Learning for VMM + WTA Embedded Classifiers
Georgia Institute of Technology Atlanta United States
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The authors present training and feed forward computation for a single layer of a VMM WTA classifier. The experimental demonstration of the one-layer universal approximator encourages the use of one-layer networks for embedded low-power classification. The results enabling correct classification of each novel acoustic signalgenerator, idle car, and idle truck. The classification structure requires, after training, less than 30microW of operational power and lower with additional fabrication.