发现转换SNN时时间错位现象，提出新神经元提升能效与性能
Temporal Misalignment in ANN-SNN Conversion and Its Mitigation via Probabilistic Spiking Neurons
- 通过概率性脉冲神经元设计，缓解转换过程中的时间错位问题
- 在CIFAR、ImageNet等数据集上达到当前最优性能
- 适合关注低功耗神经网络与脉冲神经网络落地的研究者
脉冲神经网络（SNN）通过模拟生物神经机制，为应对大规模神经模型日益增长的能耗问题提供了更节能的解决方案。然而，其离散信号处理和时间动态特性使充分挖掘SNN潜力仍具挑战。目前，通过人工神经网络（ANN）到SNN的转换已成为实用路径，使SNN在复杂任务中表现接近顶尖水平。本文首次揭示了该转换框架中存在一种称为时间错位的现象：随机的脉冲重排反而可提升性能。基于此发现，我们提出双阶段概率（TPP）脉冲神经元，进一步优化转换过程。通过在CIFAR-10/100、CIFAR10-DVS及ImageNet上对多种架构的全面实验，从理论与实证两方面验证了所提方法的优势，实现当前最优结果。
原文摘要 · Abstract (English)
Spiking Neural Networks (SNNs) offer a more energy-efficient alternative to Artificial Neural Networks (ANNs) by mimicking biological neural principles, establishing them as a promising approach to mitigate the increasing energy demands of large-scale neural models. However, fully harnessing the capabilities of SNNs remains challenging due to their discrete signal processing and temporal dynamics. ANN-SNN conversion has emerged as a practical approach, enabling SNNs to achieve competitive performance on complex machine learning tasks. In this work, we identify a phenomenon in the ANN-SNN conversion framework, termed temporal misalignment, in which random spike rearrangement across SNN layers leads to performance improvements. Based on this observation, we introduce biologically plausible two-phase probabilistic (TPP) spiking neurons, further enhancing the conversion process. We demonstrate the advantages of our proposed method both theoretically and empirically through comprehensive experiments on CIFAR-10/100, CIFAR10-DVS, and ImageNet across a variety of architectures, achieving state-of-the-art results.
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