Diffusion Models on the Edge: Challenges, Optimizations, and Applications

Fuente: arXiv
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Auteur principal: Zheng, Dongqi
Format: Preprint
Publié: 2025
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author Zheng, Dongqi
author_facet Zheng, Dongqi
contents Diffusion models have shown remarkable capabilities in generating high-fidelity data across modalities such as images, audio, and video. However, their computational intensity makes deployment on edge devices a significant challenge. This survey explores the foundational concepts of diffusion models, identifies key constraints of edge platforms, and synthesizes recent advancements in model compression, sampling efficiency, and hardware-software co-design to make diffusion models viable on edge devices. We also review promising applications and suggest future research directions.
format Preprint
id arxiv_https___arxiv_org_abs_2504_15298
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Diffusion Models on the Edge: Challenges, Optimizations, and Applications
Zheng, Dongqi
Distributed, Parallel, and Cluster Computing
Networking and Internet Architecture
Diffusion models have shown remarkable capabilities in generating high-fidelity data across modalities such as images, audio, and video. However, their computational intensity makes deployment on edge devices a significant challenge. This survey explores the foundational concepts of diffusion models, identifies key constraints of edge platforms, and synthesizes recent advancements in model compression, sampling efficiency, and hardware-software co-design to make diffusion models viable on edge devices. We also review promising applications and suggest future research directions.
title Diffusion Models on the Edge: Challenges, Optimizations, and Applications
topic Distributed, Parallel, and Cluster Computing
Networking and Internet Architecture
url https://arxiv.org/abs/2504.15298