Intelligence at the Edge.
Build, deploy, and test artificial intelligence on small, resource-constrained devices that interact with the physical world. 25 hands-on projects. Real hardware. Working code.
Read it now
Four chapters are available through the No Starch Press early access program. Get started with the foundations and your first project today.
Who this book is for
Embedded Engineers
You know hardware. You need a clear path into AI that works on MCUs, not GPUs.
Bridge your firmware expertise to on-device intelligence with projects that run on the boards you already own.
AI/ML Practitioners
You know machine learning. Deploying to constrained devices is a different discipline.
Learn to quantise, optimise, and deploy your models where they actually run: on edge hardware with real memory and power budgets.
Makers and Hobbyists
You want to add intelligence to your projects but lack a clear starting point.
Build devices that sense, decide, and act, starting with affordable hardware and step-by-step instructions.
Students
Academic ML feels disconnected from the real world.
Ground your machine learning knowledge in deployable, testable embedded systems you can hold in your hand.
What you will build – 25 Projects
Every chapter includes complete, buildable projects with parts lists, wiring diagrams, and tested source code. Here are six that define the scope of the book.
Person Detection Using CNNs
Arduino · TensorFlow Lite Micro
Train and deploy a convolutional neural network for visual person detection on a microcontroller. Covers model architecture, training, quantisation, and on-device inference.
Orientation Using a Complementary Filter
IMU · Sensor Fusion
Fuse accelerometer, gyroscope, and magnetometer data to estimate orientation in real time. Compare complementary, Madgwick, Mahony, and Kalman filter approaches.
Robot Arm Anomaly Detection
Sensors · ML Classification
Apply machine learning to IMU sensor data to detect anomalous behaviour in a robotic arm. A practical introduction to sensor-based ML in industrial contexts.
Real-Time Audio Noise Suppression
Raspberry Pi Pico 2 · PDM Microphone
Build an RNN-based audio noise suppression system that runs in real time on a Raspberry Pi Pico 2. Includes PDM microphone interfacing, display output, and benchmarking.
Build a Hardware AI MIDI Synthesizer
RP2040/RP2350 · GAN · Custom PCB
Design and build a hardware synthesizer that uses a GAN to generate music. Covers model quantisation, PIO programming, USB MIDI enumeration, and custom PCB design across eight sub-projects.
Compressed Sensing
Adaptive Sensing Framework
Implement compressed sensing to reduce data acquisition requirements on constrained devices. Part of the book’s three-layer adaptive sensing framework.
Chapter Contents
Embedded AI GitHub Repository
Reefwing-Software/Embedded-AI
All example code, trained models, wiring diagrams, and project files from the book. Clone the repo, connect your hardware, and build. MIT licensed.
View on GitHub →