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 12x and 6x improvement on the CIFAR-10 and the CelebA datasets, respectively. Moreover, we propose a threshold-guided strategy that can further improve the performances by 2.69% 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. Our code is available at https://github.com/AndyCao1125/SDDPM.

翻译：脉冲神经网络（SNN）由于其二进制和生物驱动的特性，相比人工神经网络（ANN）具有超低能耗和高度生物合理性。以往研究主要聚焦于提升SNN在分类任务中的性能，而SNN的生成潜力仍相对未被充分探索。本文提出脉冲去噪扩散概率模型（SDDPM），这是一类基于SNN的新型生成模型，能够实现高样本质量。为充分利用SNN的能效优势，我们提出一种纯脉冲U-Net架构，该架构仅需4个时间步即可达到与ANN对应体相当的性能，从而显著降低能耗。大量实验结果表明，我们的方法在生成任务上达到最优水平，且大幅优于其他基于SNN的生成模型：在CIFAR-10和CelebA数据集上分别实现了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2.69%的性能。SDDPM标志着SNN生成领域的重要进展，为该领域注入了新的视角和潜在探索方向。我们的代码开源于https://github.com/AndyCao1125/SDDPM。