Hardware Reference
In-Depth Information
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AREA
Fig. 3.7
Pareto fronts obtained with the reduced (
diamonds
) and the complete problem (
circles
).
The Area and the Total Cycle objectives values are plotted on the horizontal and vertical axes,
respectively. The Power Dissipation objective is represented by the size of the points: a larger point
means an higher value. The highlighted points are non-dominated. The values of the metrics are
normalized since the model is proprietary of STM
shows to be able of much faster convergence. An optimization run of the original
problem, without the restriction introduced on the input variables, can support this ob-
servation. Indeed, MFGA after only 3,000 evaluation can produce a non-dominated
set which outperforms the one obtained by the procedure just described [
16
]. In
Fig.
3.7
the two sets are plotted on a 2D plot where the third object is represented by
the size of the points.
Moreover, the analysis of the input variable values producing this new and en-
hanced solution set contributes to the insight in the problem structures more than
the statistical calculation previously carried on. A better front is found violating the
prescription proposed by this latter one.
Notwithstanding these results, the validation process cannot be considered as
concluded. On the contrary, it must be a complementary tool to the research for new
and better algorithms. The two processes should provide new problems each other.
Once a detailed validation has stated the reliability of the optimization technique over
one applicative field, new fields could open and new algorithms become necessary.
Following these ideas, an industrial validation step will be described in Chap. 8.
3.5
Conclusions
The results described in the present chapter shows the expertise gained during
the MULTICUBE project in handling optimization problems arising in MP-SoC
architecture design. Two different but complementary meanings can be associated
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