DGNN-Booster: A Generic FPGA Accelerator Framework For Dynamic Graph Neural Network Inference. (arXiv:2304.06831v1 [cs.AR])

Dynamic Graph Neural Networks (DGNNs) are becoming increasingly popular due
to their effectiveness in analyzing and predicting the evolution of complex
interconnected graph-based systems. However, hardware deployment of DGNNs still
remains a challenge. First, DGNNs do not fully utilize hardware resources
because temporal data dependencies cause low hardware parallelism.
Additionally, there is currently a lack of generic DGNN hardware accelerator
frameworks, and existing GNN accelerator frameworks have limited ability to
handle dynamic graphs with changing topologies and node features. To …

accelerator arxiv challenge challenges data dependencies deployment dynamic evolution features fpga framework frameworks graph graph-based graph neural network graph neural networks graphs hardware hardware accelerator inference low network networks neural network neural networks node popular resources systems temporal