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A Quantitative Exploration of Collaborative Pruning and Approximation Computing Towards Energy Efficient Neural Networks 期刊论文
IEEE DESIGN & TEST, 2020, 卷号: 37, 期号: 1, 页码: 36-45
作者:  He, Xin;  Yan, Guihai;  Lu, Wenyan;  Zhang, Xuan;  Liu, Ke
收藏  |  浏览/下载:40/0  |  提交时间:2020/12/10
Resilience  Energy consumption  Approximate computing  Collaboration  Computational modeling  Artificial neural networks  Optimization  Neural network  Energy efficient computing  Network pruning  Approximate computing  
SqueezeFlow: A Sparse CNN Accelerator Exploiting Concise Convolution Rules 期刊论文
IEEE TRANSACTIONS ON COMPUTERS, 2019, 卷号: 68, 期号: 11, 页码: 1663-1677
作者:  Li, Jiajun;  Jiang, Shuhao;  Gong, Shijun;  Wu, Jingya;  Yan, Junchao;  Yan, Guihai;  Li, Xiaowei
收藏  |  浏览/下载:41/0  |  提交时间:2020/12/10
Convolutional neural networks  accelerator architecture  hardware acceleration  
Promoting the Harmony between Sparsity and Regularity: A Relaxed Synchronous Architecture for Convolutional Neural Networks 期刊论文
IEEE TRANSACTIONS ON COMPUTERS, 2019, 卷号: 68, 期号: 6, 页码: 867-881
作者:  Lu, Wenyan;  Yan, Guihai;  Li, Jiajun;  Gong, Shijun;  Jiang, Shuhao;  Wu, Jingya;  Li, Xiaowei
收藏  |  浏览/下载:249/0  |  提交时间:2019/08/16
Convolutional neural networks  accelerator  architecture  parallelism  sparsity  
SynergyFlow: An Elastic Accelerator Architecture Supporting Batch Processing of Large-Scale Deep Neural Networks 期刊论文
ACM TRANSACTIONS ON DESIGN AUTOMATION OF ELECTRONIC SYSTEMS, 2019, 卷号: 24, 期号: 1, 页码: 27
作者:  Li, Jiajun;  Yan, Guihai;  Lu, Wenyan;  Gong, Shijun;  Jiang, Shuhao;  Wu, Jingya;  Li, Xiaowei
收藏  |  浏览/下载:70/0  |  提交时间:2019/04/03
Deep neural networks  convolutional neural networks  accelerator  architecture  resource utilization  complementary effect