Title :
A programmable gm-C CNN implementation
Author :
Lím, Drahoslav ; Moschytz, George S.
Author_Institution :
Lab. of Signal & Inf. Process., Eidgenossische Tech. Hochschule, Zurich, Switzerland
Abstract :
An implementation of a programmable cellular neural network is reported. It overcomes some of the limiting characteristics and restrictions inherent in CMOS VLSI technologies, and allows an arbitrarily large continuous-time analog CNN to be built up by modularly connecting CNN chips with a modest number of cells. The template values are implemented as sets of unit and half-unit OTAs and are digitally step-wise programmable. The design incorporates an offset compensation and initialization circuit. All external input, output and control signals are electrical and digital. The design was carried out in a 0.8 μ CMOS technology. Each cell occupies 0.78 mm2, including all support circuitry. Matching accuracy was measured and operation was verified on numerous uncoupled and propagation-type templates
Keywords :
CMOS analogue integrated circuits; VLSI; analogue processing circuits; cellular neural nets; neural chips; neural net architecture; 0.8 μ CMOS technology; 0.8 mum; CMOS VLSI technologies; initialization circuit; matching accuracy; offset compensation; programmable cellular neural network; programmable gm-C CNN implementation; propagation-type templates; CMOS technology; Cellular neural networks; Circuits; Costs; Information processing; Joining processes; Limiting; Piecewise linear techniques; Signal processing; Voltage control;
Conference_Titel :
Cellular Neural Networks and Their Applications Proceedings, 1998 Fifth IEEE International Workshop on
Conference_Location :
London
Print_ISBN :
0-7803-4867-2
DOI :
10.1109/CNNA.1998.685390
