Predictive Compositional Method to Design and Re-optimize Complex Behavioral Dataflows

Shuangnan Liu, Francis Lau, Benjamin Carrion Schafer

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

In this article, we introduce an automatic stream computing reoptimization flow from ASICs to field-programmable gate arrays (FPGAs). Complex VLSI designs need to be prototyped and/or emulated on FPGAs. The main problem that we address in this article is that configurations optimized when targeting ASICs are often, as we will show in this article, highly un-optimal when remapped onto an FPGA. Thus, this article proposes a method to first generate a variety of dataflow configurations targeting an ASIC given multiple behavioral descriptions for high-level synthesis (HLS) and then, based on a compositional predictive model, automatically reoptimize the dataflow when mapped onto an FPGA. The experimental results show that our proposed method works well and that it is very fast.

Original languageEnglish
Article number8957671
Pages (from-to)2615-2627
Number of pages13
JournalIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Volume39
Issue number10
DOIs
Publication statusPublished - Oct 2020

Keywords

  • ASICs
  • dataflow
  • field-programmable gate arrays (FPGAs)
  • high-level synthesis (HLS)
  • predictive models (PMs)
  • stream computing

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering

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