Interactive presentation: An FPGA implementation of decision tree classification
Proceedings of the conference on Design, automation and test in Europe
C is for circuits: capturing FPGA circuits as sequential code for portability
Proceedings of the 16th international ACM/SIGDA symposium on Field programmable gate arrays
Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia
A highly parallel algorithm for frequent itemset mining
MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition
ACM Transactions on Architecture and Code Optimization (TACO) - Special Issue on High-Performance Embedded Architectures and Compilers
An FPGA-Based Accelerator for Frequent Itemset Mining
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
Hi-index | 0.00 |
The Apriori algorithm is a fundamental correlation-based data mining kernel used in a variety of fields. The innovation in this paper is a highly parallel custom architecture implemented on a reconfigurable computing system. Using this "bitmapped CAM," the time and area required for executing the subset operations fundamental to data mining can be significantly reduced. The bitmapped CAM architecture implementation on an FPGA-accelerated high performance workstation provides a performance acceleration of orders of magnitude over software-based systems. The bitmapped CAM utilizes redundancy within the candidate data to efficiently store and process many subset operations simultaneously. The efficiency of this operation allows 140 units to process about 2,240 subset operations simultaneously. Using industry-standard benchmarking databases, we have tested the bitmapped CAM architecture and shown the platform provides a minimum of 24x (and often much higher) time performance advantage over the fastest software Apriori implementations.