STM32Cube.AI workshop

STM32Cube.AI workshop MOOC

STM32Cube.AI workshop Learn how to use STM32CubeMX and X-Cube-AI tools to work with Neural Networks on STM32

Focusing on STM32L4 family and STM32CubeMX code generator tool, this online course demonstrates how 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 120 minutes to complete, depending on your proficiency.

Course outline

• Introduction to Artificial Intelligence (AI) and Neural Networks (NN)
• Out of the box lab
• The five steps development behind Neural Networks
• How to collect and label learning dataset lab
• Neural Network Model creation and learning
• Introduction to STM32CubeMX
• Basic lab on STM32CubeMX
• STM32CubeMX and X-Cube-AI lab

Prerequisites

Complete preparation process is described at the end of document with slides for this session (STM32Cube.AI workshop – slides.pdf).

• For STM32 Development (present in dedicated installer AI_workshop_1.0.exe located here):
  • FP-AI-SENSING1 (v3.0.0)
  • STM32CubeMX + Cube L4 Embedded Software Package
  • X-CUBE-AI (v4.0.0)
  • ST-Link Drivers
  • STM32CubeProgrammer (v2.1.0)
  • Atollic TrueStudio v9.3.0
  • A serial terminal application. E.g. TeraTerm
• Hands-on parts can be successfully done using the following software as well:
  • FP-AI-SENSING1 (v4.0.2)
  • X-CUBE-AI (v4.0.0)
  • STM32CubeMX (v6.0.1) + Cube L4 Embedded Software Package
  • STM32CubeIDE (v1.4.0)
• Hardware requirements:
  • A Windows computer
  • STM32 IoT Node (B-L475E-IOT01A2 Discovery kit)
  • 2x USB 2.0 Type-A to Micro-B cable (2^nd for datalog)
• Smartphone App
  • ST BLE Sensor (iOS or Android)
    • https://www.st.com/en/embedded-software/stblesensor.html

Training materials

Training materials (slides, hands-on projects) can be downloaded from this link.