Embedded AI: 25 Hands-On Projects to Make Your Hardware Smart
Adding AI to an embedded device takes more than training a model. You must choose the right hardware, collect and prepare data, deploy models to resource-constrained devices, and integrate everything into a reliable system. Drawing on 30+ years of embedded engineering experience, David Such guides you through the complete engineering process with over 25 hands-on projects—from a wake-word detector and real-time AI noise suppressor to an AI-powered MIDI synthesizer and a person detector running a neural network on a camera board. No ML experience required; source code, schematics, PCB designs, and datasets included.
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.
- chenzheng
Nice to see NSP cover this space. Curious if it goes into quantization for real hardware targets, or stays at the framework level.
- seamossfet
Has anyone read this yet? I'm extremely skeptical of any book published after ~2024 just because of how much AI slop is in physical books these days.
Looks interesting, but I'd like to see some reviews on the content first before buying.
- dominotw
kind of the expensive side.
honest question, how is it better than posting toc on chatgpt and asking it to explain it to you