Exploring Architecture, Dataflow, and Sparsity for GCN Accelerators: A Holistic Framework
Lingxiang Yin, Jun Wang, Hao Zheng · 2023
Recent years have seen an increasing number of Graph Convolutional Network (GCN) models employed in various real-world applications. However, designing efficient architectures for GCN acceleration remains challenging due to the varied sparsity across graph datasets. Despite significant efforts, very few of the existing works have considered a holistic view of the entire GCN accelerator design, and therefore, the dynamic interactions between architecture, dataflow (i.e., data reuse and parallelization strategies), and compression format are not well studied