Recursive Cellular Nonlinear Neural Networks for Ultra-low Noise Digital Arithmetic
J.J. Yeboah, GRAHAM A. JULLIEN, J.W. Haslett · 2006
An innovative method of designing a class of analog cellular neural networks - recursive cellular nonlinear (neural) networks (RCNNs) - for ultra-low noise digital arithmetic computation is presented. The intended application of the RCNN is for digital arithmetic in a sensitive mixed-signal environment, such as a digital interface to a low output sensor, where digital noise is to be kept to a minimum. Our ultra low-noise approach essentially employs asynchronous analog circuit concepts. In this paper we have analyzed and summarized the recursive architecture of our RCNN networks at the mathematical and circuit level. An analog CMOS design of a 4-bit RCNN adder, which will be used to verify the low-noise behaviour of our approach, is also presented