Graph neural networks (GNNs) have recently exploded in popularity thanks to their broad applicability to ubiquitous graph-related problems such as quantum chemistry, drug discovery, and high energy physics. However, meeting demand for novel GNN models and fast inference simultaneously is challenging because of the gap between the difficulty in developing efficient FPGA accelerators and the rapid pace of creation of new GNN models. Prior art focuses on the acceleration of specific classes of GNNs but lacks the generality to work across existing models or to extend to new and emerging GNN models. In this work, we propose a generic GNN acceleration framework using High-Level Synthesis (HLS), named GenGNN, with two-fold goals. First, we aim to deliver ultra-fast GNN inference without any graph pre-processing for real-time requirements. Second, we aim to support a diverse set of GNN models with the extensibility to flexibly adapt to new models. The framework features an optimized message-passing structure applicable to all models, combined with a rich library of model-specific components. We verify our implementation on-board on the Xilinx Alveo U50 FPGA and observe a speed-up of up to 25x against CPU (6226R) baseline and 13x against GPU (A6000) baseline. Our HLS code will be open-source on GitHub upon acceptance.

翻译：最近,由于对量子化学、药物发现和高能物理学等无处不在的图形相关问题的广泛适用性,地心网络最近受到欢迎。然而,同时满足对新型GNN模型和快速推断的需求具有挑战性,因为开发高效的FPGA加速器的困难与创建新的GNN模型的快速速度之间存在差距。先行艺术侧重于加速特定类别GNN的GNN,但缺乏在现有模式中工作或扩展到新的和新出现的GNN模型的通用通用通用GNN加速框架。在这项工作中,我们提议使用名为GENGNN的高级合成(HLS)的通用GNNN加速框架,其双重目标。首先,我们的目标是在不为实时需求预处理任何图形前处理的情况下提供超快GNNN的推断。第二,我们的目标是支持一套多样化的GNNN模型,该模型可灵活地适应新的模型。框架包括适用于所有模型的优化的开放信息传递结构,并与一个内容丰富的模型库库库。我们核查了在船上执行的超快GLNNS的25S基线和GPUFS的GS标准。我们将在25SUFA上对25FS的S的运行进行快速观测。