WuJie Wen

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Florida International University ∙ University of Connecticut

Assistant Professor, Department of Electrical and Computer Engineering at Florida International University

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Yanzhi Wang
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Tiny but Accurate: A Pruned, Quantized and Optimized Memristor Crossbar Framework for Ultra Efficient DNN Implementation

The state-of-art DNN structures involve intensive computation and high m...
08/27/2019 ∙ by Xiaolong Ma, et al. ∙ 19 ∙ share

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Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM

Weight pruning and weight quantization are two important categories of D...
03/23/2019 ∙ by Shaokai Ye, et al. ∙ 16 ∙ share

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Security Analysis and Enhancement of Model Compressed Deep Learning Systems under Adversarial Attacks

DNN is presenting human-level performance for many complex intelligent t...
02/14/2018 ∙ by Qi Liu, et al. ∙ 0 ∙ share

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On the Universal Approximation Property and Equivalence of Stochastic Computing-based Neural Networks and Binary Neural Networks

Large-scale deep neural networks are both memory intensive and computati...
03/14/2018 ∙ by Yanzhi Wang, et al. ∙ 0 ∙ share

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A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers

Weight pruning methods for deep neural networks (DNNs) have been investi...
04/10/2018 ∙ by Tianyun Zhang, et al. ∙ 0 ∙ share

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Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples

Deep Neural Networks (DNNs) have achieved remarkable performance in a my...
03/14/2018 ∙ by Zihao Liu, et al. ∙ 0 ∙ share

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MT-Spike: A Multilayer Time-based Spiking Neuromorphic Architecture with Temporal Error Backpropagation

Modern deep learning enabled artificial neural networks, such as Deep Ne...
03/14/2018 ∙ by Tao Liu, et al. ∙ 0 ∙ share

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PT-Spike: A Precise-Time-Dependent Single Spike Neuromorphic Architecture with Efficient Supervised Learning

One of the most exciting advancements in AI over the last decade is the ...
03/14/2018 ∙ by Tao Liu, et al. ∙ 0 ∙ share

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Defensive Dropout for Hardening Deep Neural Networks under Adversarial Attacks

Deep neural networks (DNNs) are known vulnerable to adversarial attacks....
09/13/2018 ∙ by Siyue Wang, et al. ∙ 0 ∙ share

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ADAM-ADMM: A Unified, Systematic Framework of Structured Weight Pruning for DNNs

Weight pruning methods of deep neural networks (DNNs) have been demonstr...
07/29/2018 ∙ by Tianyun Zhang, et al. ∙ 0 ∙ share

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E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs

Recurrent Neural Networks (RNNs) are becoming increasingly important for...
12/12/2018 ∙ by Zhe Li, et al. ∙ 0 ∙ share

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2.4GHZ Class AB power Amplifier For Healthcare Application

The objective of this research was to design a 2.4 GHz class AB Power Am...
05/09/2016 ∙ by Wei Cai, et al. ∙ 0 ∙ share

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DeepN-JPEG: A Deep Neural Network Favorable JPEG-based Image Compression Framework

As one of most fascinating machine learning techniques, deep neural netw...
03/14/2018 ∙ by Zihao Liu, et al. ∙ 0 ∙ share

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Machine Vision Guided 3D Medical Image Compression for Efficient Transmission and Accurate Segmentation in the Clouds

Cloud based medical image analysis has become popular recently due to th...
04/09/2019 ∙ by Zihao Liu, et al. ∙ 0 ∙ share

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Thread Batching for High-performance Energy-efficient GPU Memory Design

Massive multi-threading in GPU imposes tremendous pressure on memory sub...
06/13/2019 ∙ by Bing Li, et al. ∙ 0 ∙ share

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