Abstract

Modern microprocessor technology is migrating from simply increasing clock speeds on a single processor to placing multiple processors on a die to increase throughput and power performance in every generation. To utilize the potential of such a system, signal processing algorithms have to be efficiently parallelized so that the load can be distributed evenly among the multiple processing units. In this paper, we study several advanced deterministic and stochastic signal processing algorithms and their computation using multiple processing units. Specifically, we consider two commonly used time-frequency signal representations, the short-time Fourier transform and the Wigner distribution, and we demonstrate their parallelization with low communication overhead. We also consider sequential Monte Carlo estimation techniques such as particle filtering, and we demonstrate that its multiple processor implementation requires large data exchanges and thus a high communication overhead. We propose a modified mapping scheme that reduces this overhead at the expense of a slight loss in accuracy, and we evaluate the performance of the scheme for a state estimation problem with respect to accuracy and scalability.

Original languageEnglish (US)
Title of host publication2008 IEEE Workshop on Signal Processing Systems, SiPS 2008, Proceedings
Pages269-274
Number of pages6
DOIs
StatePublished - Dec 26 2008
Event2008 IEEE Workshop on Signal Processing Systems, SiPS 2008 - Washington, DC, United States
Duration: Oct 8 2008Oct 10 2008

Publication series

NameIEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation
ISSN (Print)1520-6130

Other

Other2008 IEEE Workshop on Signal Processing Systems, SiPS 2008
CountryUnited States
CityWashington, DC
Period10/8/0810/10/08

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing
  • Applied Mathematics
  • Hardware and Architecture

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