Strikersoft contributes to AI on the edge EU project
The StorAIge project has received approval with a top rating from EU body Electronic Components and Systems for European Leadership (ECSEL). Comprising a collaboration among 41 organisations from eight EU countries, the project will develop hardware and software for what is known as “AI on the edge”.
The concept involves situating AI logic locally, rather than centrally in a cloud, where it can be used easily and safely in everyday working environments without the need for a data connection.
“Relocating AI to local environments and the ability to leverage its capabilities even in places where data traffic and data security measures aren’t available is truly a big step forward,” says Strikersoft CEO Fredrik Wångberg. “Sensors that can intelligently and independently analyse measurement data from inside a car battery or from a rotor blade on a wind turbine will cause AI’s relevance and popularity to explode, and will revolutionise the basis for local decision-making,” he adds.
In addition to Strikersoft, other Swedish organisations contributing to the StorAIge project are Atlas Copco, KTH Royal Institute of Technology and Uppsala University. The project will run for three years.
“Having Strikersoft in our project team is really valuable, since they have broad interfaces with so many different industries where AI on the edge will be relevant,” explains Martin Törngren, Professor in Embedded Control Systems at KTH, and StorAIge’s Swedish project manager. “The exchange of expertise between industry and academia is important in both directions, so we look forward to collaborating with Strikersoft,” Törngren adds.
“StorAIge” is short for embedded storage elements on next MCU generation ready for AI on the edge. Co-financed by the EU, the project aims to develop hardware and software that will bring AI to local environments by situating data collection and analysis at their source. Concerning hardware, the project includes the development of next-generation chipset technology for FDSOI 28nm and a new generation of embedded Phase-Change Memory (ePCM) for handling AI on the edge’s complex AI and security algorithms locally.