Summary of Adascale: Dynamic Context-aware Dnn Scaling Via Automated Adaptation Loop on Mobile Devices, by Yuzhan Wang et al.
AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices
by Yuzhan Wang, Sicong Liu, Bin Guo, Boqi Zhang, Ke Ma, Yasan Ding, Hao Luo, Yao Li, Zhiwen Yu
First submitted to arxiv on: 1 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 introduces AdaScale, an elastic inference framework that automates the adaptation of deep neural networks (DNNs) to dynamic mobile device contexts. By leveraging a self-evolutionary model, diverse compression operator combinations, and resource availability awareness, AdaScale streamlines network creation, reduces training overhead, and optimizes performance for varied devices. The framework demonstrates significant enhancements in accuracy, latency, energy efficiency, and training speed compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making deep learning work better on smartphones and other devices that don’t have a lot of power or memory. It’s hard to make these devices use deep learning because they need different things like quick results, good accuracy, and low energy use. The solution is called AdaScale, which helps adapt deep models to the changing conditions of mobile devices. This makes it better for users and also saves time and energy. |
Keywords
» Artificial intelligence » Deep learning » Inference
