让脉冲神经网络在任意推理时长下保持高精度，无需重新训练。
Efficient Logit-based Knowledge Distillation of Deep Spiking Neural Networks for Full-Range Timestep Deployment
- 基于逻辑值的新型知识蒸馏框架，利用脉冲网络时空特性。
- 跨全时长范围部署性能稳定，在多个数据集上达领先水平。
- 适合需灵活调整推理时长的边缘设备部署场景。
脉冲神经网络（SNN）作为类脑计算的新范式，因其在神经形态硬件上的潜在能效优势受到关注。然而，与传统人工神经网络相比，SNN常面临精度下降问题，且固定推理时长导致部署灵活性差，调整需重新训练。为此，本文针对SNN固有的时空特性，提出一种新的深度SNN知识蒸馏框架，可在全范围推理时长下实现性能优化而无需特定重训练，显著提升模型效能与部署适应性。我们提供了理论分析与实证验证，证明训练可确保所有隐含模型在全时长范围内的收敛性。在CIFAR-10、CIFAR-100、CIFAR10-DVS和ImageNet上的实验表明，该方法在基于蒸馏的SNN训练中达到当前最优性能。代码已开源：https://github.com/Intelli-Chip-Lab/snn_temporal_decoupling_distillation。
原文摘要 · Abstract (English)
Spiking Neural Networks (SNNs) are emerging as a brain-inspired alternative to traditional Artificial Neural Networks (ANNs), prized for their potential energy efficiency on neuromorphic hardware. Despite this, SNNs often suffer from accuracy degradation compared to ANNs and face deployment challenges due to fixed inference timesteps, which require retraining for adjustments, limiting operational flexibility. To address these issues, our work considers the spatio-temporal property inherent in SNNs, and proposes a novel distillation framework for deep SNNs that optimizes performance across full-range timesteps without specific retraining, enhancing both efficacy and deployment adaptability. We provide both theoretical analysis and empirical validations to illustrate that training guarantees the convergence of all implicit models across full-range timesteps. Experimental results on CIFAR-10, CIFAR-100, CIFAR10-DVS, and ImageNet demonstrate state-of-the-art performance among distillation-based SNNs training methods. Our code is available at https://github.com/Intelli-Chip-Lab/snn\_temporal\_decoupling\_distillation.
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