Accurate Cost Estimation of Memory Systems Inspired by Machine Learning for Computer Vision
Sprache des Vortragstitels:
Englisch
Original Tagungtitel:
In Design, Automation and Test in Europe (DATE)
Sprache des Tagungstitel:
Englisch
Original Kurzfassung:
Hardware/software co-designs are usually defined at
high levels of abstractions at the beginning of the design process
in order to allow plenty of options how to eventually realize a
system. This allows for design exploration which in turn heavily
relies on knowing the costs of different design configurations
(with respect to hardware usage as well as firmware metrics).
To this end, methods for cost estimation are frequently applied
in industrial practice. However, currently used methods for
cost estimation oversimplify the problem and ignore important
features ? leading to estimates which are far off from the real
values. In this work, we address this problem for memory
systems. To this end, we borrow and re-adapt solutions based
on Machine Learning (ML) which have been found suitable
for problems from the domain of Computer Vision (CV) ? in
particular age determination of persons depicted in images. We
show that, for an ML approach, age determination from the CV
domain is actually very similar to cost estimation of a memory
system.
Forschungs-