A near CXL memory processing architecture for distributed graph neural network inference and training

Distributed Graph Neural Networks (GNNs) require efficient handling of both fine-grained memory accesses and cross memory-device communication, particularly when scaling to large graphs. However, existing acceleration solutions fail to adequately address low bandwidth utilization and scalability acr...

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Autors principals:	Haoyang Wang, Shengbing Zhang, Xiaoya Fan, Meng Zhang
Format:	Artigo
Idioma:	Inglês
Publicat:	Taylor & Francis Group 2026-12-01
Col·lecció:	Connection Science
Matèries:	Near-data processing graph accelerator graph neural networks distributed GNN
Accés en línia:	https://www.tandfonline.com/doi/10.1080/09540091.2026.2650981
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