Alphaenhancer: A Resource-Aware Game Agent for Single Image Super Resolution for Next-Generation Edge Communication Networks

Embedded resources have been becoming part of the Internet of Things networks, where they are increasingly taking part in various kinds of decision-making using Tiny Machine Learning (TinyML) models. Although offloading the TinyML model for these devices includes removing many layers that have less impact on the overall performance, they often lead to a sacrifice on the overall performance of the model. In this paper, we propose a novel device-aware training strategy to customize the training based on the resources on which the model will be applied. We proposed AlphaEnhancer, a resource-aware game agent for medical image super-resolution. We baseline our approach on the Residual Feature Distillation Model (RFDN) and propose a device efficacy metrics, which is based on the learned actions of the agent. The model with the highest efficacy is deemed appropriate for that particular device. Our preliminary results show that our methods performed significantly well with respect to the baseline and other recent state-of-the-art.