by Eckart Zitzler, Jurgen Teich, Shuvra S. Bhattacharyya
In Proc. GECCO'99, the Genetic and Evolutionary Computation Conference
ftp://ftp-date.uni-paderborn.de/pub/people/teich/paper/ZTB99a.ps.gz
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Abstract:
The simultaneous exploration of tradeoffs between program memory, data memory and execution time requirements (3D) for DSP (digital signal processing) algorithms in embedded computing environments is a demanding application and example par excellence of a multi-objective optimization problem. In order to solve this problem, two evolutionary algorithms are shown to be successfully applicable for exploring Pareto-optimal solutions. For different well-known target DSP processors, the trade-off fronts are analyzed. The two approaches are quantitatively compared.
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Software SyntheThe following systems have been considered: 1) fractional decimation; 2) Laplacian pyramid; 3) nonuniform filterbank (1/3, 2/3 splits, 4 channels); 4) QMF nonuniformtree filterbank; 5) QMF filterbank (one-sided tree); 6) QMF analysis only;
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Software Synthe11 The following systems have been considered: 1) fractional decimation� 2) Laplacian pyramid� 3) nonuniform lterbank (1/3, 2/3 splits, 4 channels)� 4) QMF nonuniformtree lterbank� 5) QMF lterbank (one-sided tree)� 6) QMF analysis only� 7)
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