FPGA implementations of data mining algorithms

In recent decades there has been an exponential growth in quantity of collected data. Various data mining procedures have been developed to extract information from such large amounts of data. Handling ever increasing amount of data generates increasing demand for computing power. There are several ways of dealing with this demand, such as multiprocessor systems, and use of graphic processing units (GPU). Another way is use of field programmable gate array (FPGA) devices as hardware accelerators. This paper gives a survey of the application of FPGAs as hardware accelerators for data mining. Three data mining algorithms were selected for this survey: classification and regression trees, support vector machines, and k-means clustering. A literature review and analysis of FPGA implementations was conducted for the three selected algorithms. Conclusions on methods of implementation, common problems and limitations, and means of overcoming them were drawn from the analysis.