C 2013

Algorithms for Efficient Computation of Convolution

KARAS, Pavel and David SVOBODA

Basic information

Original name

Algorithms for Efficient Computation of Convolution

Name in Czech

Algoritmy pro efektivní výpočet konvoluce

Authors

KARAS, Pavel (203 Czech Republic, belonging to the institution) and David SVOBODA (203 Czech Republic, guarantor, belonging to the institution)

Edition

1st ed. Rijeka (CRO), Design and Architectures for Digital Signal Processing, p. 179-208, 30 pp. 2013

Publisher

InTech

Other information

Language

English

Type of outcome

Kapitola resp. kapitoly v odborné knize

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Croatia

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

printed version "print"

References:

RIV identification code

RIV/00216224:14330/13:00065930

Organization unit

Faculty of Informatics

ISBN

978-953-51-0874-0

Keywords (in Czech)

konvoluce; algoritmy; FFT; separabilní konvoluce; rekurzivní filtry; paralelizace; dekompozice

Keywords in English

convolution; algorithms; FFT; separable convolution; recursive filters; parallelization; decomposition

Tags

Tags

International impact, Reviewed
Změněno: 28/4/2014 11:28, RNDr. Pavel Šmerk, Ph.D.

Abstract

V originále

Convolution is an important mathematical tool in both fields of signal and image processing. It is em-ployed in filtering, denoising, edge detection, correlation, compression, deconvolution, simulation, and in many other applications. Although the concept of convolution is not new, the efficient computation of convolution is still an open topic. As the amount of processed data is constantly increasing, there is considerable request for fast manipulation with huge data. Moreover, there is demand for fast algorithms which can exploit computational power of modern parallel architectures. The aim of this chapter is to review the algorithms and approaches for computation of convolution with regards to various properties such as signal and kernel size or kernel separability (when pro-cessing n-dimensional signals). Target architectures include superscalar and parallel processing units (namely CPU, DSP, and GPU), programmable architectures (e.g. FPGA), and distributed systems (such as grids). The structure of the chapter is designed to cover various applications with respect to the signal size, from small to large scales.

Links

GBP302/12/G157, research and development project
Name: Dynamika a organizace chromosomů během buněčného cyklu a při diferenciaci v normě a patologii
Investor: Czech Science Foundation
MUNI/A/0760/2012, interní kód MU
Name: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace II. (Acronym: FI MAV II.)
Investor: Masaryk University, Category A
MUNI/A/0914/2009, interní kód MU
Name: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace (Acronym: SV-FI MAV)
Investor: Masaryk University, Category A