Summary of Efficient Deep Learning Infrastructures For Embedded Computing Systems: a Comprehensive Survey and Future Envision, by Xiangzhong Luo et al.
Efficient Deep Learning Infrastructures for Embedded Computing Systems: A Comprehensive Survey and Future Envision
by Xiangzhong Luo, Di Liu, Hao Kong, Shuo Huai, Hui Chen, Guochu Xiong, Weichen Liu
First submitted to arxiv on: 3 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper discusses the challenges of deploying powerful deep neural networks (DNNs) on resource-constrained embedded computing systems. Despite achieving impressive accuracy, DNNs are becoming increasingly computationally expensive to train and infer. This gap makes it difficult to deploy DNNs on embedded systems for ubiquitous embedded intelligence. The authors focus on efficient deep learning infrastructures for embedded computing systems, covering manual and automated network design, compression, on-device learning, large language models, software and hardware, and intelligent applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how deep neural networks are getting better at doing things like recognizing pictures and understanding language. But these powerful networks need a lot of computer power to work well. This is a problem because many computers used in real-life situations don’t have enough power for these networks. The authors look at ways to make it easier to use powerful networks on smaller computers, like designing networks that are more efficient or using special software and hardware. |
Keywords
* Artificial intelligence * Deep learning
