Machine learning for sensors

August 12, 2019 //By Julien Happich
Machine learning
Researchers at the Fraunhofer Institute for Microelectronic Circuits and Systems IMS have developed an artificial intelligence (AI) concept for microcontrollers and sensors that contains a completely configurable artificial neural network.

AifES (Artificial Intelligence for Embedded Systems), as it is called, 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-perfor-mance 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.

The new machine learning library, programmed in C, 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.

“We’ve reduced the source code to a minimum, which means the ANN can be trained directly on the microcontroller or the sensor, i.e. the embedded system. In addition the source code is universally valid and can be compiled for almost any platform. Because the same algorithms are always used, an ANN generated for example on a PC can easily be ported to a microcontroller. Until now this has been impossible in this form with commercially available software solutions,” says Dr. Pierre Gembaczka, research associ-ate at Fraunhofer IMS.

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