K-Means clustering is a classical unsupervised learning method widely used for its simplicity, efficiency, and broad applicability. In this work, we first analyze the numerical distributions of representative K-Means datasets and identify an opportunity for low-precision acceleration through hardware-native microscalin...
Rong-Tian Fu, Dong-Bo Lv, Xue-Ying Wang et al.· Proceedings of the Internati...· 0 citations
Graph Neural Networks (GNNs) have achieved widespread success from social networks to AI-for-Science. Most existing GNN frameworks adopt scatter-first (edge-centric) or gather-first (vertex-centric) scheduling paradigms for message passing. However, these paradigms are closely tied to traditional CUDA-core execution mo...
Jin-Liang Shi, Shi-Gang Li, Rong-Tian Fu et al.· IEEE Transactions on Paralle...· 0 citations
K-Means clustering is a classical unsupervised learning method widely used for its simplicity, efficiency, and broad applicability. In this work, we first analyze the numerical distributions of representative K-Means datasets and identify an opportunity for low-precision acceleration through hardware-native microscalin...
Rong-Tian Fu, Dong-Bo Lv, Xue-Ying Wang et al.· Proceedings of the Internati...· 0 citations
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