Implementations of artificial neural networks using current-mode pulse width modulation technique

Neural Networks, IEEE Transactions(1997)

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Abstract
The use of a current-mode pulse width modulation (CM-PWM) technique to implement analog artificial neural networks (ANNs) is presented. This technique can be used to efficiently implement the weighted summation operation (WSO) that are required in the realization of a general ANN. The sigmoidal transformation is inherently performed by the nonlinear transconductance amplifier, which is a key component in the current integrator used in the realization of WSO. The CM-PWM implementation results in a minimum silicon area, and therefore is suitable for very large scale neural systems. Other pronounced features of the CM-PWM implementation are its easy programmability, electronically adjustable gains of neurons, and modular structures. In this paper, all the current-mode CMOS circuits (building blocks) required for the realization of CM-PWM ANNs are presented and simulated. Four modules for modular design of ANNs are introduced. Also, it is shown that the CM-PWM technique is an efficient method for implementing discrete-time cellular neural networks (DT-CNNs). Two application examples are given: a winner-take-all circuit and a connected component detector
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Key words
CMOS analogue integrated circuits,cellular neural nets,neural chips,pulse width modulation,connected component detector,current-mode CMOS circuits,current-mode PWM,discrete-time cellular neural networks,modular structures,neural networks,nonlinear transconductance amplifier,pulse width modulation,weighted summation operation,winner-take-all circuit,
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