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.

翻译：语义通信旨在传输有意义且有效的信息，而非聚焦于单个符号或比特。相较于传统通信，这带来了更低延迟、更少带宽占用以及更高吞吐量等优势。然而，由于需要通用指标来基准化语义信息损失与实际能耗的综合效应，语义通信面临重大挑战。本研究提出了一种名为"能量优化语义损失"（EOSL）的新型多目标损失函数，以应对平衡语义信息损失与能耗的挑战。通过在Transformer模型上进行的全面实验（包括CPU和GPU能耗分析），结果表明，基于EOSL的编码器模型选择在本实验的推理过程中可节省高达90%的能耗，同时语义相似性性能提升44%。这项工作为能效导向的神经网络选择以及更绿色语义通信架构的发展奠定了基础。