Imagine if your machines could think and make decisions instantly without needing to send data to a far-away cloud server. It's like giving a factory robot a brain that processes information on the spot rather than waiting for instructions from a central office. This tool lets people build these smart systems easily, even if they aren't expert coders.
Industries generate too much real-time data at the edge to sustainably send to the cloud. There is a lack of easy-to-use tools for non-coders to deploy AI directly onto microcontrollers.
An end-to-end low-code edge AI platform and a methodology for rapid hardware target expansion, including VS Code plug-ins.
If you are a vehicle manufacturer dealing with massive amounts of sensor data from car fleets—this project developed a platform that enables real-time AI models on microcontrollers to optimize vehicle performance.
If you are a smart factory operator dealing with the impracticality of sending all production data to the cloud—this project developed a low-code edge analytics tool that extracts valuable insights directly on the shop floor.
If you are an embedded systems developer dealing with the slow process of adapting AI to new hardware—this project developed a methodology to quickly add new hardware targets and plug-ins for tools like VS Code.
Based on available project data, specific pricing details are not provided; however, the project was supported by a EUR 2,499,999 EU contribution to reach commercial readiness.
Yes, the platform is specifically designed to enable real-time AI and analytics models across massive fleets of edge devices and microcontrollers.
The core technology is based on extensive academic research from Uppsala and Stanford Universities, developed by co-founder Professor Emeritus Tore Risch.
The project includes plug-in technology for common development tools, specifically mentioning Microsoft's VS Code, so developers can use familiar environments.
The project period ended on 2024-11-30, with the goal of reaching a readiness level to accelerate commercialization and secure the next funding round for international scaling.
The project is led by a single Swedish SME, Stream Analyze Sweden AB, with a 100% industry ratio. The team consists of 15 experienced engineers and serial entrepreneurs, leveraging academic roots from Uppsala and Stanford Universities to drive the commercialization of the platform.
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