Spiking neural networks (SNNs) have ultra-low energy consumption and high biological plausibility due to their binary and bio-driven nature compared with artificial neural networks (ANNs). While previous research has primarily focused on enhancing the performance of SNNs in classification tasks, the generative potential of SNNs remains relatively unexplored. In our paper, we put forward Spiking Denoising Diffusion Probabilistic Models (SDDPM), a new class of SNN-based generative models that achieve high sample quality. To fully exploit the energy efficiency of SNNs, we propose a purely Spiking U-Net architecture, which achieves comparable performance to its ANN counterpart using only 4 time steps, resulting in significantly reduced energy consumption. Extensive experimental results reveal that our approach achieves state-of-the-art on the generative tasks and substantially outperforms other SNN-based generative models, achieving up to $12\times$ and $6\times$ improvement on the CIFAR-10 and the CelebA datasets, respectively. Moreover, we propose a threshold-guided strategy that can further improve the performances by 16.7% in a training-free manner. The SDDPM symbolizes a significant advancement in the field of SNN generation, injecting new perspectives and potential avenues of exploration.

翻译：尖峰神经网络（SNN）相比人工神经网络（ANN）具有超低能耗和高度生物可解释性，这得益于其二进制及生物驱动的特性。尽管先前研究主要聚焦于提升SNN在分类任务中的性能，但其生成潜力仍相对未得到探索。本文提出尖峰去噪扩散概率模型（SDDPM），这是一类基于SNN的新型生成模型，能够实现高样本质量。为充分挖掘SNN的能效优势，我们提出一种纯尖峰U-Net架构，该架构仅需4个时间步即可达到与其ANN对应模型相当的性能，显著降低能耗。广泛实验结果表明，本方法在生成任务上达到最优水平，并大幅超越其他基于SNN的生成模型，在CIFAR-10和CelebA数据集上分别实现高达$12$倍和$6$倍的性能提升。此外，我们提出一种阈值引导策略，可在无需训练的情况下进一步提升16.7%的性能。SDDPM标志着SNN生成领域的重大进展，为该领域注入新视角与潜在探索方向。