Accurate Cost Estimation of Memory Systems Utilizing Machine Learning and Solutions from Computer Vision for Design Automation

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.