Fully Spiking Denoising Diffusion Implicit Models
Ryo Watanabe Yusuke Mukuta Tatsuya Harada
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.