Learn how to train and deploy TinyML models on microcontrollers with this comprehensive step-by-step guide. Discover data collection, model training, TensorFlow Lite conversion, and deployment for efficient edge AI applications.
TinyML—the practice of running machine learning models on tiny, resource-constrained devices like microcontrollers—is revolutionizing edge AI. From voice recognition on earbuds to predictive maintenance on IoT sensors, TinyML enables smart applications where cloud connectivity is limited or power efficiency is critical.
In this blog, you’ll learn how to train a TinyML model and deploy it on a microcontroller, covering the full workflow from data collection to inference.
What Is TinyML?
TinyML refers to machine learning models optimized to run on devices with extremely limited compute power, memory (tens to hundreds of KB), and energy budgets. These devices often lack operating systems or GPUs, so models must be small, efficient, and fast.
Why Train Your Own TinyML Model?
Pretrained models are useful, but custom models tailored to your sensor data or specific use case offer better accuracy and relevance. Training your own model enables:
- Custom sensor inputs (accelerometer, microphone, etc.)
- Unique event detection (gesture, keyword spotting)
- Optimized size/performance trade-offs
Step 1: Define Your Use Case and Collect Data
Decide what you want your microcontroller to detect or classify. For example, detect whether a person is walking or running using accelerometer data.
- Collect Data: Use your microcontroller or a connected sensor to record labeled examples.
- Format Data: Store data in CSV or TFRecord format, ensuring each example has a label.
Step 2: Preprocess and Feature Engineer
Raw sensor data often needs cleaning and transformation:
- Normalize values (scale between 0 and 1)
- Extract features (e.g., MFCCs for audio, FFT for vibration)
- Segment data into fixed-length windows
Python libraries like numpy and scipy are helpful here.
Step 3: Choose a Model Architecture
For TinyML, smaller architectures work best, such as:
- Tiny Convolutional Neural Networks (CNNs)
- Fully Connected Neural Networks (Dense layers)
- Decision Trees or Random Forests (for classic ML)
Use TensorFlow Lite for Microcontrollers (TFLM) compatible models.
Step 4: Train Your Model on a Desktop
Use TensorFlow or PyTorch on your computer:
import tensorflow as tf
model = tf.keras.Sequential([
tf.keras.layers.Input(shape=(input_shape,)),
tf.keras.layers.Dense(32, activation='relu'),
tf.keras.layers.Dense(num_classes, activation='softmax')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(train_data, train_labels, epochs=20, validation_data=(val_data, val_labels))
Step 5: Convert Model to TensorFlow Lite Format
TinyML devices usually run TensorFlow Lite models with optimizations like quantization:
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()
with open('model.tflite', 'wb') as f:
f.write(tflite_model)
Step 6: Deploy to Microcontroller
Use the TensorFlow Lite Micro framework to run your model on microcontrollers such as ARM Cortex-M or ESP32.
- Convert the
.tflitefile to a C array. - Integrate into your microcontroller firmware.
- Use TFLM API for inference.
Example tools: xxd to convert .tflite to .cc file.
Step 7: Run Inference and Optimize
- Test the model on your device with live sensor data.
- Measure latency and power consumption.
- Optimize model size further if needed by pruning or reducing layers.
Summary
Training a TinyML model involves:
- Defining a task and collecting data
- Preprocessing and feature extraction
- Training a small, efficient model
- Converting and quantizing for TinyML
- Deploying and running inference on a microcontroller
With TinyML, you unlock AI at the edge — opening possibilities for smarter, faster, and more energy-efficient devices.
