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SSV CEO Klaus-Dieter Walter will explain in his presentation how machine learning can be implemented on embedded systems with the help of TinyML and what needs to be taken into account.
Countless microcontroller-based IoT condition monitoring sensor solutions in machines and plants only capture raw data of motors, bearings, fans, pumps, etc. and transfer these data to external cloud services to determine the current state of operation.
Such kind of sensor application, that use AI algorithms in the cloud generate an unnecessary huge amount of communication data overhead in internet communication and in some cases unacceptable latency problems. AI algorithms concepts, such as Supervised Machine Learning, consist of two individual functions: a learning phase for build a machine learning model and an inference phase to use this model for regression or classification.
SSV CEO Klaus-Dieter Walter shows an example of how a supervised machine learning model is created in a public cloud and then used on a microcontroller directly in the sensor to carry out state determinations in a real-time inference phase.
The presentation will take place on Nov. 16, 2022, from 10:30 a.m. to 11 a.m. at the electronica trade show in Munich, Germany.
Please find here all information about the electronica and the lecture.
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