Efficient-DASH: Automated Radar Neural Network Design Across Tasks and Datasets

This work shows the benefit of Neural Architecture Search (NAS) for robust and efficient radar neural network design. Radar-based neural networks have received increasing attention in the development of Advanced Driver-Assistance Systems. However, many state-of-the-art networks are designed for specific tasks and datasets, often disregarding network efficiency, leading to a lack of robust methods for efficient radar network design. This paper leverages a differentiable NAS method called DASH to automate the design of radar neural networks. We evaluate our method across multiple tasks and datasets to assess its robustness. Additionally, we introduce Efficient-DASH, which leverages compound scaling to discover more efficient networks. Our results show that DASH outperforms a state-of-the-art radar network across tasks, while maintaining strong performance when generalizing to other datasets. Efficient-DASH finds architectures that are more efficient than the baseline network, while still reporting performance gains over 4.5%. This demonstrates the potential of NAS for robust and efficient radar neural network design.