Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Implementation of a Perceptron-based Artificial Neural Network Classifier Circuit on FPGA Hardware

Author : Amit R. Chavan 1 Ashwini Kumar Arya 2

Date of Publication :21st February 2018

Abstract: This paper elaborates the implementation of an unsupervised Artificial Neural Network (ANN) on FPGA hardware for data classification. ANN is the best option to classify a large amount of data into several desired classes as per the characteristics and parameters of the given data samples. Implementation of an unsupervised ANN on a chip eliminates the additional stage of software simulation of the ANN for the given dataset, i.e. training of ANN using a software and then implementation of trained ANN on FPGA chip. The Unsupervised ANN is implemented on Xilinx Virtex-4 FPGA, which consumes less on-chip resources, consuming less power at optimum speed

Reference :

    1. Omondi, Rajapakshe, FPGA Implementations of Neural Networks, Springer.
    2. Brownlee, Master Machine Learning Algorithms.
    3. Ayman Youssef, Mohammed, Nasar, “Two Novel Generic, Reconfigurable Neural Network FPGA Architectures”, IEEE 4th International Conference on Artificial Intelligence with Applications in Engineering and Technology, 2014.
    4. Dinu, Cirstea, “A Digital Neural Network FPGA Direct Hardware Implementation Algorithm”, IEEE International Symposium on Industrial Electronics (ISIE), 2007.
    5. Kanwal, Generalized Functions: Theory and Technique, 2nd ed. Boston, MA: Birkhäuser, 1998.
    6. Chaitra, “Hardware Implementation of Artificial Neural Networks using Back Propagation Algorithm on FPGA”, International Journal of Research in Engineering and Technology, Vol. 05 Sp. Issue 04, pp. 211-214, May 2016.

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