Enhancing Fault Tolerance of Neural Networks for Security-Critical Applications

Manaar Alam, Arnab Bag, Debapriya Basu Roy, Dirmanto Jap, Jakub Breier, Shivam Bhasin, Debdeep Mukhopadhyay

Neural Networks (NN) have recently emerged as backbone of several sensitive applications like automobile, medical image, security, etc. NNs inherently offer Partial Fault Tolerance (PFT) in their architecture; however, the biased PFT of NNs can lead to severe consequences in applications like cryptography and security critical scenarios. In this paper, we propose a revised implementation which enhances the PFT property of NN significantly with detailed mathematical analysis. We evaluated the performance of revised NN considering both software and FPGA implementation for a cryptographic primitive like AES SBox. The results show that the PFT of NNs can be significantly increased with the proposed methodology.

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