| Autor: |
Köylü, T.C., Hamdioui, S., Taouil, M. |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Proceedings of the 2022 IEEE European Test Symposium (ETS) |
| DOI: |
10.1109/ets54262.2022.9810380 |
| Popis: |
Artificial neural networks (ANNs) are used to accomplish a variety of tasks, including safety critical ones. Hence, it is important to protect them against faults that can influence decisions during operation. In this paper, we propose smart and low-cost redundancy schemes that protect the most vulnerable ANN parts against fault attacks. Experimental results show that the two proposed smart schemes perform similarly to dual modular redundancy (DMR) at a much lower cost, generally improve on the state of the art, and reach protection levels in the range of 93% to 99%. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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