Hybrid hardware for a highly parallel search in the context of learning classifiers Original Research Article

Based on a comparison of input data with a set of prototypes, classifier systems identify the most appropriate representative for a given sample pattern. One remarkable classifier is Kohonenʹs Self-Organizing Map and the related learning vector quantizer, as these algorithms are highly parallel. For real-time applications the classifier search may be one of the time critical processes. We discuss specialized hardware being able to execute such a search in a fully parallel manner. Also the learning and updating of prototypes is performed in parallel controlled by a propagating front. Finally, we present experimental results concerning an unsupervised learning vector quantizer (LVQ) and a self-organizing map (SOM) obtained from our thyristor-based analog-digital hybrid system.