  Graph neural networks (GNNs) have recently exploded in popularity thanks to
their broad applicability to 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 due to the gap between
developing efficient accelerators and the rapid creation of new GNN models.
Prior art focuses on accelerating specific classes of GNNs, such as Graph
Convolutional Networks (GCN), but lacks generality to support a wide range of
existing or new GNN models. Furthermore, most works rely on graph
pre-processing to exploit data locality, making them unsuitable for real-time
applications. To address these limitations, in this work, we propose a generic
dataflow architecture for GNN acceleration, named FlowGNN, which is
generalizable to the majority of message-passing GNNs. The contributions are
three-fold. First, we propose a novel and scalable dataflow architecture, which
generally supports a wide range of GNN models with message-passing mechanism.
The architecture features a configurable dataflow optimized for simultaneous
computation of node embedding, edge embedding, and message passing, which is
generally applicable to all models. We also propose a rich library of
model-specific components. Second, we deliver ultra-fast real-time GNN
inference without any graph pre-processing, making it agnostic to dynamically
changing graph structures. Third, we verify our architecture on the Xilinx
Alveo U50 FPGA board and measure the on-board end-to-end performance. We
achieve a speed-up of up to 24-254x against CPU (6226R) and 1.3-477x against
GPU (A6000) (with batch sizes 1 through 1024); we also outperform the SOTA GNN
accelerator I-GCN by 1.26x speedup and 1.55x energy efficiency over four
datasets. Our implementation code and on-board measurement are publicly
available on GitHub.
