Running TensorFlow Lite on a Microcontroller (Without a PhD)

Microcontrollers with tiny RAM budgets (under 256KB) can now run quantized neural networks for keyword spotting, gesture classification, and predictive maintenance.

Using TensorFlow Lite for Microcontrollers (TFLM), we quantize FP32 models to INT8 weights, reducing model footprint by 75% with minimal accuracy loss.

This practical guide walks through collecting IMU sensor datasets, training a model in Google Colab, converting to C byte array header files, and executing inference loops directly on hardware.

The result is a gesture-recognition demo that runs at 100 inferences per second on an Arduino Nano 33 BLE — no cloud connection required.