Comparison of spiking neural networks with different topologies based on anti-disturbance ability under external noise

Guo, Lei; Liu, Dongzhao; Wu, Youxi; Xu, Guizhi

doi:10.1016/j.neucom.2023.01.085

Cited by 5 publications

(1 citation statement)

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“…Conductance drift [ 24 ] also presents a significant challenge to network training. Boybat [ 9 ] studied the conductance drift of PCM devices and demonstrated the possibility of resetting the drift history by applying partial SET pulses.…”

Section: Related Workmentioning

confidence: 99%

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

Zhang,

Sun,

Xie

et al. 2023

Advanced Intelligent Systems

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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.

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Section: Related Workmentioning

confidence: 99%