Energy Efficiency Evaluation of Artificial Intelligence Algorithms

Kalin Penev; Alexander Gegov; Olufemi Isiaq; Raheleh Jafari

doi:10.20944/preprints202406.1808.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 25 June 2024 / Approved: 26 June 2024 / Online: 26 June 2024 (10:04:18 CEST)
Version 2 : Received: 26 June 2024 / Approved: 27 June 2024 / Online: 27 June 2024 (11:35:36 CEST)

This article continues research efforts on performance and energy efficiency of intelligent algorithms and its role for green and sustainable computing. The introduction focusses on Bremermann's Limit and its effect on extensive approach for improvement of computers performance. The article aims to identify role of Intelligent Algorithms energy efficiency, how it differs from general software energy efficiency. An improved empirical investigation on heuristic methods for search and optimisation illustrates algorithms' energy efficiency. Experimental results and consideration of further work conclude the article.

green computing; green and sustainable software; software energy efficiency; Artificial Intelligence; Free Search; Bremermann's Limit; Planck's constant; energy efficiency; sustainability.

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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Energy Efficiency Evaluation of Artificial Intelligence Algorithms

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