Chapter 5 Robust Performance Tailoring with Tuning - SSL - MIT

parameters obtained by tuning the model and the performance variance of the nominal
and tuned models as well as that of the tuned hardware. These results are plotted
graphically in Figure 4-9(b) with open circles, filled circles and black x’s representing
the nominal model, tuned model and tuned hardware performance RMS, respectively.
The tuned hardware preformance is obtained by applying the tuning parameters
resulting from a given tuning algorithm to the hardware simulation. The requirement
is indicated by the solid line, and the untuned hardware performance by the dashed
line.
It is clear from both the table and the figure that none of the model-only tuning
schemes result in a tuning configuration that successfully tunes this hardware simula-
tion since none of the x’s lie below the requirement line. This result is not surprising
for the nominal, AO and worst-case model methods since there no hardware data
are used at all in those techniques. Both the nominal and worst-case model tuning
provide tuning configurations that improve the model performance, but make the
hardware performance worse. The AO tuning fails to find a tuning configuration that
improves all of the uncertainty vertices. The untuned configuration is actually the
most robust since the uncertainty space is so large. Therefore the nominal and tuned
model performance (open and filled circles) are directly on top of each other and the
hardware performance (black x) stays at its nominal value.
It is a bit more surprising that optimal model tuning also fails to improve hardware
performance. The optimal model has uncertainty parameters chosen specifically to
result in a model prediction equal to the hardware performance. This goal is met as
indicated in Figure 4-9(b) by the fact that the open circle is directly on the dashed line
for the optimized model case. However, despite the fact that the performances are the
same, the tuned configuration obtained from the optimized model does not also tune
the hardware. In fact, in the figure, the x associated with this method is well above
the HW performance line indicating that tuning has made the performance worse.
This result suggests that the uncertainty parameters found by the optimization are
a non-unique solution and there are multiple uncertainty configurations that result
in the same performance. This problem of uniqueness is well-known in the field of
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