2008
DOI: 10.1002/cta.564
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On the implementation of linear diffusion in transconductance‐based cellular nonlinear networks

Abstract: SUMMARYIn theory, cellular nonlinear networks (CNN) are well capable of implementing discrete-space linear diffusion by means of the appropriate templates. In practice, good results have not been demonstrated with transconductance-based circuits. In this paper, we prove that inherent mismatch to very large scale integration implementation is the reason. Although previous works consider that the small perturbations of the network parameters lead to small deviations from the ideal behavior, we consider that this… Show more

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Cited by 10 publications
(5 citation statements)
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“…Hence, one can calculate diffusions in separate regions by properly fencing these regions on the mask image. Similar solution can be found in [5].…”
Section: Implementation On a Focal-plane Sensor-processor Devicesupporting
confidence: 73%
“…Hence, one can calculate diffusions in separate regions by properly fencing these regions on the mask image. Similar solution can be found in [5].…”
Section: Implementation On a Focal-plane Sensor-processor Devicesupporting
confidence: 73%
“…Finally, the filtering performed by a resistive grid can be theoretically emulated by the CNN framework [15]. However, the unavoidable mismatch present at any VLSI implementation prevents the typical transconductance-based approach from achieving Gaussian filtering with enough accuracy, specially for large widths [16].…”
Section: Introductionmentioning
confidence: 99%
“…One peculiar aspect of these chips is that they are based on the full-range (FR) model of CNNs [2]- [7]. Differently from the standard (S) CNNs introduced by Chua and Yang [8], where the neuron activations are modeled by a typical threesegment pwl function, FRCNNs exploit hard-limiter devices to limit the allowable range for the time evolution of the neuron state variables.…”
Section: Introductionmentioning
confidence: 99%