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 provides an efficient approach to embrace various computationally demanding robust learning objectives that have been proposed for ANNs. During a post-conversion robust finetuning phase, our method adversarially optimizes both layer-wise firing thresholds and synaptic connectivity weights of the SNN to maintain transferred robustness gains from the pre-trained ANN. We perform experimental evaluations in a novel setting proposed to rigorously assess the robustness of SNNs, where numerous adaptive adversarial attacks that account for the spike-based operation dynamics are considered. Results show that our approach yields a scalable state-of-the-art solution for adversarially robust deep SNNs with low-latency.

翻译：脉冲神经网络（SNN）为基于人工神经网络（ANN）的各类人工智能应用提供了一种能效更优的替代方案。随着基于SNN的神经形态计算在应用中的扩展，SNN的对抗鲁棒性问题日益凸显。与广泛研究的端到端对抗训练解决方案不同，我们通过提出一种具有对抗鲁棒性的ANN-SNN转换算法，解决了可扩展鲁棒SNN训练方法进展有限的问题。该方法为整合针对ANN提出的各类计算密集型鲁棒学习目标提供了一种高效途径。在转换后的鲁棒微调阶段，该方法通过对抗优化SNN的逐层发放阈值和突触连接权重，维持从预训练ANN迁移而来的鲁棒增益。我们在一种旨在严格评估SNN鲁棒性的新设定下进行了实验评估，该设定考虑了多种考虑脉冲操作动力学的自适应对抗攻击。结果表明，我们的方法为低延迟的深层对抗鲁棒SNN提供了可扩展的先进解决方案。