2007 IEEE/ACM/IFIP Workshop on Embedded Systems for Real-Time Multimedia 2007
DOI: 10.1109/estmed.2007.4375802
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Adaptive mapping to resource availability for dynamic wavelet-based applications

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Cited by 2 publications
(13 citation statements)
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“…While it is feasible to remove these explicit non-linear loop and index expressions through code rewriting techniques, in this case by unrolling the WT level loop, this does not change the fact that the corresponding dependencies remain implicitly present in the code, since a level-k sample will still depend on an exponentially increasing amount of samples from each lower WT level. This makes automated optimization strategies difficult to apply to wavelet-based applications, because they cannot suitably handle these non-linear dependencies and because the code unrolling techniques increase the complexity of the internal representation used in these strategies, making it difficult to find a good solution, as was shown for the automated memory hierarchy mapping tool MH [Geelen 2006] and for the automated memory compaction tool [Ferentinos 2003. Each level of the FWT consists of a lowpass (L) and a highpass (H) filtering followed by a subsampling operation.…”
Section: Scalable Codingmentioning
confidence: 99%
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“…While it is feasible to remove these explicit non-linear loop and index expressions through code rewriting techniques, in this case by unrolling the WT level loop, this does not change the fact that the corresponding dependencies remain implicitly present in the code, since a level-k sample will still depend on an exponentially increasing amount of samples from each lower WT level. This makes automated optimization strategies difficult to apply to wavelet-based applications, because they cannot suitably handle these non-linear dependencies and because the code unrolling techniques increase the complexity of the internal representation used in these strategies, making it difficult to find a good solution, as was shown for the automated memory hierarchy mapping tool MH [Geelen 2006] and for the automated memory compaction tool [Ferentinos 2003. Each level of the FWT consists of a lowpass (L) and a highpass (H) filtering followed by a subsampling operation.…”
Section: Scalable Codingmentioning
confidence: 99%
“…• Problem formulation and the proposed switching principle [Ferentinos 2003, Geelen 2006]: The difficulty of automated tools to find optimal data mappings, for applications with non-linear dependencies like the Wavelet Transform, has been demonstrated and initially solved by subdividing (localizing) the basic processing data-block (Chapter 3). By generalizing the initial approach, we have proposed a run-time switching principle between different implementation mechanisms (localizations) of wavelet-based applications.…”
Section: Summary Of Contributionsmentioning
confidence: 99%
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