| Citation: | LI Dandan, YAO Shuzhen, WANG Ying, et al. Multicore design space exploration via semi-supervised ensemble learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(4): 792-801. doi: 10.13700/j.bh.1001-5965.2017.0297(in Chinese)
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With the increasing complexity of microprocessor architecture, the design space is growing exponentially and the software simulation technology is extremely time-consuming. Design space exploration becomes one major challenge when processors are designed. The paper proposed an efficient design space exploration method combining semi-supervised learning and ensemble learning techniques. Specifically, it includes two phases:uniform random sampling method is firstly employed to select a small set of representative design points, and then simulation is conducted with the points to constitute the training set; semi-supervised learning based AdaBoost (SSLBoost) model is further proposed to predict the responses of the configurations that have not been simulated. Then the optimal processor design configuration is found. The experimental results demonstrate that compared with the prediction models based on the existing artificial neural network and support vector machine (SVM), the proposed SSLBoost model can build a comparable accurate model using fewer simulations. When the number of simulation examples is fixed, the prediction accuracy of SSLBoost model is higher.
| [1] |
NOONBURG D B, SHEN J P. Theoretical modeling of superscalar processor performance[C]//Proceeding of International Symposium on Microarchitecture. New York: ACM, 1994: 52-62.
|
| [2] |
KARKHANIS T S, SMITH J E. Automated design of application specific superscalar processors: An analytical approach[C]//Proceedings of the 34th International Symposium on Computer Architecture. New York: ACM, 2007: 402-411.
|
| [3] |
HAMERLY G, PERELMAN E, CALDER B.How to use SimPoint to pick simulation points[J].ACM Sigmetrics Performance Evaluation Review, 2004, 31(4):25-30. doi: 10.1145/1054907
|
| [4] |
WUNDERLICH R E, WENISCH T F, FALSAFI B, et al. SMARTS: Accelerating microarchitecture simulation via rigorous statistical sampling[C]//Proceedings of the 30th Annual International Symposium on Computer Architecture. New York: ACM, 2003: 84-97.
|
| [5] |
WANG S, HU X, YU P S, et al. MMRate: Inferring multi-aspect diffusion networks with multi-pattern cascades[C]//ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2014: 1246-1255.
|
| [6] |
WANG S, LI Z, CHAO W, et al. Applying adaptive over-sampling technique based on data density and cost-sensitive SVM to imbalanced learning[C]//International Symposium on Neural Networks. Piscataway, NJ: IEEE Press, 2012: 1-8.
|
| [7] |
JOSEPH P J, VASWANI K, THAZHUTHAVEETIL M J. Construction and use of linear regression models for processor performance analysis[C]//Proceedings of the 12th International Symposium on High-Performance Computer Architecture. Piscataway, NJ: IEEE Press, 2006: 99-108.
|
| [8] |
JOSEPH P J, VASWANI K, THAZHUTHAVEETIL M J. A predictive performance model for superscalar processors[C]//Proceedings of the 39th Annual IEEE/ACM International Symposium on Microarchitecture. Piscataway, NJ: IEEE Press, 2006: 161-170.
|
| [9] |
LEE B C, BROOKS D M. Accurate and efficient regression modeling for microarchitectural performance and power prediction[C]//Proceedings of 12th International Conference on Architectural Support for Programming Language and Operating Systems. New York: ACM, 2006: 185-194.
|
| [10] |
LEE B C, COLLINS J, WANG H, et al. CPR: Composable performance regression for scalable multiprocessor models[C]//Proceedings of the 41 st Annual IEEE/ACM International Symposium on Microarchitecture. Piscataway, NJ: IEEE Press, 2008: 270-281.
|
| [11] |
ÏPEK E, MCKEE S A, CARUANA R, et al. Efficiently exploring architectural design spaces via predictive modeling[C]//Proceedings of 12th International Conference on Architectural Support for Programming Language and Operating Systems. New York: ACM, 2006: 195-206.
