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Hardware implementation of Similarity Functions (2005)
| Venue: | IADIS International Conference on Applied Computing |
| Citations: | 6 - 2 self |
BibTeX
@INPROCEEDINGS{Freeman05hardwareimplementation,
author = {Michael Freeman and Michael Weeks and Jim Austin},
title = {Hardware implementation of Similarity Functions},
booktitle = {IADIS International Conference on Applied Computing},
year = {2005},
pages = {2005}
}
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Abstract
A number of applications varying from music to document classification, require the similarity between a collection of objects to be calculated. To achieve this, features about these objects are extracted e.g. keywords, shapes, colours, frequency components etc, to produce an N-dimensional feature vector, representing a point in a N-dimensional feature space. A database containing these feature vectors can be constructed, allowing query vectors to be applied and the distance between this vector and those stored in the database to be calculated. From the results of these comparisons, similar objects can now be identified and retrieved from the database for further processing by the application. There exists a number of commonly used distance or similarity measures e.g. city block, Euclidean, weighted cosine distance etc, with varying processing requirements and performance characteristics. This paper investigates the possibility of accelerating these distance measures by using FPGA based hardware IP cores and compares this to a software implementation based on a Sun Blade 2000 computer.
Keyphrases
similarity function hardware implementation hardware ip core distance measure processing requirement sun blade similar object similarity measure query vector n-dimensional feature vector cosine distance etc frequency component city block n-dimensional feature space performance characteristic software implementation feature vector
