Nowadays, neuromorphic systems based on Spiking Neural Networks (SNNs) attract attentions of many researchers. There are many studies to improve performances of neuromorphic systems. These studies have been showing satisfactory results. To magnify performances of neuromorphic systems, developing actual neuromorphic systems is essential. For developing them, memristors play key role due to their useful characteristics. Although memristors are essential for actual neuromorphic systems, they are vulnerable to faults. However, there are few studies analyzing effects of fault elements in neuromorphic systems using memristors. To solve this problem, we analyze performance of a memristive neuromorphic system with fault elements changing fault ratios, types, and positions. We choose neurons and synapses to inject faults. We inject two types of faults to synapses: SA0 and SA1 faults. The fault synapses appear in random and important positions. Through our analysis, we discover the following four interesting points. First, memristive characteristics increase vulnerability of neuromorphic systems to fault elements. Second, fault neuron ratios reducing performance sharply exist. Third, performance degradation by fault synapses depends on fault types. Finally, SA1 fault synapses improve performance when they appear in important positions.

翻译：如今，基于脉冲神经网络（SNN）的神经形态系统吸引了众多研究者的关注。已有许多研究致力于提升神经形态系统的性能，并取得了令人满意的成果。为了进一步放大神经形态系统的性能，开发实际的神经形态系统至关重要。忆阻器因其有用特性在其中扮演关键角色。尽管忆阻器对于实际神经形态系统不可或缺，但它们易受故障影响。然而，目前针对忆阻神经形态系统中故障元件影响的研究较少。为解决这一问题，我们通过改变故障比例、类型和位置，分析了含有故障元件的忆阻神经形态系统的性能。我们选择神经元和突触作为故障注入对象，对突触注入两种类型的故障：SA0故障和SA1故障。故障突触出现在随机位置和关键位置。通过分析，我们发现了以下四个有趣现象：第一，忆阻特性增加了神经形态系统对故障元件的脆弱性；第二，存在导致性能急剧下降的故障神经元比例临界值；第三，故障突触造成的性能退化取决于故障类型；最后，当SA1故障突触出现在关键位置时，反而能提升系统性能。