17 citations · 51 across the 31 of their papers we have counts for
16 papers · 1 filter
ESR-HGNN: Eliminating Semantic Redundancy for Efficient Mini-batch HGNN Inference
Dengke Han, Mingyu Yan, Duo Wang +3
Heterogeneous graph neural networks (HGNNs) are highly effective in processing heterogeneous graph data and have been widely adopted in critical domains. As real-world graph data c…
TLV-HGNN: Thinking Like a Vertex for Memory-efficient HGNN Inference
Dengke Han, Duo Wang, Mingyu Yan +2
Heterogeneous graph neural networks (HGNNs) excel at processing heterogeneous graph data and are widely applied in critical domains. In HGNN inference, the neighbor aggregation sta…
Accelerating GNN Training through Locality-aware Dropout and Merge
Gongjian Sun, Mingyu Yan, Dengke Han +4
Graph Neural Networks (GNNs) have demonstrated significant success in graph learning and are widely adopted across various critical domains. However, the irregular connectivity bet…
MetaDSE: A Few-shot Meta-learning Framework for Cross-workload CPU Design Space Exploration
Runzhen Xue, Hao Wu, Mingyu Yan +3
Cross-workload design space exploration (DSE) is crucial in CPU architecture design. Existing DSE methods typically employ the transfer learning technique to leverage knowledge fro…
Multilayer Dataflow: Orchestrate Butterfly Sparsity to Accelerate Attention Computation
Haibin Wu, Wenming Li, Kai Yan +9
Recent neural networks (NNs) with self-attention exhibit competitiveness across different AI domains, but the essential attention mechanism brings massive computation and memory de…
Accelerating Mini-batch HGNN Training by Reducing CUDA Kernels
Meng Wu, Jingkai Qiu, Mingyu Yan +5
Heterogeneous graph neural networks (HGNNs) are essential for capturing the structure and semantic information in heterogeneous graphs. However, existing GPU-based solutions, such…