DeepNetQoE: Self-adaptive QoE Optimization Framework of Deep Networks

TL;DR

DeepNetQoE introduces a self-adaptive framework that optimizes deep network training by balancing accuracy and computational resources, enhancing user experience under resource constraints.

Contribution

It presents a novel self-adaptive QoE model and resource allocation method for deep networks, guiding training based on experience value and resource availability.

Findings

DeepNetQoE effectively adapts to limited resources to maintain high experience value.

Experimental results on four models validate the framework's ability to optimize resource use.

The approach guides resource allocation to improve model training efficiency.

Abstract

Future advances in deep learning and its impact on the development of artificial intelligence (AI) in all fields depends heavily on data size and computational power. Sacrificing massive computing resources in exchange for better precision rates of the network model is recognized by many researchers. This leads to huge computing consumption and satisfactory results are not always expected when computing resources are limited. Therefore, it is necessary to find a balance between resources and model performance to achieve satisfactory results. This article proposes a self-adaptive quality of experience (QoE) framework, DeepNetQoE, to guide the training of deep networks. A self-adaptive QoE model is set up that relates the model's accuracy with the computing resources required for training which will allow the experience value of the model to improve. To maximize the experience value when computer resources are limited, a resource allocation model and solutions need to be established. In addition, we carry out experiments based on four network models to analyze the experience values with respect to the crowd counting example. Experimental results show that the proposed DeepNetQoE is capable of adaptively obtaining a high experience value according to user needs and therefore guiding users to determine the computational resources allocated to the network models.