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...
保存先:
| 主要な著者: | , , , |
|---|---|
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Taylor & Francis Group
2026-12-01
|
| シリーズ: | Connection Science |
| 主題: | |
| オンライン･アクセス: | https://www.tandfonline.com/doi/10.1080/09540091.2026.2650981 |
| タグ: |
タグなし, このレコードへの初めてのタグを付けませんか!
|