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Article

Accurate software performance estimation using domain classification and neural networks

Authors:

Márcio Seiji Oyamada,

Felipe Zschornack,

Flávio Rech WagnerAuthors Info & Claims

SBCCI '04: Proceedings of the 17th symposium on Integrated circuits and system design

Pages 175 - 180

https://doi.org/10.1145/1016568.1016617

Published: 04 September 2004 Publication History

Abstract

For the design of an embedded system, there is a variety of available processors, each one offering a different trade-off concerning factors such as performance and power consumption. High-level performance estimation of the embedded software implemented in a particular architecture is essential for a fast design space exploration, including the choice of the most appropriate processor. However, advanced architectures present many features, such as deep pipelines, branch prediction mechanisms and cache sizes, that have a non-linear impact on the execution time, which becomes hard to evaluate. In order to cope with this problem, this paper presents a neural network based approach for high-level performance estimation, which easily adapts to the non-linear behavior of the execution time in such advanced architectures. A method for automatic classification of applications is proposed, based on topological information extracted from the control flow graph of the application, enabling the utilization of domain-specific estimators and thus resulting in more accurate estimates. Practical experiments on a variety of benchmarks show estimation results with a mean error of 6.41% and a maximum error of 32%, which is more precise than previous work based on linear and non-linear approaches.

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Cited By

Kumar V(2022)Estimation of an Early WCET Using Different Machine Learning ApproachesAdvances on P2P, Parallel, Grid, Cloud and Internet Computing10.1007/978-3-031-19945-5_30(297-307)Online publication date: 18-Oct-2022
https://doi.org/10.1007/978-3-031-19945-5_30
Kumar V(2021)Deep Neural Network Approach to Estimate Early Worst-Case Execution Time2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC)10.1109/DASC52595.2021.9594326(1-8)Online publication date: 3-Oct-2021
https://doi.org/10.1109/DASC52595.2021.9594326
Kim JHa S(2020)Fast Performance Estimation and Design Space Exploration of SSD Using AI TechniquesEmbedded Computer Systems: Architectures, Modeling, and Simulation10.1007/978-3-030-60939-9_1(1-17)Online publication date: 7-Oct-2020
https://doi.org/10.1007/978-3-030-60939-9_1
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Index Terms

Accurate software performance estimation using domain classification and neural networks
1. Computer systems organization
  1. Embedded and cyber-physical systems
  2. Real-time systems

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Information & Contributors

Information

Published In

cover image ACM Conferences

SBCCI '04: Proceedings of the 17th symposium on Integrated circuits and system design

September 2004

296 pages

ISBN:1581139470

DOI:10.1145/1016568

General Chair:
Edna Natividade da Silva Barros
Federal University of Pernambuco, Brazil
,
Program Chairs:
Flàvio Rech Wagner
Federal University of Rio Grande do Sul, Brazil
,
Luigi Carro
Federal University of Rio Grande do Sul, Brazil
,
Franz Josef Rammig
University of Paderborn, Germany

Copyright © 2004 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 September 2004

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SBCCI04

Sponsor:

SBCCI04: 17th Symposium on Integrated Circuits and System Design

September 7 - 11, 2004

Pernambuco, Brazil

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Overall Acceptance Rate 133 of 347 submissions, 38%

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Cited By

Kumar V(2022)Estimation of an Early WCET Using Different Machine Learning ApproachesAdvances on P2P, Parallel, Grid, Cloud and Internet Computing10.1007/978-3-031-19945-5_30(297-307)Online publication date: 18-Oct-2022
https://doi.org/10.1007/978-3-031-19945-5_30
Kumar V(2021)Deep Neural Network Approach to Estimate Early Worst-Case Execution Time2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC)10.1109/DASC52595.2021.9594326(1-8)Online publication date: 3-Oct-2021
https://doi.org/10.1109/DASC52595.2021.9594326
Kim JHa S(2020)Fast Performance Estimation and Design Space Exploration of SSD Using AI TechniquesEmbedded Computer Systems: Architectures, Modeling, and Simulation10.1007/978-3-030-60939-9_1(1-17)Online publication date: 7-Oct-2020
https://doi.org/10.1007/978-3-030-60939-9_1
Huybrechts TCassimon AMercelis SHellinckx P(2018)Introduction of Deep Neural Network in Hybrid WCET AnalysisAdvances on P2P, Parallel, Grid, Cloud and Internet Computing10.1007/978-3-030-02607-3_38(415-425)Online publication date: 17-Oct-2018
https://doi.org/10.1007/978-3-030-02607-3_38
Tetzlaff DGlesner S(2013)Intelligent prediction of execution times2013 Second International Conference on Informatics & Applications (ICIA)10.1109/ICoIA.2013.6650262(234-239)Online publication date: Sep-2013
https://doi.org/10.1109/ICoIA.2013.6650262
Franke B(2012)Statistical Performance Modeling in Functional Instruction Set SimulatorsACM Transactions on Embedded Computing Systems10.1145/2180887.218089911S:1(1-22)Online publication date: 1-Jun-2012
https://dl.acm.org/doi/10.1145/2180887.2180899
Bohm IFranke BTopham N(2010)Cycle-accurate performance modelling in an ultra-fast just-in-time dynamic binary translation instruction set simulator2010 International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation10.1109/ICSAMOS.2010.5642102(1-10)Online publication date: Jul-2010
https://doi.org/10.1109/ICSAMOS.2010.5642102
Powell DFranke BRosenstiel WWakabayashi K(2009)Using continuous statistical machine learning to enable high-speed performance prediction in hybrid instruction-/cycle-accurate instruction set simulatorsProceedings of the 7th IEEE/ACM international conference on Hardware/software codesign and system synthesis10.1145/1629435.1629478(315-324)Online publication date: 11-Oct-2009
https://dl.acm.org/doi/10.1145/1629435.1629478
Wang ZSanchez AHerkersdorf AAvritzer AWeyuker EWoodside M(2008)SciSimProceedings of the 7th international workshop on Software and performance10.1145/1383559.1383565(33-42)Online publication date: 23-Jun-2008
https://dl.acm.org/doi/10.1145/1383559.1383565
Franke BFalk H(2008)Fast cycle-approximate instruction set simulationProceedings of the 11th international workshop on Software & compilers for embedded systems10.1145/1361096.1361109(69-78)Online publication date: 13-Mar-2008
https://dl.acm.org/doi/10.1145/1361096.1361109
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