Optimization of optical convolution kernel of optoelectronic hybrid convolution neural network

被引：1
|
作者：
XU Xiaofeng [1 ]
ZHU Lianqing [1 ,2 ]
ZHUANG Wei [2 ]
ZHANG Dongliang [2 ]
LU Lidan [3 ]
YUAN Pei [3 ]
机构：
[1] School of Electro-Optical Engineering, Changchun University of Science and Technology
[2] Beijing Laboratory of Optical Fiber Sensing and System, Beijing Information Science and Technology University
[3] Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University
来源：
Optoelectronics Letters | 2022年 / 03期
基金：
中国国家自然科学基金;
关键词：
D O I：
暂无
中图分类号：
TP183 [人工神经网络与计算]; TN791 [];
学科分类号：
080902 ; 081104 ; 0812 ; 0835 ; 1405 ;
摘要：
To enhance the optical computation’s utilization efficiency, we develop an optimization method for optical convolution kernel in the optoelectronic hybrid convolution neural network(OHCNN). To comply with the actual calculation process, the convolution kernel is expanded from single-channel to two-channel, containing positive and negative weights. The Fashion-MNIST dataset is used to test the network architecture’s accuracy, and the accuracy is improved by 7.5% with the optimized optical convolution kernel. The energy efficiency ratio(EER) of two-channel network is 46.7% higher than that of the single-channel network, and it is 2.53 times of that of traditional electronic products.
引用
收藏
页码：181 / 186
页数：6
相关论文
共 50 条
  • [1] Optimization of optical convolution kernel of optoelectronic hybrid convolution neural network
    Xiaofeng Xu
    Lianqing Zhu
    Wei Zhuang
    Dongliang Zhang
    Lidan Lu
    Pei Yuan
    [J]. Optoelectronics Letters, 2022, 18 : 181 - 186
  • [2] Optimization of optical convolution kerne of optoelectronic hybrid convolution neural network
    Xu Xiaofeng
    Zhu Lianqing
    Zhuang Wei
    Zhang Dongliang
    Lu Lidan
    Yuan Pei
    [J]. OPTOELECTRONICS LETTERS, 2022, 18 (03) : 181 - 186
  • [3] Deep Rotating Kernel Convolution Neural Network
    Shin, Crino
    Yun, Jongpil
    [J]. 2019 THIRD IEEE INTERNATIONAL CONFERENCE ON ROBOTIC COMPUTING (IRC 2019), 2019, : 441 - 442
  • [4] Infrared object classification with a hybrid optical convolution neural network
    Chen, Jianbo
    Talley, Jennifer
    Kelly, Kevin F.
    [J]. APPLIED OPTICS, 2021, 60 (25) : G224 - G231
  • [5] Compact lensless convolution processor for an optoelectronic convolutional neural network
    Zhang, Zaikun
    Kong, Depeng
    Da, Zhengshang
    Wang, Ruiduo
    Wang, Shijie
    Geng, Yi
    He, Zhengquan
    [J]. JOURNAL OF PHYSICS D-APPLIED PHYSICS, 2023, 56 (35)
  • [6] Hybrid optical convolutional neural network with convolution kernels trained in the spatial domain
    Gene, Jinhwa
    Park, Suntak
    Shin, Hyung Cheol
    Sohn, Jong Moo
    [J]. NEUROCOMPUTING, 2024, 573
  • [7] Convolutional Neural Network with Spatial-Variant Convolution Kernel
    Dai, Yongpeng
    Jin, Tian
    Song, Yongkun
    Sun, Shilong
    Wu, Chen
    [J]. REMOTE SENSING, 2020, 12 (17) : 1 - 25
  • [8] Optimization of Convolution Neural Network Algorithm Based on FPGA
    Tang, Feixue
    Zhang, Weichao
    Tian, Xiaogang
    Fan, Xiaoye
    Cao, Xixin
    [J]. EMBEDDED SYSTEMS TECHNOLOGY, ESTC 2017, 2018, 857 : 131 - 140
  • [9] Memory optimization at Edge for Distributed Convolution Neural Network
    Naveen, Soumyalatha
    Kounte, Manjunath R.
    [J]. TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES, 2022, 33 (12):
  • [10] Single Image Dehazing Using Hybrid Convolution Neural Network
    Juneja, Akshay
    Kumar, Vijay
    Singla, Sunil Kumar
    [J]. MULTIMEDIA TOOLS AND APPLICATIONS, 2023, 83 (13) : 38355 - 38386
12345