Accurate Cost Estimation of Memory Systems Inspired by Machine Learning for Computer Vision
Lorenzo Servadei1,2,a, Elena Zennaro1,b, Keerthikumara Devarajegowda1,3,c, Martin Manzinger1,4,d, Wolfgang Ecker1,4,e and Robert Wille1,2,f
1Infineon Technologies AG
2Johannes Kepler University Linz
3TU Kaiserslautern
4TU Munich
aLorenzo.Servadei@infineon.com
bElena.Zennaro@infineon.com
cKeerthikumara.Devarajegowda@infineon.com
dMartin.Manzinger@infineon.com
eWolfgang.Ecker@infineon.com
frobert.wille@jku.at
ABSTRACT
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 VisionM (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.
