扩散模型神经网络加速策略综述OA

With the development of neural networks,diffusion models have achieved remarkable success in image generation tasks due to their unique diffusion mechanism.However,to achieve outstanding task performance,they introduce substantial computational overheads and complex network structures,which severely hinders their widespread application,particularly on edge devices with limited resources.High-efficiency model acceleration algorithms and hardware-software co-design frameworks for accelerators have emerged as effective solutions.Based on various diffusion model acceleration and efficient deployment strategies,an overview of state-of-the-art acceleration techniques for diffusion models is provided,covering both high-efficiency algorithmic designs for general-purpose computing platforms and hardware-software framework co-designs.