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Creators/Authors contains: "Senthilkumar, Prithvi"

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  1. ; ; ; ; ; ; ; ; (, IEEE)
    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 framework 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. 
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