Summary of Online Resource Allocation For Edge Intelligence with Colocated Model Retraining and Inference, by Huaiguang Cai et al.
Online Resource Allocation for Edge Intelligence with Colocated Model Retraining and Inference
by Huaiguang Cai, Zhi Zhou, Qianyi Huang
First submitted to arxiv on: 25 May 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 This paper tackles the challenge of deploying AI models at the edge, where they need to adapt to changing data distributions and tasks while serving users accurately. A key issue arises when retraining models on newly arrived data, as this can degrade inference accuracy if not balanced with resource allocation. The authors propose a lightweight and explainable algorithm called ORRIC that optimizes resource allocation for adaptive model training and inference, outperforming traditional methods in competitive ratio terms. This has implications for scenarios where data drift persists over time. Notably, ORRIC can be translated into various heuristic algorithms depending on the resource environment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI models are getting smarter, but deploying them at the edge is tricky. The problem is that the data and tasks change over time, which makes it hard to keep the model accurate. One way to handle this is by retraining the model with new data, but this can be a big task that requires lots of resources. This paper introduces an algorithm called ORRIC that helps balance the need for retraining and serving users with accuracy. It’s like finding the right balance between learning new things and using what you already know. |
Keywords
» Artificial intelligence » Inference
