SoC-FPGA-based self-reconstruction K-means cluster technology realization method

Abstract

The invention discloses an SoC-FPGA-based self-reconstruction K-means cluster technology realization method. The method comprises the following steps: S1, reconstructing an SoC-FPGA heterogeneous platform model through which an ARM host end cooperates with an FPGA equipment end; S2, the ARM end constructing an OpenCL host program, creating a core and finishing memory distribution and mapping; S3, the host program scheduling a core program of the FPGA equipment end to transmit data to the FPGA equipment end; S4, a first OpenCL core program calculating an Euclidean distance in a parallel pipelined mode, and generating a distance matrix; S5, self-reconstructing a second OpenCL core program, and screening a minimum element of each row and recording a corresponding mass center; S6, self-reconstructing a third OpenCL core program to realize distance accumulation and quantity statistics work of all sample points in each mass center cluster; S7, the host program calculating new mass center data; and S8, the host program performing iteration judgment. The method provided by the invention improves the execution speed of a K-means cluster algorithm, obtains higher energy efficiency, and solves the problem of insufficient FPGA hardware resources through self-reconstruction of the core.

Description

technical field [0001] The invention relates to the technical field of data mining, in particular to a method for realizing a SoC-FPGA-based self-reconfiguration K-means clustering technology. Background technique [0002] As the most commonly used clustering algorithm, the K-means algorithm has been widely used in the fields of pattern recognition, machine learning and data mining due to its simplicity and effectiveness, such as automatic document classification, neural network basis function center determination, nuclear magnetic Resonance image segmentation processing, etc. K-means clustering belongs to unsupervised learning, and its process is as follows figure 1 As shown, the basic idea is based on the K centroids in the space, and the samples are classified according to the distance between the sample point and each centroid, and the new centroids of the current categories are calculated. The cluster centroids are updated multiple times until the centroids converge. ...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to overcome the deficiencies in the prior art, provide a kind of execution speed that has promoted K-means clustering algorithm, obtain the self-reconfiguration K-means clustering technology realization based on SoC-FPGA of higher energy efficiency The method solves the problems of the existing K-means clustering algorithm in the prior art, such as large amount of computation, large hardware resource occupation, large power consumption, and large system delay.

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