Over the past decade, the dominance of deep learning has prevailed across various domains of artificial intelligence, including natural language processing, computer vision, and biomedical signal processing. While there have been remarkable improvements in model accuracy, deploying these models on lightweight devices, such as mobile phones and microcontrollers, is constrained by limited resources. In this survey, we provide comprehensive design guidance tailored for these devices, detailing the meticulous design of lightweight models, compression methods, and hardware acceleration strategies. The principal goal of this work is to explore methods and concepts for getting around hardware constraints without compromising the model's accuracy. Additionally, we explore two notable paths for lightweight deep learning in the future: deployment techniques for TinyML and Large Language Models. Although these paths undoubtedly have potential, they also present significant challenges, encouraging research into unexplored areas.
@article{arxiv.2404.07236,
title = {Lightweight Deep Learning for Resource-Constrained Environments: A Survey},
author = {Hou-I Liu and Marco Galindo and Hongxia Xie and Lai-Kuan Wong and Hong-Han Shuai and Yung-Hui Li and Wen-Huang Cheng},
journal= {arXiv preprint arXiv:2404.07236},
year = {2024}
}