Edge AI on Microcontrollers: Why This is the Next Big Thing in Embedded Hardware

Meta description: AI is coming to microcontrollers, not just the cloud: here's what this means for Arduino, ESP32 and Raspberry Pi developers in 2026

Introduction

When people think about AI, they often imagine sending information to a server for processing. This will continue to be an option, but an important change is taking place in 2026: major semiconductor manufacturers are starting to produce microcontrollers with specialized processing units for AI algorithms. These microcontrollers have a significant impact on the development of embedded systems, both in an industrial context and in the sphere of personal electronics.

What This Actually Means

The ability to run AI algorithms on microcontrollers means that in the past, to run such algorithms, one had to have at least a Raspberry Pi, but preferably an industrial computer or an entire cloud server. Microcontrollers such as Arduino or even ESP32 were unable to perform these kinds of tasks.

This is where the situation is changing: chip manufacturers are rolling out microcontrollers with specialized chips, often referred to as NPUs (neural processing units), which allow for accelerating AI algorithms. Such microcontrollers can perform relatively simple AI algorithms many times faster than a regular microcontroller. This is not marginal information: the giants of the semiconductor industry, among them Texas Instruments, STMicroelectronics, NXP, and Renesas, have already released such microcontrollers this year.

Why This Might Be Interesting to You

This technological change has a direct effect on the microcontroller development sphere: there are three main reasons why this might be interesting to you as a hobbyist.

The ESP32 has already been positioning itself as an alternative to more powerful computers, and the ability to use it for machine learning tasks is another confirmation that this microcontroller is much more powerful than its 32-bit core suggests. Some ESP32 models, for example, the recently released ESP32-S3, have vector instruction sets that can be used to implement machine learning algorithms.

The Raspberry Pi is facing competition from other microcontrollers, specifically in the "low-end vision sensor + ML" segment. The release of more powerful microcontrollers will lead to the fact that more and more raspberry pi owners will use their Pi for more demanding computer vision algorithms. The raspberry pi will still have its place in the ML/microcontroller sphere, but now its position is being challenged by more powerful microcontrollers.

New opportunities for weekend experiments are being created: now microcontrollers can be used not only for the tasks traditionally associated with them but also for relatively demanding ML algorithms. Of course, few people will try to run a vision algorithm on a microcontroller, but such algorithms can be used, for example, for speech recognition, gesture control, or the simplest identification of objects or scenes. These are all relatively simple tasks that can be solved with a microcontroller, thereby opening up new opportunities for weekend experiments.

What This Actually Means

TinyML is the name of the movement that brings machine learning to microcontrollers. It involves adapting ML models so that they can run on a microcontroller rather than in the cloud or on a PC. A TinyML model typically has three characteristics:

• It is small (typically no more than 1 MB)
• It is focused on a specific task (such as recognizing a particular phrase)
• It is optimized for the microcontroller (which typically have much less RAM and computing power than a PC)

This does not mean that you have to abandon existing computer vision algorithms and start rewriting them in C++ for microcontrollers. On the contrary, there are ways to make existing ML models suitable for use on a microcontroller.

How to Get Started

There are several options to get started with ML on microcontrollers without buying any new hardware:

• TensorFlow Lite for Microcontrollers allows you to run existing TensorFlow.js models on ESP32, which makes it possible to implement relatively simple speech and gesture recognition algorithms
• Edge Impulse is a no-code development environment for creating TinyML models
• Raspberry Pi + an AI acceleration board gives you an opportunity to experiment with more complex ML algorithms without spending a lot of money on new hardware

What to Look Out For

As mentioned above, there are already microcontrollers that can perform relatively simple ML algorithms. These microcontrollers are not cheap, but as their popularity grows, we can expect to see more and more boards that offer similar capabilities at affordable prices. The same ESP32, for example, has made Wi-Fi a standard for all microcontroller boards: we can expect the same to happen to microcontrollers with integrated AI accelerators in the near future.

FAQs

Do I need special hardware to experiment with edge AI?

You can start experimenting with edge AI right now without any special hardware, since some of the ESP32 boards already have the necessary hardware, and there are development environments (such as TensorFlow Lite for Microcontrollers) that allow you to run your own tiny ML models on microcontrollers. Of course, AI-specific microcontrollers allow for much more interesting experiments, but they are not a necessary condition for working with edge AI.

Is edge AI the same thing as running ChatGPT on a microcontroller?

No, you cannot run ChatGPT on a microcontroller, at least not yet. ChatGPT is an example of a large language model (LLM), which is a type of artificial intelligence. These models are huge in size (usually several gigabytes) and require tremendous computing power to run (usually a graphics card or a specialized chip). TinyML models, on the other hand, are optimized to run on small devices like microcontrollers and are much simpler in terms of functionality.

Should I buy new hardware to experiment with edge AI or is there something I can use right now?

If you have an ESP32 (preferably the new S3 version) or a Raspberry Pi, you can start working with edge AI right now! If you want to try something more interesting, you can experiment with an AI-specific microcontroller.