Exploiting Edge Devices for Data-Intensive Applications

The work on running data-intensive applications on very powerful, expensive, and power-hungry server hardware is very popular thanks to the growing size of data centers and high-performance computing (HPC) platforms. However, with the rise of new generation internet of things (IoT) applications, the lower-power and lower-budget hardware devices that specifically target IoT, the edge platforms, embedded devices, etc. have also become quite powerful today offering non-negligible data processing capability. For example, NVIDIA Jetsons and Coral devices allow GPU- and TPU-acceleration of AI applications at the edge and come with hardware resources that can match the resources of a laptop. In addition, the ecosystem build around these devices has also improved drastically and allow user-friendly coding. For example, Xilinx’ PYNQ offer a python interface for data scientists on top of an FPGA.

In this broad project topic, our goal is to investigate thoroughly the data processing capabilities of edge devices. Based on mutual interests and hardware availability, we can determine the specific data-intensive application domain, workloads, datasets, and device to use for this goal in the context of your project or thesis.

If you are interested in resource-constrained hardware, benchmarking, and data-intensive applications (databases or machine learning in general), this project would be a great fit for you.

This project would be suitable as a standalone project or BSc or MSc thesis at ITU during Fall 2024.