Enhanced HW-NAS for Affordance Segmentation on Wearable Robotics

The automatic design of deep neural network architectures executed in real-time on mobile processors would pave the way for new developments in wearable robotics. Processing information from cameras in real-time is essential to implement semiautonomous control pipelines. This work presents a hardware-aware neural architecture search suitable to generate architectures for affordance segmentation supported by mobile processors. The procedure uses a weight-sharing mechanism to speed up the search procedure and improves the convergence capability of the network selection procedure. In addition, the proposed search space has been design to induce multi-resolution features. These factors allow the network generation procedure to select architectures with a better trade-off between generalization performance and hardware requirements when compared to existing solutions.