Neuromorphic computing using analog non-volatile memory (NVM) devices has been the subject of various studies due to its potential ability to achieve extremely low power consumption less than that of traditional von Neumann architecture. However, using NVM devices, such as phase change memory (PCM) and resistive-RAM devices, presents various challenges, such as limitations in the number of conductance steps and device variability resulting from process variation and electro/thermo-dynamics. Limitations in the number of conductance steps and device variability could reduce the accuracy of neural network training. It is necessary to quantitatively analyze the effect of the number of conductance steps and synaptic device variability on the accuracy of neural network training and assess requirements for NVM devices to make NVM-based neuromorphic computing successful. We conducted the analysis using simulations focusing on a spiking neural network (SNN) based restricted Boltzmann machine (RBM) with PCM devices using the PCM-refresh method. The results of our quantitative simulation, which used the MNIST dataset, showed that having more than 500 conductance steps achieves comparable performance to that when there are more than 1000 conductance steps. We also found that less than 10% conductance update variation in the synaptic devices is required to achieve the comparable accuracy with the no variation case. These results can provide guidelines for designing and optimizing a synaptic device for realizing NVM-based neuromorphic computing.