STMicroelectronics has expanded the machine-learning techniques in the latest release of its STM32Cube.AI development environment to help users solve classification, clustering, and novelty-detection challenges more efficiently.
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 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,” said the company.
STM32Cube.AI lets developers drive machine-learning workloads from the cloud into STM32-based edge devices to reduce latency, save energy, increase cloud utilization, and protect privacy by minimizing data exchanges over the Internet, said STMicroelectronics. With the addition of machine-learning techniques for on-device analytics, STM32 MCUs are now suited for always-on use cases and smart battery-powered applications.
The new STM32Cube.AI version 7.0 can be downloaded for free at www.st.com.
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