DiTile-DGNN: An Efficient Accelerator for Distributed Dynamic Graph Neural Network Inference

Dynamic Graph Neural Networks (DGNNs) have recently emerged as a promising model for learning complex temporal and spatial relationships in evolving graphs.The performance of DGNNs is enabled by the simultaneous integration of both graph neural networks (GNNs) and recurrent neural networks (RNNs).Despite the theoretical advancements, the design space of such complex models has significantly exploded due to the combinatorial challenges of heterogeneous computation kernels and intricate data dependency (i.e., intra-and inter-snapshot data dependency).This makes the computations of DGNN hard to scale, posing significant challenges in parallelism, data reuse, and communication.To address this challenge, we propose DiTile-DGNN, an efficient accelerator for large-scale DGNN execution.The proposed DiTile-DGNN consists of a redundancy-free parallelism strategy, workload balance optimization, and a reconfigurable accelerator architecture.Specifically, we propose a redundancy-free framework that can efficiently find an efficient parallelism strategy that can fully eliminate the data redundancy between graph snapshots while minimizing the communication complexity.Additionally, we propose a workload balance optimization for DGNN models to enhance resource utilization and eliminate synchronization overhead between snapshots.Lastly, we propose a reconfigurable accelerator architecture, with a flexible interconnect, that can be dynamically configured in support of various DGNN dataflows.Our simulations demonstrate that DiTile-DGNN achieves 48.4%, 56.1%, 23.2%, and 36.1% reductions in execution time and 83.4%, 84.0%, 75.6%, and 71.4% improvements in energy efficiency compared to state-of-the-art accelerators, including ReaDy [20], DGNN-Booster [8], RACE [51], and MEGA [12], on average across multiple DGNN datasets.