arXiv Machine Learning
Carry-Through Checksum: A Lightweight Fault-Detection for CNN Inference at the Edge
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Faults That Fortify: CNN Adversarial Robustness via GPU Undervolting
The paper demonstrates that undervolting GPUs during CNN training introduces stochastic faults that act as implicit regularization, improving adversarial robustness while reducing power consumption. Experiments on LeNet, VGG-6, and MobileNetV3 trained on MNIST and CIFAR-10 show that undervolted models consistently outperform nominal-voltage models in both standard and adversarial training regimes. The approach offers a hardware-level defense that requires no algorithmic changes and yields significant energy savings due to the quadratic relationship between dynamic power and supply voltage.
arXiv AI
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TEE-X: TEE-aware Acceleration Framework for Large Vision Models at the Edge
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arXiv Machine Learning
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Deep4ge: DNN Training Trajectories for Fault Detection and Diagnosis
arXiv:2607. 12868v1 Announce Type: cross Abstract: Deep learning systems often fail due to subtle implementation faults that alter training behavior.
arXiv AI
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TreeFI: Value-Aware Statistical Fault Injection for Deep Neural Networks
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arXiv Machine Learning
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A combination of noise and bilateral filters achieve supralinear and scalable adversarial robustness in CNNs
arXiv:2606. 02267v1 Announce Type: new Abstract: The vulnerability of deep neural networks to adversarial examples poses a significant challenge for real-world deployment.
arXiv Machine Learning
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Achieving Near-Zero-Overhead Multi-Model Hierarchical Classification in Real-Time Detection Pipelines
arXiv:2608. 11770v1 Announce Type: cross Abstract: Edge-deployed vision systems in target recognition, surveillance, autonomous vehicles, and drone domains require hierarchical inference pipelines where a detection model identifies objects of interest and downstream classifiers provide fine-grained attribute analysis.