MCU Deployment in AI & Artificial Intelligence

Since microcontrollers typically lack a file system (like NTFS or ext4), they cannot 'open' a file at runtime. Instead, we use a process called Binary Inlining. By running a tool like xxd -i model.tflite, we generate a C header file containing a large const unsigned char array. This array represents the exact bytes of the model, which are compiled directly into the binary firmware and flashed into the MCU's permanent memory.

On the device, the TFLite Micro Interpreter takes the memory address of the byte array. Unlike desktop systems that might copy the model into RAM, TFLite Micro is designed for Execute-In-Place (XIP). It reads the model's graph structure directly from the Flash memory, saving precious SRAM. This requires the model array to be properly aligned (usually 4-byte or 16-byte alignment) to prevent processor crashes.

The typical MCU deployment follows a strict lifecycle: 1. Sensor Sampling: Read raw data (like IMU or Microphone). 2. Pre-processing: Scale and normalize data into the input tensor. 3. Invoke: Call the mathematical solver to process the graph. 4. Post-processing: Interpret the output tensor (e.g., if class 2 > 0.8, turn on an LED). This loop must run fast enough to satisfy real-time requirements while consuming minimal power.

You have trained a TensorFlow Lite model. But microcontrollers don't have operating systems to load files. How do we get it on the chip?

We must convert the .tflite binary file into a C/C++ byte array. We use a command line tool called 'xxd' to dump the binary into a header file.

Checkpoint: Why do we convert the model into a C byte array for MCU deployment?

• →C code executes faster than binary
• →Microcontrollers lack a traditional file system

In our Arduino C++ code, we instantiate a 'MicroInterpreter' which will parse the array and run inferences directly from memory.

Finally, inside the main loop(), we write our sensor data into the input tensor, call Invoke(), and read the output prediction.

Checkpoint: What is the purpose of the 'tensor_arena' in TFLite Micro?

• →Provides static memory for intermediate tensors
• →Stores the permanent model weights

MCU deployment logic mastered! You've learned how to flash AI models to physical hardware. Ready for ONNX Runtime?

Check a Real Microcontroller Memory Budget. Finish checking whether a model fits inside a microcontroller's tiny memory budget.