A Comparison Between CPU and GPU Computing in DNN-Based DoA Estimation

Georgios Kokkinis, Qasim Z. Ahmed, Alistair Sambell, Ioannis P. Chochliouros, Pavlos I. Lazaridis, Zaharias D. Zaharis

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Deep Learning algorithms have recently become accessible to modern Telecommunication systems due to the advancement in both hardware and software technology. The massively parallel computational tasks of Artificial Intelligence (AI) training are now directly performed in Graphic Processing Units (GPUs), which contain many processing cores. This paper tests the improvement in computing time for Deep Neural Network (DNN) architectures that solve the Direction of Arrival (DoA) estimation problem as a classification task by utilizing the Nvidia CUDA framework. The training and testing data are generated by receiving signal information from a simulated antenna array. The input of the DNN is a 64 × 64 image with two channels, whereas the problem is modelled as a multi-label classification task. The purpose of this research is to observe the acceleration that the GPU provides in the DoA estimation classification problem. The project includes benchmarks from a wide range of computing systems and the results demonstrate the potential of DNN-based DoA estimators as a modern technology that can be deployed on available commercial hardware.

Original languageEnglish
Title of host publicationProceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 2
EditorsAndrew D. Ball, Huajiang Ouyang, Jyoti K. Sinha, Zuolu Wang
PublisherSpringer, Cham
Number of pages10
ISBN (Electronic)9783031494215
ISBN (Print)9783031494208, 9783031494239
Publication statusPublished - 29 May 2024
EventThe UNIfied Conference of DAMAS, InCoME and TEPEN Conferences - Huddersfield, United Kingdom, Huddersfield, United Kingdom
Duration: 29 Aug 20231 Sep 2023

Publication series

NameMechanisms and Machine Science
Volume152 MMS
ISSN (Print)2211-0984
ISSN (Electronic)2211-0992


ConferenceThe UNIfied Conference of DAMAS, InCoME and TEPEN Conferences
Abbreviated titleUNIfied 2023
Country/TerritoryUnited Kingdom
Internet address

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