Simplified AI on Microcontrollers with STM32Cube.AI

Simplified AI on Microcontrollers with STM32Cube.AI

Table of Contents:

1. Introduction
2. What is Artificial Intelligence and Machine Learning? 2.1 Applications of AI and ML
3. Challenges with Cloud-Based ai Processing 3.1 Power consumption and bandwidth limitations 3.2 Privacy concerns 3.3 Latency issues
4. Moving AI Processing to the Edge 4.1 Benefits of Edge Computing 4.2 Introducing STM32Cube.AI
5. How STM32Cube.AI Works 5.1 Steps for Neural Network Deployment 5.2 Importing Trained Neural Networks 5.3 Converting Networks into Microcontroller Code 5.4 Running Inference on Microcontrollers 5.5 Over-the-Air Upgrade and Continuous Learning
6. Applications of STM32 Microcontrollers in AI 6.1 Sensor Analysis and Activity Recognition 6.2 Predictive Maintenance 6.3 Audio Applications
7. Supported Neural Network Frameworks
8. Hardware Tools and Development Boards 8.1 SensorTile 8.2 IOT Node 8.3 LS M6DSOX Motion Sensor
9. Function Packs and Software Examples 9.1 Human Activity Recognition 9.2 Image Classification
10. Future Improvements and Roadmap
11. Conclusion
12. Resources and Additional Information

📝 Introduction

Artificial Intelligence (AI) and Machine Learning (ML) have become integral parts of our daily lives, revolutionizing various industries such as facial recognition, autonomous driving, stock market trading, and Handwriting recognition. Traditionally, AI processing has been done in the cloud, requiring significant processing power and internet bandwidth. However, there are challenges associated with this approach, including power consumption, privacy concerns, and latency issues. As a solution, there is a growing trend to Move Ai processing to the edge, using microcontrollers with optimized AI capabilities.

🤖 What is Artificial Intelligence and Machine Learning?

Artificial Intelligence (AI) refers to the ability of machines to mimic or simulate human-like intelligence, enabling them to perform tasks that typically require human intelligence. Machine Learning (ML) is a subset of AI that focuses on algorithms and statistical models that allow machines to learn from data and improve their performance over time.

📚 Applications of AI and ML

AI and ML are used in various applications, including:

• Waste
• Face recognition
• Autonomous driving
• Stock market trading
• Handwriting recognition

💡 Challenges with Cloud-based AI Processing

Cloud-based AI processing involves transferring raw data through a gateway to the cloud for processing. While this approach has its merits, there are several challenges, including:

🔌 Power consumption and bandwidth limitations: Cloud-based processing requires a significant amount of power and internet bandwidth. This can be impractical or costly, especially in applications where power is limited or internet connectivity is unreliable.

🔒 Privacy concerns: Sending data to the cloud raises privacy concerns as sensitive information may be exposed. There is a growing need for on-device processing to ensure data privacy.

⌚ Latency issues: Cloud-based processing introduces latency, as data needs to be transmitted back and forth between the edge device and the cloud. Real-time applications require low latency for instant decision-making.

🚀 Moving AI Processing to the Edge

To address the challenges associated with cloud-based AI processing, there is a shift towards moving AI processing to the edge. Edge computing brings processing closer to the source of data, reducing power consumption, improving response times, and enhancing data privacy.

🌟 Introducing STM32Cube.AI

STMicroelectronics (ST) offers a comprehensive solution for bringing AI processing to the edge with their STM32Cube.AI software development tool. This tool allows users to import trained neural networks and convert them into microcontroller code, enabling inference directly on STM32 microcontrollers.

🔎 How STM32Cube.AI Works

STM32Cube.AI simplifies the process of deploying neural networks onto microcontrollers with restricted hardware capabilities. The key steps involved are:

1. Neural Network Importer: Users can import trained neural networks from popular frameworks like TensorFlow, Caffe, Keras, Lasagne, and ONNX. The tool validates, optimizes, and compresses the imported networks.

