• Title/Summary/Keyword: Embedded AI processor

Search Result 3, Processing Time 0.021 seconds

Design of Stand-alone AI Processor for Embedded System (독립운용이 가능한 임베디드 인공지능 프로세서 설계)

  • Cho, Kwon Neung;Choi, Do Young;Jeong, Young Woo;Lee, Seung Eun
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
    • /
    • 2021.05a
    • /
    • pp.600-602
    • /
    • 2021
  • With the development of the mobile industry and growing interest in artificial intelligence (AI) technology, a lot of research for AI processors which applicable to embedded systems is under study. When implementing AI to embedded systems, the design should be considered the restriction of resource and power consumption. Moreover, it is efficient to include a dedicated hardware accelerator in order to complement the low computational performance of the embedded system. In this paper, we propose an stand-alone embedded AI processor. The proposed AI processor includes a hardware accelerator that is dedicated to the distance-based AI algorithm and a general-purpose MCU that supports flexible programmability for application to various embedded systems. The AI processor was designed with Verilog HDL and verified by implementing on Field Programmable Gate Array (FPGA).

  • PDF

Performance Analyzer for Embedded AI Processor (내장형 인공지능 프로세서를 위한 성능 분석기)

  • Hwang, Dong Hyun;Yoon, Young Hyun;Han, Chang Yeop;Lee, Seung Eun
    • Journal of Internet Computing and Services
    • /
    • v.21 no.5
    • /
    • pp.149-157
    • /
    • 2020
  • Recently, as interest in artificial intelligence has increased, many studies have been conducted to implement AI processors. However, the AI processor requires functional verification as well as performance verification on whether the AI processor is suitable for the application. In this paper, We propose an AI processor performance analyzer that can verify the application performance and explore the limitations of the processor. By Using the performance analyzer, we explore the limitations of the AI processor and optimize the AI model to fit an AI processor in image recognition and speech recognition applications.

An Edge AI Device based Intelligent Transportation System

  • Jeong, Youngwoo;Oh, Hyun Woo;Kim, Soohee;Lee, Seung Eun
    • Journal of information and communication convergence engineering
    • /
    • v.20 no.3
    • /
    • pp.166-173
    • /
    • 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 high-performance 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.