Field-programmable gate arrays
Field-programmable gate arrays
The implementation of fuzzy systems, neural networks and fuzzy neural networks using
Information Sciences: an International Journal
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
IEEE Design & Test
FPGA Implementation of a Neural Network for a Real-Time Hand Tracking System
DELTA '02 Proceedings of the The First IEEE International Workshop on Electronic Design, Test and Applications (DELTA '02)
Neural identification of dynamic systems on FPGA with improved PSO learning
Applied Soft Computing
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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.