Summary of Skip2-lora: a Lightweight On-device Dnn Fine-tuning Method For Low-cost Edge Devices, by Hiroki Matsutani et al.
Skip2-LoRA: A Lightweight On-device DNN Fine-tuning Method for Low-cost Edge Devices
by Hiroki Matsutani, Masaaki Kondo, Kazuki Sunaga, Radu Marculescu
First submitted to arxiv on: 28 Oct 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 A novel approach is proposed in this paper, called Skip2-LoRA, which aims to bridge the gap between pre-trained and deployed deep neural networks. The method involves inserting LoRA adapters between layers to enhance expressiveness while keeping computation costs low. This architecture leverages caching intermediate results for efficient forward passes and allows skipping seen samples as training progresses. The proposed combination of architecture and cache is tested on a $15 single board computer, demonstrating an average fine-tuning time reduction of 90.0% compared to the counterpart with similar parameters, while maintaining accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better neural networks by reducing the difference between how they work in the lab and how they are used in real life. The new method is called Skip2-LoRA and it makes neural networks more powerful without using too much computer power. This means we can train the networks faster on smaller devices, like a $15 single board computer! The results show that this approach works well, taking only a few seconds to train while keeping the same level of accuracy. |
Keywords
* Artificial intelligence * Fine tuning * Lora
