Adversarially Robust Spiking Neural Networks Through Conversion

arXiv:2311.09266v2 Announce Type: replace-cross
Abstract: Spiking neural networks (SNNs) provide an energy-efficient alternative to a variety of artificial neural network (ANN) based AI applications. As the progress in neuromorphic computing with SNNs expands their use in applications, the problem of adversarial robustness of SNNs becomes more pronounced. To the contrary of the widely explored end-to-end adversarial training based solutions, we address the limited progress in scalable robust SNN training methods by proposing an adversarially robust ANN-to-SNN conversion algorithm. Our method …

abstract adversarial ai applications ann applications artificial arxiv computing conversion cs.ai cs.lg cs.ne energy network networks neural network neural networks neuromorphic neuromorphic computing progress robust robustness spiking neural networks through type