Special Session: Reliability of Hardware-Implemented Spiking Neural Networks (SNN)

Abstract

The research work presented in this paper deals with the fault analysis in hardware-implemented Spiking Neural Networks with special emphasis on circuits designed to perform unsupervised, on-line learning. The paper describes the benefits of such neuromorphic systems, the possibilities of their hardware integration, but more importantly, it underlines the main concerns related to their resilience face to different types of faults. An overview of pertinent fault models and a methodology for conducting fault injection campaigns is described and different scenarios of faulty behaviors occurring after/before the STDP learning are shown.