A project-driven guide to designing, training, and deploying artificial intelligence directly on embedded hardware, showing how to build intelligent, autonomous systems under real-world constraints. 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
Autorentext
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, including senior roles at Serco Australia, Honeywell, and Tyco. His technical writing is followed by thousands of engineers and makers building at the edge.