Accurate Operation Delay Prediction for FPGA HLS Using Graph Neural Networks

Modern heterogeneous FPGA architectures incorporate a variety of hardened blocks for boosting the performance of arithmetic-intensive designs, such as DSP blocks and carry blocks. Since hardened blocks can be configured in different ways, a variety of datapath patterns can be mapped into these blocks. We observe that existing high-level synthesis (HLS) tools often fail to capture some of the operation mapping patterns, leading to limited estimation accuracy in terms of resource usage and delay. To address this deficiency, we propose to exploit graph neural networks (GNN) to automatically learn operation mapping patterns. We apply GNN models that are trained on microbenchmarks directly to realistic designs through inductive learning. Experimental results show that our approach can effectively infer various valid mapping patterns on both microbenchmarks and realistic designs. Furthermore, the proposed framework is exploited to improve the accuracy of delay estimation in HLS.