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.
For many practical applications, we face the problem that computer vision systems must be installed in the wild, without or with a limited permanent power supply. Therefore, computationally and energy efficient solutions are needed. In particular, in this work, we show that the meaningful use of single-board computers (SBCs) can help achieve these goals. This is in line with the goals of Green AI. In particular, we show that the computer vision algorithms adopted on SBCs yield competitive results compared to high-performance computing devices. To this end, in addition to quantitative performance evaluations, we also measured and compared the power consumption of the algorithmic and technical setup used for various practical problems. These examples demonstrate the practical sustainability of SBCs. They show their performance, reduced power consumption, and lower environmental impact, while still providing real-time performance.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.