Summary of Performance Control in Early Exiting to Deploy Large Models at the Same Cost Of Smaller Ones, by Mehrnaz Mofakhami et al.
Performance Control in Early Exiting to Deploy Large Models at the Same Cost of Smaller Ones
by Mehrnaz Mofakhami, Reza Bayat, Ioannis Mitliagkas, Joao Monteiro, Valentina Zantedeschi
First submitted to arxiv on: 26 Dec 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 This study explores Early Exiting (EE), a technique for speeding up inference by adapting compute resources to data points based on their difficulty. The approach enables predictions to exit at earlier layers for simpler samples, reserving more computation for challenging ones. The authors present a novel perspective on EE, showing that larger models deployed with EE can achieve higher performance than smaller models while maintaining similar computational costs. To improve the controllability of the compute-performance trade-off, they introduce Performance Control Early Exiting (PCEE), a method that bases decisions on average accuracy rather than confidence levels. Experiments demonstrate PCEE’s effectiveness in providing better control over performance and allowing for scalable model sizes to yield performance gains while reducing computational cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about making computers think faster by using something called Early Exiting. It’s like having a superpower that helps the computer focus on hard problems and ignore easy ones. The researchers found that bigger models with this power can do better than smaller ones, but use just as much energy. To make it even better, they created a new way to control how accurate the computer is, called Performance Control Early Exiting. This allows computers to be more precise and efficient, and even work with bigger models. |
Keywords
» Artificial intelligence » Inference
