Summary of Lightweight Deep Learning For Resource-constrained Environments: a Survey, by Hou-i Liu et al.
Lightweight Deep Learning for Resource-Constrained Environments: A Survey
by Hou-I Liu, Marco Galindo, Hongxia Xie, Lai-Kuan Wong, Hong-Han Shuai, Yung-Hui Li, Wen-Huang Cheng
First submitted to arxiv on: 8 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper surveys recent advancements in artificial intelligence, focusing on designing models that can be deployed on resource-constrained devices like mobile phones and microcontrollers. Despite impressive accuracy gains, deep learning models struggle to run efficiently on these devices due to limited memory, processing power, and energy budgets. The authors provide a comprehensive guide for developing lightweight models, compressing data, and accelerating hardware to overcome these constraints without sacrificing model performance. Key topics include deployment strategies for TinyML and Large Language Models, which promise significant opportunities but also pose significant challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making artificial intelligence work on small devices like smartphones and tiny computers. Right now, these devices can’t run AI programs as well as bigger machines because they don’t have enough power or memory. The authors want to help by giving tips and ideas for creating special AI models that can fit on these devices without losing their ability to recognize things or make good decisions. They also look at two new ways of using AI on small devices: one for tiny computers and another for very large language models. These ideas are exciting, but they’re also hard problems to solve. |
Keywords
* Artificial intelligence * Deep learning
