Design robustness analysis of digital spiking neural circuits

This paper presents a method to evaluate the impact of process and environmental variations on the overall performance of biologically inspired spiking neural networks, implemented predominantly ind digital CMOS. In this method, transistor-level and behavioral level analysis are carried out. Then, the results of the transistor-level simulation are projected on the application layer to determine the effect of variability on the performance of the system. Monte Carlo analysis of a neuromorphic circuit in the presence of voltage, and temperature (PVT) variations is performed. The functionality of the circuit is demonstrated through a behavioral model of a neural network that implements a character recognition system. Errors are injected in the network to obtain its fault resilience characteristics. The result from PVT variations analysis is projected into behavioral model to estimate the effect of the circuit failures on the operation of the neural network. The experimental results have demonstrated the robustness of the networks with respect to the targeted variation effects.