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
N1 - Funding Information:
Further author information: Send correspondence to John Brunhaver, E-mail: John.Brunhaver@asu.edu This material is based on research sponsored by Air Force Research Laboratory (AFRL) and Defence Advanced Research Projects Agency (DARPA) under agreement number FA8650-18-2-7960. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusion contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of Air Force Research Laboratory (AFRL) and Defence Advanced Research Projects Agency (DARPA) or the U.S. Government.
Publisher Copyright:
© 2019 SPIE.
PY - 2019
Y1 - 2019
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 -
