Cost estimation for configurable model-driven SoC designs using machine learning

Lorenzo Servadei, Edoardo Mosca, Keerthikumara Devarajegowda, Michael Werner, Wolfgang Ecker, Robert Wille

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

Abstract

The complexity of today's System on Chips (SoCs) forces designers to use higher levels of abstractions. Here, early design decisions are conducted on abstract models while different configurations describe how to actually realize the desired SoC. Since those decisions severely affect the final costs of the resulting SoC (in terms of utilized area, power consumption, etc.), a fast and accurate cost estimation is essential at this design stage. Additionally, the resulting costs heavily depend on the adopted logic synthesis algorithms, which optimize the design towards one or more cost objectives. But how to structure a cost estimation method that supports multiple configurations of an SoC, implemented by use of different synthesis strategies, remains an open question. In this work, we address this problem by providing a cost estimation method for a configurable SoC using Machine Learning (ML). A key element of the proposed method is a data representation which describes SoC configurations in a way that is suited for advanced ML algorithms. Experimental evaluations conducted within an industrial environment confirm the accuracy as well as the efficiency of the proposed method.

Original languageEnglish
Title of host publicationGLSVLSI 2020 - Proceedings of the 2020 Great Lakes Symposium on VLSI
PublisherAssociation for Computing Machinery
Pages405-410
Number of pages6
ISBN (Electronic)9781450379441
DOIs
StatePublished - 7 Sep 2020
Event30th Great Lakes Symposium on VLSI, GLSVLSI 2020 - Virtual, Online, China
Duration: 7 Sep 20209 Sep 2020

Publication series

NameProceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI

Conference

Conference30th Great Lakes Symposium on VLSI, GLSVLSI 2020
Country/TerritoryChina
CityVirtual, Online
Period7/09/209/09/20

Keywords

  • Design Automation
  • Hardware-Software codesign
  • Machine Learning

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