Semantic communication aims to transmit meaningful and effective information rather than focusing on individual symbols or bits, resulting in benefits like reduced latency, bandwidth usage, and higher throughput compared to traditional communication. However, semantic communication poses significant challenges due to the need for universal metrics for benchmarking 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，而非关注单个符号或比特，与传统通信相比，可降低延迟、减少带宽消耗并提升吞吐量。然而，由于需要通用指标来权衡语义信息损失与实际能耗的联合影响，语义通信面临重大挑战。本研究提出一种名为“能量优化语义损失”（EOSL）的新型多目标损失函数，以解决语义信息损失与能耗之间的平衡难题。通过在Transformer模型（涵盖CPU与GPU能耗）上的全面实验证明，基于EOSL的编码器模型选择可在推理过程中节省高达90%的能耗，同时将语义相似度性能提升44%。这一工作为能效导向的神经网络选择及更绿色的语义通信架构开发奠定了基础。