Spiking Diffusion

Spiking diffusion models aim to leverage the energy efficiency of spiking neural networks (SNNs) for generative tasks, particularly image generation, traditionally dominated by computationally expensive artificial neural networks (ANNs). Current research focuses on adapting diffusion model architectures, such as U-Nets and Transformers, to the SNN framework, often incorporating techniques like temporal encoding and learnable thresholds to improve performance and reduce energy consumption. This burgeoning field promises significant advancements in energy-efficient AI, potentially enabling low-power generative applications on edge devices and neuromorphic hardware.