Transformers for Green Semantic Communication: Less Energy, More Semantics
This work addresses the problem of energy efficiency in semantic communication systems, which is crucial for developing greener communication architectures, though it appears incremental as it builds on existing transformer models with a novel loss function.
The paper tackles the challenge of balancing semantic information loss and energy consumption in semantic communication by introducing a multi-objective loss function called Energy-Optimized Semantic Loss (EOSL). It demonstrates that using EOSL for encoder model selection can save up to 90% of energy while improving semantic similarity performance by 44% during inference.
Semantic communication aims to transmit meaningful and effective information, rather than focusing on individual symbols or bits. This results in benefits like reduced latency, bandwidth usage, and higher throughput compared with traditional communication. However, semantic communication poses significant challenges due to the need for universal metrics to benchmark the joint effects of semantic information loss and practical energy consumption. This research presents a novel multi-objective loss function named "Energy-Optimized Semantic Loss" (EOSL), addressing the challenge of balancing semantic information loss and energy consumption. Through comprehensive experiments on transformer models, including CPU and GPU energy usage, it is demonstrated that EOSL-based encoder model selection can save up to 90% of energy while achieving a 44% improvement in semantic similarity performance during inference in this experiment. This work paves the way for energy-efficient neural network selection and the development of greener semantic communication architectures.