Chapter 5 Robust Performance Tailoring with Tuning - SSL - MIT

eal-time hardware optimizations that do not utilize the model at all, are explored.
Hardware simulations are generated with the SCI development model, and it is found
that although the model-only methods are attractive in that few costly hardware tests
are required, they are not consistently successful, and sometimes the resulting tuning
configurations actually degrade the hardware performance. Hardware optimization
methods, on the other hand, find a working tuning configuration (given that one ex-
ists), but generally require a large number of costly hardware tests. As an alternative,
a hybrid method called isoperformance tuning is developed that utilizes the limited
hardware performance data that is available to reduce the uncertainty space so that a
robust tuning optimization performed on the model results in a tuning configuration
that successfully improves the hardware performance. This method is superior to the
model-only and hardware optimization methods in that is consistently successful for
a large number of hardware simulations and requires only a small number of hardware
tests.
Finally the concepts of robust performance tailoring and dynamic tuning are com-
bined to create a design methodology called Robust Performance Tailoring for Tuning
(RPTT). This design optimization anticipates the fact that hardware tuning may be
employed and tailors the design to balance robustness to uncertainty and tuning au-
thority thereby utilizing a two-step uncertainty mitigation scheme . The additional
knowledge of the uncertainty provided by the hardware is anticipated by the tailor-
ing optimization and the design variables are augmented to include different tuning
parameters for each uncertainty realization in order to minimize the worst-case tuned
performance over the uncertainty space. The result is a design that produces a robust
system, instead of simply a robust design. Three different mathematical formulations
of the RPTT optimization are presented and compared through application to the
SCI development problem with both SA and SQP algorithms. The nominal, worst-
case and tuned performances of the resulting RPTT design are compared to those
of the PT and RPT designs over a range of uncertainty bounds. The nominal and
worst-case performances of the RPTT design lie in between those of the PT and RPT
designs. The nominal performance RMS is a little higher than that of the PT design,
214