I-DGNN: A Graph Dissimilarity-based Framework for Designing Scalable and Efficient DGNN Accelerators

Dynamic Graph Neural Networks (DGNNs) have recently been used in numerous application domains, comprehending the intricate dynamics of time-evolving graph data. Despite their theoretical advancements, effectively implementing scalable DGNNs continues to be a formidable challenge due to the constantly evolving graph data and heterogeneous computation kernels. Recent efforts attempted to either exploit the graph data reuse to reduce memory access or eliminate the redundant computations between consecutive graph snapshots to scale the DGNN acceleration. These efforts are still falling short. In prior work, each graph snapshot, regardless of its size and connectivity, passes through the entire DGNN computation pipeline from layer to layer. Consequently, substantial intermediate data is generated throughout the DGNN computation, which leads to excessive offchip memory access. To address this crucial challenge, we argue that the computations between evolving graph snapshots should be decoupled from the DGNN execution pipeline. In this paper, we propose I-DGNN, a theoretical, architectural, and algorithmic framework with the aim of designing scalable and efficient accelerators for DGNN execution with improved performance and energy efficiency. On the theory side, the key idea is to identify essential computations between consecutive graph snapshots and encapsulate them as a separate kernel independent from the DGNN model. Specifically, the proposed one-pass DGNN computing model extracts the process of graph update as a chained matrix multiplication between evolving graphs through rigorous mathematical derivations. Consequently, consecutive snapshots utilize a onepass computation kernel instead of passing through the entire DGNN execution pipeline, thereby eliminating the costly data movement of intermediate results across DGNN layers. On the architecture side, we propose a unified accelerator architecture that can be dynamically configured to support the computation characteristics of the proposed I-DGNN computing model with improved data and pipeline parallelism. On the algorithm side, we propose a new dataflow and mapping tailored for I-DGNN to further improve the data locality of inter-kernel data across the DGNN pipeline. Simulation results show that the proposed accelerator achieves $\mathbf{6 5. 9 \%}, \mathbf{7 1. 1 \%}$, and $\mathbf{5 8. 8 \%}$ reductions in execution time and $88.4 \%, 87.0 \%$, and $\mathbf{8 5. 9 \%}$ improvements in energy efficiency on average across multiple DGNN datasets compared to state-of-the-art-accelerators [1]–[3].