Preliminary results from an analog implementation of first‐order TDCNN dynamics

This work falls into the category of linear cellular neural network (CNN) implementations. We detail the first investigative attempt on the CMOS analog VLSI implementation of a recently proposed network formalism, which introduces time-derivative ‘diffusion’ between CNN cells for nonseparable spatiotemporal filtering applications—the temporal-derivative CNNs (TDCNNs). The reported circuit consists of an array of Gm-C filters arranged in a regular pattern across space. We show that the state–space coupling between the Gm-C-based array elements realizes stable and linear first-order (temporal) TDCNN dynamics. The implementation is based on linearized operational transconductance amplifiers and Class-AB current mirrors. Measured results from the investigative prototype chip that confirms the stability and linearity of the realized TDCNN are provided. The prototype chip has been built in the AMS 0.35 µm CMOS technology and occupies a total area of 12.6 mm sq, while consuming 1.2 µW per processing cell. Copyright © 2010 John Wiley & Sons, Ltd.

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