Building Approachable, Hands-On Embedded Machine Learning Curriculum Using Edge Impulse and Arduino
Embedded machine learning (ML) is the process of running machine learning algorithms on low-cost, resource-constrained microcontrollers and single board computers and is currently used to solve unique problems in industry and academia. As a result, there is a growing demand to teach embedded ML in higher education institutions to prepare developers, engineers, and researchers for these emerging data-driven approaches.
This lab provides hands-on experience creating an end-to-end embedded ML system using a combination of Edge Impulse and Arduino. In the lab, we will create and deploy a full image classification system by performing data collection using Arduino, model training with Edge Impulse, and live inference with Arduino. This will provide researchers and educators with the knowledge and tools to develop an approachable curriculum around embedded ML.
Attendees should have an interest in finding ways to make ML easier and approachable to students, especially those outside of the computer science field. Such attendees could be professors or lecturers from other disciplines or those teaching ML in cross-cutting programs. Some programming experience will be needed (ideally C/C++ or Arduino). No machine learning experience is required.
Schedule
| Time | Topic | Materials |
|---|---|---|
8:30:00 AM |
Introduction to Embedded ML | |
8:45:00 AM |
Hands-on Image Data Collection | |
9:30:00 AM |
Hands-on Image Data Augmentation | |
9:50:00 AM |
Hands-on Model Training in Edge Impulse | |
10:00:00 AM |
Computer Vision 102 | |
10:30:00 AM |
Break | |
10:50:00 PM |
Hands-on Model Deployment | |
11:25:00 AM |
TinyML Optimizations and Next Steps | |
11:50:00 AM |
Hands-on Using Model Outputs | |
12:20:00 PM |
Session Closing |
Team
Registration
Register for our tutorial and lab form through the main AAAI-23 website.
Supporters
We would like to thank ICTP, Harvard SEAS, and the tinyML Foundation for their continued support of TinyML educational content!
Questions?
Contact edu@tinyml.org with any questions regarding this workshop.