Neural Network Implementation in Hardware Using FPGAs

  • Suhap Sahin
  • Yasar Becerikli
  • Suleyman Yazici
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)


The usage of the FPGA (Field Programmable Gate Array) for neural network implementation provides flexibility in programmable systems. For the neural network based instrument prototype in real time application, conventional specific VLSI neural chip design suffers the limitation in time and cost. With low precision artificial neural network design, FPGAs have higher speed and smaller size for real time application than the VLSI design. In addition, artificial neural network based on FPGAs has fairly achieved with classification application. The programmability of reconfigurable FPGAs yields the availability of fast special purpose hardware for wide applications. Its programmability could set the conditions to explore new neural network algorithms and problems of a scale that would not be feasible with conventional processor. The goal of this work is to realize the hardware implementation of neural network using FPGAs. Digital system architecture is presented using Very High Speed Integrated Circuits Hardware Description Language (VHDL) and is implemented in FPGA chip. The design was tested on a FPGA demo board.


Field Programmable Gate Array Real Time Application Hardware Implementation Logic Block Float Point Arithmetic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Suhap Sahin
    • 1
  • Yasar Becerikli
    • 1
  • Suleyman Yazici
    • 1
  1. 1.Department of Computer Eng.Kocaeli UniversityIzmitTurkey

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