Digital arithmetic using analog arrays

Abstract

This paper describes techniques for using locally connected analog cellular neural networks (CNNs) to implement digital arithmetic arrays; the arithmetic is implemented using a recently disclosed Double-Base Number System (DBNS). The CNN arrays are targeted for low power low-noise DSP applications where lower slew rate during transitions is a potential advantage. Specifically, we demonstrate that a CNN array, using a simple nonlinear feedback template, with hysteresis, can perform arbitrary length arithmetic with good performance in terms of stability and robustness. The principles presented in this paper can also be used to implement arithmetic in other number systems such as the binary number system.