STMicroelectronics STM32Cube.AI version 7.0 development environment

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STMicroelectronics has expanded the variety of machine-learning techniques available to users of the STM32Cube.AI development environment, giving extra flexibility to solve classification, clustering and novelty-detection challenges as efficiently as possible.

In addition to enabling development of neural networks for edge inference on STM32 microcontrollers (MCUs), the latest STM32Cube.AI release (version 7.0) supports supervised and semi-supervised methods that work with smaller datasets 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 challenges quickly by enabling fast turnaround time with easy-to-use techniques to convert, validate and deploy various types of models on STM32 microcontrollers.

The release lets developers drive machine-learning workloads from the cloud into STM32-based edge devices to reduce latency, save energy, increase cloud utilisation and safeguard privacy by minimising data exchanges over the internet. Now with extra flexibility to choose the most efficient machine-learning techniques for on-device analytics, STM32 MCUs are suitable for always-on use cases and smart battery-powered applications.

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