1 citations · 1 across the 13 of their papers we have counts for
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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…
Large Processor Chip Model
Kaiyan Chang, Mingzhi Chen, Yunji Chen +40
Computer System Architecture serves as a crucial bridge between software applications and the underlying hardware, encompassing components like compilers, CPUs, coprocessors, and R…
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…
StreamDCIM: A Tile-based Streaming Digital CIM Accelerator with Mixed-stationary Cross-forwarding Dataflow for Multimodal Transformer
Shantian Qin, Ziqing Qiang, Zhihua Fan +4
Multimodal Transformers are emerging artificial intelligence (AI) models designed to process a mixture of signals from diverse modalities. Digital computing-in-memory (CIM) archite…
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…
SiHGNN: Leveraging Properties of Semantic Graphs for Efficient HGNN Acceleration
Runzhen Xue, Mingyu Yan, Dengke Han +3
Heterogeneous Graph Neural Networks (HGNNs) have expanded graph representation learning to heterogeneous graph fields. Recent studies have demonstrated their superior performance a…
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…
Survey on Characterizing and Understanding GNNs from a Computer Architecture Perspective
Meng Wu, Mingyu Yan, Wenming Li +3
Characterizing and understanding graph neural networks (GNNs) is essential for identifying performance bottlenecks and facilitating their deployment in parallel and distributed sys…