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)
Version 3
: Received: 13 September 2024 / Approved: 13 September 2024 / Online: 13 September 2024 (16:55:04 CEST)
How to cite:
Penev, K.; Gegov, A.; Isiaq, O.; Jafari, R. Energy Efficiency Evaluation of Artificial Intelligence Algorithms. Preprints2024, 2024061808. https://doi.org/10.20944/preprints202406.1808.v3
Penev, K.; Gegov, A.; Isiaq, O.; Jafari, R. Energy Efficiency Evaluation of Artificial Intelligence Algorithms. Preprints 2024, 2024061808. https://doi.org/10.20944/preprints202406.1808.v3
Penev, K.; Gegov, A.; Isiaq, O.; Jafari, R. Energy Efficiency Evaluation of Artificial Intelligence Algorithms. Preprints2024, 2024061808. https://doi.org/10.20944/preprints202406.1808.v3
APA Style
Penev, K., Gegov, A., Isiaq, O., & Jafari, R. (2024). Energy Efficiency Evaluation of Artificial Intelligence Algorithms. Preprints. https://doi.org/10.20944/preprints202406.1808.v3
Chicago/Turabian Style
Penev, K., Olufemi Isiaq and Raheleh Jafari. 2024 "Energy Efficiency Evaluation of Artificial Intelligence Algorithms" Preprints. https://doi.org/10.20944/preprints202406.1808.v3
Abstract
Abstract: This article advances the discourse on sustainable and energy-efficient software by examining the performance and energy efficiency of intelligent algorithms within the framework of green and sustainable computing. Building on previous research, it explores the theoretical implications of Bremermann's Limit on efforts to enhance computer performance through more extensive methods. The study presents an empirical investigation into heuristic methods for search and optimisation, demonstrating the energy efficiency of various algorithms in both simple and complex tasks. It also identifies key factors influencing the energy consumption of algorithms and their potential impact on computational processes. Furthermore, the article discusses cognitive concepts and their interplay with computational intelligence, highlighting the role of cognition in the evolution of intelligent algorithms. The conclusion offers insights into the future directions of research in this area, emphasising the need for continued exploration of energy-efficient computing methodologies.
Keywords
green computing; software energy efficiency; sustainable and responsible artificial intelligence; Free Search; Bremermann's limit.
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
