The Accuracy-Efficiency Paradox Quantifying Net Energy Loss in on-Device Energy Forecasting

A new arXiv paper (2608.26134v1) identifies the Accuracy-Efficiency Paradox, showing that high-precision energy forecasting models can cause a net energy deficit in on-device edge environments, including military systems, due to inference energy consumption and battery aging. The authors propose a Total Cost of Ownership (TCO) framework that treats battery aging as energy loss, demonstrating that in thermally sensitive settings, the energy saved by complex models' accuracy is often outweighed by their operational energy costs.

arXiv:2608.26134v1 Announce Type: new Abstract: 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.