activity
20162023
most citedCirCNN: Accelerating and Compressing Deep Neural Networks Using Block-CirculantWeight Matrices

177 citations · 340 across the 24 of their papers we have counts for

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8 papers · 1 filter

cs.CV2020★ 2 cited

MAGNet: Multi-Region Attention-Assisted Grounding of Natural Language Queries at Phrase Level

Amar Shrestha, Krittaphat Pugdeethosapol, Haowen Fang +1

Grounding free-form textual queries necessitates an understanding of these textual phrases and its relation to the visual cues to reliably reason about the described locations. Spa…

cs.CV2020

Mission-Aware Spatio-Temporal Deep Learning Model for UAS Instantaneous Density Prediction

Ziyi Zhao, Zhao Jin, Wentian Bai +4

The number of daily sUAS operations in uncontrolled low altitude airspace is expected to reach into the millions in a few years. Therefore, UAS density prediction has become an eme…

cs.CV2019★ 1 cited

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…

cs.CV2018★ 1 cited

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…

cs.CV2018★ 3 cited

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…

cs.CV2017★ 177 cited

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…

cs.CV2017★ 2 cited

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.…

cs.CV2016★ 35 cited

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…