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 numerous adaptive adversarial settings that account
for the spike-based operation dynamics of SNNs, and show that our approach
yields a scalable state-of-the-art solution for adversarially robust deep SNNs
with low-latency.

我们提出了一种通过 ANN-to-SNN 转换算法实现对深度 SNN 的鲁棒性训练的方法，该方法在后转换的鲁棒微调阶段，通过对 SNN 的层内发射阈值和突触连接权重进行敌对优化，以保持从预训练 ANN 传递的鲁棒性增益，实现了扩展性的鲁棒 SNN 训练方法。在考虑 SNN 的脉冲操作动力学的多个自适应敌对设置的实验评估中，我们的方法展示了一种具有低延迟的可扩展高鲁棒性深度 SNN 的最新解决方案。

通过转化实现对抗性鲁棒性的尖峰神经网络

Adversarially Robust Spiking Neural Networks Through Conversion

Spiking Neural Networks (SNNs) have recently emerged as a new generation of
low-power deep neural networks, which is suitable to be implemented on
low-power mobile/edge devices. As such devices have limited memory storage,
neural pruning on SNNs has been widely explored in recent years. Most existing
SNN pruning works focus on shallow SNNs (2~6 layers), however, deeper SNNs (>16
layers) are proposed by state-of-the-art SNN works, which is difficult to be
compatible with the current SNN pruning work. To scale up a pruning technique
towards deep SNNs, we investigate Lottery Ticket Hypothesis (LTH) which states
that dense networks contain smaller subnetworks (i.e., winning tickets) that
achieve comparable performance to the dense networks. Our studies on LTH reveal
that the winning tickets consistently exist in deep SNNs across various
datasets and architectures, providing up to 97% sparsity without huge
performance degradation. However, the iterative searching process of LTH brings
a huge training computational cost when combined with the multiple timesteps of
SNNs. To alleviate such heavy searching cost, we propose Early-Time (ET) ticket
where we find the important weight connectivity from a smaller number of
timesteps. The proposed ET ticket can be seamlessly combined with a common
pruning techniques for finding winning tickets, such as Iterative Magnitude
Pruning (IMP) and Early-Bird (EB) tickets. Our experiment results show that the
proposed ET ticket reduces search time by up to 38% compared to IMP or EB
methods. Code is available at Github.

该研究使用彩票票据原理，提出了一种适用于深度脉冲神经网络（SNN）的神经剪枝技术，即运用早期时间（ET）票据来减少彩票票据原理的搜索时间，该方法在不降低性能的情况下使得 SNN 的鲁棒性得到了极大提高。

探索突触传递网络中的抽奖票假设

Exploring Lottery Ticket Hypothesis in Spiking Neural Networks

Spiking Neural Networks (SNNs) are an emerging domain of biologically
inspired neural networks that have shown promise for low-power AI. A number of
methods exist for building deep SNNs, with Artificial Neural Network
(ANN)-to-SNN conversion being highly successful. MaxPooling layers in
Convolutional Neural Networks (CNNs) are an integral component to downsample
the intermediate feature maps and introduce translational invariance, but the
absence of their hardware-friendly spiking equivalents limits such CNNs'
conversion to deep SNNs. In this paper, we present two hardware-friendly
methods to implement Max-Pooling in deep SNNs, thus facilitating easy
conversion of CNNs with MaxPooling layers to SNNs. In a first, we also execute
SNNs with spiking-MaxPooling layers on Intel's Loihi neuromorphic hardware
(with MNIST, FMNIST, & CIFAR10 dataset); thus, showing the feasibility of our
approach.

本文介绍了两种硬件友好的方法来在深度脉冲神经网络中实现 Max-Pooling，并在 Intel 的 Loihi 神经形态硬件上执行 SNNs，证明了我们的方法的可行性。