  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.
