Summary of Fl-nas: Towards Fairness Of Nas For Resource Constrained Devices Via Large Language Models, by Ruiyang Qin et al.
FL-NAS: Towards Fairness of NAS for Resource Constrained Devices via Large Language Models
by Ruiyang Qin, Yuting Hu, Zheyu Yan, Jinjun Xiong, Ahmed Abbasi, Yiyu Shi
First submitted to arxiv on: 9 Feb 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 proposed FL-NAS framework leverages large language models to automate the design of deep neural networks for various applications, particularly those constrained by limited computing resources. By considering three critical metrics – model accuracy, fairness, and hardware deployment efficiency simultaneously, FL-NAS can find high-performing DNNs that outperform state-of-the-art models by orders of magnitude across most design considerations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computers to help create new artificial intelligence (AI) systems. The goal is to make AI systems better and more efficient for things like mobile phones and other devices with limited power. The researchers are trying to find the best way to combine different types of information to get the best results. They created a new method called FL-NAS that helps them do this. |
