Machine Learning on microcontrollers using MicroPython and emlearn [PyCon DE & PyData Berlin 2024]

MicroPython provides Python implementation for microcontrollers with at least 16KB RAM, supporting Python 3.6 level functionality

Modern microcontrollers (like ESP32) have significant capabilities - megabytes of RAM, WiFi/Bluetooth connectivity, and processing power comparable to early Pentium processors

emlearn library enables machine learning on microcontrollers:

• Supports common ML tasks like classification and anomaly detection
• Compatible with scikit-learn training workflow
• 100x faster than pure Python implementations
• Includes k-nearest neighbors, random forests, and simple neural networks

Key applications include:

• Sensor data analysis (accelerometer, sound, images)
• Activity tracking and gesture recognition
• Predictive maintenance
• Smart home automation
• Livestock monitoring

Benefits of on-device ML processing:

• Privacy preservation (no raw data transmission)
• Low latency
• Standalone operation
• Extended battery life
• Low cost scalability

Development workflow:

1. Train models on computer using standard tools
2. Export model to microcontroller format
3. Load and run on device using MicroPython
4. Access hardware features through MicroPython modules

Hardware considerations:

• Memory constraints require optimized models
• Built-in buffers help with sensor data collection
• Internal flash storage available for data logging
• Garbage collection timing impacts real-time operations

Best practices:

• Start with ESP32 family devices for development
• Use embedded storage buffers for sensor data
• Validate with real device data
• Ensure preprocessing compatibility between training and deployment