Role of synaptic variability in spike-based neuromorphic circuits with unsupervised learning

Resistive Random Access Memory (RRAM)-based artificial Neural Networks (NNs) have been shown to be intrinsically robust to RRAM variability but no study has been done to clearly explain and quantify this robustness. In this paper, we fully characterize a 4kbit RRAM array under different programming conditions. The impact of the electrical characteristics of RRAM (resistance variability, memory window, endurance performance) on the detection rate of a NN designed for object tracking and trained with a stochastic Spike-Timing Dependent Plasticity (STDP) rule is studied. We introduce a new parameter called the Synaptic Window (SW), defined as the ratio between the arithmetic mean conductance values of the low and high resistance distributions. The network performance was found only to be sensitive to the value of the SW (a SW>100 is required to achieve the maximum NN performance). Moreover, we demonstrate that a high resistance variability increases the SW for a given window margin.