Zobrazeno 1 - 10
of 815
pro vyhledávání: '"Li, WenMing"'
Autor:
Wu, Haibin, Li, Wenming, Yan, Kai, Fan, Zhihua, Wu, Peiyang, Liu, Yuqun, Liu, Yanhuan, Qiang, Ziqing, Wu, Meng, Liu, Kunming, Ye, Xiaochun, Fan, Dongrui
Recent neural networks (NNs) with self-attention exhibit competitiveness across different AI domains, but the essential attention mechanism brings massive computation and memory demands. To this end, various sparsity patterns are introduced to reduce
Externí odkaz:
http://arxiv.org/abs/2411.00734
Autor:
Wu, Meng, Qiu, Jingkai, Yan, Mingyu, Li, Wenming, Zhang, Yang, Zhang, Zhimin, Ye, Xiaochun, Fan, Dongrui
Heterogeneous graph neural networks (HGNNs) are essential for capturing the structure and semantic information in heterogeneous graphs. However, existing GPU-based solutions, such as PyTorch Geometric, suffer from low GPU utilization due to numerous
Externí odkaz:
http://arxiv.org/abs/2408.08490
Characterizing graph neural networks (GNNs) is essential for identifying performance bottlenecks and facilitating their deployment. Despite substantial work in this area, a comprehensive survey on GNN characterization is lacking. This work presents a
Externí odkaz:
http://arxiv.org/abs/2408.01902
Recently, large language models (LLMs) have demonstrated excellent performance in understanding human instructions and generating code, which has inspired researchers to explore the feasibility of generating RTL code with LLMs. However, the existing
Externí odkaz:
http://arxiv.org/abs/2407.12022
Autor:
Xue, Runzhen, Han, Dengke, Yan, Mingyu, Zou, Mo, Yang, Xiaocheng, Wang, Duo, Li, Wenming, Tang, Zhimin, Kim, John, Ye, Xiaochun, Fan, Dongrui
Heterogeneous graph neural networks (HGNNs) have emerged as powerful algorithms for processing heterogeneous graphs (HetGs), widely used in many critical fields. To capture both structural and semantic information in HetGs, HGNNs first aggregate the
Externí odkaz:
http://arxiv.org/abs/2307.12765
Heterogeneous graph neural networks (HGNNs) deliver powerful capacity in heterogeneous graph representation learning. The execution of HGNNs is usually accelerated by GPUs. Therefore, characterizing and understanding the execution pattern of HGNNs on
Externí odkaz:
http://arxiv.org/abs/2208.04758
Publikováno v:
In International Journal of Heat and Mass Transfer January 2025 236 Part 2
Autor:
Sun, Gongjian, Yan, Mingyu, Wang, Duo, Li, Han, Li, Wenming, Ye, Xiaochun, Fan, Dongrui, Xie, Yuan
Limited by the memory capacity and compute power, singe-node graph convolutional neural network (GCN) accelerators cannot complete the execution of GCNs within a reasonable amount of time, due to the explosive size of graphs nowadays. Thus, large-sca
Externí odkaz:
http://arxiv.org/abs/2207.07258
Publikováno v:
In Construction and Building Materials 20 December 2024 456
Autor:
Li, Ce, Xu, Chenyang, Guan, Rui, Jiao, Ruijie, Wang, Yin, Cui, Chengfu, Cao, Shengda, Chang, Fen, Wei, Ran, Li, Zinan, Liu, Zhiwei, Gross, Neil D, Li, Guojun, Li, Wenming, Wei, Dongmin, Lei, Dapeng
Publikováno v:
In International Immunopharmacology 5 December 2024 142 Part B