A new technical paper "AutoGNN: End-to-End Hardware-Driven Graph Preprocessing for Enhanced GNN Performance" was published by researchers at KAIST, Panmnesia, Peking University, Hanyang University, and Pennsylvania State University.
Abstract
"Graph neural network (GNN) inference faces significant bottlenecks in preprocessing, which often dominate overall inference latency. We introduce Au...
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