Enhancing Robustness of Memristor Crossbar‐Based Spiking Neural Networks against Nonidealities: A Hybrid Approach for Neuromorphic Computing in Noisy Environments

Abstract

Memristor crossbar‐based spiking neural networks (SNNs) face challenges caused by nonidealities associated with their hardware‐based neurons and synapses. The key nonidealities include electric‐field noise, conductance noise, and conductance drift. This study investigates the robustness of fully connected, convolutional, residual, and spike‐timing‐dependent plasticity‐based SNNs against hardware nonidealities using the MNIST, Fashion MNIST, and CIFAR10 datasets. In response to these challenges, a novel hybrid residual SNN (HRSNN) is proposed that incorporates a new neuron circuit and a weight‐dependent loss function. The HRSNN in a high‐intensity noise environment is evaluated using the neuromorphic DVS128 Gesture dataset. The achieved accuracy rate of 92.71% is only 2.15% lower than that of the noise‐free environment. These results demonstrate the robustness of the proposed HRSNN under high‐intensity noise conditions and present new possibilities for the advancement of neuromorphic computing in noisy environments.