<link rel="stylesheet" href="styles.cf390c6bfbe7555f.css"> Skip to main content

學術出版;;Publications

Permanent URI for this communityhdl:11536/1

收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作,涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

Browse

Search Results

Now showing 1 - 2 of 2
  • Item type:Item,
    A SYSTOLIC ALGORITHM FOR SOLVING KNAPSACK-PROBLEMS
    (1994)
    LIN, CJ
    ;
    CHEN, SJ
    ;
    應用數學系
    ;
    Department of Applied Mathematics
    A systolic algorithm for solving the O/1-knapsack problems with n items is presented. The computational model used is a tree structure which consists of 2(n) identical processing elements (PEs). Each PE executes the same program at any time step. The time complexity varies from n to 3n - 2 steps which includes all the input/output data communication time. The design process and the correctness verification of this algorithm are considered in detail.
  • Item type:Item,
    RESIDUE SYSTOLIC IMPLEMENTATIONS FOR NEURAL NETWORKS
    (1995)
    ZHANG, CN
    ;
    WANG, M
    ;
    TSENG, CC
    ;
    資訊工程學系
    ;
    Department of Computer Science
    In this work we propose two techniques for improving VLSI implementations for artificial neural networks (ANNs). By making use of two kinds of processing elements (PEs), one dedicated to the basic operations (addition and multiplication) and another to evaluate the activation function, the total time and cost for the VLSI array implementation of ANNs can be decreased by a factor of two compared with previous work. Taking the advantage of residue number system, the efficiency of each PE can be further increased. Two RNS-based array processor designs are proposed. The first is built by look-up tables, and the second is constructed by binary adders accomplished by the mixed-radix conversion (MRC), such that the hardwares are simple and high speed operations are obtained. The proposed techniques are general enough to be extended to cover other forms of loading and learning algorithms.