Evaluating the Impact of Collocating Deep Learning Training Tasks on Jetson Orion Nano GPUs

This project investigates how running multiple deep learning training tasks simultaneously (collocation) affects performance on resource-constrained edge devices, specifically the NVIDIA Jetson Orion Nano. Students will deploy and benchmark various models (e.g., CNNs, Transformers) in isolated vs. different collocated scenarios, measure metrics such as GPU utilization, memory usage, training time, and analyze the trade-offs between throughput, fairness, and system efficiency.

This project would be suitable as a BSc or MSc thesis. If you are interested in machine learning systems and their efficiency in general, this project would be a great fit for you.