AGF: Adaptive GNN Framework via Subgraph and Model Partitioning for Edge Analysis

AI has found extensive application in edge analytics, with Deep Neural Networks effectively processing and analyzing data. Some applications need to deal with unstructured data like graphs. This complexity and diversity of unstructured features pose challenges for traditional DNN models. Therefore, Graph Neural Networks have emerged as a significant solution. However, GNNs encounter two primary challenges in edge analytics scenarios: scalability and efficiency. These challenges arise primarily from significant structural differences among various graphs and models. To address these issues, we propose AGF, an Adaptive GNN Framework. AGF consists of three components: subgraph partitioning and model partitioning, scalable hardware architecture, and design space exploration. Subgraph partitioning and model partitioning transform complex and diverse GNNs into a uniform computing flow and subgraphs. These can be directly mapped onto the hardware kernel, leveraging GNN characteristics to achieve independent parallelism. The hardware architecture is based on a multi-level processing element structure, which can efficiently parallelize subgraphs and vertices based on the partitioning. The design space exploration selects kernel allocation strategies and setups based on the hardware, dataset, and model. We validated AGF using six datasets and three GNN models. The results demonstrate that our framework achieves an acceleration ranging from 4.54 to 53.17× compared to the GPU-based GNN framework. When compared to other FPGA-based GNN accelerators, AGF achieves a latency reduction ranging from 1.16 to 33.6× in model execution and from 1.09 to 2.37× in end-to-end scenarios.