An Efficient FPGA-Based Architecture for Convolutional Neural Networks

被引：0
|
作者：
Hwang, Wen-Jyi [1 ]
Jhang, Yun-Jie [1 ]
Tai, Tsung-Ming [1 ]
机构：
[1] Natl Taiwan Normal Univ, Dept Comp Sci & Informat Engn, Taipei, Taiwan
来源：
2017 40TH INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS AND SIGNAL PROCESSING (TSP) | 2017年
关键词：
artificial vision systems; convolutional neural networks; field programmable gate array; hardware architecture; pipeline operations; COPROCESSOR;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
The goal of this paper is to implement an efficient FPGA-based hardware architectures for the design of fast artificial vision systems. The proposed architecture is capable of performing classification operations of a Convolutional Neural Network (CNN) in realtime. To show the effectiveness of the architecture, some design examples such as hand posture recognition, character recognition, and face recognition are provided. Experimental results show that the proposed architecture is well suited for embedded artificial computer vision systems requiring high portability, high computational speed, and accurate classification.
引用
收藏
页码：582 / 588
页数：7
相关论文
共 50 条
  • [41] Optimizing FPGA-Based Convolutional Neural Network Performance
    Kao, Chi-Chou
    JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS, 2023, 32 (15)
  • [42] FPGA-based Convolutional Neural Network Design and Implementation
    Yan, Ruitao
    Yi, Jianjun
    He, Jie
    Zhao, Yifan
    2023 3RD ASIA-PACIFIC CONFERENCE ON COMMUNICATIONS TECHNOLOGY AND COMPUTER SCIENCE, ACCTCS, 2023, : 456 - 460
  • [43] An Efficient FPGA-based Depthwise Separable Convolutional Neural Network Accelerator with Hardware Pruning
    Liu, Zhengyan
    Liu, Qiang
    Yan, Shun
    Cheung, Ray C. C.
    ACM TRANSACTIONS ON RECONFIGURABLE TECHNOLOGY AND SYSTEMS, 2024, 17 (01)
  • [44] A High Performance FPGA-based Accelerator for Large-Scale Convolutional Neural Networks
    Li, Huimin
    Fan, Xitian
    Jiao, Li
    Cao, Wei
    Zhou, Xuegong
    Wang, Lingli
    2016 26TH INTERNATIONAL CONFERENCE ON FIELD PROGRAMMABLE LOGIC AND APPLICATIONS (FPL), 2016,
  • [45] Optimizing FPGA-based Convolutional Neural Networks Accelerator for Image Super-Resolution
    Chang, Jung-Woo
    Kang, Suk-Ju
    2018 23RD ASIA AND SOUTH PACIFIC DESIGN AUTOMATION CONFERENCE (ASP-DAC), 2018, : 343 - 348
  • [46] Light-OPU: An FPGA-based Overlay Processor for Lightweight Convolutional Neural Networks
    Yu, Yunxuan
    Zhao, Tiandong
    Wang, Kun
    He, Lei
    2020 ACM/SIGDA INTERNATIONAL SYMPOSIUM ON FIELD-PROGRAMMABLE GATE ARRAYS (FPGA '20), 2020, : 122 - 132
  • [47] Fast and Efficient Implementation of Convolutional Neural Networks on FPGA
    Podili, Abhinav
    Zhang, Chi
    Prasanna, Viktor
    2017 IEEE 28TH INTERNATIONAL CONFERENCE ON APPLICATION-SPECIFIC SYSTEMS, ARCHITECTURES AND PROCESSORS (ASAP), 2017, : 11 - 18
  • [48] Efficient Utilization of FPGA Multipliers for Convolutional Neural Networks
    Boulasikis, M. A.
    Birbas, M.
    Tsafas, N.
    Kanakaris, N.
    2021 10TH INTERNATIONAL CONFERENCE ON MODERN CIRCUITS AND SYSTEMS TECHNOLOGIES (MOCAST), 2021,
  • [49] Efficient Design of Pruned Convolutional Neural Networks on FPGA
    Vestias, Mario
    JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY, 2021, 93 (05): : 531 - 544
  • [50] Efficient Design of Pruned Convolutional Neural Networks on FPGA
    Mário Véstias
    Journal of Signal Processing Systems, 2021, 93 : 531 - 544
12345