VLSI Implementation of Fuzzy Adaptive Resonance and Learning Vector Quantization

Author(s): Cauwenberghs, G. | Lubkin, J. |

Year: 2002

Citation: Analog Integrated Circuits and Signal Processing, Vol. 30, Issue 2, 149-157

Abstract: We present a mixed-mode VLSI chip performing unsupervised clustering and classification, implementing models of fuzzy adaptive resonance theory (ART) and learning vector quantization (LVQ), and extending to variants such as Kohonen self-organizing maps (SOM). The parallel processor classifies analog vectorial data into a digital code in a single clock, and implements on-line learning of the analog templates, stored locally and dynamically using the same adaptive circuits for on-chip quantization and refresh. The unit cell performing fuzzy choice and vigilance functions, adaptive resonance learning and long-term analog storage, measures 71 μm71 μm in 2 μm CMOS. Experimental learning results are included from a 16-input, 16-category prototype on a 2.2 mm2.2 mm chip, operating at 10 ksample/s parallel data rate and 2 mW power dissipation.

Topics: Neural Hardware, Models: ART 2 / Fuzzy ART, Self Organizing Maps,

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