Deep learning models are widely used for automated driving, but achieving the goal of performance in embedded Systems-on-Chip (SoCs) has a lot of challenges due to the tradeoff between accuracy and run-time. This paper addresses the task of finding a suitable neural architecture for heterogeneous Systems-on-Chip (SoCs) in order to strike a balance between accuracy and run-time efficiency. Our appr...Show More
