GCoDE: Efficient Device-Edge Co-Inference for GNNs via Architecture-Mapping Co-Search

作者：Ao Zhou, Jianlei Yang, Tong Qiao, Yingjie Qi, Zhi Yang, Weisheng Zhao, Chunming Hu · 发表于：IEEE Transactions on Computers · 年份：2025 · DOI：10.1109/tc.2025.3624262 · 被引用次数：2 · 研究领域：Advanced Graph Neural Networks、Graph Theory and Algorithms、Advanced Neural Network Applications

Graph Neural Networks (GNNs) have emerged as the state-of-the-art graph learning method. However, achieving efficient GNN inference on edge devices poses significant challenges, limiting their application in real-world edge scenarios. This is due to the high computational cost of GNNs and limited hardware resources on edge devices, which prevent GNN inference from meeting real-time and energy requirements. As an emerging paradigm, device-edge co-inference shows potential for improving inference efficiency and reducing energy consumption on edge devices. Despite its potential, research on GNN device-edge co-inference remains scarce, and our findings show that traditional model partitioning methods are ineffective for GNNs. To address this, we propose GCoDE, the first automatic framework forGNN architecture-mappingCo-design and deployment onDevice-Edge hierarchies. By abstracting the device communication process into an explicit operation, GCoDE fuses the architecture and mapping scheme in a unified design space for joint optimization. Additionally, GCoDE’s system performance awareness enables effective evaluation of architecture efficiency across diverse heterogeneous systems. By analyzing the energy consumption of various GNN operations, GCoDE introduces an energy prediction method that improves energy assessment accuracy and identifies energy-efficient solutions. Using a constraint-based random search strategy, GCoDE identifies the optimal solution in 1.5 hours, balancing ac...