Parameter Tuning in SVM-Based Power Macro-Modeling
R – INESC-ID Lisboa –
We describe a methodology that uses of support vector machines
(SVMs) to determine simpler and better fit power macromodels of
functional units for high-level power estimation. The basic approach
is first to obtain the power consumption of the module for a large
number of points in the input signal space. Least-Squares SVMs are
then used to compute the best model to fit this set of points. We
have performed extensive experiments in order to determine the best
parameters for the kernels. Based on this analysis, we propose an
iterative method of improving the model by selectively adding new
support vectors and increasing the sharpness of the model. The
macromodels obtained confirm the excellent modelling capabilities of
the proposed kernel-based method, providing both excellent accuracy
on maximum error (close to 17%) and average (2% error), which
represents an improvement over the state-of-the-art. Furthermore, we
present an analysis of the dynamic range of power consumption for
the benchmarks circuits, which serves to confirm that the model is
able to accommodate circuits exhibiting a more skewed power
- Date and local
- Friday, March, 6 2009, 14h00, room 336 at INESC-ID, Lisbon.
- More info
Seminars page of INESC-ID
Seminar organized by the
ALGOS group (algos.inesc-id.pt)
Date: 2009-Mar-06 Time: 14:00:00 Room: 336
For more information:
- +351 213100399
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