High-level synthesis performance prediction using GNNs: benchmarking, modeling, and advancing

Agile hardware development requires fast and accurate circuit quality evaluation from early design stages. Existing work of high-level synthesis (HLS) performance prediction usually requires extensive feature engineering after the synthesis process. To expedite circuit evaluation from as early design stage as possible, we propose rapid and accurate performance prediction methods, which exploit the representation power of graph neural networks (GNNs) by representing C/C++ programs as graphs. The contribution of this work is three-fold. (1) Benchmarking. We build a standard benchmark suite with 40k C programs, which includes synthetic programs and three sets of real-world HLS benchmarks. Each program is synthesized and implemented on FPGA to obtain post place-and-route performance metrics as the ground truth. (2) Modeling. We formally formulate the HLS performance prediction problem on graphs and propose multiple modeling strategies with GNNs that leverage different trade-offs between prediction timeliness (early/late prediction) and accuracy. (3) Advancing. We further propose a novel hierarchical GNN that does not sacrifice timeliness but largely improves prediction accuracy, significantly outperforming HLS tools. We apply extensive evaluations for both synthetic and unseen real-case programs; our proposed predictor largely outperforms HLS by up to 40X and excels existing predictors by 2X to 5X in terms of resource usage and timing prediction. The benchmark and explored GNN models are publicly available at https://github.com/lydiawunan/HLS-Perf-Prediction-with-GNNs.