498 citations · 731 across the 19 of their papers we have counts for
19 papers
Defense against Adversarial Cloud Attack on Remote Sensing Salient Object Detection
Huiming Sun, Lan Fu, Jinlong Li +5
Detecting the salient objects in a remote sensing image has wide applications for the interdisciplinary research. Many existing deep learning methods have been proposed for Salient…
Loss Attitude Aware Energy Management for Signal Detection
Baocheng Geng, Chen Quan, Tianyun Zhang +2
This work considers a Bayesian signal processing problem where increasing the power of the probing signal may cause risks or undesired consequences. We employ a market based approa…
Compact Multi-level Sparse Neural Networks with Input Independent Dynamic Rerouting
Minghai Qin, Tianyun Zhang, Fei Sun +4
Deep neural networks (DNNs) have shown to provide superb performance in many real life applications, but their large computation cost and storage requirement have prevented them fr…
Load-balanced Gather-scatter Patterns for Sparse Deep Neural Networks
Fei Sun, Minghai Qin, Tianyun Zhang +6
Deep neural networks (DNNs) have been proven to be effective in solving many real-life problems, but its high computation cost prohibits those models from being deployed to edge de…
Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search
Zheng Zhan, Yifan Gong, Pu Zhao +9
Though recent years have witnessed remarkable progress in single image super-resolution (SISR) tasks with the prosperous development of deep neural networks (DNNs), the deep learni…
Efficient Transformer-based Large Scale Language Representations using Hardware-friendly Block Structured Pruning
Bingbing Li, Zhenglun Kong, Tianyun Zhang +4
Pre-trained large-scale language models have increasingly demonstrated high accuracy on many natural language processing (NLP) tasks. However, the limited weight storage and comput…
Computation on Sparse Neural Networks: an Inspiration for Future Hardware
Fei Sun, Minghai Qin, Tianyun Zhang +3
Neural network models are widely used in solving many challenging problems, such as computer vision, personalized recommendation, and natural language processing. Those models are…
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…
BLK-REW: A Unified Block-based DNN Pruning Framework using Reweighted Regularization Method
Xiaolong Ma, Zhengang Li, Yifan Gong +8
Accelerating DNN execution on various resource-limited computing platforms has been a long-standing problem. Prior works utilize l1-based group lasso or dynamic regularization such…
An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices
Xiaolong Ma, Wei Niu, Tianyun Zhang +8
Weight pruning has been widely acknowledged as a straightforward and effective method to eliminate redundancy in Deep Neural Networks (DNN), thereby achieving acceleration on vario…
An Ultra-Efficient Memristor-Based DNN Framework with Structured Weight Pruning and Quantization Using ADMM
Geng Yuan, Xiaolong Ma, Caiwen Ding +7
The high computation and memory storage of large deep neural networks (DNNs) models pose intensive challenges to the conventional Von-Neumann architecture, incurring substantial da…
Adversarial Attack Generation Empowered by Min-Max Optimization
Jingkang Wang, Tianyun Zhang, Sijia Liu +4
The worst-case training principle that minimizes the maximal adversarial loss, also known as adversarial training (AT), has shown to be a state-of-the-art approach for enhancing ad…
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…
ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Method of Multipliers
Ao Ren, Tianyun Zhang, Shaokai Ye +5
To facilitate efficient embedded and hardware implementations of deep neural networks (DNNs), two important categories of DNN model compression techniques: weight pruning and weigh…
A Unified Framework of DNN Weight Pruning and Weight Clustering/Quantization Using ADMM
Shaokai Ye, Tianyun Zhang, Kaiqi Zhang +6
Many model compression techniques of Deep Neural Networks (DNNs) have been investigated, including weight pruning, weight clustering and quantization, etc. Weight pruning leverages…
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…
Systematic Weight Pruning of DNNs using Alternating Direction Method of Multipliers
Tianyun Zhang, Shaokai Ye, Yipeng Zhang +2
We present a systematic weight pruning framework of deep neural networks (DNNs) using the alternating direction method of multipliers (ADMM). We first formulate the weight pruning…