research-article

Effective and efficient microprocessor design space exploration using unlabeled design configurations

Authors:

Zhiwei XuAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 5, Issue 1

Article No.: 20, Pages 1 - 18

https://doi.org/10.1145/2542182.2542202

Published: 03 January 2014 Publication History

Abstract

Ever-increasing design complexity and advances of technology impose great challenges on the design of modern microprocessors. One such challenge is to determine promising microprocessor configurations to meet specific design constraints, which is called Design Space Exploration (DSE). In the computer architecture community, supervised learning techniques have been applied to DSE to build regression models for predicting the qualities of design configurations. For supervised learning, however, considerable simulation costs are required for attaining the labeled design configurations. Given limited resources, it is difficult to achieve high accuracy. In this article, inspired by recent advances in semisupervised learning and active learning, we propose the COAL approach which can exploit unlabeled design configurations to significantly improve the models. Empirical study demonstrates that COAL significantly outperforms a state-of-the-art DSE technique by reducing mean squared error by 35% to 95%, and thus, promising architectures can be attained more efficiently.

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Cited By

Li LFlynn THoisie A(2024)Learning Generalizable Program and Architecture Representations for Performance ModelingProceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis10.1109/SC41406.2024.00072(1-15)Online publication date: 17-Nov-2024
https://dl.acm.org/doi/10.1109/SC41406.2024.00072
Huang SYe YYan HShi L(2024)ARS-Flow: A Design Space Exploration Flow for Accelerator-rich System based on Active Learning2024 29th Asia and South Pacific Design Automation Conference (ASP-DAC)10.1109/ASP-DAC58780.2024.10473989(213-218)Online publication date: 22-Jan-2024
https://doi.org/10.1109/ASP-DAC58780.2024.10473989
Harbin JGerasimou SMatragkas NZolotas TCalinescu RAlpizar Santana M(2023)Model-driven design space exploration for multi-robot systems in simulationSoftware and Systems Modeling (SoSyM)10.1007/s10270-022-01041-w22:5(1665-1688)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1007/s10270-022-01041-w
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Index Terms

Effective and efficient microprocessor design space exploration using unlabeled design configurations
1. Computer systems organization
  1. Architectures
2. Computing methodologies
  1. Machine learning

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Information

Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 5, Issue 1

Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining

December 2013

520 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2542182

Editors:
Dr. Hui Xiong
Rutgers University http://datamining.rutgers.edu/
,
Dr. Shashi Shekhar
University of Minnesota http://www-users.cs.umn.edu/∼shekhar/
,
Dr. Alexander Tuzhilin
New York University http://pages.stern.nyu.edu/∼atuzhili/
,
Francesco Bonchi
Yahoo! Research Barcelona, Spain
,
Wray Buntine
NICTA, Australia and The Australian National University
,
Ricard Gavaldá
Technical University of Catalonia, Spain
,
Shengbo Guo
Xerox Research Centre Europe, France

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Association for Computing Machinery

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Publication History

Published: 03 January 2014

Accepted: 01 May 2012

Revised: 01 March 2012

Received: 01 January 2012

Published in TIST Volume 5, Issue 1

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National Natural Science Foundation of China
Chinese Academy of Sciences
Ministry of Science and Technology of the People's Republic of China
National S&T Major Project of China (2009ZX01028-002-003, 2009ZX01029-001-003, and 2010ZX01036-001-002)

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Cited By

Li LFlynn THoisie A(2024)Learning Generalizable Program and Architecture Representations for Performance ModelingProceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis10.1109/SC41406.2024.00072(1-15)Online publication date: 17-Nov-2024
https://dl.acm.org/doi/10.1109/SC41406.2024.00072
Huang SYe YYan HShi L(2024)ARS-Flow: A Design Space Exploration Flow for Accelerator-rich System based on Active Learning2024 29th Asia and South Pacific Design Automation Conference (ASP-DAC)10.1109/ASP-DAC58780.2024.10473989(213-218)Online publication date: 22-Jan-2024
https://doi.org/10.1109/ASP-DAC58780.2024.10473989
Harbin JGerasimou SMatragkas NZolotas TCalinescu RAlpizar Santana M(2023)Model-driven design space exploration for multi-robot systems in simulationSoftware and Systems Modeling (SoSyM)10.1007/s10270-022-01041-w22:5(1665-1688)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1007/s10270-022-01041-w
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Rajmohan SRamasubramanian NNaganathan N(2020)Hybrid Evolutionary Design Space Exploration Algorithm With Defence Against Third Party IP VulnerabilitiesIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2019.296034039:10(2602-2614)Online publication date: Oct-2020
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Sheidaeian HFatemi O(2020)Toward a general framework for jointly processor-workload empirical modelingThe Journal of Supercomputing10.1007/s11227-020-03475-9Online publication date: 5-Nov-2020
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Rajmohan SRamasubramanian N(2019)A Memetic Algorithm-Based Design Space Exploration for Datapath Resource Allocation During High-Level SynthesisJournal of Circuits, Systems and Computers10.1142/S021812662050001229:01(2050001)Online publication date: 15-Mar-2019
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Yan KTan JFu X(2019)Bridging mobile device configuration to the user experience under budget constraintPervasive and Mobile Computing10.1016/j.pmcj.2019.05.004Online publication date: May-2019
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Meng HMeng HDing PWang MWang D(2018)A Design Space Exploration Method for On-Chip Memory System Based on Task Scheduling2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS)10.1109/ICSESS.2018.8663909(912-915)Online publication date: Nov-2018
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