突发脉冲神经网络
Burst Spiking Neural Networks
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中文总结 AI 辅助
研究SNN的准确性 - 鲁棒性问题，提出基于突发增强脉冲神经元和动态权重约束机制的BuSNN，通过理论分析和实验表明其在准确性、鲁棒性及低功耗方面优势显著，推进了SNN在相关应用中的可行性。
中文摘要 AI 辅助
当前脉冲神经网络（SNN）研究的一个核心目标是提高其准确性以成为人工神经网络（ANN）的低功耗替代方案。本文认为实现这一目标不仅需要提高准确性，还需增强鲁棒性，即输入受扰时保持正确预测的能力。现有SNN方法存在削弱鲁棒性的两个关键问题：二元脉冲激活在小扰动下会产生大的激活状态变化；缺乏有效的权重约束使网络输出对输入变化更敏感。为此，提出基于突发增强脉冲神经元（BSN）和动态权重约束（DWC）机制的突发脉冲神经网络（BuSNN）。BSN通过突发发放提供分级脉冲模式，减轻扰动引起的激活状态转变，增强鲁棒性。DWC根据激活状态惩罚连接权重，有效降低权重大小，在保持准确性的同时提高鲁棒性。理论分析支持了这些鲁棒性效果。实验结果表明：在CIFAR - 10等较小规模基准测试中，BuSNN在准确性和鲁棒性上优于SNN和ANN；在大规模ImageNet上，具有MS ResNet - 34骨干的BuSNN在top - 1准确性和抗干扰鲁棒性上分别比相应SNN基线提高3.18%和2.66%，超越4位激活量化的ANN基线并接近8位ANN基线，还保留了SNN的低功耗优势。该研究推进了SNN在鲁棒和节能应用中的实际可行性。
英文摘要
A central goal of current Spiking Neural Network (SNN) research is to improve their accuracy toward becoming low-power alternatives to Artificial Neural Networks (ANNs). This work further argues that realizing this ambition requires improving not only accuracy but also robustness, defined as the ability to maintain correct predictions under input perturbations. We identify two key issues in existing SNN methods that undermine robustness. First, binary spiking activations can produce large activation-state changes under small perturbations. Second, the lack of effective weight constraints makes network outputs more sensitive to input variations. To this end, we propose Burst Spiking Neural Networks (BuSNNs), built upon Burst-enhanced Spiking Neurons (BSNs) and a Dynamic Weight Constraint (DWC) mechanism. BSNs incorporate burst firing to provide a graded spiking pattern. This spiking mechanism mitigates perturbation-induced transitions in activation states and thereby enhances robustness. DWC penalizes connection weights based on activation states, effectively reducing weight magnitudes and improving robustness while preserving accuracy. We provide theoretical analyses to support these robustness effects. Experimental results further show that, on smaller-scale benchmarks such as CIFAR-10, BuSNNs outperform both SNN and ANN counterparts in accuracy and robustness. On large-scale ImageNet, BuSNN with the MS ResNet-34 backbone further improves top-1 accuracy and corruption robustness over the corresponding SNN baseline by 3.18% and 2.66%, respectively. Despite using spike-based activations, BuSNNs surpass 4-bit activation-quantized ANN baselines and approach 8-bit ANN baselines on ImageNet. They also preserve SNNs' low-power advantage. This work studies the accuracy-robustness problem in SNNs, advancing their practical viability in robust and energy-efficient applications.
发表机构
- Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
- School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
- State Key Laboratory of Media Convergence and Communication, Communication University of China（中国传媒大学媒体融合与传播国家重点实验室）
- School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）
- Peng Cheng Laboratory（鹏城实验室）
- Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）
机构由 AI 辅助整理，请以论文原文为准。
