A CNN approach to computing arbitrary Boolean functions
Eero Lehtonen, Jussi H. Poikonen, Mika Laiho · 2010
In this paper, a novel approach to computing arbitrary Boolean functions using a binary-state cellular neural/nonlinear/nanoscale network (CNN) architecture with local static memory is presented. We define explicitly how to map a given Boolean function and its input values to the cells of a specific type of binary CNN, and the global rules used to perform parallel calculations. Each of the computation steps can be performed asynchronously. Additionally, the total CNN area is readily split into subsections, each of which perform individual computations of different Boolean functions. The main benefits of our approach are simple implementation of arbitrary Boolean functions, built-in parallelism both in local and global scale of the computation and the possibility for asynchronous operation.