-Learning-Driven Path-Based Timing Analysis Results Predictor from Graph-Based Timing Analysis

With diminishing margins in advanced technology nodes, the performance of static timing analysis (STA) is a serious concern, including accuracy and runtime. The STA can generally be divided into graph-based analysis (GBA) and path-based analysis (PBA). For GBA, the timing results are always pessimistic, leading to overdesign during design optimization. For PBA, the timing pessimism is re-duced via propagating real path-specific slews with the cost of severe runtime overheads relative to GBA. In this work, we present a fast and accurate predictor of post-layout PBA timing results from inex-pensive GBA based on deep edge-featured graph attention network, namely deep EdgeGAT. Compared with the conventional machine and graph learning methods, deep EdgeGAT can learn global timing path information. Experimental results demonstrate that our pre-dictor has the potential to substantially predict PBA timing results accurately and reduce timing pessimism of GBA with maximum error reaching 6.81 ps, and our work achieves an average 24.80x speedup faster than PBA using the commercial STA tool.