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 (1 cm2) 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.
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© 1999 Springer-Verlag Berlin Heidelberg
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Serrano-Gotarredona, T., Linares-Barranco, B. (1999). Adaptive resonance theory microchips. In: Mira, J., Sánchez-Andrés, J.V. (eds) Foundations and Tools for Neural Modeling. IWANN 1999. Lecture Notes in Computer Science, vol 1606. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0098232
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DOI: https://doi.org/10.1007/BFb0098232
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