The move sees extra flexibility being added to solve classification, clustering, and novelty-detection challenges as efficiently as possible.
As well as enabling development of neural networks for edge inference on STM32* microcontrollers (MCUs), this 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 will help developers solve their challenges more quickly by enabling fast turnaround time with, according to ST, easy-to-use techniques to convert, validate, and deploy various types of models on STM32 microcontrollers.
STM32Cube.AI has been developed to let designers drive machine-learning workloads from the cloud into STM32-based edge devices to reduce latency, save energy, increase cloud utilization, and safeguard privacy by reducing data exchanges over the Internet.
With this extra flexibility to choose the most efficient machine-learning techniques for on-device analytics, STM32 MCUs will be suitable for always-on use cases and smart battery-powered applications.
