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.

References

[1]

Austin, T., Larson, E., and Ernst, D. 2002. Simplescalar: An infrastructure for computer system modeling. Computer 35, 2, 59--67.

Digital Library

[2]

Balcan, M.-F., Beygelzimer, A., and Langford, J. 2006. Agnostic active learning. In Proceedings of 23th International Conference on Machine Learning. 65--72.

Digital Library

[3]

Blum, A. and Mitchell, T. 1998. Combining labeled and unlabeled data with co-training. In Proceedings of the 11th Annual Conference on Learning Theory. 92--100.

Digital Library

[4]

Chapelle, O. and Zien, A. 2005. Semi-supervised learning by low density separation. In Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics. 57--64.

[5]

Cho, C.-B., Zhang, W., and Li, T. 2007. Informed microarchitecture design space exploration using workload dynamics. In Proceedings of the 40th International Symposium on Microarchitecture. 274--285.

Digital Library

[6]

Dagan, I. and Engelson, S. P. 1995. Committee-based sampling for training probabilistic classifiers. In Proceedings of the 12th International Conference on Machine Learning. 150--157.

[7]

Dasgupta, S. and Hsu, D. 2008. Hierarchical sampling for active learning. In Proceedings of the 25th International Conference on Machine Learning. 208--215.

Digital Library

[8]

Dasgupta, S., Hsu, D., and Monteleoni, C. 2007. A general agnostic active learning algorithm. In Advances in Neural Information Processing Systems 21, 353--360.

[9]

Donmez, P., Carbonell, J. G., and Bennett, P. N. 2007. Dual strategy active learning. In Proceedings of the 18th European Conference on Machine Learning. 116--127.

Digital Library

[10]

Dubach, C., Jones, T., and O'Boyle, M. 2011. An empirical architecture-centric approach to microarchitectural design space exploration. IEEE Trans. Comput. 60, 10, 1445--1458.

Digital Library

[11]

Freund, Y., Seung, H. S., Shamir, E., and Tishby, N. 1997. Selective sampling using the query by committee algorithm. Machine Learn. 28, 2--3, 133--168.

Digital Library

[12]

Fujino, A., Ueda, N., and Saito, K. 2005. A hybrid generative/discriminative approach to semisupervised classifier design. In Proceedings of the 20th National Conference on Artificial Intelligence. 764--769.

Digital Library

[13]

Genbrugge, D. and Eeckhout, L. 2009. Chip multiprocessor design space exploration through statistical simulation. IEEE Trans. Comput. 58, 12, 1668--1681.

Digital Library

[14]

Guo, Q., Chen, T., Chen, Y., Zhou, Z.-H., Hu, W., and Xu, Z. 2011. Effective and efficient microprocessor design space exploration using unlabeled design configurations. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence. 1671--1677.

Digital Library

[15]

Hamerly, G., Perelman, E., Lau, J., Calder, B., and Sherwood, T. 2006. Using machine learning to guide architecture simulation. J. Machine Learn. Res. 7, 343--378.

Digital Library

[16]

Hanneke, S. 2007. A bound on the label complexity of agnostic active learning. In Proceedings of the 24th International Conference on Machine Learning. 353--360.

Digital Library

[17]

Hennessy, J. L. and Patterson, D. A. 2003. Computer Architecture: A Quantitative Approach 3rd Ed. Morgan Kaufmann, San Francisco, CA.

Digital Library

[18]

Hu, W., Wang, J., Gao, X., Chen, Y., Liu, Q., and Li, G. 2009. Godson-3: A scalable multicore RISC processor with X86 emulation. IEEE Micro 29, 2, 17--29.

Digital Library

[19]

Huang, S.-J., Jin, R., and Zhou, Z.-H. 2010. Active learning by querying informative and representative examples. In Advances in Neural Information Processing Systems 23, 892--900.

[20]

Ïpek, E., Mckee, S. A., Caruana, R., Supinski, B. R., and Schulz, M. 2006. Efficiently exploring architectural design spaces via predictive modeling. In Proceedings of the 12th International Conference on Architectural Support for Programming Languages and Operating Systems. 195--206.

Digital Library

[21]

Joachims, T. 1999. Transductive inference for text classification using support vector machines. In Proceedings of International Conference on Machine Learning. 200--209.

Digital Library

[22]

Joseph, P., Kapil, V., and Thazhuthaveetil, M. 2006. Construction and use of linear regression models for processor performance analysis. In Proceedings of the 12th International Symposium on High Performance Computer Architecture. 99--108.

[23]

Joshi, A., Phansalkar, A., Eeckhout, L., and John, L. K. 2006. Measuring benchmark similarity using inherent program characteristics. IEEE Trans. Comput. 55, 6, 769--782.

Digital Library

[24]

Khan, S., Xekalakis, P., Cavazos, J., and Cintra, M. 2007. Using predictivemodeling for cross-program design space exploration in multicore systems. In Proceedings of the 16th International Conference on Parallel Architecture and Compilation Techniques. 327--338.

