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

Allen-Jasmin Farcas, Geffen Cooper, Hyun Joon Song, Afnan Mir, Vincent Liew, Chloe Tang, Prithvi Senthilkumar, Tiani Chen-Troester, Radu Marculescu

Abstract: 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.