• DocumentCode
    1799933
  • Title

    Implementation of a feed-forward Artificial Neural Network in VHDL on FPGA

  • Author

    Dondon, Philippe ; Carvalho, Julien ; Gardere, Remi ; Lahalle, Paul ; Tsenov, Georgi ; Mladenov, Valeri

  • Author_Institution
    ENSEIRB-MATMECA, Ecole Nat. Super. d´Electron., France
  • fYear
    2014
  • fDate
    25-27 Nov. 2014
  • Firstpage
    37
  • Lastpage
    40
  • Abstract
    Describing an Artificial Neural Network (ANN) using VHDL allows a further implementation of such a system on FPGA. Indeed, the principal point of using FPGA for ANNs is flexibility that gives it an advantage toward other systems like ASICS which are entirely dedicated to one unique architecture and allowance to parallel programming, which is inherent to ANN calculation system and one of their advantages. Usually FPGAs do not have unlimited logical resources integrated in a single package and this limitation forcesrequirement for optimizations for the design in order to have the best efficiency in terms of speed and resource consumption. This paper deals with the VHDL designing problems which can be encountered when trying to describe and implement such ANNs on FPGAs.
  • Keywords
    feedforward neural nets; field programmable gate arrays; hardware description languages; parallel programming; ANN calculation system; ASICS; FPGA; VHDL designing problems; feedforward artificial neural network; logical resources; parallel programming; resource consumption; Artificial neural networks; Biological neural networks; Field programmable gate arrays; MATLAB; Neurons; Random access memory; Read only memory; FPGA implementation; VHDL; neural networks; nonlinear systems;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Network Applications in Electrical Engineering (NEUREL), 2014 12th Symposium on
  • Conference_Location
    Belgrade
  • Print_ISBN
    978-1-4799-5887-0
  • Type

    conf

  • DOI
    10.1109/NEUREL.2014.7011454
  • Filename
    7011454