In addition to enabling development of neural networks for edge inference on STM32 microcontrollers (MCUs), the latest STM32Cube.AI release (version 7.0) supports new supervised and semi-supervised methods that work with smaller data sets and fewer CPU cycles. These include isolation forest (iForest) and One Class Support Vector Machine (OC SVM) for novelty detection and K-means and SVM Classifier algorithms for classification which users can now implement without laborious manual coding.
The addition of these classical machine-learning algorithms on top of neural networks helps developers solve their challenges more quickly by enabling fast turnaround time with easy-to-use techniques to convert, validate, and deploy various types of models on STM32 microcontrollers.
