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

• URL: http://arxiv.org/abs/2406.16962v1
• Date: Sat, 22 Jun 2024 00:49:40 GMT
• Title: MetaGreen: Meta-Learning Inspired Transformer Selection for Green Semantic Communication
• Authors: Shubhabrata Mukherjee, Cory Beard, Sejun Song,
• Abstract summary: Energy-d Semantic Loss'' function balances semantic information loss and energy consumption. We show that EOSL-based transformer model selection up to 83% better similarity-to-power ratio (SPR) compared to BLEU score-based selection. We extend the applicability of EOSL to diverse and varying contexts, inspired by the principles of Meta-Learning.
• Score: 0.3850666668546735
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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