Resource Management on Tiny Hardware

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

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 powerful hardware device in the cloud for further data processing and training machine learning models. Processing the data closer to the source would reduce data movement. This, in turn, would reduce latency, costs, and power required to deploy data-intensive applications at the edge.

To enable efficient data processing on resource-constrained devices, though, has many challenges. One is the management of hardware resources as we run several data-intensive tasks from SQL-based data processing to machine learning model inference.

In this project, we would like to explore hardware resource management challenges at the edge. We are flexible in terms of the set of edge devices or data-intensive tasks to explore. We can adjust this set based on the project group size (number of students taking part in the project) and duration of the project (BSc or MSc thesis, regular project course, etc.). If you are interested in edge IoT, benchmarking, hardware, and data management, in general, this project would be a great fit for you.