Hardware Kits

Hands-on Embedded ML Labs for Real Devices

Read Online | PDF

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What This Is

The Hardware Kits teach you how to deploy ML models to real embedded devices. You will face actual hardware constraints: limited memory, power budgets, and latency requirements that do not exist in cloud environments.

This is where AI systems meet the physical world.

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🎓 What You Will Learn

<table> <thead> <tr> <th width="25%">Concept</th> <th width="75%">What You Do</th> </tr> </thead> <tbody> <tr> <td align="center"><b>🖼️ Image Classification</b></td> <td>Deploy CNN models to classify images in real-time on microcontrollers</td> </tr> <tr> <td align="center"><b>🎯 Object Detection</b></td> <td>Run YOLO-style detection on camera-equipped boards</td> </tr> <tr> <td align="center"><b>🗣️ Keyword Spotting</b></td> <td>Build always-on wake word detection with audio DSP</td> </tr> <tr> <td align="center"><b>👋 Motion Classification</b></td> <td>Use IMU sensors for gesture and activity recognition</td> </tr> <tr> <td align="center"><b>🗜️ Model Optimization</b></td> <td>Quantize and compress models to fit in KB of RAM</td> </tr> <tr> <td align="center"><b>🔋 Power Management</b></td> <td>Balance accuracy vs battery life for edge deployment</td> </tr> </tbody> </table>

🛠️ Hardware Platforms

<table> <thead> <tr> <th width="25%">Platform</th> <th width="50%">Description</th> <th width="25%">Best For</th> </tr> </thead> <tbody> <tr> <td align="center"><b>Arduino Nicla Vision</b></td> <td>Compact AI camera board with STM32H7</td> <td>Vision projects, ultra-low power</td> </tr> <tr> <td align="center"><b>Seeed XIAO ESP32S3</b></td> <td>Tiny ESP32-S3 with camera support</td> <td>WiFi-connected vision</td> </tr> <tr> <td align="center"><b>Grove Vision AI V2</b></td> <td>No-code AI vision module</td> <td>Rapid prototyping</td> </tr> <tr> <td align="center"><b>Raspberry Pi</b></td> <td>Full Linux SBC for edge AI</td> <td>Complex pipelines, prototyping</td> </tr> </tbody> </table>

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🚀 Quick Start

For Learners

1. Pick a platform from the labs
2. Follow the setup guide for your hardware
3. Complete the labs in order: Setup → Image Classification → Object Detection → Keyword Spotting

For Contributors

<kbd>cd kits</kbd>

Build HTML site <kbd>ln -sf config/_quarto-html.yml _quarto.yml</kbd> <kbd>quarto render</kbd>

Build PDF <kbd>ln -sf config/_quarto-pdf.yml _quarto.yml</kbd> <kbd>quarto render --to titlepage-pdf</kbd>

Preview with live reload <kbd>quarto preview</kbd>

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🔬 Labs Overview

Each platform includes progressive labs:

<table> <thead> <tr> <th width="25%">Lab</th> <th width="50%">What You Build</th> <th width="25%">Skills</th> </tr> </thead> <tbody> <tr> <td align="center"><b>Setup</b></td> <td>Hardware setup and environment configuration</td> <td>Toolchain, flashing, debugging</td> </tr> <tr> <td align="center"><b>Image Classification</b></td> <td>CNN-based image recognition</td> <td>Model deployment, inference</td> </tr> <tr> <td align="center"><b>Object Detection</b></td> <td>Real-time object detection</td> <td>YOLO, bounding boxes</td> </tr> <tr> <td align="center"><b>Keyword Spotting</b></td> <td>Audio wake word detection</td> <td>DSP, MFCC features</td> </tr> <tr> <td align="center"><b>Motion Classification</b></td> <td>IMU-based gesture recognition</td> <td>Sensor fusion, time series</td> </tr> </tbody> </table>

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📂 Directory Structure

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📚 Documentation

<table> <tbody> <tr> <td width="20%" align="center"><b>Learners</b></td> <td><a href="https://mlsysbook.ai/kits">Online Labs</a> ・ <a href="https://mlsysbook.ai/kits/assets/downloads/Hardware-Kits.pdf">PDF</a></td> </tr> <tr> <td width="20%" align="center"><b>Contributors</b></td> <td>See build instructions above</td> </tr> </tbody> </table>

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🤝 Contributing

We welcome contributions to the hardware labs! To contribute:

1. Fork and clone the repository
2. Add or improve lab content in contents/
3. Test your changes with <kbd>quarto preview</kbd>
4. Submit a PR with a clear description

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🔗 Related

<table> <tbody> <tr> <td width="25%" align="center"><b><a href="../README.md">Main README</a></b></td> <td>Project overview and ecosystem</td> </tr> <tr> <td width="25%" align="center"><b><a href="../book/">Textbook</a></b></td> <td>ML Systems concepts and theory</td> </tr> <tr> <td width="25%" align="center"><b><a href="../tinytorch/">TinyTorch</a></b></td> <td>Build ML frameworks from scratch</td> </tr> <tr> <td width="25%" align="center"><b><a href="https://mlsysbook.ai/kits">Website</a></b></td> <td>Read labs online</td> </tr> </tbody> </table>

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Contributors

Thanks to these wonderful people who helped improve the hardware kits!

Legend: 🪲 Bug Hunter · ⚡ Code Warrior · 📚 Documentation Hero · 🎨 Design Artist · 🧠 Idea Generator · 🔎 Code Reviewer · 🧪 Test Engineer · 🛠️ Tool Builder

<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section --> <!-- prettier-ignore-start --> <!-- markdownlint-disable --> <table> <tbody> <tr> <td align="center" valign="top" width="14.28%"><a href="https://github.com/profvjreddi"> _{<b>Vijay Janapa Reddi</b>}</a> 🪲 🧑‍💻 🎨 ✍️ 🧪 🛠️</td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/Mjrovai"> _{<b>Marcelo Rovai</b>}</a> ✍️ 🧑‍💻 🎨 tutorial</td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/salmanmkc"> _{<b>Salman Chishti</b>}</a> 🧑‍💻</td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/Pratham-ja"> _{<b>Pratham Chaudhary</b>}</a> 🧑‍💻</td> </tr> </tbody> </table> <!-- markdownlint-restore --> <!-- prettier-ignore-end --> <!-- ALL-CONTRIBUTORS-LIST:END -->

Recognize a contributor: Comment on any issue or PR:

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License

Content is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).

See LICENSE.md for details.