Elena-Ioana Vatajelu, CNRS/TIMA Laboratory, Grenoble
The work presented in this talk deals with the fault analysis in hardware-implemented Spiking Neural Networks with special emphasis on circuits designed to perform unsupervised, on-line learning. The talk 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 also described and different scenarios of faulty behaviors occurring after/before the STDP learning are shown. Finally, main challenges and issues related to the manufacturing testing of SNN chips will be discussed.