@inproceedings{dd8fe61034294d6db9c319ecf5c3d7ac,
title = "Machine understanding of domain computation for Domain-Specific System-on-Chips (DSSoC)",
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.",
keywords = "Domain-specific SoC (DSSoC), Machine learning, Ontological inference, Parallel systems",
author = "Richard Uhrie and Bliss, {Daniel W.} and Chaitali Chakrabarti and Ogras, {Umit Y.} and John Brunhaver",
year = "2019",
month = jan,
day = "1",
doi = "10.1117/12.2519264",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Raja Suresh",
booktitle = "Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2018",
note = "24th Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation Conference 2018 ; Conference date: 16-04-2019 Through 18-04-2019",
}