Spiking neural networks (SNNs) have garnered considerable attention owing to their ability to run on neuromorphic devices with super-high speeds and remarkable energy efficiencies. SNNs can be used in conventional neural network-based time- and energy-consuming applications. However, research on generative models within SNNs remains limited, despite their advantages. In particular, diffusion models are a powerful class of generative models, whose image generation quality surpass that of the other generative models, such as GANs. However, diffusion models are characterized by high computational costs and long inference times owing to their iterative denoising feature. Therefore, we propose a novel approach fully spiking denoising diffusion implicit model (FSDDIM) to construct a diffusion model within SNNs and leverage the high speed and low energy consumption features of SNNs via synaptic current learning (SCL). SCL fills the gap in that diffusion models use a neural network to estimate real-valued parameters of a predefined probabilistic distribution, whereas SNNs output binary spike trains. The SCL enables us to complete the entire generative process of diffusion models exclusively using SNNs. We demonstrate that the proposed method outperforms the state-of-the-art fully spiking generative model.

翻译：脉冲神经网络（SNNs）因其能在神经形态设备上实现超高速度和显著能效而备受关注。SNNs可用于传统神经网络中耗时且耗能的应用场景。然而，尽管SNNs具有优势，但其中生成模型的研究仍然有限。特别地，扩散模型是一类强大的生成模型，其图像生成质量超越了GANs等其他生成模型。然而，扩散模型因其迭代去噪特性而具有计算成本高和推理时间长的问题。因此，我们提出了一种新颖的全脉冲去噪扩散隐式模型（FSDDIM），在SNNs中构建扩散模型，并通过突触电流学习（SCL）利用SNNs的高速低能耗特性。SCL填补了以下空白：扩散模型使用神经网络来估计预定义概率分布的实值参数，而SNNs输出二进制脉冲序列。SCL使我们能够仅使用SNNs完成扩散模型的整个生成过程。我们证明，所提出的方法优于当前最先进的全脉冲生成模型。