An Edge AI Device Based Intelligent Transportation System
Published in Journal of information and communication convergence engineering (JICCE), 2022
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 field-programmable 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.
Youngwoo Jeong, Hyun Woo Oh, Soohee Kim, Seung Eun Lee. "An Edge AI Device Based Intelligent Transportation System." Journal of information and communication convergence engineering (JICCE), 2022.