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

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

doi:10.1117/12.2519264

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 proceeding › Conference contribution

2 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 language	English (US)
Title of host publication	Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2018
Editors	Raja Suresh
Publisher	SPIE
ISBN (Electronic)	9781510626959
DOIs	https://doi.org/10.1117/12.2519264
State	Published - Jan 1 2019
Event	24th Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation Conference 2018 - Baltimore, United States Duration: Apr 16 2019 → Apr 18 2019

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	11015
ISSN (Print)	0277-786X
ISSN (Electronic)	1996-756X

Conference

Conference	24th Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation Conference 2018
Country	United States
City	Baltimore
Period	4/16/19 → 4/18/19

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

Access to Document

10.1117/12.2519264

Fingerprint Dive into the research topics of 'Machine understanding of domain computation for Domain-Specific System-on-Chips (DSSoC)'. Together they form a unique fingerprint.

Cite this

Uhrie, R., Bliss, D. W., Chakrabarti, C., Ogras, U. Y., & Brunhaver, J. (2019). Machine understanding of domain computation for Domain-Specific System-on-Chips (DSSoC). In R. Suresh (Ed.), Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2018 [110150O] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11015). SPIE. https://doi.org/10.1117/12.2519264

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

Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2018. ed. / Raja Suresh. SPIE, 2019. 110150O (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Uhrie, R, Bliss, DW , Chakrabarti, C, Ogras, UY & Brunhaver, J 2019, Machine understanding of domain computation for Domain-Specific System-on-Chips (DSSoC). in R Suresh (ed.), Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2018., 110150O, Proceedings of SPIE - The International Society for Optical Engineering, vol. 11015, SPIE, 24th Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation Conference 2018, Baltimore, United States, 4/16/19. https://doi.org/10.1117/12.2519264

Uhrie R, Bliss DW , Chakrabarti C, Ogras UY, Brunhaver J. Machine understanding of domain computation for Domain-Specific System-on-Chips (DSSoC). In Suresh R, editor, Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2018. SPIE. 2019. 110150O. (Proceedings of SPIE - The International Society for Optical Engineering). https://doi.org/10.1117/12.2519264

Uhrie, Richard ; Bliss, Daniel W. ; Chakrabarti, Chaitali ; Ogras, Umit Y. ; Brunhaver, John. / Machine understanding of domain computation for Domain-Specific System-on-Chips (DSSoC). Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2018. editor / Raja Suresh. SPIE, 2019. (Proceedings of SPIE - The International Society for Optical Engineering).

@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",

}

TY - GEN

T1 - Machine understanding of domain computation for Domain-Specific System-on-Chips (DSSoC)

AU - Uhrie, Richard

AU - Bliss, Daniel W.

AU - Chakrabarti, Chaitali

AU - Ogras, Umit Y.

AU - Brunhaver, John

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

KW - Domain-specific SoC (DSSoC)

KW - Machine learning

KW - Ontological inference

KW - Parallel systems

UR - http://www.scopus.com/inward/record.url?scp=85073911218&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85073911218&partnerID=8YFLogxK

U2 - 10.1117/12.2519264

DO - 10.1117/12.2519264

M3 - Conference contribution

AN - SCOPUS:85073911218

T3 - Proceedings of SPIE - The International Society for Optical Engineering

BT - Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2018

A2 - Suresh, Raja

PB - SPIE

T2 - 24th Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation Conference 2018

Y2 - 16 April 2019 through 18 April 2019

ER -