Accurate Cost Estimation of Memory Systems Utilizing Machine Learning and Solutions from Computer Vision for Design Automation
Sprache des Titels:
Hardware/software co-designs are usually defined
at high levels of abstractions at the beginning of the design
process in order to provide a variety of options of how to realize
a system. This allows for design exploration which relies on
knowing the costs of different design configurations (with respect
to hardware usage and firmware metrics). To this end, methods
for cost estimation are frequently applied in industrial practice.
However, currently used methods oversimplify the problem and
ignore important features, leading to estimates which are far
off from 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). Based
on that, an approach is proposed which outperforms existing
methods for cost estimation. Experimental evaluations within an
industrial context show that, while the accuracy of the stateof-the-art approach is frequently off by more than 20% for
area estimation and more than 15% for firmware estimation,
the method proposed in this work comes rather close to the
actual values (just 5-7% off for both area and firmware).
Furthermore, our approach outperforms existing methods for
scalability, generalization and decrease in manual effort.