MNEMEE - Electronic Systems - Technische Universiteit Eindhoven

The problem with the aforementioned DMMs is that they are developed in order to satisfy a wide
range of run-time data storage needs of various applications. They propose one-size-fits-all solutions
without focusing and using application specific information. Additionally, these off-the-shelf DMMs
do not use any platform specific information either, thus failing to exploit any useful information
about the memory hierarchy of an embedded system. In most of the cases (as is the case for DMMs of
general purpose systems), one single flat memory hierarchy is assumed. The result is that while these
general designs serve a wide range of software application demands equally good, they fail to satisfy
the special needs of specific multimedia and network applications mapped on specific memory
hierarchies of an embedded system.
As a result, the performance of all those DMMs is very low, on the one hand, and the memory
footprint, the memory accesses and especially the energy consumption are unacceptably high, on the
other hand.
9.7. MNEMEE extensions to current approaches
9.7.1. Dynamic data access optimization
Most of the work done until now, regards the behavioural analysis of target applications. As stated in
detail in section 9.3, the designer needs an accurate view of the DDT behavior in order to make a
successful choice in DDT implementations. Thus, we have developed a methodology that provides a
higher level view of the DDTs and exposes their particular behavioural characteristics. The
methodology is explained in detail in D2.1 “Profiling framework and dynamic data access
optimization methodology” of the MNEMEE project. Here, we present only the relevant part to
dynamic data access optimization, which is the set metadata needed for the behavioural analysis of
DDTs. In detail we have:
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