1 citations · 1 across the 10 of their papers we have counts for
10 papers
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…
DraftExpert: Expansion-Aware Self-Speculative Decoding for End-Device MoE Inference
Dengke Han
Large Mixture-of-Experts (MoE) language models are attractive for end-device deployment because only a small subset of experts is active per token, but their routed expert weights…
A Systematic Characterization of LLM Inference on GPUs
Haonan Wang, Xuxin Xiao, Mingyu Yan +8
This work presents a systematic characterization of Large Language Model (LLM) inference to address fragmented understanding. Through comprehensive experiments, we establish a four…
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…
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…
Characterizing and Understanding HGNN Training on GPUs
Dengke Han, Mingyu Yan, Xiaochun Ye +1
Owing to their remarkable representation capabilities for heterogeneous graph data, Heterogeneous Graph Neural Networks (HGNNs) have been widely adopted in many critical real-world…
ADE-HGNN: Accelerating HGNNs through Attention Disparity Exploitation
Dengke Han, Meng Wu, Runzhen Xue +3
Heterogeneous Graph Neural Networks (HGNNs) have recently demonstrated great power in handling heterogeneous graph data, rendering them widely applied in many critical real-world d…
GDR-HGNN: A Heterogeneous Graph Neural Networks Accelerator Frontend with Graph Decoupling and Recoupling
Runzhen Xue, Mingyu Yan, Dengke Han +4
Heterogeneous Graph Neural Networks (HGNNs) have broadened the applicability of graph representation learning to heterogeneous graphs. However, the irregular memory access pattern…
HiHGNN: Accelerating HGNNs through Parallelism and Data Reusability Exploitation
Runzhen Xue, Dengke Han, Mingyu Yan +8
Heterogeneous graph neural networks (HGNNs) have emerged as powerful algorithms for processing heterogeneous graphs (HetGs), widely used in many critical fields. To capture both st…