Summary of Goal-oriented Communications Based on Recursive Early Exit Neural Networks, by Jary Pomponi et al.
Goal-oriented Communications based on Recursive Early Exit Neural Networks
by Jary Pomponi, Mattia Merluzzi, Alessio Devoto, Mateus Pontes Mota, Paolo Di Lorenzo, Simone Scardapane
First submitted to arxiv on: 27 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 This novel framework for goal-oriented semantic communications leverages recursive early exit models to dynamically partition computations and offload samples to a server based on layer-wise prediction dynamics. The approach is built on two key components: an innovative early exit strategy that detects samples where confidence is not increasing fast enough, and a Reinforcement Learning-based online optimization framework that jointly determines early exit points, computation splitting, and offloading strategies while accounting for wireless conditions, inference accuracy, and resource costs. Numerical evaluations in an edge inference scenario demonstrate the method’s adaptability and effectiveness in striking an excellent trade-off between performance, latency, and resource efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows a new way to improve communication by using recursive early exit models. It has two main parts: a strategy that decides when to stop computing on samples where confidence is not increasing fast enough, and an optimization framework that figures out the best places to split computations and offload samples to a server while considering wireless conditions, accuracy, and resource usage. The results show that this approach works well in edge inference scenarios, balancing performance, latency, and resource efficiency. |
Keywords
» Artificial intelligence » Inference » Optimization » Reinforcement learning