2. Converting Networks into Microcontroller Code: STM32Cube.AI converts the imported neural network into optimized microcontroller code, considering the specific memory capabilities of different STM32 devices.

3. Running Inference on Microcontrollers: The generated code allows users to run inference directly on STM32 microcontrollers, even with limited processing power. Pre-trained models can be executed without the need for actual learning on the device.

4. Over-the-Air Upgrade and Continuous Learning: The STM32 microcontrollers can collect additional data during runtime, which can be sent back to the cloud for retraining and model improvements. The updated models can then be deployed over-the-air to the microcontrollers.

⚙️ Applications of STM32 Microcontrollers in AI

While STM32 microcontrollers may not have the processing power of GPUs or specialized accelerators, they are well-suited for specific AI applications. Some of the applications where STM32 microcontrollers are a good fit include:

✅ Sensor Analysis and Activity Recognition: STM32 microcontrollers enable sensor analysis and activity recognition applications. From fitness wearables to maintenance modules, the microcontrollers provide the necessary capabilities for monitoring and analyzing sensor data in real-time.

✅ Predictive Maintenance: In predictive maintenance applications, STM32 microcontrollers can monitor machine vibrations or noise to detect anomalies. Machine learning algorithms can then determine the appropriate actions, such as scheduling maintenance or replacing faulty components.

✅ Audio Applications: STM32 microcontrollers can be used for audio applications like keyword detection (such as Alexa or Siri) and object detection. While microcontrollers may not handle high frame rates or resolutions, they are suitable for specific audio-based tasks.

🖥️ Supported Neural Network Frameworks

STM32Cube.AI supports various neural network frameworks, including:

• Caffe
• Keras
• Lasagne
• TensorFlow
• TensorFlow Lite
• ONNX

🛠️ Hardware Tools and Development Boards

ST provides a range of hardware tools and development boards that complement STM32Cube.AI. These include:

1. SensorTile: SensorTile is a compact board that integrates an STM32 microcontroller with a microphone, a Bluetooth Low-Energy radio, and a nine-axis motion sensor. It is suitable for running AI applications and allows for easy development and prototyping.

2. IOT Node: The IOT Node is an STM32-based development board with additional connectivity options, such as Wi-Fi, NFC, and a sub-gigahertz radio. It is designed for applications that require enhanced connectivity capabilities.

3. LS M6DSOX Motion Sensor: ST recently introduced the LS M6DSOX motion sensor, which includes a machine-learning state machine. When combined with an STM32 microcontroller, it provides optimized power usage for certain AI applications.

📦 Function Packs and Software Examples

STM32Cube.AI includes function packs and software examples that demonstrate the capabilities of STM32 microcontrollers in AI applications. Some of the available function packs are:

1. Human Activity Recognition: The Human Activity Recognition function pack allows the detection of different states such as standing, walking, running, driving, or cycling. It enables developers to easily implement activity recognition in their applications using STM32 microcontrollers.

2. Image Classification: Image Classification function pack enables developers to classify images using STM32 microcontrollers. It supports the detection of various objects and uses popular neural network models like MobileNet for accurate classification.

🚧 Future Improvements and Roadmap

ST is continuously working to improve the capabilities of STM32Cube.AI. Planned enhancements and updates include:

• Full ONNX support
• Support for MXNet, PyTorch, and other frameworks
• User interface improvements
• Optimization for additional hardware boards

🔚 Conclusion

STM32Cube.AI provides a powerful solution for deploying AI processing on STM32 microcontrollers at the edge. By leveraging the capabilities of these microcontrollers, developers can overcome the limitations of cloud-based processing, reduce power consumption, improve response times, and ensure data privacy. With an extensive range of hardware tools, development boards, function packs, and software examples, STM32Cube.AI empowers developers to explore the potential of AI in a variety of applications.

📚 Resources and Additional Information

• For more information, visit STMicroelectronics website

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