Enhancing Fault Tolerance of Neural Networks for Security-Critical Applications
- Indian Institute of Technology Kharagpur（印度理工学院卡格普尔分校）
- Nanyang Technological University（南洋理工大学）
- Underwriters Laboratories（美国保险商实验室(UL)新加坡分部）
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英文摘要：
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.
