Parallel implementation of the feedforward back-propagation algorithm on pyramid networks

被引：0
|
作者：
Maelainin, SA
Bellaachia, A
机构：
来源：
PARALLEL AND DISTRIBUTED COMPUTING SYSTEMS - PROCEEDINGS OF THE ISCA 9TH INTERNATIONAL CONFERENCE, VOLS I AND II | 1996年
关键词：
pyramid; interconnection networks; neural networks; backpropagation; feedforward; hopfield network;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In this paper, we present a parallel implementation of the FeedForward BackPropagation algorithm on pyramids. The proposed algorithm is quite simple and easy to implement. For an ANN of L layers and a maximum of N nodes per layer, our algorithm requires a pyramid of (4N(2)-1)/3 processors to perform backpropagation algorithm in O(LN) time complexity. Note that our solution requires less than the number of nodes required by the best known solution for a mesh of appendixed trees topology of 3N(2) processors. In addition, the algorithm can be adapted to quadtrees with slight modifications. The technique used in our implementation can also be applied to the Hopfield network.
引用
收藏
页码：444 / 449
页数：6
相关论文
共 50 条
  • [21] Parallel Implementation of Feedforward Neural Networks on GPUs
    Gurgel, Saskya T. A.
    Formiga, Andrei de A.
    2013 BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS), 2013, : 143 - 149
  • [22] Application of back-propagation networks in debris flow prediction
    Chang, Tung-Chueng
    Chao, Ru-Jen
    ENGINEERING GEOLOGY, 2006, 85 (3-4) : 270 - 280
  • [23] Back-propagation is not efficient
    Sima, J
    NEURAL NETWORKS, 1996, 9 (06) : 1017 - 1023
  • [24] Layer multiplexing FPGA implementation for deep back-propagation learning
    Ortega-Zamorano, Francisco
    Jerez, Jose M.
    Gomez, Ivan
    Franco, Leonardo
    INTEGRATED COMPUTER-AIDED ENGINEERING, 2017, 24 (02) : 171 - 185
  • [25] A comparison between the back-propagation and counter-propagation networks in the modeling of the TIG welding process
    Juang, SC
    Tarng, YS
    Lii, HR
    JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 1998, 75 (1-3) : 54 - 62
  • [26] Theories of Error Back-Propagation in the Brain
    Whittington, James C. R.
    Bogacz, Rafal
    TRENDS IN COGNITIVE SCIENCES, 2019, 23 (03) : 235 - 250
  • [27] Inferring species membership using DNA sequences with back-propagation neural networks
    Zhang, A. B.
    Sikes, D. S.
    Muster, C.
    Li, S. Q.
    SYSTEMATIC BIOLOGY, 2008, 57 (02) : 202 - 215
  • [28] Study on initial value setting of the back-propagation learning algorithm
    Jia, Qi, 1600, (22):
  • [29] THE UNPREDICTABILITY OF STANDARD BACK-PROPAGATION NEURAL NETWORKS IN CLASSIFICATION APPLICATIONS
    WANG, SH
    MANAGEMENT SCIENCE, 1995, 41 (03) : 555 - 559
  • [30] Chicken S-BP: An Efficient Chicken Swarm Based Back-Propagation Algorithm
    Khan, Abdullah
    Nawi, Nazri Mohd
    Shah, Rahmat
    Akhter, Nasreen
    Ullah, Atta
    Rehman, M. Z.
    AbdulHamid, Norhamreeza
    Chiroma, Haruna
    RECENT ADVANCES ON SOFT COMPUTING AND DATA MINING, 2017, 549 : 122 - 129
12345