Analytic and VLSI Specific Design of Robust CNN Templates

Abstract

Parameter learning or design is a key issue in cellular neural network (CNN) theory. If the CNN is implemented as an analog VLSI chip, additional constraints are posed due to its restricted accuracy. Only robust parameters will still guarantee the correct network behavior. We present an analytical design approach for the class of bipolar CNNs which yields optimally robust template parameters. We give a rigorous definition of absolute and relative robustness and show that all well-defined CNN tasks are characterized by a finite set of linear and homogeneous inequalities. This system of inequalities can be analytically solved for the most robust template by simple matrix algebra. Focusing on a particular implementation of the CNN universal chip, we demonstrate that the proposed method can cope with the manufacturing inaccuracies.