Beyond performance comparing the costs of applying Deep and Shallow Learning

Abstract

The rapid growth of mobile network traffic and the emergence of complex applications, such as self-driving cars and augmented reality, demand ultra-low latency, high throughput, and massive device connectivity, which traditional network design approaches struggle to meet. These issues were initially addressed in Fifth-Generation (5G) and Beyond-5G (B5G) networks, where Artificial Intelligence (AI), particularly Deep Learning (DL), is proposed to optimize the network and to meet these demanding requirements. However, the resource constraints and time limitations inherent in telecommunication networks raise questions about the practicality of deploying large Deep Neural Networks (DNNs) in these contexts. This paper analyzes the costs of implementing DNNs by comparing them with shallow ML models across multiple datasets and evaluating factors such as execution time and model interpretability. Our findings demonstrate that shallow ML models offer comparable performance to DNNs, with significantly reduced training and inference times, achieving up to 90% acceleration. Moreover, shallow models are more interpretable, as explainability metrics struggle to agree on feature importance values even for high-performing DNNs.