高精度模型反致能耗亏损，新框架统一计算推理与电池损耗。
The Accuracy-Efficiency Paradox Quantifying Net Energy Loss in on-Device Energy Forecasting
- 构建总拥有成本框架，统一度量推理能耗与电池老化损耗。
- 复杂模型因高算力消耗，净能耗反而高于低精度模型。
- 适用于军事等对能效敏感的边缘计算场景。
能源预测旨在通过提升精度来减少能源浪费，以实现能效最大化，这一目标在军事等关键边缘环境中同样重要。然而本文揭示了‘精度-效率悖论’：高精度能源预测模型可能反而引发净能量亏空，根源在于边缘AI的推理能耗和电池老化。为此，我们提出一种总拥有成本（TCO）框架，将推理能耗与电池老化统一视为能量损失——后者代表系统未来储电能力的物理耗散。实验表明，在热敏感边缘环境，复杂架构虽预测精度更高，但其高运行强度导致的总能耗损失往往超过节能收益。
原文摘要 · Abstract (English)
Energy forecasting aims to maximize accuracy to ensure energy efficiency by reducing energy waste, an objective that applies equally to on-device forecasting for mission-critical edge environments, including military systems. However, this paper identifies the Accuracy-Efficiency Paradox: high-precision energy forecasting models can ironically trigger a net energy deficit. This stems from both edge AI's inference energy consumption and battery aging. We propose a Total Cost of Ownership (TCO) framework for energy forecasting, designed to minimize net energy loss. This framework treats not only inference energy consumption but also battery aging as a unified form of energy loss, as degradation represents a physical dissipation of the system's future energy-carrying capacity. We demonstrate that in thermally sensitive edge environments, energy saved by the superior precision of complex architectures is often outweighed by the total energy lost through their high operational intensity.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。