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, Raspberry Pi 4B+ comes with 4GB of RAM and NVIDIA Jetson allow GPU-acceleration of AI applications at the edge. In addition, the ecosystem build around these devices has also improved drastically. 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 your interests, 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.