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Accelerating Convolutional Neural Networks by Exploiting the Sparsity of Output Activation 期刊论文
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2023, 卷号: 34, 期号: 12, 页码: 3253-3265
作者:  Fan, Zhihua;  Li, Wenming;  Wang, Zhen;  Liu, Tianyu;  Wu, Haibin;  Liu, Yanhuan;  Wu, Meng;  Wu, Xinxin;  Ye, Xiaochun;  Fan, Dongrui;  Sun, Ninghui;  An, Xuejun
收藏  |  浏览/下载:2/0  |  提交时间:2024/05/20
Accelerator  output activation  prediction  sparse convolutional neural network  
DRONE: An Efficient Distributed Subgraph-Centric Framework for Processing Large-Scale Power-law Graphs 期刊论文
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2023, 卷号: 34, 期号: 2, 页码: 463-474
作者:  Zhang, Shuai;  Jiang, Zite;  Hou, Xingzhong;  Li, Mingyu;  Yuan, Mengting;  You, Haihang
收藏  |  浏览/下载:13/0  |  提交时间:2023/07/12
Fault tolerance  graph partition  large-scale power-law graph  parallel graph computation  subgraph-centric model  
Graphine: Programming Graph-Parallel Computation of Large Natural Graphs for Multicore Clusters 期刊论文
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2016, 卷号: 27, 期号: 6, 页码: 1647-1659
作者:  Yan, Jie;  Tan, Guangming;  Mo, Zeyao;  Sun, Ninghui
收藏  |  浏览/下载:42/0  |  提交时间:2019/12/13
Graph-parallel  parallel framework  computational model