Advancing green computer vision: principles and practices for sustainable development for real-time computer vision applications

Mark A. M. Kramer; Peter M. Roth

doi:10.1117/12.3025256

20 June 2024 Advancing green computer vision: principles and practices for sustainable development for real-time computer vision applications

Mark A. M. Kramer, Peter M. Roth

Author Affiliations +

Proceedings Volume 13000, Real-time Processing of Image, Depth, and Video Information 2024; 1300004 (2024) https://doi.org/10.1117/12.3025256
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

Recent algorithmic developments, specifically in deep learning, have propelled computer vision forward for practical applications. However, the high computational complexity and the resulting power consumption are often overlooked issues. This is not only a problem if the systems need to be installed in the wild, where often only a limited electricity supply is available, but also in the context of high energy consumption. To address both aspects, we explore the intersection of green artificial intelligence and real-time computer vision, focusing on the use of single-board computers. To this end, we need to take into account the limitations of single-board computers, including limited processing power and storage capacity, and demonstrate how the algorithm and data optimization ensure high-quality results, however, at a drastically reduced computational effort. Energy efficiency can be increased, aligning with the goals of Green AI and making such systems less dependent on a permanent electrical power supply.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mark A. M. Kramer and Peter M. Roth "Advancing green computer vision: principles and practices for sustainable development for real-time computer vision applications", Proc. SPIE 13000, Real-time Processing of Image, Depth, and Video Information 2024, 1300004 (20 June 2024); https://doi.org/10.1117/12.3025256

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