Summary of Etuner: a Redundancy-aware Framework For Efficient Continual Learning Application on Edge Devices, by Sheng Li et al.
etuner: A Redundancy-Aware Framework for Efficient Continual Learning Application on Edge Devices
by Sheng Li, Geng Yuan, Yawen Wu, Yue Dai, Tianyu Wang, Chao Wu, Alex K. Jones, Jingtong Hu, Yanzhi Wang, Xulong Tang
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Distributed, Parallel, and Cluster Computing (cs.DC)
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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 proposes ETuner, an efficient edge continual learning framework that optimizes inference accuracy, fine-tuning execution time, and energy efficiency for deploying deep neural networks (DNNs) on edge devices. The proposed framework addresses the challenges of handling streaming-in inference requests and adapting deployed models to changing deployment scenarios. ETuner achieves this through inter-tuning and intra-tuning optimizations, reducing overall fine-tuning execution time by 64%, energy consumption by 56%, and improving average inference accuracy by 1.75% compared to immediate model fine-tuning approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a world where robots can help take care of elderly people and recognize objects more accurately. To make this happen, special computer models called deep neural networks (DNNs) need to be used on devices like smartphones or smartwatches. These devices have limited power and memory, so the DNNs need to be fine-tuned to work well in different situations. The problem is that current methods can take a long time and use too much energy. In this paper, researchers propose a new way called ETuner that makes these models more efficient while still keeping them accurate. This means robots can help people better and devices can run longer on their batteries. |
Keywords
* Artificial intelligence * Continual learning * Fine tuning * Inference
