Object Detection on FPGAs and GPUs by Using Accelerated Deep Learning

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dc.contributor.authorCambay, V. Yusuf
dc.contributor.authorUcar, Aysegul
dc.contributor.authorArserim, M. Ali
dc.date.accessioned2024-04-24T17:11:23Z
dc.date.available2024-04-24T17:11:23Z
dc.date.issued2019
dc.departmentDicle Üniversitesien_US
dc.descriptionInternational Conference on Artificial Intelligence and Data Processing (IDAP) -- SEP 21-22, 2019 -- Inonu Univ, Malatya, TURKEYen_US
dc.description.abstractObject detection and recognition is one of the main tasks in many areas such as autonomous unmanned ground vehicles, robotic and medical image processing. Recently, deep learning has been used by many researchers in these areas when the data measure is large. In particular, one of the most up-to-date structures of deep learning, Convolutional Neural Networks (CNNs) has achieved great success in this field. Real-time works related to CNNs are carried out by using GPU-Graphics Processing Units. Although GPUs provides high stability, they requires high power, energy consumption, and large computational load problems. In order to overcome this problem, it has started to used the Field Programmable Gate Arrays (FPGAs). In this article, object detection and recognition procedures were performed using the ZYNQ XC7Z020 development board including both the ARM processor and the FPGA. Real-time object recognition has been made with the Movidius USB-GPU externally plugged into the FPGA. The results are given with figures.en_US
dc.description.sponsorshipIEEE Turkey Sect,Anatolian Sci,Inonu Univ, Comp Sci Dept,Inonu Univ, Muhendisli Fakultesien_US
dc.description.sponsorshipFirat University [MF.17.33]; Xilinx Universityen_US
dc.description.sponsorshipThis research was supported/partially in MF.17.33 project by Firat University and the Xilinx Universityen_US
dc.identifier.doi10.1109/idap.2019.8875870
dc.identifier.scopus2-s2.0-85074890677
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/idap.2019.8875870
dc.identifier.urihttps://hdl.handle.net/11468/17457
dc.identifier.wosWOS:000591781100002
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2019 International Conference on Artificial Intelligence and Data Processing (Idap 2019)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFpgaen_US
dc.subjectMovidiusen_US
dc.subjectObject Recognitionen_US
dc.subjectObject Detectionen_US
dc.subjectDeep Neural Networksen_US
dc.titleObject Detection on FPGAs and GPUs by Using Accelerated Deep Learningen_US
dc.titleObject Detection on FPGAs and GPUs by Using Accelerated Deep Learning
dc.typeConference Objecten_US

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