Tagged with: tinyML

PROPOSAL

How much Data Movement is Being Saved?

In this project, we would specifically like to quantify the data movement savings of applying techniques like compression and model-based data filtering in the context of resource-constrained hardware and edge/IoT applications. Today many data sources are small low-powered and hardware-constrained devices such as mobile phones, wearable or self-driving smart platforms, etc. Processing the data on …
Supervisors: Pınar Tözün, Robert Bayer
Semester: Fall 2024
Tags: resource-constrained hardware, data management, ML model updates, tinyML

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

PROPOSAL

Resource Management on Tiny Hardware

Today many data sources are small low-powered and hardware-constrained devices such as mobile phones, wearable or self-driving smart platforms, etc. Edge computing is a broad term that refers to computations performed on such edge devices. It becomes increasingly important to enable techniques that get more value out of data at the edge rather than always sending the data to a remote and more …
Supervisors: Pınar Tözün, Robert Bayer
Semester: Fall 2024
Tags: resource-constrained hardware, data management, resource management, tinyML