
SME: ReRAMbased SparseMultiplicationEngine to SqueezeOut Bit Sparsity of Neural Network
Resistive RandomAccessMemory (ReRAM) crossbar is a promising technique...
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RADAR: Runtime Adversarial Weight Attack Detection and Accuracy Recovery
Adversarial attacks on Neural Network weights, such as the progressive b...
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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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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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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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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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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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Zhezhi He
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