Summary of Recommending Pre-trained Models For Iot Devices, by Parth V. Patil et al.
Recommending Pre-Trained Models for IoT Devices
by Parth V. Patil, Wenxin Jiang, Huiyun Peng, Daniel Lugo, Kelechi G. Kalu, Josh LeBlanc, Lawrence Smith, Hyeonwoo Heo, Nathanael Aou, James C. Davis
First submitted to arxiv on: 25 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
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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 addresses the challenges in deploying pre-trained machine learning (ML) models on resource-constrained Internet of Things (IoT) devices. By recognizing that engineers often lack the time and resources to evaluate each model’s suitability, techniques like quantization and distillation have expanded the applicability of pre-trained models (PTMs) to IoT hardware. However, current approaches for model selection, such as LogME, LEEP, and ModelSpider, largely ignore hardware constraints, limiting their effectiveness in IoT settings. To address this limitation, the authors introduce a novel, hardware-aware method for PTM selection and propose a research agenda to guide the development of effective model recommendation systems for IoT applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making it easier to choose the right machine learning model for a job on tiny devices like those used in the Internet of Things. Right now, people have to try out many different models to see which one works best, but that takes a lot of time and resources. To make things faster and more efficient, some techniques have been developed to shrink models so they can run on smaller devices. However, these methods don’t take into account the limitations of the devices themselves, like how much memory or processing power they have. The authors of this paper want to change that by developing a new way to choose models that takes those hardware constraints into account. |
Keywords
» Artificial intelligence » Distillation » Machine learning » Quantization
