Fault tolerant CNN template design and optimization based on chip measurements

Abstract

Proposes a generic method for finding non-propagating cellular neural network (CNN) templates that can be implemented reliably on a given CNN Universal Machine chip. The method has two main components: (i) adaptive optimization of templates based on measurements of actual CNN chips, (ii) simplification and decomposition of Boolean operators into a sequence of simpler ones that work correctly and more robustly on a given chip. Examples are presented using two stored-program CNNUM chips to demonstrate the effectiveness of the proposed method, whose advantages and limitations are also discussed.