TensorFlow Lite Micro Integration

Overview

Use this skill to bring up TensorFlow Lite for Microcontrollers (TFLM / LiteRT for Microcontrollers) on an MCU: register exactly the ops the model uses, size the tensor_arena correctly, and quantize inputs so Invoke() returns valid output. Most failures are missing ops, an undersized arena, or unquantized input, not model logic. TFLM is Google's official microcontroller runtime and is integrated by several vendor toolchains — Espressif esp-tflite-micro (with ESP-NN kernels), NXP eIQ, and ST X-CUBE-AI, where TFLM is available as an optional runtime alongside ST's own proprietary Cube.AI runtime. Use tinymaix-integration instead when you want a lighter, dependency-free runtime.

When To Use

Use this skill when:

• The user runs tensorflow/tflite-micro and works with tflite::MicroInterpreter, tflite::MicroMutableOpResolver, AllocateTensors(), or Invoke().
• The build or runtime hits Didn't find op for builtin opcode 'CONV_2D', 'QUANTIZE', arena/AllocateTensors failures, or output that is all zeros or drifting.
• The project embeds a .tflite model as a C array (xxd -i) and needs int8 scale/zero_point handling.

Do not use this skill when the model is not yet converted and quantized to .tflite; run the TFLite converter and validate on the host first.

First Questions

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