The STM32Cube.AI Developer Cloud provides access to an extensive suite of online development tools built around the STM32 family of microcontrollers (MCUs).
“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,” said Ricardo De Sa Earp, Executive Vice President General-Purpose Microcontroller Sub-Group, STMicroelectronics. “This is 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 optimised 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 number of industry-firsts:
- An online interface to generate optimised C-code for STM32 microcontrollers, without requiring prior software installation. Data Scientists and developers benefit from the STM32Cube.AI's proven Neural Network optimisation 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 optimised for STM32.
Users will have access to the world’s first online benchmarking service for edge-AI Neural Networks on STM32 boards and the board farm features a broad range of STM32 boards, refreshed regularly, that will allow data scientists and developers to remotely measure the actual performance of the optimised models.
