AIMSM - A Mechanism to Optimize Systems with Multiple AI Models: A Case Study in Computer Vision for Autonomous Mobile Robots

• Type: Article in International Conference Proceedings Book
• Date: 2024

Authors

Ferreira, BG (Author) Other; Armando Jorge Sousa (Author) FEUP; reis, lp (Author) FEUP; António Augusto de Sousa (Author) FEUP; Rui Rodrigues (Author) FEUP; Rosaldo J. F. Rossetti (Author) FEUP

Conference proceedings International

Title: Progress in Artificial Intelligence - 23rd EPIA Conference on Artificial Intelligence, EPIA 2024, Viana do Castelo, Portugal, September 3-6, 2024, Proceedings, Part III

Pages: 53-64

23rd EPIA Conference on Artificial Intelligence, EPIA 2024

Viana do Castelo, 3 September 2024 through 6 September 2024

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-017-CNF

DOI: 10.1007/978-3-031-73503-5_5

Abstract (EN): This article proposes the Artificial Intelligence Models Switching Mechanism (AIMSM), a novel approach to optimize system resource utilization by allowing systems to switch AI models during runtime in dynamic environments. Many real-world applications utilize multiple data sources and various AI models for different purposes. In many of those applications, every AI model doesn¿t have to operate all the time. The AIMSM strategically allows the system to activate and deactivate these models, focusing on system resource optimization. The switching of each AI model can be based on any information, such as context or previous results. In the case study of an autonomous mobile robot performing computer vision tasks, the AIMSM helps the system to achieve a significant increment in performance, with a 50% average increase in frames per second (FPS) rate, for this specific case study, assuming that no erroneous switching occurred. Experimental results have demonstrated that the AIMSM can improve system resource utilization efficiency when properly implemented, optimize overall resource consumption, and enhance system performance. The AIMSM presented itself as a better alternative to permanently loading all the models simultaneously, improving the adaptability and functionality of the systems. It is expected that using the AIMSM will yield a performance improvement that is particularly relevant to systems with multiple AI models of a complex nature, where such models do not need to be all continuously executed or systems that will benefit from lower resource usage. Code is available at https://github.com/BrunoGeorgevich/AIMSM. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11