Zhengang Li
Zhengang Li
Verified email at husky.neu.edu
Title
Cited by
Cited by
Year
Progressive dnn compression: A key to achieve ultra-high weight pruning and quantization rates using admm
S Ye, X Feng, T Zhang, X Ma, S Lin, Z Li, K Xu, W Wen, S Liu, J Tang, ...
arXiv preprint arXiv:1903.09769, 2019
252019
Rtmobile: Beyond real-time mobile acceleration of rnns for speech recognition
P Dong, S Wang, W Niu, C Zhang, S Lin, Z Li, Y Gong, B Ren, X Lin, ...
2020 57th ACM/IEEE Design Automation Conference (DAC), 1-6, 2020
92020
ResNet Can Be Pruned 60: Introducing Network Purification and Unused Path Removal (P-RM) after Weight Pruning
X Ma, G Yuan, S Lin, Z Li, H Sun, Y Wang
2019 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH), 1-2, 2019
62019
Non-structured DNN weight pruning considered harmful
Y Wang, S Ye, Z He, X Ma, L Zhang, S Lin, G Yuan, SH Tan, Z Li, D Fan, ...
arXiv preprint arXiv:1907.02124, 2019
52019
Non-Structured DNN Weight Pruning--Is It Beneficial in Any Platform?
X Ma, S Lin, S Ye, Z He, L Zhang, G Yuan, SH Tan, Z Li, D Fan, X Qian, ...
IEEE Transactions on Neural Networks and Learning Systems, 2021
32021
Efficient Transformer-based Large Scale Language Representations using Hardware-friendly Block Structured Pruning
B Li, Z Kong, T Zhang, J Li, Z Li, H Liu, C Ding
arXiv preprint arXiv:2009.08065, 2020
22020
BLK-REW: A Unified Block-based DNN Pruning Framework using Reweighted Regularization Method
X Ma, Z Li, Y Gong, T Zhang, W Niu, Z Zhan, P Zhao, J Tang, X Lin, B Ren, ...
arXiv preprint arXiv:2001.08357, 2020
22020
StructADMM: Achieving Ultrahigh Efficiency in Structured Pruning for DNNs
T Zhang, S Ye, X Feng, X Ma, K Zhang, Z Li, J Tang, S Liu, X Lin, Y Liu, ...
IEEE Transactions on Neural Networks and Learning Systems, 2021
12021
A Privacy-Preserving-Oriented DNN Pruning and Mobile Acceleration Framework
Y Gong, Z Zhan, Z Li, W Niu, X Ma, W Wang, B Ren, C Ding, X Lin, X Xu, ...
Proceedings of the 2020 on Great Lakes Symposium on VLSI, 119-124, 2020
12020
RT3D: Achieving Real-Time Execution of 3D Convolutional Neural Networks on Mobile Devices
W Niu, M Sun, Z Li, JA Chen, J Guan, X Shen, Y Wang, S Liu, X Lin, ...
arXiv preprint arXiv:2007.09835, 2020
12020
SS-Auto: A Single-Shot, Automatic Structured Weight Pruning Framework of DNNs with Ultra-High Efficiency
Z Li, Y Gong, X Ma, S Liu, M Sun, Z Zhan, Z Kong, G Yuan, Y Wang
arXiv preprint arXiv:2001.08839, 2020
12020
Real-Time Mobile Acceleration of DNNs: From Computer Vision to Medical Applications
H Li, G Yuan, W Niu, Y Cai, M Sun, Z Li, B Ren, X Lin, Y Wang
2021 26th Asia and South Pacific Design Automation Conference (ASP-DAC), 581-586, 2021
2021
NPAS: A Compiler-aware Framework of Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration
Z Li, G Yuan, W Niu, P Zhao, Y Li, Y Cai, X Shen, Z Zhan, Z Kong, Q Jin, ...
arXiv preprint arXiv:2012.00596, 2020
2020
A SOT-MRAM-based Processing-In-Memory Engine for Highly Compressed DNN Implementation
G Yuan, X Ma, S Lin, Z Li, C Ding
arXiv preprint arXiv:1912.05416, 2019
2019
NPS: A Compiler-aware Framework of Unified Network Pruning for Beyond Real-Time Mobile Acceleration
Z Li, G Yuan, W Niu, Y Li, P Zhao, Y Cai, X Shen, Z Zhan, Z Kong, Q Jin, ...
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