Adaptive resonance theory microchips

Author(s): LinaresBarranco, B. | SerranoGotarredona, T. |

Year: 1999

Citation: Foundations and Tools for Neural Modeling, 1999

Abstract: Recently, a real-time clustering microchip based on the ART1 algorithm has been reported. That chip was able to classify 100-bit input patterns into up to 18 categories. However, its high area comsumption (lcm 2) caused a very poor yield (6%). In this paper, an improved prototype is presented. In this chip, a different approach has been used to implement the most area consuming elements. The new chip can cope with 50-bit input patterns and classify them into up to 10 categories. Its area is 15 times less than that of the first prototype and it exhibits a yield performance of 98%. Due to its higher robustness, multichip systems are easily assembled.

Topics: Neural Hardware, Models: ART 1,

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