Abstract
During development, processor architectures can be tuned and configured by many different parameters. For benchmarking, automatic design space explorations (DSEs) with heuristic algorithms are a helpful approach to find the best settings for these parameters according to multiple objectives, e.g. performance, energy consumption, or real-time constraints. But if the setup is slightly changed and a new DSE has to be performed, it will start from scratch, resulting in very long evaluation times.
To reduce the evaluation times we extend the NSGA-II algorithm in this article, such that automatic DSEs can be supported with a set of transformation rules defined in a highly readable format, the fuzzy control language (FCL). Rules can be specified by an engineer, thereby representing existing knowledge. Beyond this, a decision tree classifying high-quality configurations can be constructed automatically and translated into transformation rules. These can also be seen as very valuable result of a DSE because they allow drawing conclusions on the influence of parameters and describe regions of the design space with high density of good configurations.
Our evaluations show that automatically generated decision trees can classify near optimal configurations for the hardware parameters of the Grid ALU Processor (GAP) and M-Sim 2. Further evaluations show that automatically constructed transformation rules can reduce the number of evaluations required to reach the same quality of results as without rules by 43%, leading to a significant saving of time of about 25%. In the demonstrated example using rules also leads to better results.
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References
Ascia, G., Catania, V., Nuovo, A.G.D., Palesi, M., Patti, D.: Efficient design space exploration for application specific systems-on-a-chip. Journal of Systems Architecture 53(10), 733–750 (2007); Embedded Computer Systems: Architectures, Modeling, and Simulation
Beltrame, G., Bruschi, D., Sciuto, D., Silvano, C.: Decision-theoretic exploration of multiprocessor platforms. In: Proceedings of the 4th International Conference on Hardware/Software Codesign and System Synthesis, CODES+ISSS 2006, pp. 205–210 (October 2006)
Calborean, H.: Multi-Objective Optimization of Advanced Computer Architectures using Domain-Knowledge. PhD thesis, Lucian Blaga University of Sibiu, Romania (PhD Supervisor: Prof. Lucian Vintan, PhD) (2011)
Calborean, H., Jahr, R., Ungerer, T., Vintan, L.: Optimizing a superscalar system using multi-objective design space exploration. In: Proceedings of the 18th International Conference on Control Systems and Computer Science (CSCS), Bucharest, Romania, Calea Grivitei, nr. 132, 78122, Sector 1, Bucuresti, May 24-27, vol. 1, pp. 339–346. Editura Politehnica Press (2011) ISSN 2066-4451
Calborean, H., Vintan, L.: An automatic design space exploration framework for multicore architecture optimizations. In: 9th Roedunet International Conference (RoEduNet), Sibiu, Romania, pp. 202–207 (June 2010)
Calborean, H., Vintan, L.: Toward an efficient automatic design space exploration frame for multicore optimization. In: ACACES 2010 Poster Abstracts, Terassa, Spain, pp. 135–138 (July 2010)
Cook, H., Skadron, K.: Predictive design space exploration using genetically programmed response surfaces. In: Proceedings of the 45th annual Design Automation Conference, DAC 2008, pp. 960–965. ACM, New York (2008)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Guthaus, M., Ringenberg, J., Ernst, D., Austin, T., Mudge, T., Brown, T.: Mibench: A free, commercially representative embedded benchmark suite. In: 4th IEEE International Workshop on Workload Characteristics, pp. 3–14 (December 2001)
Hall, M.: Correlation-based Feature Selection for Machine Learning. PhD thesis, University of Waikato (1999)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. Newsl. 11, 10–18 (2009)
IEC 1131 - programmable controllers, part 7 - fuzzy control programming (January 1997)
Jahr, R., Ungerer, T., Calborean, H., Vintan, L.: Automatic multi-objective optimization of parameters for hardware and code optimizations. In: Smari, W.W., McIntire, J.P. (eds.) Proceedings of the 2011 International Conference on High Performance Computing & Simulation (HPCS 2011), pp. 308–316. IEEE (July 2011) ISBN 978-1-61284-381-0
