Energy per Successful Goal: Goal-Level Energy Accounting for Agentic AI Systems
A new paper introduces "Energy per Successful Goal," a method for tracking energy consumption in AI systems at the goal level. This allows for a more nuanced understanding of the energy cost associated with specific AI tasks. The paper is available on arXiv, an open-access research platform.
A new research paper titled "Energy per Successful Goal: Goal-Level Energy Accounting for Agentic AI Systems" has been published. Authored by Deepak Panigrahy and another researcher, the paper introduces a novel method for measuring energy consumption in AI systems.
This methodology focuses on "goal-level energy accounting," allowing for the tracking of energy expenditure for each successful task completed by an AI agent. This provides a more granular understanding of the energy footprint of AI, moving beyond system-wide measurements.
The paper is accessible through arXiv, a prominent open-access repository for scientific preprints. It is available in PDF format, with experimental HTML and TeX source options also provided.
arXiv is a platform dedicated to fostering open science and collaboration. It partners with initiatives like arXivLabs, which allows external collaborators to develop and integrate new features, adhering to principles of openness, community, excellence, and user data privacy.
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