
This fall, Assistant Professor of Electrical and Computer Engineering Omiya Hassan taught a new course on artificial intelligence (AI) and machine learning (ML) hardware systems. Students worked all semester to develop devices that showcase the implementations and inference of optimized AI/ML on edge devices such as microcontrollers with a power consumption requirement of max 50mW.
“The outcome of this course was to equip students with the knowledge necessary to implement AI/machine-learning algorithms on resource-constrained hardware systems by focusing on optimization techniques to enhance energy efficiency,” said Hassan. “These projects helped students grasp the trade-offs between efficiency and accuracy. They also successfully deployed optimized AI/ML models on microcontrollers. As an instructor, I think it is an amazing success.”
Projects included gesture and voice control for remote sensing, an automated waste management system, and a robot that makes pancakes using the “Mixture of Experts (MoE)” machine learning model. The course culminated with live demonstrations of seven successful projects.