Summary of Qute: Quantifying Uncertainty in Tinyml with Early-exit-assisted Ensembles For Model-monitoring, by Nikhil P Ghanathe and Steven J E Wilton
QUTE: Quantifying Uncertainty in TinyML with Early-exit-assisted ensembles for model-monitoring
by Nikhil P Ghanathe, Steven J E Wilton
First submitted to arxiv on: 19 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 novel architecture, QUTE, tackles the challenge of uncertainty quantification (UQ) for tinyML models deployed without access to true labels. By introducing additional output blocks at the final exit of the base network, QUTE distills early-exit knowledge into a diverse yet lightweight ensemble, delivering superior uncertainty quality on tiny models. This resource-efficient approach achieves comparable performance on larger models with 59% smaller model sizes than the closest prior work. Furthermore, when deployed on a microcontroller, QUTE demonstrates a 31% reduction in latency on average. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary QUTE is a new way to make tinyML models better at predicting how uncertain they are without needing lots of data. Right now, tinyML devices that don’t have much power or memory can’t do this well because the methods we use take up too many resources. QUTE helps by being more efficient and using less energy. It also does a great job of detecting when accuracy starts to drop off. |
