Object Detection on FPGAs and GPUs by Using Accelerated Deep Learning
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Tarih
2019
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Object 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.
Açıklama
International Conference on Artificial Intelligence and Data Processing (IDAP) -- SEP 21-22, 2019 -- Inonu Univ, Malatya, TURKEY
Anahtar Kelimeler
Fpga, Movidius, Object Recognition, Object Detection, Deep Neural Networks
Kaynak
2019 International Conference on Artificial Intelligence and Data Processing (Idap 2019)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
