TinyML
Deploy machine learning on microcontrollers — TensorFlow Lite Micro, Edge Impulse, model quantization, and on-device inference for keyword spotting, gesture recognition, and image classification.
What is TinyML, the ML-on-MCU ecosystem, hardware constraints, and real-world use cases.
End-to-end pipeline: data collection → training → conversion → deployment → inference.
Add TensorFlow Lite for Microcontrollers to an Arduino, STM32, or ESP32 project.
Create a project, connect a device, and use the Edge Impulse Studio for end-to-end ML.
Capture sensor data from IMU, microphone, and camera; label datasets; handle class imbalance.
Build and train a small neural network with Keras, evaluate accuracy, and export to TFLite.
Post-training int8 quantization, quantization-aware training, and weight pruning techniques.
Use TFLiteConverter, generate a C byte array, and link it into your firmware build system.
Train a yes/no or custom wake-word model, deploy on a Cortex-M board with a PDM microphone.
Classify hand gestures from accelerometer/gyroscope data in real time on-device.
Use autoencoders and K-means for predictive maintenance on vibration and temperature sensors.
Run MobileNet V1 person detection on ESP32-CAM or Arducam using TFLite Micro.
Deploy an Edge Impulse model on ESP32, integrate with FreeRTOS tasks, and stream results via MQTT.
Use STM32Cube.AI to convert Keras models, generate optimized C code, and run inference on Cortex-M4.
Reduce latency and RAM usage: operator fusion, CMSIS-NN kernels, and flash-friendly model layout.
Integrate TFLite Micro into a Zephyr application, use Zephyr sensor drivers, and manage inference threads.