Neural system design with the integrated neurocomputing architecture

Design and implementation issues of high-performance VLSI systems in the form of deployable neural networks for pattern recognition applications are addressed, in particular the integrated neurocomputing architecture (INCA), which was developed with a complete system approach involving the integration of custom analog IC design, digital and analog board-level design, neural network development software, and application-specific hardware and software elements, is considered. The design methodology is discussed to demonstrate the capability of INCA as a usable neuroprocessing system. It serves as an example of a system that highlights many design automation issues for future mixed-signal, highly integrated systems.<
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