|
| [12] |
郭崎, 陈天石, 陈云霁.基于模型树的多核设计空间探索技术[J].计算机辅助设计与图形学学报, 2012, 24(6):710-720. http://www.cnki.com.cn/Article/CJFDTOTAL-JSJF201206001.htm
GUO Q, CHEN T S, CHEN Y J.Model tree based multi-core design space exploration[J].Journal of Computer-Aided Design & Computer Graphics, 2012, 24(6):710-720(in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-JSJF201206001.htm
|
| [13] |
PANG J F, LI X F, XIE J S, et al.Microarchitectural design space exploration via support vector machine[J].Acta Scientiarum Naturalium Universitatis Pekinensis, 2010, 46(1):55-63.
|
| [14] |
PALERMO G, SILVANO C, ZACCARIA V.ReSPIR:A response surface-based Pareto iterative refinement for application-specific design space exploration[J].IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2009, 28(12):1816-1829. doi: 10.1109/TCAD.2009.2028681
|
| [15] |
XYDIS S, PALERMO G, ZACCARIA V, et al.SPIRIT:Spectral-aware Pareto iterative refinement optimization for supervised high-level synthesis[J].IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2015, 34(1):155-159. doi: 10.1109/TCAD.2014.2363392
|
| [16] |
GUO Q, CHEN T, ZHOU Z H, et al.Robust design space modeling[J].ACM Transactions on Design Automation of Electronic Systems, 2015:20(2):18.
|
| [17] |
LI D, YAO S, LIU Y H, et al. Efficient design space exploration via statistical sampling and AdaBoost learning[C]//Design Automation Conference. New York: ACM, 2016: 1-6.
|
| [18] |
KHAN S, XEKALAKIS P, CAVAZOS J, et al. Using predictivemodeling for cross-program design space exploration in multicore systems[C]//Proceedings of the 22nd International Conference on Parallel Architectures and Compilation Techniques. Piscataway, NJ: IEEE Press, 2007: 327-338.
|
| [19] |
DUBACH C, JONES T, OBOYLE M. Microarchitectural design space exploration using an architecture-centric approach[C]//Proceedings of the 40th Annual IEEE/ACM International Symposium on Microarchitecture. Piscataway, NJ: IEEE Press, 2007: 262-271.
|
| [20] |
LI D, WANG S, YAO S, et al. Efficient design space exploration by knowledge transfer[C]//Eleventh IEEE/ACM/IFIP International Conference on Hardware/software Codesign and System Synthesis. New York: ACM, 2016: 1-10.
|
| [21] |
SHRESTHA D L, SOLOMATINE D P.Experiments with AdaBoost.RT, an improved boosting scheme for regression[J].Neural Computation, 2006, 18(7):1678-1710. doi: 10.1162/neco.2006.18.7.1678
|
| [22] |
ZHOU Z H, LI M.Semi-supervised learning by disagreement[J].Knowledge and Information Systems, 2010, 24(3):415-439. doi: 10.1007/s10115-009-0209-z
|
| [23] |
BINKERT N, BECKMANN B, BLACK G, et al.The gem5 simulator[J].ACM SIGARCH Computer Architecture News, 2011, 39(2):1-7. doi: 10.1145/2024716
|
| [24] |
BIENIA C, KUMAR S, SINGH J P, et al. The PARSEC benchmark suite: Characterization and architectural implications[C]//Proceedings of the 17th International Conference on Parallel Architecture and Compilation Techniques. New York: ACM, 2008: 72-81.
|
| [25] |
HAMED V, RONG J, ANIL K. Semi-supervised boosting for multi-class classification[C]//European Conference on Principles of Data Mining and Knowledge Discovery, 2008: 522-537.
|
| [26] |
ZHOU Z H, LI M. Semi-supervised regression with co-training[C]//Proceedings of the 19th International Joint Conference on Artificial Intelligence. New York: ACM, 2005: 908-913.
|
| [27] |
CHANG C C, LIN C J.LIBSVM:A library for support vector machines[J].ACM Transactions on Intelligent Systems and Technology, 2011, 2(3):27-1-27-27.
|
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