Summary of Edge-llm: Enabling Efficient Large Language Model Adaptation on Edge Devices Via Layerwise Unified Compression and Adaptive Layer Tuning and Voting, by Zhongzhi Yu et al.
EDGE-LLM: Enabling Efficient Large Language Model Adaptation on Edge Devices via Layerwise Unified Compression and Adaptive Layer Tuning and Voting
by Zhongzhi Yu, Zheng Wang, Yuhan Li, Haoran You, Ruijie Gao, Xiaoya Zhou, Sreenidhi Reedy Bommu, Yang Katie Zhao, Yingyan Celine Lin
First submitted to arxiv on: 22 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 proposed Edge-LLM framework enables efficient adaptation of large language models (LLMs) on edge devices, crucial for continuous and privacy-preserving adaptation and inference. Existing tuning techniques are hindered by high computation and memory overheads. Edge-LLM addresses this by introducing three core components: layer-wise unified compression (LUC), adaptive layer tuning and voting scheme, and complementary hardware scheduling strategy. These innovations lead to a 2.92x speedup and 4x memory overhead reduction compared to vanilla tuning methods, while maintaining task accuracy. This framework has significant implications for applications that require real-time processing and data analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Edge-LLM helps make language models work on devices like smartphones or smart home assistants. These devices need to process lots of information quickly without taking up too much memory. The problem is that current methods take a long time and use a lot of memory. Edge-LLM solves this by using special techniques to reduce the amount of computation and memory needed. This makes it faster and more efficient. The results show that Edge-LLM can make language models work 2.92 times faster and use 4 times less memory compared to other methods, while still being accurate. |
Keywords
* Artificial intelligence * Inference
