A reconfigurable architecture mapping multilayer CNN paradigms

A digital VLSI implementation of linear template cellular neural nets (CNNs) is presented. A reconfigurable architecture is organized as 12 layers of 64/spl times/64 cells. The CNNs are reformulated introducing sets of generalized cloning templates to enucleate more sharply the structure of both intra- and inter-layer cooperative computations. In this way it is possible to develop CNN algorithms for complex vision machine tasks. Various applications are considered in edge and connected component detection and in texture segregation.<>