Spiking Denoising Diffusion Probabilistic Models
This work advances energy-efficient generative AI for neuromorphic computing applications, though it is incremental as it adapts existing diffusion models to SNNs.
The paper tackles the under-explored generative potential of spiking neural networks (SNNs) by introducing Spiking Denoising Diffusion Probabilistic Models (SDDPM), which achieve state-of-the-art performance on generative tasks with up to 12x and 6x improvement on CIFAR-10 and CelebA datasets compared to other SNN-based models.
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.