TaGNN: An Efficient Topology-aware Accelerator for High-performance Dynamic Graph Neural Network

Dynamic Graph Neural Networks (DGNNs) have become powerful tools for analyzing continuously evolving graph data, combining Graph Neural Network (GNN) models to extract structural information and Recurrent Neural Network (RNN) models to capture temporal semantics across snapshots. However, despite extensive research, existing DGNN solutions still face significant limitations, particularly low data parallelism caused by their snapshot-by-snapshot execution. This sequential paradigm exacerbates memory contention due to irregular, repeated vertex feature accesses and enforces strict temporal dependencies. In this paper, we propose TaGNN, an efficient topology-aware DGNN accelerator that addresses these performance bottlenecks. Specifically, we present a topology-aware concurrent execution approach into the accelerator design that calculates the final features of affected vertices while ensuring that unaffected vertices are loaded and computed only once per layer across multiple snapshots, maximizing data parallelism while minimizing memory usage. TaGNN employs a cache-friendly storage format that compactly organizes affected vertices across multiple snapshots by their timestamps and topological characteristics, reducing indexing overhead and enhancing data locality. In addition, TaGNN further proposes a similarity-aware cell skipping strategy to alleviate the stringent temporal data dependencies. It selectively reuses the RNN results from the previous snapshot to bypass RNN operations in the current snapshot when the output features of the GNN module across two consecutive snapshots are similar, achieving significant efficiency gains with minimal accuracy loss. We have implemented and assessed TaGNN on a Xilinx Alveo U280 FPGA card. Experimental results show that TaGNN achieves average speedups of 535.2x and 84.3x, and energy savings of 742.6x and 104.9x over state-of-the-art software DGNNs on Intel Xeon CPUs and NVIDIA A100 GPUs, respectively. Compared to leading DGNN accelerators (i.e., DGNN-Booster, E-DGCN, and Cambricon-DG), TaGNN delivers average speedups of 13.5x, 10.2x, and 6.5x, and energy savings of 15.9x, 11.7x, and 7.8x, respectively.