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Today microcontrollers can be found in almost any technical device, from washing machines to blood pressure meters and wearables. Researchers at the Fraunhofer Institute for Microelectronic Circuits and Systems IMS have developed AIfES, an artificial intelligence (AI) concept for microcontrollers and sensors that contains a completely configurable artificial neural network. AIfES is a platform-independent machine learning library which can be used to realize self-learning microelectronics requiring no connection to a cloud or to high-performance computers. The sensor-related AI system recognizes handwriting and gestures, enabling for example gesture control of input when the library is running on a wearable.
A wide variety of software solutions currently exist for machine learning, but as a rule they are only available for the PC and are based on the programming language Python. There is still no solution which makes it possible to execute and train neural networks on embedded systems such as microcontrollers. Nevertheless, it can be useful to conduct the training directly in the embedded system, for example when an implanted sensor is to calibrate itself. The vision is sensor-related AI that can be directly integrated in a sensor system. A team of researchers at Fraunhofer IMS has made this vision a reality in the form of AIfES (Artificial Intelligence for Embedded Systems), a machine learning library programmed in C that can run on microcontrollers, but also on other platforms such as PCs, Raspberry PI and Android. The library currently contains a completely configurable artificial neural network (ANN), which can also generate deep networks for deep learning when necessary. An ANN is an attempt to mathematically simulate the human brain using algorithms in order to make functional contexts learnable for the algorithms. AIfES has been optimized specifically for embedded systems.
Source - Code - Minimum - ANN
"We've reduced the source code to a minimum, which means the ANN can be trained...
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