MetaGreen: Meta-Learning Inspired Transformer Selection for Green Semantic Communication

TL;DR

This paper introduces EOSL, a multi-objective loss function for transformer models in semantic communication, optimizing energy use and semantic fidelity, and employs meta-learning for adaptable, energy-efficient model selection.

Contribution

The paper proposes EOSL, a novel energy-aware loss function, and demonstrates its effectiveness for transformer model selection in green semantic communication, incorporating meta-learning for adaptability.

Findings

EOSL improves similarity-to-power ratio by up to 83% over BLEU-based methods.

EOSL-based selection reduces energy consumption while maintaining semantic quality.

Meta-learning enables EOSL to adapt across diverse contexts.

Abstract

Semantic Communication can transform the way we transmit information, prioritizing meaningful and effective content over individual symbols or bits. This evolution promises significant benefits, including reduced latency, lower bandwidth usage, and higher throughput compared to traditional communication. However, the development of Semantic Communication faces a crucial challenge: the need for universal metrics to benchmark the joint effects of semantic information loss and energy consumption. This research introduces an innovative solution: the ``Energy-Optimized Semantic Loss'' (EOSL) function, a novel multi-objective loss function that effectively balances semantic information loss and energy consumption. Through comprehensive experiments on transformer models, including energy benchmarking, we demonstrate the remarkable effectiveness of EOSL-based model selection. We have established that EOSL-based transformer model selection achieves up to 83\% better similarity-to-power ratio (SPR) compared to BLEU score-based selection and 67\% better SPR compared to solely lowest power usage-based selection. Furthermore, we extend the applicability of EOSL to diverse and varying contexts, inspired by the principles of Meta-Learning. By cumulatively applying EOSL, we enable the model selection system to adapt to this change, leveraging historical EOSL values to guide the learning process. This work lays the foundation for energy-efficient model selection and the development of green semantic communication.