Graph-OPU: A Highly Flexible FPGA-Based Overlay Processor for Graph Neural Networks

Field-programmable gate arrays (FPGAs) are an ideal candidate for accelerating graph neural networks (GNNs). However, the FPGA redeployment process is time-consuming when updating or switching between diverse GNN models across different applications. Existing GNN processors eliminate the need for FPGA redeployment when switching between different GNN models. However, adapting matrix multiplication types by switching processing units decreases hardware utilization. In addition, the bandwidth of DDR limits further improvements in hardware performance. This article proposes a highly flexible FPGA-based overlay processor for GNN accelerations. Graph-OPU provides excellent flexibility and programmability for users, as the executable code of GNN models is automatically compiled and reloaded without requiring FPGA redeployment. First, we customize the compiler and instruction sets for the inference process of different GNN models. Second, we customize the datapath and optimize the data format in the microarchitecture to fully leverage the advantages of high bandwidth memory (HBM). Third, we design a unified matrix multiplication to handle both sparse-dense matrix multiplication (SpMM) and general matrix multiplication (GEMM), enhancing Graph-OPU performance. During Graph-OPU execution, the computational units are shared between SpMM and GEMM instead of being switched, which improves the hardware utilization. Finally, we implement a hardware prototype on the Xilinx Alveo U50 and test the mainstream GNN models using various datasets. Experimental results show that Graph-OPU achieves up to 1,654x and 63x speedup, as well as up to 5,305x and 422x energy efficiency boosts, compared to implementations on CPU and GPU, respectively. Graph-OPU outperforms state-of-the-art (SOTA) end-to-end overlay accelerators for GNN, reducing latency by an average of 1.36x and improving energy efficiency by 1.41x on average. Moreover, Graph-OPU exhibits an average 1.45x speed improvement in end-to-end latency over the SOTA GNN processor. Graph-OPU represents an