A machine learning approach for area prediction of hardware designs from abstract specifications

Elena Zennaro, Lorenzo Servadei, Keerthikumara Devarajegowda, Wolfgang Ecker

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

19 Scopus citations

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.

Original languageEnglish
Title of host publicationProceedings - 21st Euromicro Conference on Digital System Design, DSD 2018
EditorsNikos Konofaos, Martin Novotny, Amund Skavhaug
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages413-420
Number of pages8
ISBN (Electronic)9781538673768
DOIs
StatePublished - 12 Oct 2018
Event21st Euromicro Conference on Digital System Design, DSD 2018 - Prague, Czech Republic
Duration: 29 Aug 201831 Aug 2018

Publication series

NameProceedings - 21st Euromicro Conference on Digital System Design, DSD 2018

Conference

Conference21st Euromicro Conference on Digital System Design, DSD 2018
Country/TerritoryCzech Republic
CityPrague
Period29/08/1831/08/18

Keywords

  • Area Estimation
  • Code Generation
  • Design Productivity
  • Machine Learning
  • Meta-Modeling
  • Model-Driven-Architecture
  • Register Interface

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