Zhengang Li
Zhengang Li
Verified email at husky.neu.edu
Cited by
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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
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
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, ...
arXiv preprint arXiv:2002.11474, 2020
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
A Privacy-Preserving DNN Pruning and Mobile Acceleration Framework
Z Zhan, Y Gong, Z Li, P Zhao, X Ma, W Niu, X Xu, B Ren, Y Wang, X Lin
arXiv preprint arXiv:2003.06513, 2020
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
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, X Lin, B Ren
arXiv preprint arXiv:2007.09835, 2020
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
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
Non-Structured DNN Weight Pruning–Is It Beneficial in Any Platform?
X Ma, S Lin, S Ye, Z He, L Zhang, G Yuan, S Huat Tan, Z Li, D Fan, X Qian, ...
arXiv, arXiv: 1907.02124, 2019
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