Analysis on Effects of Fault Elements in Memristive Neuromorphic Systems
This work addresses the vulnerability of memristors in neuromorphic systems, which is crucial for developing reliable hardware, but it is incremental as it focuses on analyzing fault effects rather than proposing a new solution.
The study analyzed how fault elements affect performance in memristive neuromorphic systems, finding that memristive characteristics increase vulnerability, specific fault neuron ratios cause sharp performance drops, and SA1 fault synapses can improve performance in important positions.
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.