Digital Library

[25]

Lee, B. C., Collins, J., Wang, H., and Brooks, D. 2008. Cpr: Composable performance regression for scalable multiprocessormodels. In Proceedings of the 41st International Symposium on Microarchitecture. 270--281.

Digital Library

[26]

Lewis, D. D. and Catlett, J. 1994. Heterogeneous uncertainty sampling for supervised learning. In Proceedings of the 11th International Conference on Machine Learning. 148--156.

[27]

Li, M. and Zhou, Z.-H. 2007. Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples. IEEE Trans. Syst. Man Cybern. Part A Syst. Humans 37, 6, 1088--1098.

Digital Library

[28]

Li, M. and Zhou, Z.-H. 2011. Towards making unlabeled data never hurt. In Proceedings of the 28th International Conference on Machine Learning (ICML). 1081--1088.

[29]

Miller, D. J. and Uyar, H. S. 1997. A mixture of experts classifier with learning based on both labelled and unlabelled data. In Advances in Neural Information Processing Systems 9, 571--577.

[30]

Muslea, I., Minton, S., and Knoblock, C. A. 2000. Active learning using pre-clustering. In Proceedings of the 17th National Conference on Artificial Intelligence. 621--626.

[31]

Nguyen, H. T. and Smeulders, A. W. M. 2004. Active learning using pre-clustering. In Proceedings of the 21th International Conference on Machine Learning. 623--630.

Digital Library

[32]

Nigam, K., McCallum, A. K., Thrun, S., and Mitchell, T. 2000. Text classification from labeled and unlabeled documents using em. Machine Learn. 39, 2--3, 103--134.

Digital Library

[33]

Seung, H. S., Opper, M., and Sompolinsky, H. 1992. Query by committee. In Proceedings of the 5th Annual Workshop on Computational Learning Theory. 287--294.

Digital Library

[34]

Sherwood, T., Perelman, E., Hamerly, G., and Calder, B. 2002. Automatically characterizing large scale program behavior. In Proceedings of the 10th International Conference on Architectural Support for Programming Languages and Operating Systems. 45--57.

Digital Library

[35]

Wang, W. and Zhou, Z.-H. 2007. Analyzing co-training style algorithms. In Proceedings of the 18th European Conference on Machine Learning. 454--465.

Digital Library

[36]

Wang, W. and Zhou, Z.-H. 2008. On multi-view active learning and the combination with semi-supervised learning. In Proceedings of the 25th International Conference on Machine Learning. 1152--1159.

Digital Library

[37]

Wang, W. and Zhou, Z.-H. 2010a. Multi-view active learning in the non-realizable case. In Advances in Neural Information Processing Systems 24, 2388--2396.

[38]

Wang, W. and Zhou, Z.-H. 2010b. A new analysis of co-training. In Proceedings of the 27th International Conference on Machine Learning. 1135--1142.

[39]

Wang, Y. and Witten, I. 1997. Induction of model trees for predicting continuous classes. In Proceedings of the 9th European Conference on Machine Learning. 128--137.

[40]

Xu, L. and Schuurmans, D. 2005. Unsupervised and semi-supervised multi-class support vector machines. In Proceedings of the 20th National Conference on Artificial Intelligence. 904--910.

Digital Library

[41]

Zhou, D., Bousquet, O., Lal, T. N., Weston, J., and Schölkopf, B. 2004. Learning with local and global consistency. In Advances in Neural Information Processing Systems 16, 321--328.

Digital Library

[42]

Zhou, Z.-H., Chen, K.-J., and Dai, H.-B. 2006. Enhancing relevance feedback in image retrieval using unlabeled data. ACM Trans Inf. Syst. 24, 2, 219--244.

Digital Library

[43]

Zhou, Z.-H. and Li, M. 2005a. Semi-supervised regression with co-training. In Proceedings of the 19th International Joint Conference on Artificial Intelligence. 908--913.

Digital Library

[44]

Zhou, Z.-H. and Li, M. 2005b. Tri-training: Exploiting unlabeled data using three classifiers. IEEE Trans. Knowl. Data Engin. 17, 11, 1529--1541.

Digital Library

[45]

Zhou, Z.-H. and Li, M. 2010. Semi-supervised learning by disagreement. Knowledge Inf. Syst. 24, 3, 415--439.

Digital Library

[46]

Zhou, Z.-H., Zhan, D.-C., and Yang, Q. 2007. Semi-supervised learning with very few labeled training examples. In Proceedings of the 22nd National Conference on Artificial Intelligence. 675--680.

Digital Library

[47]

Zhu, X., Ghahramani, Z., and Lafferty, J. D. 2003. Semi-supervised learning using Gaussian fields and harmonic functions. In Proceedings of 20th International Conference on Machine Learning. 912--919.

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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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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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Funding Sources

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

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https://doi.org/10.1016/j.vlsi.2024.102315
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
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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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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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