Intro to TinyML and Arduino in AI & Artificial Intelligence

TinyML is the intersection of Machine Learning and Embedded Systems. Unlike mobile phones, microcontrollers don't have gigabytes of RAM or complex operating systems. They operate on Milliwatts of power, often sleeping for 99% of the time and waking only when a sensor threshold is met. Running AI here requires TensorFlow Lite for Microcontrollers, a highly optimized library that avoids dynamic memory allocation (new/delete) to ensure stability and predictability in constrained 'Bare-metal' environments.

Because MCUs have limited RAM (often 128KB - 512KB), every byte counts. TinyML uses a Tensor Arena—a pre-allocated static buffer where the interpreter places all the data it needs during a prediction. If your model's peak memory usage exceeds this arena, the code won't run. This forces the engineer to use Quantization and Pruning as fundamental tools, not just optional optimizations. Mastering the balance between model complexity and the Tensor Arena is the core skill of a TinyML developer.

Can a $5 chip see, hear, and feel? In this lesson, we'll master TinyML—the cutting edge of AI running on microcontrollers like Arduino and ESP32.

TinyML is about sub-milliwatt AI. We use TensorFlow Lite for Microcontrollers (TFLM) to run inference on devices with only 256KB of RAM.

Unlike mobile AI, TinyML doesn't have an OS. Everything is written to the flash memory as a C++ array of bytes representing the model weights.

Checkpoint: Why do we convert models to C++ arrays in TinyML?

• →To encrypt the model
• →To store the model in the microcontroller's Flash memory for direct 'bare-metal' access

The main constraint is memory. We must carefully manage the 'Tensor Arena'—a shared memory space where input, output, and intermediate tensors live.

By mastering TinyML on Arduino, you've learned to bring intelligence to everyday objects. You're ready to build the smart environment of the future.

Checkpoint: True or False: You can easily run a standard ResNet-50 model on an Arduino Uno.

• →True
• →False (Microcontrollers have thousands of times less memory than needed for ResNet-50)

TinyML foundations mastered! Now, let's get our hands dirty and learn the actual deployment steps: Deploying to Microcontrollers.

Next, we'll explore the deployment workflow—from C++ generation to hardware flashing.

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