Demo Abstract: Online Training and Inference for On-Device Monocular Depth Estimation

A central challenge in machine learning deployment is maintaining accurate and updated models as the deployment environment changes over time. We present a hardware/software framework for simultaneous training and inference for monocular depth estimation on edge devices. Our proposed frame-work can be used as a hardware/software co-design tool that enables continual and online federated learning on edge devices. Our results show real-time training and inference performance, demonstrating the feasibility of online learning on edge devices.