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Proceedings Paper

Machine understanding of domain computation for Domain-Specific System-on-Chips (DSSoC)
Author(s): Richard Uhrie; Daniel W. Bliss; Chaitali Chakrabarti; Umit Y. Ogras; John Brunhaver
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Paper 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.

Paper Details

Date Published: 30 April 2019
PDF: 8 pages
Proc. SPIE 11015, Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2019, 110150O (30 April 2019); doi: 10.1117/12.2519264
Show Author Affiliations
Richard Uhrie, Arizona State Univ. (United States)
Daniel W. Bliss, Arizona State Univ. (United States)
Chaitali Chakrabarti, Arizona State Univ. (United States)
Umit Y. Ogras, Arizona State Univ. (United States)
John Brunhaver, Arizona State Univ. (United States)

Published in SPIE Proceedings Vol. 11015:
Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2019
Raja Suresh, Editor(s)

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