English(EN) TopGQ: Fast GNN Post-Training Quantization Leveraging Topology Information

TopGQ 框架通过拓扑感知分组加速 GNN 量化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了 TopGQ，一种用于图神经网络 (GNN) 训练后量化的新颖框架，可显著降低量化开销。该方法采用双轴尺度吸收，将激活量化集成到邻接矩阵中，并引入 TopPIN（局部节点结构的代理）来对具有相似拓扑的节点进行分组。据报道，这种方法在保持精度的同时，将量化时间缩短了一个数量级。 AI

影响这项研究可能有助于在资源受限的环境中更有效地部署图神经网络。

排序理由该集群包含一篇详细介绍 GNN 量化新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Dain Kwon, Kanghyun Choi, Hyeyoon Lee, Sunjong Park, Seoyong Lee, Sukjin Kim, Jinho Lee · 2026-09-01 04:00

TopGQ：利用拓扑信息进行快速 GNN 训练后量化

arXiv:2608.30394v1 Announce Type: cross Abstract: Existing GNN quantization methods suffer from considerable quantization overhead, which severely limits their practical usage in real-world scenarios. To this end, we present TopGQ, an accurate post-training GNN quantization frame…