Summary of Transformers For Green Semantic Communication: Less Energy, More Semantics, by Shubhabrata Mukherjee et al.
Transformers for Green Semantic Communication: Less Energy, More Semantics
by Shubhabrata Mukherjee, Cory Beard, Sejun Song
First submitted to arxiv on: 11 Oct 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Networking and Internet Architecture (cs.NI)
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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 In this research, the authors propose a novel multi-objective loss function called “Energy-Optimized Semantic Loss” (EOSL) to balance semantic information loss and energy consumption in semantic communication. They demonstrate that EOSL-based encoder model selection can save up to 90% of energy while achieving a 44% improvement in semantic similarity performance during inference. This work has implications for developing more efficient neural networks and greener semantic communication architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study aims to make communication faster, more efficient, and kinder to the environment. It’s like finding a way to send messages that gets the point across better, using less energy and data. The researchers created a new way to measure how well this works, called EOSL, and tested it on special kinds of computer models. They found that using EOSL can save up to 90% of energy while still getting good results. |
Keywords
* Artificial intelligence * Encoder * Inference * Loss function
