Smart Redundancy Schemes for ANNs against Fault Attacks
Troya Çağıl Köylü (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Said Hamdioui (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Mottaqiallah Taouil (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
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%.