Joseph, K.G., Sharkey, J., Ponomarev, D.: M-sim: A flexible, multithreaded architectural simulation environment. Technical Report CS-TR-05-DP01, State University of New York at Binghamton (October 2005)
Mariani, G., Brankovic, A., Palermo, G., Jovic, J., Zaccaria, V., Silvano, C.: A correlation-based design space exploration methodology for multi-processor systems-on-chip. In: Proceedings of the 47th Design Automation Conference, DAC 2010, pp. 120–125. ACM, New York (2010)
Mariani, G., Palermo, G., Silvano, C., Zaccaria, V.: Meta-model assisted optimization for design space exploration of multi-processor systems-on-chip. In: Proceedings of the 2009 12th Euromicro Conference on Digital System Design, Architectures, Methods and Tools, DSD 2009, pp. 383–389. IEEE Computer Society, Washington, DC (2009)
Mariani, G., Palermo, G., Silvano, C., Zaccaria, V.: Multi-processor system-on-chip design space exploration based on multi-level modeling techniques. In: International Symposium on Systems, Architectures, Modeling, and Simulation, SAMOS 2009, pp. 118 –124 (July 2009)
Mariani, G., Palermo, G., Zaccaria, V., Silvano, C.: An efficient design space exploration methodology for multi-cluster vliw architectures based on artificial neural networks. In: Proc. IFIP International Conference on Very Large Scale Integration VLSI - SoC 2008, Rhodes Island, Greece, October 13-15 (2008)
Nebro, A., Durillo, J., Garcıa-Nieto, J., Coello, C.A., Luna, F., Alba, E.: SMPSO: A new pso-based metaheuristic for multi-objective optimization. In: Proceedings of the IEEE Symposium Series on Computational Intelligence, pp. 66–73 (2009)
Ozisikyilmaz, B., Memik, G., Choudhary, A.: Efficient system design space exploration using machine learning techniques. In: Proceedings of the 45th Annual Design Automation Conference, DAC 2008, pp. 966–969. ACM, New York (2008)
Palermo, G., Silvano, C., Zaccaria, V.: Discrete particle swarm optimization for multi-objective design space exploration. In: Proceedings of the 2008 11th EUROMICRO Conference on Digital System Design Architectures, Methods and Tools, pp. 641–644. IEEE Computer Society, Washington, DC (2008)
Quinlan, J.R.: C4.5: Programs for Machine Learning, 1st edn. Morgan Kaufmann Series in Machine Learning. Morgan Kaufmann (January 1993)
Roychowdhury, S., Pedrycz, W.: A survey of defuzzification strategies. International Journal of Intelligent Systems 16(6), 679–695 (2001)
Sengupta, A., Sedaghat, R., Zeng, Z.: Rapid design space exploration by hybrid fuzzy search approach for optimal architecture determination of multi objective computing systems. Microelectronics Reliability 51, 502–512 (2010); 2010 Reliability of Compound Semiconductors (ROCS) Workshop; Prognostics and Health Management
Shehan, B., Jahr, R., Uhrig, S., Ungerer, T.: Reconfigurable grid ALU processor: Optimization and design space exploration. In: Proceedings of the 13th Euromicro Conference on Digital System Design (DSD), Lille, France (2010)
Sierra, M.R., Coello Coello, C.A.: Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and ε-Dominance. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 505–519. Springer, Heidelberg (2005)
Uhrig, S., Shehan, B., Jahr, R., Ungerer, T.: The two-dimensional superscalar gap processor architecture. International Journal on Advances in Systems and Measurements 3(1 and 2), 71–81 (2010)
Waterman, T.: Adaptive compilation and inlining. PhD thesis. Houston, TX, USA, Adviser-Cooper Keith D. (2006)
Zeng, Z., Sedaghat, R., Sengupta, A.: A framework for fast design space exploration using fuzzy search for vlsi computing architectures. In: Proceedings of 2010 IEEE International Symposium on Circuits and Systems (ISCAS), May 30-June 2, pp. 3176–3179 (2010)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength pareto evolutionary algorithm. Technical Report 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Zurich, Switzerland (2001)
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Jahr, R., Calborean, H., Vintan, L., Ungerer, T. (2012). Boosting Design Space Explorations with Existing or Automatically Learned Knowledge. In: Schmitt, J.B. (eds) Measurement, Modelling, and Evaluation of Computing Systems and Dependability and Fault Tolerance. MMB&DFT 2012. Lecture Notes in Computer Science, vol 7201. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28540-0_16
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DOI: https://doi.org/10.1007/978-3-642-28540-0_16
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