  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.
