498 citations · 2.5k across the 114 of their papers we have counts for
30 papers · 1 filter
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…
E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs
Zhe Li, Caiwen Ding, Siyue Wang +8
Recurrent Neural Networks (RNNs) are becoming increasingly important for time series-related applications which require efficient and real-time implementations. The two major types…
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…
Interpreting Adversarial Robustness: A View from Decision Surface in Input Space
Fuxun Yu, Chenchen Liu, Yanzhi Wang +2
One popular hypothesis of neural network generalization is that the flat local minima of loss surface in parameter space leads to good generalization. However, we demonstrate that…
Structured Adversarial Attack: Towards General Implementation and Better Interpretability
Kaidi Xu, Sijia Liu, Pu Zhao +6
When generating adversarial examples to attack deep neural networks (DNNs), Lp norm of the added perturbation is usually used to measure the similarity between original image and a…
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…
Towards Robust Training of Neural Networks by Regularizing Adversarial Gradients
Fuxun Yu, Zirui Xu, Yanzhi Wang +2
In recent years, neural networks have demonstrated outstanding effectiveness in a large amount of applications.However, recent works have shown that neural networks are susceptible…
Adversarial Meta-Learning
Chengxiang Yin, Jian Tang, Zhiyuan Xu +1
Meta-learning enables a model to learn from very limited data to undertake a new task. In this paper, we study the general meta-learning with adversarial samples. We present a meta…
Towards Budget-Driven Hardware Optimization for Deep Convolutional Neural Networks using Stochastic Computing
Zhe Li, Ji Li, Ao Ren +5
Recently, Deep Convolutional Neural Network (DCNN) has achieved tremendous success in many machine learning applications. Nevertheless, the deep structure has brought significant i…
Learning Topics using Semantic Locality
Ziyi Zhao, Krittaphat Pugdeethosapol, Sheng Lin +4
The topic modeling discovers the latent topic probability of the given text documents. To generate the more meaningful topic that better represents the given document, we proposed…
An ADMM-Based Universal Framework for Adversarial Attacks on Deep Neural Networks
Pu Zhao, Sijia Liu, Yanzhi Wang +1
Deep neural networks (DNNs) are known vulnerable to adversarial attacks. That is, adversarial examples, obtained by adding delicately crafted distortions onto original legal inputs…
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…
Structured Weight Matrices-Based Hardware Accelerators in Deep Neural Networks: FPGAs and ASICs
Caiwen Ding, Ao Ren, Geng Yuan +5
Both industry and academia have extensively investigated hardware accelerations. In this work, to address the increasing demands in computational capability and memory requirement,…
Efficient Recurrent Neural Networks using Structured Matrices in FPGAs
Zhe Li, Shuo Wang, Caiwen Ding +3
Recurrent Neural Networks (RNNs) are becoming increasingly important for time series-related applications which require efficient and real-time implementations. The recent pruning…
Image Dataset for Visual Objects Classification in 3D Printing
Hongjia Li, Xiaolong Ma, Aditya Singh Rathore +5
The rapid development in additive manufacturing (AM), also known as 3D printing, has brought about potential risk and security issues along with significant benefits. In order to e…
Security Analysis and Enhancement of Model Compressed Deep Learning Systems under Adversarial Attacks
Qi Liu, Tao Liu, Zihao Liu +3
DNN is presenting human-level performance for many complex intelligent tasks in real-world applications. However, it also introduces ever-increasing security concerns. For example,…
C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs
Shuo Wang, Zhe Li, Caiwen Ding +4
Recently, significant accuracy improvement has been achieved for acoustic recognition systems by increasing the model size of Long Short-Term Memory (LSTM) networks. Unfortunately,…
DeepN-JPEG: A Deep Neural Network Favorable JPEG-based Image Compression Framework
Zihao Liu, Tao Liu, Wujie Wen +4
As one of most fascinating machine learning techniques, deep neural network (DNN) has demonstrated excellent performance in various intelligent tasks such as image classification.…
Model-Free Control for Distributed Stream Data Processing using Deep Reinforcement Learning
Teng Li, Zhiyuan Xu, Jian Tang +1
In this paper, we focus on general-purpose Distributed Stream Data Processing Systems (DSDPSs), which deal with processing of unbounded streams of continuous data at scale distribu…
C3PO: Database and Benchmark for Early-stage Malicious Activity Detection in 3D Printing
Zhe Li, Xiaolong Ma, Hongjia Li +5
Increasing malicious users have sought practices to leverage 3D printing technology to produce unlawful tools in criminal activities. Current regulations are inadequate to deal wit…
Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples
Zihao Liu, Qi Liu, Tao Liu +4
Image compression-based approaches for defending against the adversarial-example attacks, which threaten the safety use of deep neural networks (DNN), have been investigated recent…
On the Universal Approximation Property and Equivalence of Stochastic Computing-based Neural Networks and Binary Neural Networks
Yanzhi Wang, Zheng Zhan, Jiayu Li +6
Large-scale deep neural networks are both memory intensive and computation-intensive, thereby posing stringent requirements on the computing platforms. Hardware accelerations of de…
Towards Ultra-High Performance and Energy Efficiency of Deep Learning Systems: An Algorithm-Hardware Co-Optimization Framework
Yanzhi Wang, Caiwen Ding, Zhe Li +8
Hardware accelerations of deep learning systems have been extensively investigated in industry and academia. The aim of this paper is to achieve ultra-high energy efficiency and pe…