Accelerating a random forest classifier: Multi-core, GP-GPU, or FPGA?

Proceedings of the 2012 IEEE 20th International Symposium on Field-Programmable Custom Computing Machines, FCCM 2012

Volumn , Issue , 2012, Pages 232-239

  Van Essen, Brian ^a   Macaraeg, Chris ^a   Gokhale, Maya ^a   Prenger, Ryan ^a  

^a LAWRENCE LIVERMORE NATIONAL LABORATORY   (United States)

Author keywords

FPGA; GP GPU; Machine learning; OpenMP

Indexed keywords

FOREST SIZE; GP-GPU; INPUT SAMPLE; LAWRENCE LIVERMORE NATIONAL LABORATORIES; LEARNING CLASSIFIERS; MACHINE LEARNING TECHNIQUES; MEMORY BOUNDS; MULTI CORE; MULTI-CHIP; MULTI-THREADING; MULTIPLE THREADS; OPENMP; RANDOM FOREST CLASSIFICATION; RANDOM FOREST CLASSIFIER; RANDOM FORESTS; SHARED MEMORY SYSTEM; TRAINING ALGORITHMS;

APPLICATION PROGRAMMING INTERFACES (API); COMPUTERS; FIELD PROGRAMMABLE GATE ARRAYS (FPGA); FORESTRY; LEARNING SYSTEMS; PROGRAM PROCESSORS;

DECISION TREES;

COMPUTERS; DECISION MAKING; TREES;

EID: 84864961784     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FCCM.2012.47     Document Type: Conference Paper

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