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Machine Learning-Based Processor Adaptability Targeting Energy, Performance, and Reliability. / Sartor, Anderson Luiz; Exenberger Becker, Pedro Henrique; Wong, Stephan; Marculescu, Radu; Schneider Beck, Antonio Carlos.

2019 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2019: Proceedings . ed. / L. O'Conner. Piscataway : IEEE, 2019. p. 158-163 8839457.

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Harvard

Sartor, AL, Exenberger Becker, PH, Wong, S, Marculescu, R & Schneider Beck, AC 2019, Machine Learning-Based Processor Adaptability Targeting Energy, Performance, and Reliability. in L O'Conner (ed.), 2019 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2019: Proceedings ., 8839457, IEEE, Piscataway, pp. 158-163, 18th IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2019, Miami, United States, 15/07/19. https://doi.org/10.1109/ISVLSI.2019.00037

APA

Sartor, A. L., Exenberger Becker, P. H., Wong, S., Marculescu, R., & Schneider Beck, A. C. (2019). Machine Learning-Based Processor Adaptability Targeting Energy, Performance, and Reliability. In L. O'Conner (Ed.), 2019 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2019: Proceedings (pp. 158-163). [8839457] IEEE. https://doi.org/10.1109/ISVLSI.2019.00037

Vancouver

Sartor AL, Exenberger Becker PH, Wong S, Marculescu R, Schneider Beck AC. Machine Learning-Based Processor Adaptability Targeting Energy, Performance, and Reliability. In O'Conner L, editor, 2019 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2019: Proceedings . Piscataway: IEEE. 2019. p. 158-163. 8839457 https://doi.org/10.1109/ISVLSI.2019.00037

Author

Sartor, Anderson Luiz ; Exenberger Becker, Pedro Henrique ; Wong, Stephan ; Marculescu, Radu ; Schneider Beck, Antonio Carlos. / Machine Learning-Based Processor Adaptability Targeting Energy, Performance, and Reliability. 2019 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2019: Proceedings . editor / L. O'Conner. Piscataway : IEEE, 2019. pp. 158-163

BibTeX

@inproceedings{7faf83f9e8ad42e5ab47b8ccb9069124,
title = "Machine Learning-Based Processor Adaptability Targeting Energy, Performance, and Reliability",
abstract = "Adaptive processors can dynamically change their hardware configuration by tuning several knobs that optimize a given metric, according to the current application. However, the complexity of choosing the best setup at runtime increases exponentially as more adaptive resources become available. Therefore, we propose a polymorphic VLIW processor coupled to a machine learning-based decision mechanism that quickly and accurately delivers the best trade-off in terms of energy, performance, and reliability. The proposed system predicts the best processor configuration in 97.37% of the test cases and achieves an efficiency that is close to an oracle (more than 93.30% on all benchmarks).",
keywords = "Adaptive processor, Energy consumption, Fault tolerance, Machine learning, Runtime optimization",
author = "Sartor, {Anderson Luiz} and {Exenberger Becker}, {Pedro Henrique} and Stephan Wong and Radu Marculescu and {Schneider Beck}, {Antonio Carlos}",
year = "2019",
month = jul,
day = "1",
doi = "10.1109/ISVLSI.2019.00037",
language = "English",
isbn = "978-1-7281-3392-8",
pages = "158--163",
editor = "L. O'Conner",
booktitle = "2019 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2019",
publisher = "IEEE",
address = "United States",
note = "18th IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2019 ; Conference date: 15-07-2019 Through 17-07-2019",

}

RIS

TY - GEN

T1 - Machine Learning-Based Processor Adaptability Targeting Energy, Performance, and Reliability

AU - Sartor, Anderson Luiz

AU - Exenberger Becker, Pedro Henrique

AU - Wong, Stephan

AU - Marculescu, Radu

AU - Schneider Beck, Antonio Carlos

PY - 2019/7/1

Y1 - 2019/7/1

N2 - Adaptive processors can dynamically change their hardware configuration by tuning several knobs that optimize a given metric, according to the current application. However, the complexity of choosing the best setup at runtime increases exponentially as more adaptive resources become available. Therefore, we propose a polymorphic VLIW processor coupled to a machine learning-based decision mechanism that quickly and accurately delivers the best trade-off in terms of energy, performance, and reliability. The proposed system predicts the best processor configuration in 97.37% of the test cases and achieves an efficiency that is close to an oracle (more than 93.30% on all benchmarks).

AB - Adaptive processors can dynamically change their hardware configuration by tuning several knobs that optimize a given metric, according to the current application. However, the complexity of choosing the best setup at runtime increases exponentially as more adaptive resources become available. Therefore, we propose a polymorphic VLIW processor coupled to a machine learning-based decision mechanism that quickly and accurately delivers the best trade-off in terms of energy, performance, and reliability. The proposed system predicts the best processor configuration in 97.37% of the test cases and achieves an efficiency that is close to an oracle (more than 93.30% on all benchmarks).

KW - Adaptive processor

KW - Energy consumption

KW - Fault tolerance

KW - Machine learning

KW - Runtime optimization

UR - http://www.scopus.com/inward/record.url?scp=85072986589&partnerID=8YFLogxK

U2 - 10.1109/ISVLSI.2019.00037

DO - 10.1109/ISVLSI.2019.00037

M3 - Conference contribution

AN - SCOPUS:85072986589

SN - 978-1-7281-3392-8

SP - 158

EP - 163

BT - 2019 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2019

A2 - O'Conner, L.

PB - IEEE

CY - Piscataway

T2 - 18th IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2019

Y2 - 15 July 2019 through 17 July 2019

ER -

ID: 62407193