New paper highlights Accuracy-Efficiency Paradox in on-device energy forecasting

A new paper introduces the Accuracy-Efficiency Paradox, which states that highly accurate energy forecasting models can paradoxically lead to a net energy deficit. This occurs due to the energy consumed during inference on edge devices and the accelerated aging of batteries. To address this, the paper proposes a Total Cost of Ownership (TCO) framework that considers both inference energy and battery degradation as forms of energy loss, aiming to minimize overall energy waste in critical edge environments. AI

IMPACT Highlights potential energy trade-offs in deploying AI models on edge devices, influencing hardware and model design choices.

RANK_REASON The cluster contains a single academic paper discussing a novel concept and framework. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• The Accuracy-Efficiency Paradox Quantifying Net Energy Loss in on-Device Energy Forecasting
• total cost of ownership

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