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.


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
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


Shawn Hymel
Shawn Hymel
Senior DevRel Engineer, Edge Impulse
Brian Plancher
Brian Plancher
Assistant Professor, Barnard College, Columbia University


Register for our tutorial and lab form through the main AAAI-23 website.


We would like to thank ICTP, Harvard SEAS, and the tinyML Foundation for their continued support of TinyML educational content!


Contact with any questions regarding this workshop.