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A Mixed Mode Neural Network Circuitry for Object Recognition Application

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Abstract

A general purpose Conic Section Function Neural Network (CSFNN) circuitry in Very Large Scale Integration (VLSI) has been designed for an object recognition application. CSFNN is capable of making open and closed decision regions by combining the propagation rules of Radial Basis Functions (RBF) and Multilayer Perceptrons (MLP) on a single neural network with a unique propagation rule. Chip-in-the-loop learning technique was used during the training process. Utilizing mixed-mode hardware techniques, the inputs of the network and the feedforward signals are all analog while the control unit and storage of the network parameters are fully digital. CSFNN circuitry architecture is problem independent and consists of 16 inputs, 16 hidden layer neurons and 8 outputs. Inheriting the merits of CSFNN, the circuitry has good recognition performance on several objects with invariance to pose, lighting, and brightness. The designed hardware achieved a good recognition performance by means of both accuracy and computational time comparable to CSFNN software.

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Acknowledgement

This work was supported by the TUBITAK—The Scientific and Technological Research Council of Turkey. Project Number: 104E133.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Yildiz Technical University, 34220, Istanbul, Turkey

    Burcu Erkmen, Revna Acar Vural, Nihan Kahraman & Tulay Yildirim

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  1. Burcu Erkmen
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  2. Revna Acar Vural
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  4. Tulay Yildirim
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Correspondence to Burcu Erkmen.

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Erkmen, B., Vural, R.A., Kahraman, N. et al. A Mixed Mode Neural Network Circuitry for Object Recognition Application. Circuits Syst Signal Process 32, 29–46 (2013). https://doi.org/10.1007/s00034-012-9458-2

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  • DOI: https://doi.org/10.1007/s00034-012-9458-2

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