Machine understanding of domain computation for Domain-Specific System-on-Chips (DSSoC)

Richard Uhrie, Daniel W. Bliss, Chaitali Chakrabarti, Umit Y. Ogras, John Brunhaver

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Heterogeneous system-on-chips (SoC) can increase the energy-efficiency of domain-specific computation by orders of magnitude compared to scalar processors. High-performance systems can be generated procedurally through example-driven inference of a domain of computation to facilitate the design of domain-specific SoCs. This paper focuses on the domain of signal processing as it plays a recurring and important role in automation. The expertise required to build processors well-suited to a specific computation domain, rather than a single application or general computation, is inferred through the statistical analysis of computation, hardware, and their affinity for each other. This paper highlights the development of an ontological inference engine to achieve this goal.

Original languageEnglish (US)
Title of host publicationOpen Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2018
EditorsRaja Suresh
PublisherSPIE
ISBN (Electronic)9781510626959
DOIs
StatePublished - 2019
Event24th Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation Conference 2018 - Baltimore, United States
Duration: Apr 16 2019Apr 18 2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11015
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference24th Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation Conference 2018
Country/TerritoryUnited States
CityBaltimore
Period4/16/194/18/19

Keywords

  • Domain-specific SoC (DSSoC)
  • Machine learning
  • Ontological inference
  • Parallel systems

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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