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Re-FeMAT: A Reconfigurable Multifunctional FeFET-Based Memory Architecture 期刊论文
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 2022, 卷号: 41, 期号: 11, 页码: 5071-5084
作者:  Zhang, Xiaoyu;  Liu, Rui;  Song, Tao;  Yang, Yuxin;  Han, Yinhe;  Chen, Xiaoming
收藏  |  浏览/下载:13/0  |  提交时间:2023/07/12
Convolutional neural network (CNN)  ferroelectric field-effect transistor (FeFET)  few-shot learning  in-memory processing  ternary content-addressable memory (TCAM)  
Rubik: A Hierarchical Architecture for Efficient Graph Neural Network Training 期刊论文
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 2022, 卷号: 41, 期号: 4, 页码: 936-949
作者:  Chen, Xiaobing;  Wang, Yuke;  Xie, Xinfeng;  Hu, Xing;  Basak, Abanti;  Liang, Ling;  Yan, Mingyu;  Deng, Lei;  Ding, Yufei;  Du, Zidong;  Xie, Yuan
收藏  |  浏览/下载:21/0  |  提交时间:2022/12/07
Deep learning accelerator  graph neural network (GNN)  
Breaking the Interaction Wall: A DLPU-Centric Deep Learning Computing System 期刊论文
IEEE TRANSACTIONS ON COMPUTERS, 2022, 卷号: 71, 期号: 1, 页码: 209-222
作者:  Du, Zidong;  Guo, Qi;  Zhao, Yongwei;  Zeng, Xi;  Li, Ling;  Cheng, Limin;  Xu, Zhiwei;  Sun, Ninghui;  Chen, Yunji
收藏  |  浏览/下载:32/0  |  提交时间:2022/06/21
Deep learning  Central Processing Unit  Process control  Task analysis  Computational modeling  Pipelines  Runtime  Neural net accelerators  system architectures  interaction wall  
A Decomposable Winograd Method for N-D Convolution Acceleration in Video Analysis 期刊论文
INTERNATIONAL JOURNAL OF COMPUTER VISION, 2021, 页码: 21
作者:  Huang, Di;  Zhang, Rui;  Zhang, Xishan;  Wu, Fan;  Wang, Xianzhuo;  Jin, Pengwei;  Liu, Shaoli;  Li, Ling;  Chen, Yunji
收藏  |  浏览/下载:35/0  |  提交时间:2021/12/01
Convolution neural networks  Model acceleration  Winograd algorithm  Video analysis  
Swallow: A Versatile Accelerator for Sparse Neural Networks 期刊论文
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 2020, 卷号: 39, 期号: 12, 页码: 4881-4893
作者:  Liu, Bosheng;  Chen, Xiaoming;  Han, Yinhe;  Xu, Haobo
收藏  |  浏览/下载:28/0  |  提交时间:2021/12/01
Accelerator  convolutional (Conv) layers  fully connected (FC) layers  sparse neural networks (SNNs)