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Spiking neural networks (SNNs) have garnered considerable attention owing totheir ability to run on neuromorphic devices with super-high speeds andremarkable energy efficiencies. SNNs can be used in conventional neuralnetwork-based time- and energy-consuming applications. However, research ongenerative models within SNNs remains limited, despite their advantages. Inparticular, diffusion models are a powerful class of generative models, whoseimage generation quality surpass that of the other generative models, such asGANs. However, diffusion models are characterized by high computational costsand 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 speedand low energy consumption features of SNNs via synaptic current learning(SCL). SCL fills the gap in that diffusion models use a neural network toestimate real-valued parameters of a predefined probabilistic distribution,whereas SNNs output binary spike trains. The SCL enables us to complete theentire generative process of diffusion models exclusively using SNNs. Wedemonstrate that the proposed method outperforms the state-of-the-art fullyspiking generative model.

Fully Spiking Denoising Diffusion Implicit Models