Incremental Model Updates on Tiny Hardware

One of the key challenges with enabling efficient machine learning on resource-constrained devices is keeping the machine learning models deployed on these devices up-to-date without frequent retraining. This requires exploring the impact of different model update mechanisms at the edge.

This project would be suitable as a standalone project or BSc or MSc thesis at ITU during Fall 2024. If you are interested in resource-constrained hardware, benchmarking, and machine learning in general, this project would be a great fit for you.