A Radiation-Hardened, Fault-Aware Quantized Neural Network That Self-Heals
Deep learning (DL) models are increasingly being deployed into radiation-challenging environments in spacecraft, particle accelerators, and, soon, space-based data centers. Instantiated as a learned orchestration of numbers stored in persistent memory, models are susceptible to continual degradation as high-energy particles corrupt data. At inference time, new data passed through the network fundamentally break the off-line assumptions and guarantees of a static model determined during training. To preserve the quality and efficacy of the models, fault tolerance against bit flips is a clear objective. In this work we developed a runtime method for repairing the quantized weights of neural...
| Authors | Peter Xiangyuan Ma, Joshua S. Bloom |
|---|---|
| Primary category | astro-ph.IM |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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