Introduction to STM32Cube.AI
Introduction to STM32Cube.AI
 | Discover the 5 steps to deploy an ANN on STM32
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Focusing on STM32L4 family and STM32CubeMX code generator tool, this online course demonstrates to create a basic Neural Network embedded system on STM32 devices.
Who should attend this course?
- Engineers interested in Neural Networks and its implementation in embedded world
- Engineers looking for ready solutions of AI implementation on STM32 devices
Benefits you will take away
- Basic information about Neural Networks and its implementation in STM32 embedded world
- First experience with STM32CubeMX and X-Cube-AI – tools dedicated to Neural Network support on STM32
On line course concept
- Course is provided in MOOC format with course material available online, mostly as videos complemented with exercises
- This course takes approximately 90 minutes to complete, depending on your proficiency
Course outline
- Overview of the Artificial Intelligence Solutions on STM32
- Presentation of STM32Cube.AI function pack
- Hands-on sessions
- Out of the box experience on the STM32L4 Discovery kit IoT
- Neural Network Model creation using Keras
- How to configure and generate code using STM32CubeMX and X-CUBE-AI
- How to update a NN model in FP-AI-SENSING using STM32CubeMX
Prerequisites
- For STM32 Development:
- FP-AI-SENSING1 (v2.2.0)
- STM32CubeMX + Cube L4 Embedded Software Package
- X-CUBE-AI (v3.4.0)
- ST-Link Drivers
- STM32CubeProgrammer (v2.1.0)
- IAR EWARM (v8.20)
- A serial terminal application. E.g. TeraTerm
- For AI development (optional):
- Python 3.5.4
- Tensorflow 1.5.0
- Keras 2.2.4
- Librosa 0.6.2
- Hardware requirements:
- A Windows computer
- STM32 IoT Node (B-L475E-IOT01A Discovery kit)
- 2x USB 2.0 Type-A to Micro-B cable (2nd for datalog)
- Smartphone App
- ST BLE Sensor (iOS or Android)
