26 citations · 30 across the 3 of their papers we have counts for
6 papers
A Unified DNN Weight Compression Framework Using Reweighted Optimization Methods
Tianyun Zhang, Xiaolong Ma, Zheng Zhan +7
To address the large model size and intensive computation requirement of deep neural networks (DNNs), weight pruning techniques have been proposed and generally fall into two categ…
Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM
Shaokai Ye, Xiaoyu Feng, Tianyun Zhang +11
Weight pruning and weight quantization are two important categories of DNN model compression. Prior work on these techniques are mainly based on heuristics. A recent work developed…
Progressive Weight Pruning of Deep Neural Networks using ADMM
Shaokai Ye, Tianyun Zhang, Kaiqi Zhang +10
Deep neural networks (DNNs) although achieving human-level performance in many domains, have very large model size that hinders their broader applications on edge computing devices…
StructADMM: A Systematic, High-Efficiency Framework of Structured Weight Pruning for DNNs
Tianyun Zhang, Shaokai Ye, Kaiqi Zhang +8
Weight pruning methods of DNNs have been demonstrated to achieve a good model pruning rate without loss of accuracy, thereby alleviating the significant computation/storage require…
A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers
Tianyun Zhang, Shaokai Ye, Kaiqi Zhang +4
Weight pruning methods for deep neural networks (DNNs) have been investigated recently, but prior work in this area is mainly heuristic, iterative pruning, thereby lacking guarante…
A Memristor-Based Optimization Framework for AI Applications
Sijia Liu, Yanzhi Wang, Makan Fardad +1
Memristors have recently received significant attention as ubiquitous device-level components for building a novel generation of computing systems. These devices have many promisin…