International audienceResource-constrained CNN implementations are subject to various reliability threats. This article provides an exploratory study investigating the impact of faults (soft errors modeled as bit flips) across the parameters of CNNs and the impact on the CNN weights, which can be used to create reliability-aware design guidelines. -Theocharis Theocharides, University of Cyprus -Muhammad Shafique, Technische Universitat Wie
The heavy burdens of computation and off-chip traffic impede deploying the large scale convolution n...
Previous studies have demonstrated that, up to a certain degree, Convolutional Neural Networks (CNNs...
Reducing the learning rate of a CNN can positively affect the validation accuracy of a machine learn...
There is an increasing interest in employing Convolutional Neural Networks (CNNs) in safety-critical...
As more deep learning algorithms enter safety-critical application domains, the importance of analyz...
The great quest for adopting AI-based computation for safety-/mission-critical applications motivate...
International audienceConvolutional Neural Networks (CNNs) are currently one of the most widely used...
Convolutional neural networks (CNNs) are becoming more and more important for solving challenging an...
International audienceThe numerical format used for representing weights and activations plays a key...
There have been an extensive use of Convolutional Neural Networks (CNNs) in healthcare applications....
There have been an extensive use of Convolutional Neural Networks (CNNs) in healthcare applications....
Deep Learning, and in particular its implementation using Convolutional Neural Networks (CNNs), is c...
International audienceDeep Neural Networks (DNNs) show promising performance in several application ...
Classification performance based on ImageNet is the de-facto standard metric for CNN development. In...
Given a pre-trained CNN without any testing samples, this paper proposes a simple yet effective meth...
The heavy burdens of computation and off-chip traffic impede deploying the large scale convolution n...
Previous studies have demonstrated that, up to a certain degree, Convolutional Neural Networks (CNNs...
Reducing the learning rate of a CNN can positively affect the validation accuracy of a machine learn...
There is an increasing interest in employing Convolutional Neural Networks (CNNs) in safety-critical...
As more deep learning algorithms enter safety-critical application domains, the importance of analyz...
The great quest for adopting AI-based computation for safety-/mission-critical applications motivate...
International audienceConvolutional Neural Networks (CNNs) are currently one of the most widely used...
Convolutional neural networks (CNNs) are becoming more and more important for solving challenging an...
International audienceThe numerical format used for representing weights and activations plays a key...
There have been an extensive use of Convolutional Neural Networks (CNNs) in healthcare applications....
There have been an extensive use of Convolutional Neural Networks (CNNs) in healthcare applications....
Deep Learning, and in particular its implementation using Convolutional Neural Networks (CNNs), is c...
International audienceDeep Neural Networks (DNNs) show promising performance in several application ...
Classification performance based on ImageNet is the de-facto standard metric for CNN development. In...
Given a pre-trained CNN without any testing samples, this paper proposes a simple yet effective meth...
The heavy burdens of computation and off-chip traffic impede deploying the large scale convolution n...
Previous studies have demonstrated that, up to a certain degree, Convolutional Neural Networks (CNNs...
Reducing the learning rate of a CNN can positively affect the validation accuracy of a machine learn...