Micron Technology Incorporated

Xilinx, Inc.

Yao Fu

Paul Glendenning

Eric Michael Jonas

Tommy Tracy, Ii

Christopher H Dick

Ashish Sirasao

Michael Wu

Bing Tian

Satyaprakash Pareek

Anup Hosangadi

Oscar Fernando C Fernandez

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

A method includes encoding an input data stream to generate an encoded input data pattern, transmitting the encoded input data pattern to a programmed automata processor, and searching the encoded input data pattern via the programmed automata processor to identify an identifiable data pattern within the encoded input data pattern as a data pattern search.

Methods and devices for reducing array size and complexity in automata processors

A method includes calculating a first position encoded pattern based on a first data pattern, and using an automata processor to compare the first position encoded pattern to a second position encoded pattern to identify a second data pattern within the first data pattern.

Circuits and method for multiplying floating point operands. An exponent adder circuit sums a first exponent and a second exponent and generates an output exponent. A mantissa multiplier circuit multiplies a first mantissa and a second mantissa and generates an output mantissa. A first conversion circuit converts the output exponent and output mantissa into a fixed point number. An accumulator circuit sums contents of an accumulation register and the fixed point number into an accumulated value and stores the accumulated value in the accumulation register.

Multiply and accumulate circuit

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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Yao Fu