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FlowGNN: A Dataflow Architecture for Real-Time Workload-Agnostic Graph Neural Network Inference. (arXiv:2204.13103v2 [cs.DC] UPDATED)
cs.LG updates on arXiv.org arxiv.org
Graph neural networks (GNNs) have recently exploded in popularity thanks to
their broad applicability to graph-related problems such as quantum chemistry,
drug discovery, and high energy physics. However, meeting demand for novel GNN
models and fast inference simultaneously is challenging due to the gap between
developing efficient accelerators and the rapid creation of new GNN models.
Prior art focuses on accelerating specific classes of GNNs, such as Graph
Convolutional Networks (GCN), but lacks generality to support a wide range of …
architecture arxiv dataflow dataflow architecture graph graph neural network inference network neural network real-time