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Exploring Winograd Convolution for Cost-Effective Neural Network Fault Tolerance 期刊论文
IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS, 2023, 卷号: 31, 期号: 11, 页码: 1763-1773
作者:  Xue, Xinghua;  Liu, Cheng;  Liu, Bo;  Huang, Haitong;  Wang, Ying;  Luo, Tao;  Zhang, Lei;  Li, Huawei;  Li, Xiaowei
收藏  |  浏览/下载：2/0  |  提交时间：2024/05/20
Fault tolerant systems  Fault tolerance  Artificial neural networks  Convolution  Reliability  Computational modeling  Neurons  Fault-tolerance  soft errors  vulnerability analysis  winograd convolution (WG-Conv)  
Scalable and Conflict-Free NTT Hardware Accelerator Design: Methodology, Proof, and Implementation 期刊论文
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 2023, 卷号: 42, 期号: 5, 页码: 1504-1517
作者:  Mu, Jianan;  Ren, Yi;  Wang, Wen;  Hu, Yizhong;  Chen, Shuai;  Chang, Chip-Hong;  Fan, Junfeng;  Ye, Jing;  Cao, Yuan;  Li, Huawei;  Li, Xiaowei
收藏  |  浏览/下载：9/0  |  提交时间：2023/12/04
Memory access pattern  number theoretic transform (NTT)  post-quantum cryptography (PQC)  scalable hardware design  
On-Line Fault Protection for ReRAM-Based Neural Networks 期刊论文
IEEE TRANSACTIONS ON COMPUTERS, 2023, 卷号: 72, 期号: 2, 页码: 423-437
作者:  Li, Wen;  Wang, Ying;  Liu, Cheng;  He, Yintao;  Liu, Lian;  Li, Huawei;  Li, Xiaowei
收藏  |  浏览/下载：14/0  |  提交时间：2023/07/12
Training  Fault detection  Computational modeling  Image edge detection  Memristors  Neural networks  Kernel  Deep neural network  hard fault  ReRAM  reliability  soft fault  
A Fast Precision Tuning Solution for Always-On DNN Accelerators 期刊论文
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 2022, 卷号: 41, 期号: 5, 页码: 1236-1248
作者:  Wang, Ying;  He, Yintao;  Cheng, Long;  Li, Huawei;  Li, Xiaowei
收藏  |  浏览/下载：25/0  |  提交时间：2022/12/07
Computer architecture  Neural networks  Computational modeling  Approximate computing  Tuning  Switches  Microprocessors  Always-on  CNN  computing-in-memory (CiM)  resistive RAM  
EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks 期刊论文
IEEE TRANSACTIONS ON COMPUTERS, 2021, 卷号: 70, 期号: 9, 页码: 1511-1525
作者:  Liang, Shengwen;  Wang, Ying;  Liu, Cheng;  He, Lei;  Li, Huawei;  Xu, Dawen;  Li, Xiaowei
收藏  |  浏览/下载：38/0  |  提交时间：2021/12/01
Neural networks  Hardware  System-on-chip  Task analysis  Feature extraction  Memory management  Graph neural network  accelerator architecture  hardware acceleration  
Architecting Effectual Computation for Machine Learning Accelerators 期刊论文
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 2020, 卷号: 39, 期号: 10, 页码: 2654-2667
作者:  Lu, Hang;  Zhang, Mingzhe;  Han, Yinhe;  Wang, Qi;  Li, Huawei;  Li, Xiaowei
收藏  |  浏览/下载：88/0  |  提交时间：2020/12/10
Computational modeling  Throughput  Adders  Machine learning  Acceleration  Kernel  Computational efficiency  Accelerator architectures  neural network hardware  multiplying circuits  
MV-Net: Toward Real-Time Deep Learning on Mobile GPGPU Systems 期刊论文
ACM JOURNAL ON EMERGING TECHNOLOGIES IN COMPUTING SYSTEMS, 2019, 卷号: 15, 期号: 4, 页码: 25
作者:  Tang, Yibin;  Wang, Ying;  Li, Huawei;  Li, Xiaowei
收藏  |  浏览/下载：47/0  |  提交时间：2020/12/10
Edge computing  online scheduling  deep learning  energy efficiency  approximate computing  
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  
An Analytical Framework for Estimating Scale-Out and Scale-Up Power Efficiency of Heterogeneous Manycores 期刊论文
IEEE TRANSACTIONS ON COMPUTERS, 2016, 卷号: 65, 期号: 2, 页码: 367-381
作者:  Ma, Jun;  Yan, Guihai;  Han, Yinhe;  Li, Xiaowei
收藏  |  浏览/下载：36/0  |  提交时间：2019/12/13
Heterogeneous manycores  scale-out  scale-up  analytical model  power efficiency  runtime management