TY - JOUR
T1 - An Edge AI Device based Intelligent Transportation System
AU - Jeong, Youngwoo
AU - Oh, Hyun Woo
AU - Kim, Soohee
AU - Lee, Seung Eun
N1 - Publisher Copyright:
© The Korea Institute of Information and Communication Engineering
PY - 2022
Y1 - 2022
N2 - Recently, studies have been conducted on intelligent transportation systems (ITS) that provide safety and convenience to humans. Systems that compose the ITS adopt architectures that applied the cloud computing which consists of a highperformance general-purpose processor or graphics processing unit. However, an architecture that only used the cloud computing requires a high network bandwidth and consumes much power. Therefore, applying edge computing to ITS is essential for solving these problems. In this paper, we propose an edge artificial intelligence (AI) device based ITS. Edge AI which is applicable to various systems in ITS has been applied to license plate recognition. We implemented edge AI on a fieldprogrammable gate array (FPGA). The accuracy of the edge AI for license plate recognition was 0.94. Finally, we synthesized the edge AI logic with Magnachip/Hynix 180nm CMOS technology and the power consumption measured using the Synopsys’s design compiler tool was 482.583mW.
AB - Recently, studies have been conducted on intelligent transportation systems (ITS) that provide safety and convenience to humans. Systems that compose the ITS adopt architectures that applied the cloud computing which consists of a highperformance general-purpose processor or graphics processing unit. However, an architecture that only used the cloud computing requires a high network bandwidth and consumes much power. Therefore, applying edge computing to ITS is essential for solving these problems. In this paper, we propose an edge artificial intelligence (AI) device based ITS. Edge AI which is applicable to various systems in ITS has been applied to license plate recognition. We implemented edge AI on a fieldprogrammable gate array (FPGA). The accuracy of the edge AI for license plate recognition was 0.94. Finally, we synthesized the edge AI logic with Magnachip/Hynix 180nm CMOS technology and the power consumption measured using the Synopsys’s design compiler tool was 482.583mW.
KW - Character recognition
KW - Edge ai device
KW - Embedded system
KW - Intelligent transportation system
KW - License plate detection
UR - https://www.scopus.com/pages/publications/85143178453
U2 - 10.56977/jicce.2022.20.3.166
DO - 10.56977/jicce.2022.20.3.166
M3 - Article
AN - SCOPUS:85143178453
SN - 2234-8255
VL - 20
SP - 166
EP - 173
JO - Journal of Information and Communication Convergence Engineering
JF - Journal of Information and Communication Convergence Engineering
IS - 3
ER -
