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Access services via an easy-to-use online platform with no SW installation.

Board farm hosted in ST premises

Evaluate the performance of models remotely, on real STM32 boards. The board farm will allow early support of upcoming MCUs (incl. STM32N6).

Turnkey AI resource on GitHub with STM32 model zoo

Find optimized models for various applications and getting started code example to easily create applications.

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STM32Cube.AI Developer Cloud (STM32CubeAI-DC) is a free-of-charge online platform and services to create, optimize, benchmark, and generate artificial intelligence (AI) for the STM32 microcontrollers based on the Arm® Cortex®‑M processor. STM32CubeAI-DC uses the STM32Cube.AI core technology. Its performance is identical to the X-CUBE-AI Expansion Package used with STM32CubeMX. Find STM32Cube.AI Developer Cloud at

  • 所有功能

    • Online GUI (no installation required) accessible with STMicroelectronics extranet user credentials
    • Network optimization and visualization prodiving the RAM and flash memory sizes needed to run on the STM32 target
    • Quantization tool to convert a floating point model into an integer model
    • Benchmark service on the STMicroelectronics hosted board farm including various STM32 boards to make the most suited hardware selection
    • Code generator including the network C code and optionally the whole STM32 project
    • STM32 model zoo:
      • Easy access to model selection, training script, and key model metrics, directly available for benchmark
      • Application code generator from the user’s model with “Getting started” code examples
      • ML workflow automation service with Python™ scripts (REST API)
    • Supports all the X-CUBE-AI features, such as:
      • Native support for various deep learning frameworks such as Keras and TensorFlow™ Lite, and support for all frameworks that can export to the ONNX standard format such as PyTorch™, MATLAB®, and more
      • Support for the 8-bit quantization of Keras networks and TensorFlow™ Lite quantized networks
      • Support for various built-in scikit-learn models such as isolation forest, support vector machine (SVM), K-means, and more
      • Possibility to use larger networks by storing weights in external flash memory and activation buffers in external RAM
      • Easy portability across different STM32 microcontroller series
    • User-friendly license terms