Embedded AI

Embedded AI | No Starch Press Skip to main content WANT SWEET DEALS? JOIN OUR MAILING LIST Embedded AI Intelligence at the Deep Edge Available September 2026, 600 pp. ISBN-13: 9781718504905 Download Chapter 9: Sensor Machine Learning You already know how to build embedded systems. Now it’s time to make them intelligent. Adding AI to an embedded device takes more than training a model. You have to choose the right hardware, collect and prepare data, deploy models to resource-constrained devices, and integrate everything into a system that performs reliably. Drawing on more than 30 years of embedded engineering experience, David Such takes you through the complete engineering process. You’ll work through more than 25 hands-on projects (complete with downloadable source code, schematics, PCB designs, and datasets); no machine learning experience required. You’ll build: A wake-word detector that responds to your voice A real-time AI noise suppressor An AI-powered MIDI synthesizer that composes music A battery monitor that collects its own training data A person detector that runs a neural network on a camera board Whether you’re an embedded developer adding AI to your products, a machine learning practitioner moving onto embedded hardware, or a maker ready to move beyond beginner projects, Embedded AI teaches you the engineering decisions behind every design. When the breadboard is flaky, the sensor data is noisy, or the tensor arena is too small, you’ll know how to fix it—and why. Prerequisites Most projects require an Arduino UNO or Raspberry Pi Pico; a few use specialized boards. You’ll also need to download some free software, including Python with TensorFlow, Arduino IDE, and Raspberry Pi Pico SDK. View the complete hardware and software requirements. David Such is an embedded systems engineer and founder of Reefwing Software, where he builds IoT devices, robotics platforms, and drone flight control systems. He has over 30 years of experience in embedded development, including senior roles at Serco Australia, Honeywell, and Tyco. His technical writing on embedded AI has a substantial following among hardware engineers working at the edge. Table of contents Acknowledgments Introduction PART I: FOUNDATIONS OF EMBEDDED AI Chapter 1: Path to Embedded AI Chapter 2: The Basics of Embedded Systems Chapter 3: Applied Machine Learning in Embedded Projects Chapter 4: Deep Learning Chapter 5: Exploratory Data Analysis PART II: WORKING WITH SENSORS AND DATA Chapter 6: Smart Sensors Chapter 7: IMU Data Preprocessing Chapter 8: Sensor Fusion PART III: BUILDING COMPLETE SYSTEMS Chapter 9: Sensor Machine Learning Chapter 10: Real-Time Audio Noise Suppression Chapter 11: Build an AI MIDI Synthesizer Chapter 12: Build a Hot Word Detection Chapter 13: Build an Arduino Battery Monitor and Logging Shield Epilogue: Where Do We Go Next? List of Projects Resources Index