STMicroelectronics, a global semiconductor leader serving customers across the spectrum of electronics applications, is continuing to expand its solutions for embedded AI developers and data scientists with a new, industry-first set of tools and services to get edge AI technology on the market faster and with less complexity by aiding hardware and software decision-making. The STM32Cube.AI Developer Cloud opens access to an extensive suite of online development tools built around the industry-leading 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. “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:
- 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.
- Access to the world’s first online benchmarking service for edge-AI Neural Networks on STM32 boards. 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.