# Lightweight Deep Learning for Resource-Constrained Environments: A Survey

**Authors:** Hou-I Liu, Marco Galindo, Hongxia Xie, Lai-Kuan Wong, Hong-Han Shuai, Yung-Yui Li, Wen-Huang Cheng  
**arXiv:** [2404.07236](https://arxiv.org/abs/2404.07236)  
**Date:** 2024-04-08  
**Summary page:** https://www.summarizepaper.com/en/paper/2404.07236v1/

## Key points


## Summary

In their comprehensive survey titled "Lightweight Deep Learning for Resource-Constrained Environments," authors Hou-I Liu, Marco Galindo, Hongxia Xie, Lai-Kuan Wong, Hong-Han Shuai, Yung-Yui Li, and Wen-Huang Cheng delve into the prevailing dominance of deep learning in various artificial intelligence domains over the past decade. They highlight the remarkable advancements in model accuracy but also point out the challenges of deploying these models on lightweight devices like mobile phones and microcontrollers due to limited resources. The authors provide detailed design guidance tailored specifically for such resource-constrained devices. This includes a meticulous exploration of lightweight model design, compression methods, and hardware acceleration strategies. Their primary objective is to find innovative methods and concepts that can overcome hardware limitations without compromising model accuracy. Furthermore, the survey delves into two significant paths for the future of lightweight deep learning: deployment techniques for TinyML and Large Language Models. While these paths show immense potential, they also come with substantial challenges that necessitate further research in unexplored areas. Overall, for researchers and practitioners looking to navigate the complexities of deploying deep learning models on lightweight devices efficiently while maintaining high levels of accuracy.