Summary of Edgerag: Online-indexed Rag For Edge Devices, by Korakit Seemakhupt et al.
EdgeRAG: Online-Indexed RAG for Edge Devices
by Korakit Seemakhupt, Sihang Liu, Samira Khan
First submitted to arxiv on: 30 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 proposes EdgeRAG, a Retrieval Augmented Generation (RAG) model designed for deployment on resource-constrained edge devices. To address the limited memory and processing power, EdgeRAG prunes embeddings within clusters and generates them on-demand during retrieval. Additionally, it pre-computes and stores embeddings for large tail clusters to reduce latency, while adaptively caching remaining embeddings to minimize redundant computations. The results show that EdgeRAG offers significant latency reduction over a baseline IVF index, while maintaining similar generation quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EdgeRAG is a new way to make computers talk like humans by using less memory and processing power. Normally, this type of technology requires a lot of resources, but EdgeRAG makes it work on small devices too. It does this by being smart about how it uses the computer’s memory and processing power. The result is that computers can understand and respond to human language faster and more efficiently. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
