FPGA Design for Autonomous Vehicle Driving Using Binarized Neural Networks

Kaijie Wei, Koki Honda, Hideharu Amano

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose an autonomous vehicle controlled by FPGAs. In our design, considering embedded systems, we apply the binarized neural networks (BNNs) which can realize a satis-fying result in high speed and accuracy to recognize pedestrians and some obstacles on a given road. To detect the traffic light, a passive camera-based pipeline is applied. Furthermore, the implementation of road lane detection is based on color selection algorithm, Canny Edge Detection, and Hough Transformation. The proposed design is realized by two Xilinx boards: PYNQ-Z1 and Zynq-Xc7Z010. These two FPGA boards cooperate with each other through a shared network cable. In the proposed design, the resource used by Zynq-Xc7Z010 can be greatly reduced and the inference time on the FPGA has been thousands times faster than the software implementation.

Original languageEnglish
Title of host publicationProceedings - 2018 International Conference on Field-Programmable Technology, FPT 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages428-431
Number of pages4
ISBN (Electronic)9781728102139
DOIs
Publication statusPublished - 2018 Dec 1
Event17th International Conference on Field-Programmable Technology, FPT 2018 - Naha, Okinawa, Japan
Duration: 2018 Dec 102018 Dec 14

Publication series

NameProceedings - 2018 International Conference on Field-Programmable Technology, FPT 2018

Conference

Conference17th International Conference on Field-Programmable Technology, FPT 2018
CountryJapan
CityNaha, Okinawa
Period18/12/1018/12/14

Fingerprint

Field programmable gate arrays (FPGA)
Neural networks
Edge detection
Embedded systems
Telecommunication traffic
Cables
Pipelines
Cameras
Color

Keywords

  • Autonomous driving
  • Binarized Neural Networks
  • Canny Edge Detection
  • FPGA
  • Hough transformation
  • Image processing
  • Pynq
  • Zynq

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Hardware and Architecture

Cite this

Wei, K., Honda, K., & Amano, H. (2018). FPGA Design for Autonomous Vehicle Driving Using Binarized Neural Networks. In Proceedings - 2018 International Conference on Field-Programmable Technology, FPT 2018 (pp. 428-431). [8742321] (Proceedings - 2018 International Conference on Field-Programmable Technology, FPT 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FPT.2018.00091

FPGA Design for Autonomous Vehicle Driving Using Binarized Neural Networks. / Wei, Kaijie; Honda, Koki; Amano, Hideharu.

Proceedings - 2018 International Conference on Field-Programmable Technology, FPT 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 428-431 8742321 (Proceedings - 2018 International Conference on Field-Programmable Technology, FPT 2018).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wei, K, Honda, K & Amano, H 2018, FPGA Design for Autonomous Vehicle Driving Using Binarized Neural Networks. in Proceedings - 2018 International Conference on Field-Programmable Technology, FPT 2018., 8742321, Proceedings - 2018 International Conference on Field-Programmable Technology, FPT 2018, Institute of Electrical and Electronics Engineers Inc., pp. 428-431, 17th International Conference on Field-Programmable Technology, FPT 2018, Naha, Okinawa, Japan, 18/12/10. https://doi.org/10.1109/FPT.2018.00091
Wei K, Honda K, Amano H. FPGA Design for Autonomous Vehicle Driving Using Binarized Neural Networks. In Proceedings - 2018 International Conference on Field-Programmable Technology, FPT 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 428-431. 8742321. (Proceedings - 2018 International Conference on Field-Programmable Technology, FPT 2018). https://doi.org/10.1109/FPT.2018.00091
Wei, Kaijie ; Honda, Koki ; Amano, Hideharu. / FPGA Design for Autonomous Vehicle Driving Using Binarized Neural Networks. Proceedings - 2018 International Conference on Field-Programmable Technology, FPT 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 428-431 (Proceedings - 2018 International Conference on Field-Programmable Technology, FPT 2018).
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