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）因其二值化与生物驱动的特性，相较于人工神经网络（ANN）具有极低能耗和高度生物可解释性。已有研究主要聚焦于提升SNN在分类任务中的性能，但其在生成任务方面的潜能尚待探索。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。为充分开发SNN的能效优势，我们设计了纯脉冲U-Net架构，仅需4个时间步即可达到与ANN架构相当的性能，显著降低了能耗。大量实验结果表明，本方法在生成任务中达到最优水平，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，可在免训练条件下进一步提升2.69%的性能。SDDPM标志着SNN生成领域的重要进展，为探索提供了新视角与潜在路径。我们的代码开源于https://github.com/AndyCao1125/SDDPM。