@inproceedings{9f76359a3d884a54ad6202fdeb88f10a,
title = "A machine learning approach for area prediction of hardware designs from abstract specifications",
abstract = "Advancements of Machine Learning (ML) in the field of computer vision have paved the way for its potential application in many other fields. Researchers and hardware domain experts are exploring possible applications of Machine Learning in optimizing many aspects of hardware development process. In this paper, we propose a novel approach for predicting the area of hardware components from specifications. The flow uses an existing RTL generation framework, for generating valid data samples that enable ML algorithms to train the learning models. The approach has been successfully employed to predict the area of real-life hardware components such as Control and Status Register (CSR) interfaces that are ubiquitous in embedded systems. With this approach we are able to predict the area with more than 98% accuracy and 600x faster than the existing methods. In addition, we are able to rank the features according to their importance in final area estimations.",
keywords = "Area Estimation, Code Generation, Design Productivity, Machine Learning, Meta-Modeling, Model-Driven-Architecture, Register Interface",
author = "Elena Zennaro and Lorenzo Servadei and Keerthikumara Devarajegowda and Wolfgang Ecker",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 21st Euromicro Conference on Digital System Design, DSD 2018 ; Conference date: 29-08-2018 Through 31-08-2018",
year = "2018",
month = oct,
day = "12",
doi = "10.1109/DSD.2018.00076",
language = "English",
series = "Proceedings - 21st Euromicro Conference on Digital System Design, DSD 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "413--420",
editor = "Nikos Konofaos and Martin Novotny and Amund Skavhaug",
booktitle = "Proceedings - 21st Euromicro Conference on Digital System Design, DSD 2018",
}