• DocumentCode
    1797619
  • Title

    Efficient diminished-1 modulo 2n+1 multiplier architectures

  • Author

    Xiaolan Lv ; Ruohe Yao

  • Author_Institution
    Sch. of Electron. & Inf. Eng., South China Univ. of Technol., Guangzhou, China
  • fYear
    2014
  • fDate
    6-11 July 2014
  • Firstpage
    481
  • Lastpage
    486
  • Abstract
    The main components of an artificial neuron are adders and multipliers. In order to implement neural network, large number of adders and multipliers are required. The efficient architectures for diminished-1 modulo 2n+1 multipliers are described. The results and operands of the new modulo 2n+1 multipliers use the diminished-1, avoiding n+1 bit circuit. And the presented multipliers can handle zero inputs and results. The proposed modulo 2n +1 multiplier are built using three major functional modules, partial products generation block, partial products reduction block and a final diminished-1 adder block. The final modulo 2n +1 addition block is built around a sparse carry computation unit for the analytical and experimental results. And this indicates that the significant area and power of the proposed multipliers is superior to the earlier proposals, with a high operation speed.
  • Keywords
    adders; multiplying circuits; neural nets; adders; artificial neuron; diminished-1 adder block; diminished-1 modulo 2n+1 multiplier architectures; functional modules; multipliers; neural network; operands; partial products generation block; partial products reduction block; sparse carry computation unit; Adders; Arrays; Biological neural networks; Delays; Logic gates; Vectors; Diminished-1 representation; Residue number system (RNS); VLSI; modular multiplier;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks (IJCNN), 2014 International Joint Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4799-6627-1
  • Type

    conf

  • DOI
    10.1109/IJCNN.2014.6889540
  • Filename
    6889540