High performance silicon intellectual property for K-Nearest Neighbor algorithm

Autor:	Tse-Wei Chen, Shao-Yi Chien, Chi-Sun Tang
Rok vydání:	2009
Předmět:	Set (abstract data type) Euclidean distance Statistical classification Computer science Pattern recognition (psychology) Clock rate System on a chip Algorithm design Algorithm k-nearest neighbors algorithm
Zdroj:	2009 IEEE 13th International Symposium on Consumer Electronics.
DOI:	10.1109/isce.2009.5156909
Popis:	K-Nearest Neighbor (K-NN) is a classification algorithm that is widely applied in pattern recognition and machine learning. Due to real-time requirements of multimedia content analysis in embedded systems for consumer electronics, it is necessary to accelerate K-NN algorithm by hardware implementations. A high performance silicon intellectual property for K-NN is proposed in this paper. The features include the distance calculator supporting both Euclidean distance and Manhattan distance, and a set of ranking processing elements with high computational efficiency. Experiments show that the proposed hardware has the maximum clock frequency 400MHz with TSMC 90nm technology.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::6d18e5c20db03ef40895baffb32d406c https://doi.org/10.1109/isce.2009.5156909 Zobrazit plný text záznamu