
DeepDup: An Adversarial Weight Duplication Attack Framework to Crush Deep Neural Network in MultiTenant FPGA
The wide deployment of Deep Neural Networks (DNN) in highperformance cl...
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PANDA: ProcessinginMRAM Accelerated De Bruijn Graph based DNA Assembly
Spurred by widening gap between data processing speed and data communica...
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TBFA: Targeted BitFlip Adversarial Weight Attack
Deep Neural Network (DNN) attacks have mostly been conducted through adv...
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DeepHammer: Depleting the Intelligence of Deep Neural Networks through Targeted Chain of Bit Flips
Security of machine learning is increasingly becoming a major concern du...
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Representable Matrices: Enabling High Accuracy Analog Computation for Inference of DNNs using Memristors
Analog computing based on memristor technology is a promising solution t...
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TBT: Targeted Neural Network Attack with Bit Trojan
Security of modern Deep Neural Networks (DNNs) is under severe scrutiny ...
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Nonstructured DNN Weight Pruning Considered Harmful
Large deep neural network (DNN) models pose the key challenge to energy ...
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Defending Against Adversarial Attacks Using Random Forests
As deep neural networks (DNNs) have become increasingly important and po...
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Robust Sparse Regularization: Simultaneously Optimizing Neural Network Robustness and Compactness
Deep Neural Network (DNN) trained by the gradient descent method is know...
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ProcessingInMemory Acceleration of Convolutional Neural Networks for EnergyEfficiency, and PowerIntermittency Resilience
Herein, a bitwise Convolutional Neural Network (CNN) inmemory accelera...
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Accelerating Bulk BitWise X(N)OR Operation in ProcessinginDRAM Platform
With VonNeumann computing architectures struggling to address computati...
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BitFlip Attack: Crushing Neural Network with Progressive Bit Search
Several important security issues of Deep Neural Network (DNN) have been...
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BitFlip Attack: Crushing Neural Network withProgressive Bit Search
Several important security issues of Deep Neural Network (DNN) have been...
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Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial Attack
Recent development in the field of Deep Learning have exposed the underl...
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Simultaneously Optimizing Weight and Quantizer of Ternary Neural Network using Truncated Gaussian Approximation
In the past years, Deep convolution neural network has achieved great su...
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Optimize Deep Convolutional Neural Network with Ternarized Weights and High Accuracy
Deep convolution neural network has achieved great success in many artif...
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Defend Deep Neural Networks Against Adversarial Examples via Fixed andDynamic Quantized Activation Functions
Recent studies have shown that deep neural networks (DNNs) are vulnerabl...
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A SemiSupervised TwoStage Approach to Learning from Noisy Labels
The recent success of deep neural networks is powered in part by larges...
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Blind PreProcessing: A Robust Defense Method Against Adversarial Examples
Deep learning algorithms and networks are vulnerable to perturbed inputs...
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Robust PreProcessing: A Robust Defense Method Against Adversary Attack
Deep learning algorithms and networks are vulnerable to perturbed inputs...
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Developing AllSkyrmion Spiking Neural Network
In this work, we have proposed a revolutionary neuromorphic computing me...
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Deliang Fan
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