AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。
--> [Submitted on 25 Jun 2026] Title:The Accuracy-Efficiency Paradox Quantifying Net Energy Loss in on-Device Energy Forecasting View a PDF of the paper titled The Accuracy-Efficiency Paradox Quantifying Net Energy Loss in on-Device Energy Forecasting, by Jaeik Jeong and 2 other authors View PDF HTML (experimental) 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. Comments: The 5th International Conference on Mobile, Military, Maritime IT Convergence (ICMIC 2026) Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.26134 [cs.AI] (or arXiv:2608.26134v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.26134 arXiv-issued DOI via DataCite Submission history From: Jaeik Jeong [view email] [v1] Thu, 25 Jun 2026 01:22:55 UTC (203 KB) Full-text links: Access Paper: View a PDF of the paper titled The Accuracy-Efficiency Paradox Quantifying Net Energy Loss in on-Device Energy Forecasting, by Jaeik Jeong and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)