Fault-Tolerant Security Mechanisms in Hardware Neural Networks

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Atika Nishat
Areej Mustafa

Abstract

Hardware Neural Networks (HNNs) are increasingly utilized in diverse applications, from autonomous systems to edge computing devices. However, their vulnerability to faults and security threats poses a significant challenge. This paper explores fault-tolerant security mechanisms in HNNs, focusing on techniques that ensure reliability and resilience under adversarial conditions. We analyze architectural strategies, redundancy techniques, error detection and correction systems, and emerging innovations in secure computation for HNNs. Furthermore, we address the trade-offs between performance, energy consumption, and security to guide future research in robust HNN designs.

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How to Cite
Atika Nishat, & Areej Mustafa. (2025). Fault-Tolerant Security Mechanisms in Hardware Neural Networks. Pioneer Research Journal of Computing Science, 1(2), 7–14. Retrieved from http://prjcs.com/index.php/prjcs/article/view/10

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