TinyML

Tiny Machine Learning (TinyML) is a research area in machine learning and describes the optimization as well as execution of AI-based processing chains on embedded systems.  

Our embeddif.[ai] technology is an established brand for TinyML. Here you can find several use cases for embeddif.[ai].

For us, artificial intelligence (AI) is not only the use of deep neural networks with millions of parameters, which require a not inconsiderable amount of energy already during their development (training), but also during operation (inference, scoring), but we also include classical machine learning with very efficient processing chains in the solution space.

At the ADA Lovelace Center, we develop prediction models for the energy requirements of AI on embedded hardware platforms to optimize the trade-off between performance and energy requirements of battery-powered sensor systems in very fast development cycles. Only the fast prediction of the energy demand allows the integration into a multi-criteria AutoML (automatic machine learning) solution.

In industry projects, we provide our customers with automation solutions for AI development for ultra-low power applications or advise data science teams on optimized workflow and reliable testing in seminars and workshops.

Intelligent and self-learning systems are becoming increasingly important in corporate processes, for example for the automation of processes or the analysis of large volumes of data. Until now, these intelligent systems have always had to be connected to a cloud, as this provides the large computing power for the mathematical algorithms. With Edge AI, short for Edge Artificial Intelligence, the next generation of intelligent systems is now coming into the house: The intelligence is being shifted directly into the end devices.