Programmable CMOS CNN chip for binary image processing

At present, Cellular Neural Networks (CNN) in VLSI technology are powerful parallel structures with real-time image processing capabilities. In this context it is necessary to work on a simplified CNN idea from the hardware point of view (derived from the original CNN model proposed by Chua et al.)...

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Bibliographic Details
Main Authors: Molitiar-Solis, J.E., Gomez-Castafieda, F., Moreno-Cadenas, J.A.
Format: Conference Proceeding
Language:English
Subjects:
Cellular neural networks
CMOS process
CMOS technology
Image processing
Integrated circuit modeling
Laboratories
Multiplexing
Real time systems
Silicon
Very large scale integration
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Summary:At present, Cellular Neural Networks (CNN) in VLSI technology are powerful parallel structures with real-time image processing capabilities. In this context it is necessary to work on a simplified CNN idea from the hardware point of view (derived from the original CNN model proposed by Chua et al.) in order to design a more feasible CNN IC with lower complexity circuits. In this work, taking into account that many binary image tasks present a linearly separable feature, the original output function can be replaced by a step function. Analog multipliers can be substituted by simple analog multiplexers and we take advantage that all the signals can be unipolar. These features reduce the complexity of the circuits but preserving the computation in selected binary-image processing tasks. The circuits in our CMOS integrated circuit belong to the class of those in the design of mixed-signal systems with current-mode representation of signal-flow. The technology for these circuits is based on the n-well, 1.2-micron CMOS process offered by MOSlS to research groups in universities.
DOI:10.1109/ICEEE.2004.1433878