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13th Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM'05)
Efficient Hardware Data Mining with the Apriori Algorithm on FPGAs
Los Alamitos
April 18-April 20
ISBN: 0-7695-2445-1
Zachary K. Baker, University of Southern California
Viktor K. Prasanna, University of Southern California

The Apriori algorithm is a popular correlation-based data-mining kernel. However, it is a computationally expensive algorithm and the running times can stretch up to days for large databases, as database sizes can extend to Gigabytes.

Through the use of a new extension to the systolic array architecture, time required for processing can be significantly reduced. Our array architecture implementation on a Xilinx Virtex-II Pro 100 provides a performance improvement that can be orders of magnitude faster than the state-of-the-art software implementations. The system is easily scalable and introduces an efficient "systolic injection" method for intelligently reporting unpredictably generated mid-array results to a controller without any chance of collision or excessive stalling.

Citation:
Zachary K. Baker, Viktor K. Prasanna, "Efficient Hardware Data Mining with the Apriori Algorithm on FPGAs," fccm, pp.3-12, 13th Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM'05), 2005
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