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Abstract:

This presentation by John Nordby introduces Python developers to the world of microcontrollers and machine learning using MicroPython. It highlights the accessibility and affordability of modern microcontrollers like the ESP32, capable of running a tailored Python 3 implementation. The talk covers setting up the development environment (including tools like Viper IDE and MP Remote), basic IoT sensor examples (like a temperature sensor using MQTT), and the MicroPython package manager (mip) for installing libraries. A key focus is overcoming performance limitations for tasks like audio or image processing by utilizing C modules, specifically MicroPython's unique ability to install native C modules at runtime. The presentation heavily features the emlearn-micropython library, developed by the speaker, which provides C-accelerated DSP and machine learning functions (Random Forests, CNNs, KNNs). A practical example of building an activity tracker demonstrates the full workflow: collecting labeled accelerometer data on the device, training a scikit-learn model on a PC, converting it using emlearn, and deploying it for on-device inference using emlearn-micropython. The talk concludes that MicroPython, augmented with C modules, is a powerful and productive platform for developing sophisticated embedded IoT and machine learning applications.

MicroPython for Microcontroller Machine Learning

• 0:00:04 Introduction: Welcome to the talk on using MicroPython for machine learning on microcontrollers, presented by John Nordby, ML engineer and co-founder of Sound Sensing.
• 0:00:55 Running Python on Small Devices: The core idea is that Python (specifically MicroPython) can run on small, low-power devices like smartwatches or sensors, not just servers or desktops.
• 0:01:52 Goal: Enable Python developers to build IoT sensors on microcontrollers using their existing skills, including implementing on-device machine learning with the emlearn-micropython library.
• 0:02:27 Modern Microcontrollers: Today's microcontrollers (e.g., ESP32S3) are powerful (32-bit, >200MHz CPU, WiFi, Bluetooth), low-cost ($2.5 chip, $6 board), and accessible.
• 0:03:28 Ready-made Boards: Affordable development boards with built-in sensors (accelerometers, microphones, cameras) simplify getting started without deep hardware knowledge.
• 0:03:57 MicroPython Overview: A lean implementation of Python 3 for microcontrollers. Can be easily flashed onto devices like the ESP32 using tools like esptool.
• 0:04:37 Development Workflow: Interact with MicroPython via USB REPL. Tools like MP Remote help manage files and run scripts. Viper IDE offers a browser-based, zero-install development environment.
• 0:05:09 Simple Example: Temperature Sensor: Demonstrates reading a sensor (built-in or external) and sending data using standard protocols.
• 0:06:48 Code Example Details: Shows using asyncio for concurrency, the machine module for hardware access (I2C), and an MQTT library (umqtt.simple) for IoT communication.
• 0:08:44 Package Management (mip): MicroPython has its own package manager (mip) to install third-party libraries (pure Python or native C modules), similar to pip. Viper IDE supports this.
• 0:10:10 Handling Advanced Tasks: MicroPython can process higher data rates (accelerometer, audio, low-res images) by leveraging C modules for performance-critical parts.
• 0:11:41 C Modules for Performance: Explains how to write C code and create bindings so it can be called from MicroPython, necessary for tasks like real-time audio filtering (e.g., IIR filters).
• 0:14:50 Native C Modules: A key MicroPython feature allowing pre-compiled C modules (.mpy files) to be installed at runtime via mip, offering C performance with Python-like convenience.
• 0:16:34 ML Example: Activity Tracker: Building a custom exercise tracker (detecting jumping jacks, lunges, squats) using the device's accelerometer.
• 0:17:24 Data Collection for ML: Using the microcontroller itself to record labeled sensor data (accelerometer readings) directly into numpy-compatible files.
• 0:19:34 ML Pipeline: Standard approach: split sensor data into time windows (e.g., 4 seconds), extract features (e.g., statistical), and train a classifier (e.g., Random Forest).
• 0:20:38 Feature Extraction: Write feature extraction code compatible with MicroPython, ensuring identical calculations during training (PC) and inference (device).
• 0:21:26 Model Training & Conversion: Train a standard scikit-learn model on a PC. Use the emlearn Python library to convert the trained model (e.g., Random Forest) into a C-friendly format (like CSV).
• 0:22:14 On-Device Inference: Copy the converted model file to the device. Use emlearn-micropython (the C module library) to load the model and perform fast, efficient inference directly on the microcontroller.
• 0:23:01 C Module Benefits for ML: The C implementation of the classifier in emlearn-micropython is significantly (~10x) faster and more memory-efficient than pure Python alternatives.
• 0:23:24 Summary & Key Takeaways: Microcontrollers are accessible; MicroPython makes them productive for Python devs (ESP32 recommended); Viper IDE is a good starting point; mip provides extensibility; C modules unlock performance for advanced tasks.
• 0:24:41 emlearn-micropython Features: Provides C modules for DSP (filters, FFT) and ML models (Random Forest, CNN, KNN), enabling complex processing on-device, including on-device learning.
• 0:25:16 Related Libraries: Mentions ulab (a numpy-like library for MicroPython) and OpenMV (computer vision library for MicroPython).
• 0:25:58 Q&A (Numba/Compilation): Numba is tied to CPython; MicroPython has its own bytecode and native code decorators (@micropython.native, @micropython.viper) for acceleration.
• 0:27:25 Q&A (Numpy): No full Numpy; ulab is a good alternative. The examples used the built-in array module.