OPTIMAL CNN TEMPLATES FOR LINEARLY-SEPARABLE ONE-DIMENSIONAL CELLULAR AUTOMATA

摘要

In this tutorial, we present optimal Cellular Nonlinear Network (CNN) templates for implementing linearly-separable one-dimensional (1-D) Cellular Automata (CA). From the gallery of CNN templates presented in this paper, one can calculate any of the 256 1-D CA Rules studied by Wolfram using a CNN Universal Machine chip that is several orders of magnitude faster than conventional programming on a digital computer.