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RSM presented dramatic speed-up in terms of evaluation. Besides, it has been found
that a peculiar mathematical transformation of input training set known as Box-Cox
λ transform [ 4 ] has a great impact on the prediction accuracy. A sub-set of the above
analytical techniques has been implemented and integrated in the MULTICUBE
open-source tool while another sub-set was already available in the modeFRON-
TIER tool. RSM techniques for DSE are described in more detail in Chap. 4 of this
topic.
1.2.4
Run-Time Resource Management
The MULTICUBE design flow has been built to provide not only design-time support
but also run-time support. In this scenario, at design time, the multi-objective design
space exploration framework generates a set of Pareto-optimal operating points (for
each application) annotated with system metrics like energy consumption, execution
time, and memory and communication bandwidth values. The Pareto set can then be
exploited at run time (while the application(s) are running) to optimise the overall
system behavior (see Fig. 1.3 ). Specifically a separate Run-time Resource Manager
(RRM) has been developed to exploit the set of operating points derived at design-
time for all applications to steer the overall system behavior according to the imposed
user requirements (quality, power, performance, etc.). The goal of the RRM is to use
the Pareto information given by the design-time exploration on the operating points
(of all applications) to make at run-time a decision to allocate the system resources
to active applications based on the user requirements in terms of Quality of Service.
Run-time Resource Management techniques are described in Chap. 5, while some
more general concepts about resources management at the Operating System layer
are presented in Chap. 6.
Application designer
Design-time
design space
exploration
MP-SoC
platform
simulator
Application
configurations
At design time
At run time
Run-time Resource
Manager
(RRM)
Fig. 1.3 Overview of the
run-time support
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