AutoDeep research project

AutoDeep stands for Automatic Design of Safe, High-Performance and Compact Deep Learning Models for Autonomous Vehicles

In this project, we will develop the AutoDeep framework to design performance-efficient DNNs suitable for deployment on embedded resources-limited computing platforms while enhancing the robustness of DNN models.

The mission is to strengthen Swedish industrial competence and competitiveness in the area of deep learning in the context of autonomous systems through close collaboration between academia and industry. AutoDeep can have a tangible impact on designing DL architectures for safety-critical applications and thus a successful demonstration of AutoDeep can increase Swedish industry’s market shares in ICT sectors that produce safe and high-performance embedded computing platforms for autonomous systems.

Project manager at MDU: Masoud Daneshtalab