VLSI Implementation of Fuzzy Adaptive Resonance and Learning Vector Quantization

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 43 μm × 43 μm in 1.2 μm CMOS technology. Experimental learning results from a fabricated 8-input, 16-category prototype are included.