Deep Learning of Telecommunication Technology for the Efficient Utilization of Embedded Systems

Abstract

Deep Learning is a subset of Machine Learning, an Artificial Intelligence approach that can work with very big and complex datasets. Since it not only processes big data but also learns from it automatically, its range of applications and frequency of use is increasing. Deep Learning is used in telecommunications for network quality improvement, network optimization, secure information transfer, big data analysis, providing a virtual assistant, offering solutions to customers by processing voice with NLP, filtering noise, adjusting bandwidth, predicting failures, and preventing security breaches, among many other solutions. Methods such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Networks for Time Series Analysis can also be used effectively in embedded system applications, especially for the prediction of failures and security violations. Fast and uninterrupted data transmission can be achieved using central servers, as the waiting times and potential delays often experienced during data transfer to cloud-based systems (IoT) are mitigated by using more powerful and energy-efficient GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units). It makes embedded systems more preferable. TPUs offer even higher efficiency and performance for deep learning workloads, while GPUs, with their parallel processing capabilities, are well-suited for matrix and vector calculations common in deep learning. Solutions such as computer vision, image processing, and natural language processing, which can also be applied in embedded systems, are becoming increasingly widespread. Edge AI Platforms are Software platforms and frameworks designed to optimize deep learning models for execution on embedded devices. © 2025 Elsevier B.V., All rights reserved.