Keyword Matters: Unveiling the Energy Sensitivity of On-Device LLM Prompting
How linguistic structure and prompt engineering can significantly reduce battery drain on mobile AI devices.
Large Language Models (LLMs) are increasingly deployed on mobile and embedded devices to improve privacy and reduce network latency. Yet on-device inference faces a fundamental constraint: high energy consumption on battery-powered, resource-limited hardware. While model compress...
ArXiv CS.AI
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This research reveals that the specific linguistic structure of prompts directly influences the computational load and battery drain of on-device large language models. By optimizing keyword selection, developers can improve the energy efficiency of mobile AI applications without needing complex model compression techniques.
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