Xiaochun Ye

ESR-HGNN: Eliminating Semantic Redundancy for Efficient Mini-Batch HGNN Inference

This work proposes a redundancy-aware HGNN sampling paradigm that leverages a metapath trie to reuse traversal paths, effectively eliminating redundant memory accesses and introduces a reusability-driven metapath grouping technique that optimally clusters metapaths to maximize reusable traversal paths within hardware channels, enhancing efficiency in scenarios with semantic parallelism.