Neural Network Hardware Implementation Using FPGA

Mehmet Ali Çavuşlu, Cihan Karakuzu, Suhap Şahin
ISEECE 2006 3rd International Symposium on Electrical, Electronic and Computer Engineering Symposium Proceedings, Nicosia, TRNC,, Pages. 287-290, 2006

Abstract: The FPGAs (Field Programmable Gate Arrays) approach for neural network implementation provides flexibility in programmable systems. For the neural based instrument prototype in real time application, conventional specific VLSI neural chip design suffers from 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 that of the VLSI design. This paper presents a novel fully parallel hardware implementations of neural network for EXOR benchmark problem using Xilinx FPGA. The validity of this approach is demonstrated by application to EXOR problem. The design is tested on an FPGA demo board.

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