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SeamAI: Seamless Integration of AI for IoT Smart Objects

Reference number
Coordinator RISE Research Institutes of Sweden AB - RISE AB - Digitala System
Funding from Vinnova SEK 7 379 631
Project duration July 2024 - June 2027
Status Ongoing
Venture Advanced digitalization - Enabling technologies
Call AI for advanced digitalization 2024

Purpose and goal

The SeamAI project aims to seamlessly integrate advanced AI into connected consumer products like robotic lawnmowers. Its key objectives are to develop AI toolchains that optimise AI deployment on resource-constrained embedded system and improve AI performance in small devices, in collaboration with Swedish industry partners. The project enhances Sweden’s leadership in AI by advancing tools and frameworks for industrial and consumer IoT applications.

Expected effects and result

The SeamAI project will create advanced AI toolchains for seamless integration in connected consumer products such as robotic lawnmowers, improving AI performance on resource-constrained embedded systems. The project will enhance the autonomy and capabilities of small devices, addressing industry needs in collaboration with Swedish partners. Expected outcomes include strengthened AI tools and frameworks, boosting Sweden´s digital innovation, industrial competitiveness, and leadership in AI and IoT applications.

Planned approach and implementation

The SeamAI project is structured into work packages focused on developing AI toolchains, optimizing AI for resource-constrained embedded systems, and enhancing performance in small devices. Collaborating with RISE, Husqvarna, ASSA ABLOY, and Antmicro, it addresses industry needs through two main use cases: improving intelligence in robotic lawnmowers with Husqvarna and developing AI solutions for smart doors with ASSA ABLOY. The project follows a phased approach from research to application, ensuring continuous improvement and knowledge sharing for the Swedish industry.

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 30 September 2024

Reference number 2024-01422