Work

TopGQ: Fast GNN Post-Training Quantization Leveraging Topology Information

Year: 2026

Type: conference-paper

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 framework, alleviating redundant quantization overhead. We propose dual-axis scale absorption, which enables activation quantization along both the outer and inner dimensions by merging one into the adjacency matrix. On top of that, we introduce TopPIN, a proxy for nodes' local structure, and use it to group nodes with similar topology during quantization. Experimental results show that TopGQ reduces quantization time by an order of magnitude while preserving accuracy. The code is available at https://github.com/meowrowan/TopGQ.

Source: ACM/IEEE Design Automation Conference (DAC)

DOI: 10.1145/3770743.3804433