Security Aspects of Neuromorphic MPSoCs

Abstract

Neural networks and deep learning are promising techniques for bringing brain inspired computing into embedded platforms. They pave the way to new kinds of associative memories, classifiers, data-mining, machine learning or search engines, which can be the basis of critical and sensitive applications such as autonomous driving. Emerging non-volatile memory technologies integrated in the so called Multi-Processor System-on-Chip (MPSoC) architectures enable the realization of such computational paradigms. These architectures take advantage of the Network-on-Chip concept to efficiently carry out communications with dedicated distributed memories and processing elements. However, current MPSoC-based neuromorphic architectures are deployed without taking security into account. The growing complexity and the hyper-sharing of hardware resources of MPSoCs may become a threat, thus increasing the risk of malware infections and Trojans introduced at design time. Specially, MPSoC microarchitectural side-channels and fault injection attacks can be exploited to leak sensitive information and to cause malfunctions. In this work we present three contributions to that issue: i) first analysis of security issues in MPSoC-based neuromorphic architectures; ii) discussion of the threat model of the neuromorphic architectures; ii) demonstration of the correlation between SNN input and the neural computation.