A behavioural modelling technique for visual microprocessor mixed-signal VLSI chips

This paper describes procedures to build custom-tailored behavioural models of cellular neural networks (CNNs), and acompanion tool to run these models. The main property of the CNNs is the emerging behaviour, i.e. new phenomena arise from the interactions of thousands of identical cells. The existence of these phenomena need is to be checked during the design phase, which requires a full network simulation and therefore constitutes a very time-consuming step of circuit verification. To solve this task as a modelling problem, we introduce a new behavioural model optimization technique. Starting from a user-defined set of block models, the proposed framework produces an optimized selection which is used to build up a full-chip model. The optimization goal is the minimization of the simulation CPU time and the maximization of the time domain precision. A dedicated environment has been developed for efficient numerical simulation; this environment is briefly described in the paper. Two case studies are also presented to demonstrate the effectivity of the technique. Copyright © 2002 John Wiley & Sons, Ltd.Contract/grant sponsor: ONR-NICOP; contract/grant number: N68171-98-C-9004. Contract/grant sponsor: DICTAM; contract/grant number: IST-1999-19007. Contract/grant sponsor: TIC; contract/grant number: 990826.Peer Reviewe