Improving the Fault Resilience of Neural Network Applications Through Security Mechanisms

Abstract

Numerous electronic systems store valuable intellectual property (IP) information inside non-volatile memories. In order to protect the integrity of such sensitive information from an unauthorized access or modification, encryption mechanisms are employed. From a reliability standpoint, such information can be vital to the system's functionality and thus, dedicated techniques are employed to detect possible reliability threats (e.g., transient faults in the memory content). In this paper we explore the capability of encryption mechanisms to guarantee protection from both unauthorized access and faults, while considering a Convolutional Neural Network application whose weights represent the valuable IP of the system. Experimental results show that it is possible to achieve very high fault detection rates, thus exploiting the benefits of security mechanisms for reliability purposes as well.