Learn how to run AI on Edge devices using Arduino Nano RP2040
Introduction
Overview
Train and deploy a TinyML audio classifier with Edge Impulse
Board connection and IDE setup
Program your first TinyML device
Next Steps

  Log an issue

  Fork and edit

  Discuss on Discord

About this Learning Path

Skill level:  Introductory
Reading time:  1 hr 30 min
Last updated:  13 Aug 2026
Skill level:
  Introductory
Reading time:
  1 hr 30 min
Last updated:
  13 Aug 2026
Author:Bright Edudzi Gershon Kordorwu
Arm IP:Cortex-M
Tags: ML Baremetal Edge Impulse tinyML Edge AI Arduino
Author:
  • Bright Edudzi Gershon Kordorwu
Arm IP:
Cortex-M
Tags:
ML Baremetal Edge Impulse tinyML Edge AI Arduino

Who is this for?

This Learning Path is for beginners in Edge AI and TinyML, including developers, engineers, hobbyists, AI/ML enthusiasts, and researchers working with embedded AI and IoT.

What will you learn?

Upon completion of this Learning Path, you will be able to:

  • Understand the basics of Edge AI and TinyML.
  • Collect and preprocess audio data using Edge Impulse.
  • Train and deploy an audio classification model on the Arduino Nano RP2040.
  • Control LEDs by turning them on and off based on model predictions.

Prerequisites

Before starting, you will need the following:

Summary

AI-assisted

This summary was drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.

Close
?
You’ll move from Edge AI and TinyML concepts to a voice-command prototype on an Arduino Nano RP2040 Connect. First, you’ll collect audio, train a classifier in Edge Impulse, and export its Arduino library. Then, you’ll add the library to a sketch, flash the board, and validate on-device inference by speaking commands that control an LED.

Frequently asked questions

AI-assisted

These FAQs were drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.

Close
?
Which Edge Impulse project type should I choose for voice commands?
Create an audio classification project in Edge Impulse. Define classes for the words you plan to recognize, such as “on” and “off,” and apply preprocessing before training.
What do I need to download from Edge Impulse for the Arduino sketch?
Download the Arduino library generated from your Edge Impulse project. Add this library to your sketch so the trained model and processing steps are available on the device.
Do I need an internet connection on the board while the model runs?
No. Inference runs locally on the device, which is a core principle of Edge AI. You need connectivity only when using Edge Impulse Studio to build and export the model.
What result should I expect after flashing the sketch?
The board performs real-time audio inference and controls an LED. When it recognizes the trained words “on” and “off,” the LED changes state.
The LED does not change when I say the command—what should I check?
Verify that the correct Edge Impulse library is included, the build succeeds, and the uploaded firmware matches your project. Confirm the labels used in the sketch match the classes you trained, then rebuild and reflash.
Next