HomeElectronics NewsNew AI Software For STM32 Development Boards

New AI Software For STM32 Development Boards

  • The NanoEdge AI Studio development environment by Cartesiam allows an embedded developer to quickly create powerful AI-based solutions
  • It allows users of STMicroelectronics’ STM32 microcontroller development boards to easily integrate machine-learning algorithms for a wide range of applications

Cartesiam, a company that creates artificial intelligence (AI) software for embedded systems, has announced a new release of its NanoEdge  AI Studio optimised for the STM32 microcontroller development boards by STMicroelectronics.

The NanoEdge AI Studio development environment removes traditional AI barriers and is designed for companies that do not have expert resources in machine learning. This new release allows an embedded developer to quickly create powerful AI-based solutions using STM32 microcontrollers. Also, live data logging in NanoEdge AI Studio is directly brought through the STM32 serial/USB port and an enhanced version of Cartesiam’s automatic data-compliance and quality-verification tool.

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Equips system designers with powerful features

The intuitive software tool allows system designers using Arm-based low-power microcontrollers to quickly, easily and inexpensively integrate machine-learning algorithms directly into a broad range of applications including connected devices, household appliances and industrial machines. The software enables on-device learning of a nominal behaviour and is capable of detecting any variation of this behaviour, even in a complex and “noisy” environment.

In this new release, developers get a new option to directly select ST’s Nucleo-F401RE or Nucleo-L432KC development boards as hardware platforms. Selecting these boards unlocks the last step of the studio process and allows the download of a custom machine-learning library ready to be run on the selected hardware platform.

Miguel Castro, AI Solutions Manager at STMicroelectronics, said, Customers developing with ST’s microcontrollers can benefit from Cartesiam’s solutions in the same ecosystem of boards and development environment they are already used to. NanoEdge AI Studio complements our AI offer by adding simple machine-learning library generation to our Neural Networks development environment, STM32Cube.AI. ST development boards are the perfect platforms for developing the next generation of smart products on low-power and cost-effective microcontrollers.”

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Joel Rubino, CEO of Cartesiam, added, Since its launch in February, NanoEdge AI Studio has been used by customers of all sizes and on all continents to develop predictive maintenance, fraud detection, and smart-security solutions. This new release of NanoEdge AI Studio custom-made for specific STM32 boards will give our customers a solid hardware and software combo to rely on when developing and testing new companion devices as well as built-in sub-systems designed for predictive maintenance.”

NanoEdge AI Studio is available today for download from the website of Cartesiam.


Vinay Prabhakar Minj
Vinay Prabhakar Minj
Vinay Prabhakar Minj is a technology writer and science communication specialist with a Master’s degree in Communication of Science and Innovation (Science Communication). He is a prolific contributor to Electronics For You, where he has authored over 1,000 articles covering electronics, semiconductors, embedded systems, IoT, and emerging technologies. With a strong foundation in science communication, Vinay focuses on translating complex engineering concepts into clear, accessible, and application-oriented content. His work spans topics such as sensor technologies, chip design, wireless systems, and next-generation electronics, making advanced innovations easier to understand for engineers, students, and industry professionals. Through his extensive contributions, he has built a reputation for delivering reliable, well-researched, and practical insights that help readers stay updated with the rapidly evolving electronics ecosystem. His writing bridges the gap between technical depth and real-world usability, supporting both learning and decision-making in the field.

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