DGMF: A Unified Dynamic Mapping Framework for Graph Neural Networks
Zimeng Fan, Min Peng · ACM Transactions on Reconfigurable Technology and Systems · 2025
Graph Neural Networks (GNNs) have seen considerable advancements across various applications. However, the computationally and storage-intensive nature of GNNs presents unique challenges for hardware design. Existing GNN accelerators frequently grapple with low execution efficiency and workload imbalance. Therefore, this article introduces DGMF, a unified dynamic mapping approach that optimizes dataflow and computation mode based on GNN models and datasets. To support this dynamic mapping approach, we also present a dedicated hardware architecture. This architecture dynamically adjusts resource allocation and parallelism based on models and datasets, thereby ensuring optimal utilization. Additionally, we propose a Design Space Exploration (DSE) algorithm that traverses the defined design space to identify the optimal design solution, effectively managing diverse design constraints and objectives. To validate our approach, we developed an accelerator based on three datasets and five types of GNN models. Experimental results demonstrate that DGMF achieves performance by 10.5×–2,548×, and 1.01×–51.7× compared to GPUs and existing accelerators. It also improves resource efficiency, energy efficiency, and DRAM access by 0.87×–1.47×, 0.92×–2.18×, and 1.01×–47.25×, respectively, compared to other works.