Smart Redundancy Schemes for ANNs against Fault Attacks

Conference paper (2022)

Authors

T.C. Köylü Computer Engineering - EEMCS
S. Hamdioui Quantum & Computer Engineering
M. Taouil Computer Engineering - EEMCS
Research Group
Computer Engineering (EEMCS) (TU Delft)
Copyright: © 2022 T.C. Köylü, S. Hamdioui, M. Taouil
DOI: https://doi.org/10.1109/ETS54262.2022.9810380
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Published Date

2022

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Quantum & Computer Engineering

Research Group

Computer Engineering

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%.

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