Winner-take-all networks of O(N) complexity
Advances in neural information processing systems 1
Vector quantization and signal compression
Vector quantization and signal compression
A real-time clustering microchip neural engine
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Adaptive resonance theory microchips: circuit design techniques
Adaptive resonance theory microchips: circuit design techniques
Analog VLSI Stochastic Perturbative Learning Architectures
Analog Integrated Circuits and Signal Processing
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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 71µm 脳 71µm in 2 µm CMOS. Experimental learning results are included from a 16-input, 16-category prototype on a 2.2mm 脳 2.2mm chip, operating at 10 ksample/s parallel data rate and 2 mW power dissipation.