"Our goal is to deliver the best hardware, software, and services to meet the challenges faced by embedded developers and data scientists so that they can develop their edge AI application faster and with less hassle," says Ricardo De Sá Earp, Executive Vice President General-Purpose Microcontroller Sub-Group, STMicroelectronics. "Today we are unveiling the world’s first MCU AI Developer Cloud, which works hand-in-glove with our STM32Cube.AI ecosystem. This new tool brings the possibility to remotely benchmark models on STM32 hardware through the cloud to save on workload and cost."
Serving the growing demand for edge AI-based systems, the STM32Cube.AI desktop front-end includes the resources for developers to validate and generate optimized STM32 AI libraries from trained Neural Networks. This is now complemented by the STM32Cube.AI Developer Cloud, an online version of the tool, delivering a range of industry-firsts, including an online interface to generate optimized C-code for STM32 microcontrollers, without requiring prior software installation. Data Scientists and developers benefit from the STM32Cube.AI's proven Neural Network optimization performance to develop edge-AI projects.
Access to the STM32 model zoo, a repository of trainable deep-learning models and demos to speed application development. At launch, available use cases include human motion sensing for activity recognition and tracking, computer vision for image classification or object detection, audio event detection for audio classification, and more. Hosted on GitHub, these enable the automatic generation of "getting started" packages optimized for STM32.
The cloud-accessible board farm features a broad range of STM32 boards, refreshed regularly, allowing data scientists and developers to remotely measure the actual performance of the optimized models, with access to the world’s first online benchmarking service for edge-AI Neural Networks.
STM32Cube-AI Developer Cloud is now freely available to registered MyST users. The tool has been undergoing testing and evaluation by several embedded development customers.
"The STM32Cube.AI Developer Cloud provides an easy way for our data scientists and embedded developers to collaborate and share their knowledge on embedded neural networks, which helps streamline our development process. The benchmarking feature also enables our data scientists to ensure that the models they create will run smoothly on microcontrollers. This allows us to remain competitive and provide the best solutions to our customers," testifies Johan A. Simonsson, Director AI Ideation & Research, Husqvarna Group AI Labs.
"Thanks to STM32Cube.AI Developer Cloud, we can confirm in a very short time the validity of our approach to create a product with embedded AI. With the board farm we are able to confirm that our model works on a microcontroller. We are also able to choose the most appropriate STM32 by performing a remote benchmark on different STM32 boards. Overall, this addition to STM32Cube.AI is really welcome and will allow us to make more innovative products in the future," states Serge Robin, Microcontroller & Digital Components Expert Engineer, Somfy.
"The use of the STM32 Model zoo can greatly ease machine-learning (ML) workflow and significantly shorten time to market by providing a collection of pre-trained models for STM32 microcontrollers that can be easily accessed and integrated into a new project, reducing the need for time-consuming training and experimentation," adds Stephane Henry, Executive VP R&D, Lacroix.
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