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Fast Context Adaptation in Cost-Aware Continual Learning
This creates friction: on the one hand, the learning process needs resources to quickly convergence to an effective strategy; on the other hand, the learning process needs to be efficient, i. e., take as few resources as possible from the user's data plane, so as not to throttle users' QoS.
The Cost of Learning: Efficiency vs. Efficacy of Learning-Based RRM for 6G
This creates a friction between the need to speed up convergence towards an effective strategy, which requires the allocation of resources to transmit learning samples, and the need to maximize the amount of resources used for data plane communication, maximizing users' Quality of Service (QoS), which requires the learning process to be efficient, i. e., minimize its overhead.
Energy Consumption of Neural Networks on NVIDIA Edge Boards: an Empirical Model
At the intersection of these trends, we hence find the energetic characterization of machine learning at the edge, which is attracting increasing attention.
