Title :
A new current mode programmable cellular neural network
Author :
Ravezzi, L. ; Betta, G. F Dalla ; Setti, G.
Author_Institution :
Dipartimento di Ingegneria dei Mater., Trento Univ., Italy
Abstract :
We report on the design of a full-analog current-mode CNN in a 1.2 μm CMOS technology, whose cell core is characterized by an intrinsic capability of weights control, low power consumption and small area occupation. Circuit simulations allowed the design approach to be validated and the electrical performance of the CNN to be predicted; moreover, it is shown that the proposed CNN can be successfully adopted for several applications in image processing. A preliminary CNN test-chip consisting of a 8×1 array for connected component detection and shadow detection, is currently being fabricated at IRST (Trento Italy) in a 2.5 μm CMOS technology
Keywords :
CMOS analogue integrated circuits; analogue processing circuits; cellular neural nets; edge detection; neural chips; 1.2 μm CMOS technology; 1.2 mum; 2.5 μm CMOS technology; connected component detection; current mode programmable cellular neural network; full-analog current-mode CNN; image processing; shadow detection; CMOS technology; Cellular neural networks; Charge coupled devices; Circuit testing; Energy consumption; Hardware; Image processing; Lab-on-a-chip; Very large scale integration; Weight 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.1998.685380
