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

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.