HAVENN: horizontally and vertically expandable neural networks
Date of Original Version
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, e.g., Journal
IEEE International Conference on Neural Networks - Conference Proceedings
Lo, Jien-Chung, and G. Fischer. "HAVENN: horizontally and vertically expandable neural networks." IEEE International Conference on Neural Networks - Conference Proceedings 4, (1994): 2074-2077. https://digitalcommons.uri.edu/ele_facpubs/239