Hardware Kits

Hands-On Embedded ML Labs for Real-World Deployment

Embedded ML Hardware Platforms

From $20 microcontrollers to powerful edge devices

These hands-on laboratories accompany the Machine Learning Systems textbook, bringing theory to life on real hardware. Deploy machine learning on embedded devices you can hold in your hand, from image classification to voice recognition to motion detection. Professional development boards costing $25-100 provide immediate, tangible feedback: LEDs light up, motors spin, and buzzers sound when your model runs successfully.

Working within the resource constraints of embedded devices (typically 2MB of RAM and 1MB of flash) forces you to confront the same engineering trade-offs that define large-scale ML systems, but in a tangible environment where every optimization decision has immediate, observable consequences.

NoteLaboratory Development

These hands-on laboratories were co-designed by Prof. Vijay Janapa Reddi and Marcelo Rovai, with Marcelo leading their development. His decades of embedded systems expertise shaped accessible, practical learning experiences that bridge theory with real-world implementation.

Hardware Platforms

Grove Vision AI V2

Grove Vision AI V2

~$25

Best for beginners. Plug & play vision AI.
XIAOML Kit

XIAOML Kit

~$40

Best value. Vision, audio, motion.
Raspberry Pi

Raspberry Pi

~$60-80

Advanced. LLMs, VLMs, edge AI.
Arduino Nicla Vision

Nicla Vision

~$95

Professional. Dual sensors, compact.

What You Will Build

👁️ Computer Vision

Image classification and object detection on microcontrollers. Train models to recognize objects, detect faces, or classify scenes.

🎤 Audio Processing

Keyword spotting and voice command recognition. Build wake-word detectors and voice interfaces that run entirely on-device.

🏃 Motion Classification

Activity and gesture recognition from IMU data. Create wearable-style applications using accelerometer and gyroscope sensors.

🤖 Large Language Models

Run LLMs and VLMs on edge devices. Experience the frontier of on-device AI with models that understand and generate text.

Getting Started

  1. Choose Hardware: Select a platform based on your budget and learning goals. See Platforms for detailed comparisons.

  2. Set Up Environment: Install Arduino IDE or platform-specific tools. Follow the IDE Setup Guide for step-by-step instructions.

  3. Build & Deploy: Work through the labs for your chosen platform. Start with Getting Started for an overview of available exercises.

Part of the MLSysBook Ecosystem

Textbook

Comprehensive theory and concepts covering the full ML systems stack.

Hardware Kits

Hands-on embedded deployment. You are here.

TinyTorch

Build your own ML framework from scratch.

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