Grayscale CNN computation of Boolean functions

In this paper, an approach to computing arbitrary Boolean functions using a continuous-state cellular neural/nonlinear/nanoscale network (CNN) architecture with local static memory is presented. We explain how any given Boolean function can be mapped to a CNN array and how the function is executed using a sequence of wave operations. Furthermore, we explain how gray-scale waves could reduce the number of CNN cells required to perform a certain logic operation. The main benefits of our approach are simple implementation of arbitrary Boolean functions, asynchronous operation, and applicability in multi-state computation.