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Achieve real-time traffic analytics across city-scale camera networks by offloading DNN inference to edge devices and using cloud-based GNNs for forecasting, all while dynamically adapting to changing conditions with federated learning.
Achieve near-optimal throughput for concurrent DNN training and inference on edge devices by intelligently time-slicing workloads and dynamically adjusting power modes, even with limited profiling.
