Deep learning is being adopted in robotics to accurately navigate indoor environments, detect and follow objects of interest, and maneuver without collisions. However, the increasing complexity of deep learning makes it challenging to accommodate these workloads on embedded systems. While you can make trade-offs between accuracy and deep learning model size, compromising accuracy to meet real-time��
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