Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM
Shaokai Ye, Yanzhi Wang(Sichuan University), Xue Lin(Northeastern University), Yongpan Liu(Tsinghua University), Makan Fardad(Syracuse University), Xiaoyu Feng(Qingdao University), Sheng Lin(Qingdao Agricultural University), Sijia Liu(Hunan University), Tianyun Zhang(Syracuse University), Zhengang Li(Northeastern University), Kaidi Xu(Northeastern University), Xiaolong Ma(Universidad del Noreste), Wujie Wen(Lehigh University), Jian Tang(Midea Group (China))
Cited by 26
Related Papers
CNTFET-Based Design of Ternary Logic Gates and Arithmetic Circuits
|IEEE Transactions on Nanotechnology|2009|599
Incidence, Clinical Course, and Predictors of Prolonged Recovery Time Following Sport-Related Concussion in High School and College Athletes
|Journal of the International Neuropsychological Society|2012|501
A Systematic DNN Weight Pruning Framework Using Alternating Direction Method of Multipliers
|Lecture notes in computer science|2018|498
Deep Reinforcement Learning for Building HVAC Control
|Unknown|2017|447
Adversarial T-Shirt! Evading Person Detectors in a Physical World
|Lecture notes in computer science|2020|296