Tagged with: ML model updates

PROPOSAL

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 …
Supervisors: Pınar Tözün, Robert Bayer
Semester: Fall 2024
Tags: resource-constrained hardware, data management, ML model updates, tinyML

PROPOSAL

Machine Learning Models for the Edge

To enable efficient data processing and machine learning on resource-constrained devices has many challenges. One is fitting the models into the restrictive memory and compute resources of these devices. In this project, first, we would like to explore the landscape of foundational, generative-AI, language, etc. models with respect to their size and compute needs to understand what could be a fit …
Supervisors: Pınar Tözün, Robert Bayer
Semester: Fall 2024
Tags: resource-constrained hardware, data management, ML model updates, tinyML