New possibilities arising with AIfES
Fraunhofer IMS has developed AIfES (Artificial Intelligence for Embedded Systems), a platform-independent and constantly growing machine learning library in the programming language C, which implies a fully configurable feedforward artificial neural network (ANN). AIfES uses standard libraries based on the GNU Compiler Collection (GCC). The program source code is reduced to a minimum, thus even the integration on a microcontroller including learning algorithms is possible.
Intelligent microelectronics and sensors
AIfES offers AI to run on small intelligent microelectronics and sensors, independent on connectivity towards a cloud or a powerful and resource-hungry processing entity while providing full AI mechanism like independent learning. This opens the door for many new applications, starting from real-time evaluation of sensor data, calibration of sensors towards recognition of pattern and their classification. On top, the development of virtual sensors becomes possible by simulating dependency of deputy measuring sizes to a new target size.
An embedded system with different tasks is possible
The construction of AIfES functions inside the library enables for example the calculation of an ANN including its parameterization with only one function call. This structure allows an embedded system to be completely reconfigured to perform a totally different task afterwards. This is realized by exchanging the weights of the ANN and - if necessary - a change of the network structure is possible. The same AIfES functions are still available and only the transferred parameters are altered.
Because processing can take place offline on the device, no sensitive data needs to be transferred.
AIfES offers a decentralization of the processing power, for example by small intelligent embedded systems that processes the data and provides calculated results to the next higher entity to avoid raw data overloading of the whole system. As a result, the system transferred amount of data is significantly reduced and the system can act faster and more efficient as a whole.
Finally it is possible to build a network from small and adaptive systems that share tasks among themselves.