Resource Dependency-Aware Scheduling for High-Level Synthesis with GNN and SDC

Resource dependency is essential for operation parallelization and resource reuse in scheduling. Existing reinforcement learning (RL)- and graph neural network (GNN)-based methods use data-driven approximation to determine resource dependency. The approximation accuracy is not guaranteed for different circuit structures. System of difference constraints (SDC) formulation has the potential to conduct exact resource dependency determination. Existing SDC method adopts heuristic transformation, degrading determination accuracy. In this paper, we propose a resource dependency-aware scheduling method. The method integrates SDC formulation with three GNN-based assistant tasks. We leverage GNN to perceive operation relations and conduct operation relation-aware resource dependency determination with three assistant tasks. These tasks enable resource optimization in SDC formulation and mitigate loss in the heuristic transformation, improving accuracy. Experiments show that our method provides an average 38.7% resource usage reduction, 8.5%, and 17.1% latency reduction, compared with the RL-based, GNN-based, and basic SDC methods, respectively.