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1. What is AI at the edge
Smart objects are changing nearly every aspect of our daily lives, helping in our homes, workplaces, cities and factories. ST has been investing in AI for many years… recognizing that microcontrollers provide enough processing power to run AI software on smart objects and enable AI on the edge.
Local processing can indeed fix many issues including:
- Latency and high communication costs on connected devices
- Potential limitations related to network bandwidth
- Energy constraints on battery-operated devices. When a device is very power-consuming and wastes too much energy on data transfers, the device’s battery size must be increased... leading to a rise in cost.
- Data privacy: when monitored data is sent over the internet, security breaches can occur. When data is locally analyzed, it is immediately transformed and does not have to be sent remotely.
To simplify the development of AI algorithms on STM32, ST has developed a solution called STM32Cube.AI.
2. Getting started with STM32 and STM32Cube.AI
STM32Cube.AI solution brings the following:
- A CubeMX extension called X-CUBE-AI to convert an NN in optimized code. It is inter-operable with state-of-the-art Deep Learning design frameworks such as Keras, TensorFlow, ONNX,... It supports quantization scheme and optimizations for STM32, reducing memory requirement up to ÷4 and decreasing latency and power consumption up to ÷3
- Software examples for Quick prototyping. Audio, Motion and Vision Function packs on ST development hardware allows to directly start your project
- STM32 Community with dedicated Neural Networks topic
- AI Partner Program that brings expertise in Machine Learning and STM32 solutions
Check our list of resources for detailed information.
4. STM32 compliant with STM32Cube.AI
STM32Cube.AI supports all Cortex M4 and Cortex M7 MCUs, as well as STM32 MP1. Other MCUs are supported in our partner ecosystem or can be added on-demand.
X-CUBE-AI is compliant with the following frameworks:
- Keras
- TensorFlow
- Caffe
- Lasagne
- ONNX: support is detailed in ONNX support page
The exact support versions are detailed in the X-CUBE-AI release note: file:///C:/Users/%USERNAME%/STM32Cube/Repository/Packs/STMicroelectronics/X-CUBE-AI/5.0.0/Documentation/faqs.html
5. Specific tools
Links and explanations for the tech domain dedicated tools
6. STMicroelectronics Resources
All resources are gathered on https://www.st.com/stm32cubeai STM32Cube.AI
7. Examples
The area where FAE's, collab... can put examples (linked to Github)