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.
By Manali Dangarikar, Cory Merkel
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.
By Noam Bires, Marcello Traiola, Angeliki Kritikakou, Elisa Fromont
arXiv:2607. 12868v1 Announce Type: cross Abstract: Deep learning systems often fail due to subtle implementation faults that alter training behavior.
By Sigma Jahan
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.
By Alan T. L. Bacellar, Sathvik Chemudupati, Shashank Nag, Allison Seigler, Priscila M. V. Lima, Felipe M. G. Fran\c{c}a, Lizy K. John
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...
By Peter Xiangyuan Ma, Joshua S. Bloom
arXiv:2511.08092v2 Announce Type: replace-cross
Abstract: We challenge the conventional view of neural network pruning as solely a compression technique, demonstrating that one-shot magnitude pruning...
By Julian Irigoyen, Arthur S\"ohler, Andreas S{\o}eborg Kirkedal