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arXiv AI

TreeFI: Value-Aware Statistical Fault Injection for Deep Neural Networks

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.

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arXiv Machine Learning
Oct 5

Exploring the Trade-Off Between Structured Pruning and Fault Tolerance in Deep Neural Networks for Space Applications

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.

By Toon Vinck, Na\"in Jonckers, Jaro De Roose, Jeffrey Prinzie, Peter Karsmakers
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arXiv AI
Jul 7

From Arithmetic to Logic: The Resilience of Logic and Lookup-Based Neural Networks Under Parameter Bit-Flips

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
efficiencybenchmarkssafety
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arXiv AI
Sep 17

REQAP: Resilient Weight Packing and Quantization for Edge DNN Acceleration

The paper introduces REQAP, a reliability‑aware quantized weight packing technique for systolic‑array DNN accelerators. It uses a sensitivity‑driven mixed‑precision quantization to assign layer‑wise bit‑widths, a deterministic register‑level packing strategy for SIMD‑within‑a‑register execution, and selective bit‑level protection that replicates critical MSBs into unused register space. Experiments on AlexNet, VGG‑11, and ResNet‑18 show up to 62% memory reduction, 56% fewer MAC operations, and improved accuracy resilience under fault injection compared to baseline and fully protected models.

By Mahdi Taheri, Samira Nazari, Mubassher Ansari, Ali Azarpeyvand, Mohsen Afsharchi, Maksim Jenihhin, Christian Herglotz
efficiency
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arXiv Machine Learning
Aug 6

Understanding Fault Tolerance of Adversarially Robust Pruned Models

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
efficiencysafety
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