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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Published in | [1993] Proceedings Third Great Lakes Symposium on VLSI-Design Automation of High Performance VLSI Systems pp. 32 - 36 |
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Main Authors | , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE Comput. Soc. Press
1993
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Subjects | |
Online Access | Get full text |
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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.< > |
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ISBN: | 9780818634307 0818634308 |
DOI: | 10.1109/GLSV.1993.224486 |