  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.
