High-Level Synthesis Performance Prediction using GNNs: Benchmarking, Modeling, and Advancing

• URL: http://arxiv.org/abs/2201.06848v1
• Date: Tue, 18 Jan 2022 09:53:48 GMT
• Title: High-Level Synthesis Performance Prediction using GNNs: Benchmarking, Modeling, and Advancing
• Authors: Nan Wu, Hang Yang, Yuan Xie, Pan Li, Cong Hao
• Abstract summary: Agile hardware development requires fast and accurate circuit quality evaluation from early design stages. We propose a rapid and accurate performance modeling, exploiting the representation power of graph neural networks (GNNs) by representing C/C++ programs as graphs. 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.
• Score: 21.8349113634555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Agile hardware development requires fast and accurate circuit quality evaluation from early design stages. Existing work of high-level synthesis (HLS) performance prediction usually needs extensive feature engineering after the synthesis process. To expedite circuit evaluation from as earlier design stage as possible, we propose a rapid and accurate performance modeling, exploiting the representation power of graph neural networks (GNNs) by representing C/C++ programs as graphs. The contribution of this work is three-fold. First, we build a standard benchmark containing 40k C synthesizable programs, which includes both synthetic programs and three sets of real-world HLS benchmarks. Each program is implemented on FPGA to generate ground-truth performance metrics. Second, 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. Third, 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.

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