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https://github.com/harvard-edge/cs249r_book.git
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ecb3af7fd2
- Add git pull --rebase before push so concurrent comment-triggered runs don't reject each other (only one of three runs had succeeded for PR 1179). - Manually add @salmanmkc as code contributor to tinytorch and kits (labs was already added by the single successful run).
171 lines
6.2 KiB
Markdown
171 lines
6.2 KiB
Markdown
# Hardware Kits
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*Hands-on Embedded ML Labs for Real Devices*
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[](https://github.com/harvard-edge/cs249r_book/actions/workflows/kits-publish-dev.yml)
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[](https://mlsysbook.ai/kits)
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[](https://mlsysbook.ai/kits/assets/downloads/Hardware-Kits.pdf)
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**[Read Online](https://mlsysbook.ai/kits)** | **[PDF](https://mlsysbook.ai/kits/assets/downloads/Hardware-Kits.pdf)**
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---
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## What This Is
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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.
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This is where AI systems meet the physical world.
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---
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## What You Will Learn
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| Concept | What You Do |
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|---------|-------------|
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| **Image Classification** | Deploy CNN models to classify images in real-time on microcontrollers |
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| **Object Detection** | Run YOLO-style detection on camera-equipped boards |
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| **Keyword Spotting** | Build always-on wake word detection with audio DSP |
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| **Motion Classification** | Use IMU sensors for gesture and activity recognition |
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| **Model Optimization** | Quantize and compress models to fit in KB of RAM |
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| **Power Management** | Balance accuracy vs battery life for edge deployment |
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### Hardware Platforms
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| Platform | Description | Best For |
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|----------|-------------|----------|
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| **Arduino Nicla Vision** | Compact AI camera board with STM32H7 | Vision projects, ultra-low power |
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| **Seeed XIAO ESP32S3** | Tiny ESP32-S3 with camera support | WiFi-connected vision |
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| **Grove Vision AI V2** | No-code AI vision module | Rapid prototyping |
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| **Raspberry Pi** | Full Linux SBC for edge AI | Complex pipelines, prototyping |
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---
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## Quick Start
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### For Learners
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1. Pick a platform from the [labs](https://mlsysbook.ai/kits)
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2. Follow the setup guide for your hardware
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3. Complete the labs in order: Setup → Image Classification → Object Detection → Keyword Spotting
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### For Contributors
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```bash
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cd kits
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# Build HTML site
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ln -sf config/_quarto-html.yml _quarto.yml
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quarto render
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# Build PDF
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ln -sf config/_quarto-pdf.yml _quarto.yml
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quarto render --to titlepage-pdf
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# Preview with live reload
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quarto preview
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```
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---
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## Labs Overview
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Each platform includes progressive labs:
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| Lab | What You Build | Skills |
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|-----|----------------|--------|
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| **Setup** | Hardware setup and environment configuration | Toolchain, flashing, debugging |
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| **Image Classification** | CNN-based image recognition | Model deployment, inference |
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| **Object Detection** | Real-time object detection | YOLO, bounding boxes |
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| **Keyword Spotting** | Audio wake word detection | DSP, MFCC features |
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| **Motion Classification** | IMU-based gesture recognition | Sensor fusion, time series |
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---
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## Directory Structure
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```
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kits/
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├── contents/ # Lab content
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│ ├── arduino/ # Arduino Nicla Vision labs
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│ ├── seeed/ # Seeed XIAO & Grove Vision labs
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│ ├── raspi/ # Raspberry Pi labs
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│ └── shared/ # Shared resources (DSP, features)
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├── assets/ # Images, styles, scripts
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├── config/ # Quarto configurations
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│ ├── _quarto-html.yml # Website config
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│ └── _quarto-pdf.yml # PDF config
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├── tex/ # LaTeX includes for PDF
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├── filters/ # Lua filters
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└── index.qmd # Landing page
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```
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---
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## Documentation
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| Audience | Resources |
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|----------|-----------|
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| **Learners** | [Online Labs](https://mlsysbook.ai/kits) ・ [PDF](https://mlsysbook.ai/kits/assets/downloads/Hardware-Kits.pdf) |
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| **Contributors** | See build instructions above |
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---
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## Contributing
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We welcome contributions to the hardware labs! To contribute:
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1. Fork and clone the repository
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2. Add or improve lab content in `contents/`
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3. Test your changes with `quarto preview`
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4. Submit a PR with a clear description
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---
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## Related
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| Component | Description |
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|-----------|-------------|
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| **[Main README](../README.md)** | Project overview and ecosystem |
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| **[Textbook](../book/)** | ML Systems concepts and theory |
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| **[TinyTorch](../tinytorch/)** | Build ML frameworks from scratch |
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| **[Website](https://mlsysbook.ai/kits)** | Read labs online |
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---
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## Contributors
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Thanks to these wonderful people who helped improve the hardware kits!
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**Legend:** 🪲 Bug Hunter · ⚡ Code Warrior · 📚 Documentation Hero · 🎨 Design Artist · 🧠 Idea Generator · 🔎 Code Reviewer · 🧪 Test Engineer · 🛠️ Tool Builder
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<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->
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<!-- prettier-ignore-start -->
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<!-- markdownlint-disable -->
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<table>
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<tbody>
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<tr>
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<td align="center" valign="top" width="14.28%"><a href="https://github.com/profvjreddi"><img src="https://avatars.githubusercontent.com/profvjreddi?v=4?s=80" width="80px;" alt="Vijay Janapa Reddi"/><br /><sub><b>Vijay Janapa Reddi</b></sub></a><br />🪲 🧑💻 🎨 ✍️ 🧪 🛠️</td>
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<td align="center" valign="top" width="14.28%"><a href="https://github.com/Mjrovai"><img src="https://avatars.githubusercontent.com/Mjrovai?v=4?s=80" width="80px;" alt="Marcelo Rovai"/><br /><sub><b>Marcelo Rovai</b></sub></a><br />✍️ 🧑💻 🎨 tutorial</td>
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<td align="center" valign="top" width="14.28%"><a href="https://github.com/salmanmkc"><img src="https://avatars.githubusercontent.com/u/32169182?v=4?v=4?s=80" width="80px;" alt="Salman Chishti"/><br /><sub><b>Salman Chishti</b></sub></a><br />🧑💻</td>
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</tr>
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</tbody>
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</table>
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<!-- markdownlint-restore -->
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<!-- prettier-ignore-end -->
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<!-- ALL-CONTRIBUTORS-LIST:END -->
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**Recognize a contributor:** Comment on any issue or PR:
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```
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@all-contributors please add @username for tool, test, video, or doc
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```
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---
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## License
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Content is licensed under **Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International** (CC BY-NC-SA 4.0).
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See [LICENSE.md](../LICENSE.md) for details.
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