arXiv:2609.16742v1 Announce Type: cross Abstract: Convolutional Neural Networks (CNNs) are increasingly deployed in safety-critical edge applications, where soft errors can silently corrupt inference...
arXiv:2608. 04173v1 Announce Type: new Abstract: Deep neural networks (DNNs) deployed on resource-constrained neuromorphic hardware face three concurrent challenges: the need for model compression through pruning, vulnerability to adversarial input perturbations, and susceptibility to hardware-induced weight faults such as stuck-at-zero errors.
arXiv:2606. 17471v1 Announce Type: new Abstract: Traditional CPU, GPU, and NPU architectures are increasingly limited by the von Neumann bottleneck.
arXiv:2603. 22770v2 Announce Type: replace-cross Abstract: The deployment of deep neural networks (DNNs) in safety-critical edge environments necessitates robustness against hardware-induced bit-flip errors.
The paper introduces Right In-Place (RiP) convolution, a memory‑efficient strategy that corrects and generalizes previous in‑place convolution formulations to arbitrary stride, dilation, padding, and rectangular kernels. RiP aligns each layer’s input and output within a shared workspace, enabling safe, row‑major access with minimal memory overhead. Experiments on 10,000 random layers and 84 layers from 25 architectures show no corruption, matching or improving on existing herringbone workspaces while reducing memory usage by up to 24.8% and lowering peak activation memory on Raspberry Pi Pico MCUs by 12.5–33.3% without affecting cycle counts.
arXiv:2607. 12868v1 Announce Type: cross Abstract: Deep learning systems often fail due to subtle implementation faults that alter training behavior.
The paper presents the first instruction-level fault injection study of large language model (LLM) inference, focusing on how GPU soft errors affect these compute- and memory-intensive models. It examines the impact of model architecture, parameter scale, and task complexity on LLM vulnerability, revealing distinct reliability characteristics compared to earlier neural networks. The study offers new insights that can guide the development of more effective fault tolerance mechanisms for LLMs.
RobustNCL is a lightweight framework that enhances the reliability of neuromorphic continual learning (NCL) systems against transient hardware faults. It detects faults during weight access from off‑chip memory and cures affected weights using one of three strategies—zero replacement, min/max value, or most‑probable value—storing curing values in radiation‑hardened memory. Experiments on Split MNIST, Split Fashion‑MNIST, Split CIFAR‑10, and Split CIFAR‑100 show that RobustNCL maintains accuracy within 0.5% of fault‑free models and significantly outperforms baseline NCL methods while adding negligible latency and energy overhead.
arXiv:2610.27621v1 Announce Type: cross Abstract: Deep learning (DL) models are increasingly being deployed into radiation-challenging environments in spacecraft, particle accelerators, and, soon, sp...
TreeFI is a value‑aware statistical fault‑injection technique for FP32 single‑bit faults in deep neural network activations and weights. It partitions each layer’s value distribution into intervals with similar expected bit‑flip behavior using regression trees, then allocates injections across these intervals based on their relevance for failure‑rate estimation. This stratified approach preserves target confidence and error margins while dramatically reducing the required injection budget—up to 72.1× for activations and 11.2× for weights compared to existing baselines.
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.
The study examines how reducing the width of deep neural networks through structured pruning affects their robustness to Single Event Upsets (SEUs). By iteratively pruning baseline models and conducting fault-injection tests at each stage, the authors find that while pruning increases per-inference sensitivity to bit-flips, the shorter execution time offsets this, lowering the overall SEU probability. Consequently, structured pruning can deliver energy and latency savings without compromising reliability for space-based AI systems.