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Efficient and Versatile FPGA Acceleration of Support Counting for Stream Mining of Sequences and Frequent Itemsets

Adrien Prost-Boucle, FRédéric Pétrot, Vincent Leroy, Hande Alemdar
2017 ACM Transactions on Reconfigurable Technology and Systems  
This accelerator can be used to accelerate data mining algorithms including itemsets and sequences mining.  ...  We validate our architecture by developing a co-designed implementation of the Apriori Frequent Itemset Mining (FIM) algorithm, and perform numerous experiments against existing hardware and software solutions  ...  Acknowledgments The authors would like to thank Olivier Menut from ST Microelectronics for his valuable inputs and continuous support.  ... 
doi:10.1145/3027485 fatcat:ujsomdbfbrfbhipe5ih7emxiha

High-Level Design Optimizations for Implementing Data Stream Sketch Frequency Estimators on FPGAs

Ali Ebrahim
2022 Electronics  
Compared to FPGA sketches in the published literature, the presented sketch is the most well-rounded sketch in terms of features and versatility.  ...  Using an Intel HLS compiler, a proposed optimized hardware version of the popular Count-Min sketch utilizing 80% of the embedded RAM in an Intel Arria 10 FPGA, achieved more than 3x the throughput of an  ...  Conflicts of Interest: The author declares no conflict of interest.  ... 
doi:10.3390/electronics11152399 fatcat:bir4w3e6h5gjfjwf5jbs3fraoi

Polychroniou_columbia_0054D_14504.pdf [article]

This thesis focuses on the design and implementation of highly efficient database systems by optimizing analytical query execution for all layers of modern hardware.  ...  Analytical database queries are at the core of business intelligence and decision support. To analyze the vast amounts of data available today, query execution needs to be orders of magnitude faster.  ...  ., 2012] proposed SIMD-based pre-filtering for frequent itemset mining. [Mühlbauer et al., 2013] optimized data loading from CSV files using SIMD instructions for efficient parsing.  ... 
doi:10.7916/d8k94qmp fatcat:glh57zlatnfo3e6lbarghk2d2i