Power Control for Edge ML Inference With Hypernetwork Meta-Parameters

In recent years, machine learning (ML) tasks have been widely deployed at the edge of wireless networks, e.g., autonomous cars and tactile robots. However, the impairments of wireless channels between devices, such as fading and noise, deteriorate the effectiveness of ML inference tasks. In this work, we propose an efficient framework with an adaptive power control mechanism, which considers the constraint of the limited energy budget of edge devices. To guarantee the inference performance of ML tasks that are transmitted through wireless channels, we design hypernetworks with meta-parameters. The hypernetwork takes the context, such as the network condition, as the input and outputs the parameters of the power control network and artificial intelligence (AI) model. The training loss is designed by minimizing the trade-off between inference performance and energy consumption. Simulation results verify the effectiveness of the proposed adaptive inference framework on energy saving while ensuring the accuracy of inferring ML tasks.