Design of Programmable Current-Mode Cellular Neural Networks
碩士 === 國立成功大學 === 電機工程研究所 === 84 === This thesis describes an analog current-mode realization of Cellular Neural Net-works (CNNs) that are programmable with continuous valued weights and operate in continuous time. At first, a Fixed-weight...
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| Format: | Others |
| Language: | en_US |
| Published: |
1996
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| Online Access: | http://ndltd.ncl.edu.tw/handle/68440301270212813262 |
| Summary: | 碩士 === 國立成功大學 === 電機工程研究所 === 84 === This thesis describes an analog current-mode realization of
Cellular Neural Net-works (CNNs) that are programmable with
continuous valued weights and operate in continuous time. At
first, a Fixed-weight current-mode cellular neural network is
proposed to implement the Connected Component Detection (CCD)
with low supply voltage (3.3 v) architecture. Base on this
work, A programmable CNN is invented. The features of our
design are described as follow: 1) The use of fully
differential architecture for reducing distortion by canceling
even-order harmonics and increasing the dynamic range. 2) The
use of a simple four-quadrant multiplier for the control and
feedback coef-ficients. 3) An efficient realization of the
scaling block to half the number of the multipliers. 4) The
use of full signal range model to implement an efficient cell
architectures. 5) The signals transmitted in the cells are
current-mode and between the cells are voltage-mode. Hence,
some important parts of the CNN Universal Machine concept are
implement. Our system is designed to operate in two phases to
reduce the hardware com-plexity. The original and modified
mathematical models are both discussed . Some implementation
methods of other researchers are mentioned and compared to our
design. All the circuit components are described and HSPICE
simulation results are given for the 0.8 mm CMOS digital
process. A layout has been designed for a chip with 8 by 8
cells on a square grid realizing a one-neighborhood with 9
feedback and 9 control coefficients.
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