Hardware/software (HW/SW) partitioning is the crucial step in HW/SW co-design, which can significantly reduce the time-to-market and improves the performance of an embedded system. Due to that the majority of previous works have large exploration time and generate often low-quality solutions for large scale systems, we propose a fast HW/SW partitioning approach based on graph convolution network (GCN) to address this problem. To the best of our knowledge, it is a new partitioning method based on GCN which is a gradient-based optimization approach. It can aggressively speed up the partitioning process. To quantify the quality of solutions, the scheduling is integrated into the partitioning process. The experiment results show that not only does our proposed method outperform existing metaheuristics approaches in terms of the efficiency (e.g., 18\(\times \) faster than Kernighan–Lin algorithm for the task graphs with 1000 nodes), but it also improves the quality of HW/SW partitioning (e.g., more than 10% acceleration ratio (AR) improvement for the 1000 nodes graphs).

硬件/软件 (HW/SW) 分区是 HW/SW 协同设计的关键步骤，它可以显着缩短产品上市时间并提高嵌入式系统的性能。由于大多数先前的工作具有大量的探索时间并且通常为大规模系统生成低质量的解决方案，我们提出了一种基于图卷积网络（GCN）的快速硬件/软件分区方法来解决这个问题。据我们所知，它是一种基于 GCN 的新分区方法，是一种基于梯度的优化方法。它可以大大加快分区过程。为了量化解决方案的质量，调度被集成到分区过程中。实验结果表明，我们提出的方法不仅在效率方面优于现有的元启发式方法（例如，18\(\times \)对于 1000 个节点的任务图比 Kernighan-Lin 算法快），但它也提高了硬件/软件分区的质量（例如，1000 个节点图的加速比（AR）提高了 10% 以上）。