Summary of Nvcim-pt: An Nvcim-assisted Prompt Tuning Framework For Edge Llms, by Ruiyang Qin et al.
NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs
by Ruiyang Qin, Pengyu Ren, Zheyu Yan, Liu Liu, Dancheng Liu, Amir Nassereldine, Jinjun Xiong, Kai Ni, Sharon Hu, Yiyu Shi
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Emerging Technologies (cs.ET)
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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 challenge of fine-tuning Large Language Models (LLMs) deployed on edge devices under limited resource constraints. Existing methods are not suitable for edge LLMs due to their reliance on high resources and low learning capacity. The authors propose a prompt tuning framework for edge LLMs, leveraging Non-Volatile Computing-in-Memory (NVCiM) architectures to accelerate matrix-matrix multiplication and improve performance. They introduce a novel NVCiM-assisted PT framework that narrows down the core operations to matrix-matrix multiplication, which can be accelerated by performing in-situ computation on NVCiM. This work addresses the open research question of how to address domain shift issues for edge LLMs with limited resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making it easier to fine-tune language models on devices like smartphones and tablets, where there’s not a lot of power or memory available. The current methods don’t work well for these devices because they require too much power and data. The researchers propose a new way to fine-tune the models using special computers called NVCiM that can do calculations faster and more efficiently. This will help improve the performance of language models on edge devices. |
Keywords
» Artificial intelligence » Fine tuning » Prompt
