Department: HMS Labs
Size: 1-4 students
Scope: Artificial Intelligence, Deep Learning, Neural Networks
Description: There is an increasing interest in intelligent applications for industrial use cases. On the factory floor of tomorrow, artificial neural networks will make billions of informed decisions in all steps of the manufacturing process. Training neural networks is a resource-intensive process. Factory floors today are filled with embedded devices that individually offer limited computing power but, together, they could have significant computational capabilities. The goal of this thesis project is to develop a solution with which neural networks could be trained and executed across several smaller devices. This way, intensive workloads like training of neural networks could be distributed across many embedded devices during periods with lower activity.
The project and scope can be adjusted to the size of the group and the participants.
Prerequisites: Bachelor or master program within computer science or similar; knowledge in artificial intelligence, deep learning, and experience in C/C++, make, CMake is an advantage.
Contact person
Felix Nilsson
+46 7667 753 51
fenil@hms.se