Micron Technology Incorporated

Tommy Tracy, Ii

Paul Glendenning

Eric Michael Jonas

Yao Fu

An apparatus includes a processing resource configured to receive a feature vector of a data stream. The feature vector includes a set of feature values. The processing resource is further configured to calculate a set of feature labels based at least in part on the set of feature values to generate a label vector, provide the label vector to another processing resource, and receive a plurality of classifications corresponding to each feature label of the label vector from the other processing resource. The plurality of classifications are generated based at least in part on a respective range of feature values of the set of feature values. The processing resource is configured to then combine the plurality of classifications to generate a final classification of the data stream.

Space efficient random decision forest models implementation utilizing automata processors

An apparatus includes a processing resource configured to receive a feature vector of a data stream. The feature vector includes a set of feature values. The processing resource is further configured to calculate a set of feature labels based at least in part on the set of feature values to generate a label vector, provide the label vector to another processing resource, and to receive a plurality of classifications corresponding to each feature label of the label vector from the other processing resource. The plurality of classifications are generated based at least in part on a respective range of feature values of the set of feature values. The processing resource is configured to then combine the plurality of classifications to generate a final classification of the data stream.

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Charlottesville, VA, United States of America

Tommy Tracy, Ii