Leda: Leveraging Tiling Dataflow to Accelerate SpMM on HBM-Equipped FPGAs for GNNs

Graph neural networks (GNNs) play a pivotal role in extracting insightful representations from graph-structured data, driving advancements across diverse domains. Central to GNNs is the sparse matrix-dense matrix multiplication (SpMM) kernel. However, challenges arise in accelerating SpMM due to the high sparsity and randomly distributed non-zeros in graph matrices. Recently, the high concurrency capability of high bandwidth memory (HBM) has provided a new opportunity for SpMM acceleration. Nonetheless, accelerating SpMM on HBM FPGAs is still non-trivial due to load imbalance and the random memory access patterns.