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 integrati...

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Bibliographic Details
Published in[1993] Proceedings Third Great Lakes Symposium on VLSI-Design Automation of High Performance VLSI Systems pp. 32 - 36
Main Authors Mukai, P., Busa, M., Kazlas, P.
Format Conference Proceeding
LanguageEnglish
Published IEEE Comput. Soc. Press 1993
Subjects
Analog integrated circuits
Application software
Application specific integrated circuits
Computer architecture
Design methodology
Digital integrated circuits
Neural network hardware
Neural networks
Pattern recognition
Very large scale integration
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Summary: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.< >
ISBN:9780818634307
0818634308
DOI:10.1109/GLSV.1993.224486