OpenCL and SoC-FPGA-Based K neighbor sorting accelerating method

Abstract

The invention discloses an OpenCL and SoC-FPGA-based K neighbor sorting accelerating method, which comprises the following steps: S1: establishing an SoC-FPGA heterogeneous platform model; S2: establishing an OpenCL host program with a control action at an ARM host port; S3: establishing an inner core by a host program of the ARM host port; S4; configuring and calling an inner core program of an FPGA equipment port by the ARM host port and transmitting data to the FPGA equipment port; S5: calculating an Euclidean distance of data of data distance training sets to be classified by a first inner core program and generating a distance matrix; S6: performing incomplete sorting on each row of the distance matrix by a second inner core program, screening K minimal distance of each row, finding corresponding training set element types and sending back to the ARM host port for processing; S7: performing type counting and classifying by the ARM host port. According to the invention, an OpenCL standard is used for realizing and optimizing the FPGA of a K neighbor sorting algorithm, a system level procedure is formed at the ARM port and the FPGA port, and compared with the traditional GPU heterogeneous computing system, the method has the advantages of lower power consumption and higher energy efficiency.

Description

technical field [0001] The invention relates to an acceleration method of K nearest neighbor classification technology based on SoC-FPGA novel heterogeneous computing system. Background technique [0002] As one of the top ten classic data mining algorithms in the 20th century, the K-nearest neighbor algorithm is widely used in text classification, pattern recognition, image and spatial classification and other fields due to its advantages of accuracy, simplicity and effectiveness. The K-nearest neighbor algorithm is based on lazy learning. Its basic idea is to find the K reference samples closest to each sample to be classified in the known training set, and determine the category of the sample to be classified according to the most category of the K reference samples. However, the K-nearest neighbor algorithm involves a lot of calculations, especially when the training set samples compared with the samples to be classified are large, it will bring a lot of calculation over...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to overcome the deficiencies of the prior art, provide a kind of acceleration method based on the K-nearest neighbor classification technology of SoC-FPGA novel heterogeneous computing system, solve the large amount of calculations and take up hardware resources that exist in the K-nearest neighbor algorithm of the prior art Multiple, high power consumption, and large system delays

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