177 citations · 382 across the 18 of their papers we have counts for
18 papers
Learning Dynamics and Structure of Complex Systems Using Graph Neural Networks
Zhe Li, Andreas S. Tolias, Xaq Pitkow
Many complex systems are composed of interacting parts, and the underlying laws are usually simple and universal. While graph neural networks provide a useful relational inductive…
Prior-aware Neural Network for Partially-Supervised Multi-Organ Segmentation
Yuyin Zhou, Zhe Li, Song Bai +5
Accurate multi-organ abdominal CT segmentation is essential to many clinical applications such as computer-aided intervention. As data annotation requires massive human labor from…
CircConv: A Structured Convolution with Low Complexity
Siyu Liao, Zhe Li, Liang Zhao +3
Deep neural networks (DNNs), especially deep convolutional neural networks (CNNs), have emerged as the powerful technique in various machine learning applications. However, the lar…
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…
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…
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…
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,…
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…
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…
An Area and Energy Efficient Design of Domain-Wall Memory-Based Deep Convolutional Neural Networks using Stochastic Computing
Xiaolong Ma, Yipeng Zhang, Geng Yuan +5
With recent trend of wearable devices and Internet of Things (IoTs), it becomes attractive to develop hardware-based deep convolutional neural networks (DCNNs) for embedded applica…
Thoracic Disease Identification and Localization with Limited Supervision
Zhe Li, Chong Wang, Mei Han +4
Accurate identification and localization of abnormalities from radiology images play an integral part in clinical diagnosis and treatment planning. Building a highly accurate predi…
CirCNN: Accelerating and Compressing Deep Neural Networks Using Block-CirculantWeight Matrices
Caiwen Ding, Siyu Liao, Yanzhi Wang +13
Large-scale deep neural networks (DNNs) are both compute and memory intensive. As the size of DNNs continues to grow, it is critical to improve the energy efficiency and performanc…
Hardware-Driven Nonlinear Activation for Stochastic Computing Based Deep Convolutional Neural Networks
Ji Li, Zihao Yuan, Zhe Li +5
Recently, Deep Convolutional Neural Networks (DCNNs) have made unprecedented progress, achieving the accuracy close to, or even better than human-level perception in various tasks.…
A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning
Ning Liu, Zhe Li, Zhiyuan Xu +5
Automatic decision-making approaches, such as reinforcement learning (RL), have been applied to (partially) solve the resource allocation problem adaptively in the cloud computing…
Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank
Liang Zhao, Siyu Liao, Yanzhi Wang +4
Recently low displacement rank (LDR) matrices, or so-called structured matrices, have been proposed to compress large-scale neural networks. Empirical results have shown that neura…
SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing
Ao Ren, Ji Li, Zhe Li +5
With recent advancing of Internet of Things (IoTs), it becomes very attractive to implement the deep convolutional neural networks (DCNNs) onto embedded/portable systems. Presently…