Accelerator and method of K-nearest neighbor

Abstract

The invention discloses an accelerating installation and method of K-nearest neighbor, which relates to the field of information retrieval, data mining and computer architecture, the device comprises a control module, used to control each module in the accelerating installation; an address calculation module, used to acquire the address of the training sample; an Euclidean distance calculation module, used to calculate distance when searching K-nearest neighbor; a result module, used to store and transmit the distance; a sequencing module, used to implement K-nearest neighbor sequencing for the distance. Wherein, the result module transmits the distance to the sequencing module, and sends the signal of the next address to the address calculation module for calculation, the address calculation module receives the signal and calculates the address of the next training sample, and the control module clears the contents in the result module. The accelerating installation and method of K-nearest neighbor adjusts KNN algorithm with samples under different dimensions through component increasing and decreasing, and meets different demands of sample algorithm under the same dimension by adjusting degree of parallelism.

Description

technical field [0001] The invention relates to the fields of information retrieval, data mining and computer architecture, in particular to an acceleration device and method for a K-nearest neighbor algorithm. Background technique [0002] KNN algorithm (K-Nearest Neighbor, K nearest neighbor algorithm) is a non-parametric classification algorithm, which has high classification accuracy in the classification of unknown distribution and low error rate in the classification of non-normal distribution, so it is widely used in machine learning , stochastic programming, pattern recognition and other fields, because KNN classification needs to calculate the distance between the feature vector to be classified and each sample in the trained sample set with category labels, resulting in computational complexity when the sample dimension and number of samples are large High, the calculation speed is slow and the real-time performance is poor when using software, which affects its ap...

AI Technical Summary

Problems solved by technology

[0003] In addition to the above-mentioned improvements to the algorithm itself, many works use dedicated hardware to further accelerate the processing of the KNN algorithm. The advantage of using hardware acceleration is that the dedicated hardware structure is often more optimized than the general-purpose CPU in terms of performance and power consumption. However, for Different applications, including the number of training samples, sample dimensions, K variables and other parameters usually need to be adjusted according to actual needs. In addition, the calculation time also needs to meet the specific needs of different applications. However, the current research on the KNN algorithm based on hardware acceleration The work pays more attention to how to improve the speed by simplifying the algorithm, which usually implements a specific KNN algorithm under fixed dimensions, which limits the adaptability to different applications

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