Summary of Temporal Decisions: Leveraging Temporal Correlation For Efficient Decisions in Early Exit Neural Networks, by Max Sponner and Lorenzo Servadei and Bernd Waschneck and Robert Wille and Akash Kumar
Temporal Decisions: Leveraging Temporal Correlation for Efficient Decisions in Early Exit Neural Networks
by Max Sponner, Lorenzo Servadei, Bernd Waschneck, Robert Wille, Akash Kumar
First submitted to arxiv on: 12 Mar 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 The paper explores the challenges of deploying deep learning models on embedded devices with limited resources, which can impact inference accuracy and latency. Early Exit Neural Networks are proposed as a potential solution, adjusting model depth dynamically through additional classifiers. The real-time termination decision mechanism is crucial for system efficiency, latency, and sustained accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to use deep learning on small devices like those in the Internet of Things. Right now, this is hard because these devices don’t have enough resources, which can make the model’s predictions less accurate or slower. To fix this, the authors suggest using Early Exit Neural Networks that adjust their complexity based on what they’re doing. The key to making this work is deciding when to stop using a model and move on. |
Keywords
* Artificial intelligence * Deep learning * Inference
