A Mixed Mode Neural Network Circuitry for Object Recognition Application

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...

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Bibliographic Details
Published in:Circuits, systems, and signal processing systems, and signal processing, 2013-02, Vol.32 (1), p.29-46
Main Authors: Erkmen, Burcu, Vural, Revna Acar, Kahraman, Nihan, Yildirim, Tulay
Format: Article
Language:English
Subjects:
Circuits and Systems
Electric circuits
Electrical Engineering
Electronics and Microelectronics
Engineering
Feedforward
Hardware
Instrumentation
Networks
Neural networks
Object linking & embedding
Object recognition
Recognition
Signal,Image and Speech Processing
Very large scale integration
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Summary: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.
ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-012-9458-2