Title

HAVENN: horizontally and vertically expandable neural networks

Document Type

Conference Proceeding

Date of Original Version

12-1-1994

Abstract

The toughest challenge facing hardware designers of artificial neural networks is the expandability problem, since no single VLSI chip is likely to accommodate all components of a real world application. In this paper, we present a microelectronic system architecture with virtually unlimited expandability at a relatively low cost in additional hardware and reduced system performance. The Horizontally And Vertically Expandable Neural Network (HAVENN) architecture consists of three types of chips: a single layer neural network chip, a summer chip and a repeater chip. The most important features of the proposed architecture are: a balanced distribution of (circuit) complexity between board level and chip level, easy implementation, true parallel operation and versatility.

Publication Title

IEEE International Conference on Neural Networks - Conference Proceedings

Volume

4

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