FPGA (Field Programmable Gate Array) implementation method based on LS-SVM (Least Squares-Support Vector Machine) algorithm restructured at runtime

Abstract

The invention relates to an FPGA (Field Programmable Gate Array) implementation method based on an LS-SVM (Least Squares Support Vector Machine) algorithm restructured at runtime, relating to the technical field of the applications of time detection and an FPGA. In the method, a static logic area and a restructuring area are arranged in an FPGA; a PowerPC440 is used as a system main controller in the static logic area; an ICAP (Internet Content Adaptation Protocol) interface instantiated as a PLB (Payload Loop Back) device is used as a configuration interface; an inner block-RAM is used as a storage area of the program and the data of the PowerPC440; the PowerPC440 is connected with a DDR2RAM through an MPMC (Micro-Processor Memory Controller) interface for controlling the reading-writing of the data of the DDR2RAM; the restructuring area is connected with the DDR2RAM through an NPI (Network Provider Interface) of the MPMC, and the interaction of the command and the data of the restructuring area and the PowerPC440 is carried out through the DDR2RAM; a process for forming a core function array in the process of training the LS-SVM algorithm is realized by using a core function array to calculate an IP (Internet Protocol) module; a process for solving the least square is realized by using a least square to solve the IP module; and two modules are uploaded in the FPGA through the restructuring technology time division to calculate and realize the LS-SVM algorithm. In the invention, the hardware of the LS-SVM algorithm is accelerated on the platform of the FPGA; and the method is capable of realizing the process of training the LS-SVM algorithm for any scale sample.

Description

technical field [0001] The present invention relates to the technical field of time prediction, and also relates to the field of FPGA application technology. Background technique [0002] In nature, engineering technology, science, and economic society, there is an important class of data-time series data. A time series refers to a sequence of random variables arranged in time order and related to each other. For example, the precipitation sequence in meteorology, the sunspot sequence in astronomy, the brain wave sequence in medicine, the daily closing price sequence of ordinary tickets, the node flow sequence in the telecommunications industry, the state detection data sequence of aircraft engines, etc. With the continuous development of computer technology and the increasing storage capacity of storage devices, time series databases are also getting larger and larger. Analyzing and processing these massive time series and mining the valuable information behind them can re...

AI Technical Summary

Problems solved by technology

[0006] In addition, most of the current time series prediction algorithms, including the LS-SVM algorithm, run on general-purpose computing platforms, but in some engineering problems, this is not the best way to achieve it, and even in some application environments it cannot be implemented at all. accomplish

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