Optimizing system-on-chip verifications with multi-objective genetic evolutionary algorithms

Journal of Industrial and Management Optimization

Volumn 10, Issue 2, 2014, Pages 383-396

  Cheng, Adriel ^a   Lim, Cheng Chew ^a  

^a UNIVERSITY OF ADELAIDE   (Australia)

Author keywords

Coverage driven verification; Genetic evolutionary algorithms; Multi objective optimization; Pareto optimization; System on chip verification

Indexed keywords

EID: 84886742584     PISSN: 15475816     EISSN: 1553166X     Source Type: Journal    
DOI: 10.3934/jimo.2014.10.383     Document Type: Article

References (26)

1. T. Bao and B. Mordukhovich, Refined necessary conditions in multi-objective optimization with applications to microeconomic modeling, Discrete Contin. Dyn. Syst., 31 (2011), 1069{ 1096.
2. J. Bergeron, Writing Testbenches using SystemVerilog, 1st edition, Springer Science + Busi- ness Media, New York, 1994.
3. H. Bonnel, and N. S. Pham, Non-smooth optimization over the (weakly or properly) Pareto set of a linear-quadratic multi-objective control problem: Explicit optimality conditions, J. Ind. Manag. Optim., 7 (2011), 789{809.
4. A. Cheng, and C. C. Lim, Markov modeling, and parameterization of genetic evolutionary test generation, J. Global Optim., 51 (2011), 743-751.
5. A. Cheng, C.-C. Lim, Y. Sun, H. He, Z. Zhou, and T. Lei, Using genetic evolutionary software application testing to verify a DSP SoC, in 4th IEEE Int. Workshop on Electronic Design, Test & Applications, IEEE Computer Society, Hong Kong, 2008, 20-25.
6. A. Cheng, A. Parashkevov, and C.-C. Lim, A software test program generator for verify- ing system-on-chips, in 10th IEEE Int. High Level Design Validation, and Test Workshop (HLDVT'05), Napa Valley, CA, 2005, 79-86.
7. C. A. C. Coello, A comprehensive survey of evolutionary-based multiobjective optimization techniques, Journal of Knowledge, and Information Systems, 1 (1999), 269-308.
8. F. Corno, E. Sanchez, M. S. Reorda, and G. Squillero, Code generation for functional validation of pipelined microprocessors, Journal of Electronic Testing: Theory, and Applications, 20 (2004), 269-278.
9. F. Corno, P. Prinetto, M. Rebaudengo, and M. S. Reorda, GATTO: A genetic algorithm for automatic test pattern generation for large synchronous sequential circuits, in IEEE Trans. Computer-Aided Design of Integrated Circuits, and Systems, Vol. 15, IEEE Council on Elec- tronic Design Automation, 1996, 991-1000.
10. S. Fine, and A. Ziv, Coverage directed test generation for functional verification using Bayesian networks, in Proc. 40th Design Automation Conference, New Orleans, LA, 2003, 286-291.
11. C. M. Fonseca, and P. J. Flemming, Genetic algorithms for multi-objective optimization: Formulation, discussion,, and generalization, in 5th Int. Conf. on Genetic Algorithms, Morgan Kaufmann, 1993, 416-423.
12. D. E. Goldberg, Genetic Algorithms in Search, Optimization,, and Machine Learning, Addison-Wesley, Massachusetts, 1989.
13. J. Horn, N. Nafpliotis, and D. E. Goldberg, A Niched Pareto genetic algorithm for multiobjec- tive optimization, in Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Computation, Orlando, FL, 1994, 82-87.
14. W. Jakob, M. Gorges-Schleuter, and C. Blume, Application of genetic algorithms to task planning, and learning, in Parallel Problem Solving from Nature, 2nd Workshop, Lecture Notes in Computer Science, 1992, 291-300.
15. T.-F. Liang, and H.-W. Cheng, Multi-objective aggregate production planning decisions using two-phase fuzzy goal programming method, J. Ind. Manag. Optim., 7 (2011), 365-383.
16. G. Nativ, S. Mittermaier, S. Ur, and A. Ziv, Cost evaluation of coverage directed test genera- tion for the IBM mainframe, in Proceedings of the 2001 IEEE International Test Conference, Baltimore, MD, 2001, 793-802.
17. T. Ray, and R. Sarker, EA for solving combined machine layout, and job assignment problems, J. Ind. Manag. Optim., 4 (2008), 631-646.
18. A. Samarah, A. Habibi, S. Tahar, and N. Kharma, Automated coverage directed test gener- ation using a cell-based genetic algorithm, in IEEE Int. High Level Design Validation, and Test Workshop (HLDVT'06), Monterey, CA, 2006, 19-26.
19. E. Sanchez, M. Schillaci, and G. Squillero, Evolutionary Optimization: The GP Toolkit, 1st edition, Springer Science + Business Media, New York, 2011.
20. E. Sanchez, and G. Squillero, Evolutionary techniques applied to hardware optimization prob- lems: Test, and verification of advanced processors, in Advances in Evolutionary Computing for System Design (eds. L. C. Jain, V. Palade, and D. Srinivasan), Studies in Computational Intelligence, 66, Springer, Berlin-Heidelberg, 2007, 83-106.
21. N. Srinivas, and K. Deb, Multiobjective optimization using nondominated sorting in genetic algorithms, Evolutionary Computation, 2 (1994), 221-248.
22. H. Tamaki, H. Kita, and S. Kobayashi, Multi-objective optimization by genetic algorithms: A review, in Proc. IEEE Int. Conference on Evolutionary Computation, Nagoya, Japan, 1996, 517-522.
23. S. Tasiran, F. Fallah, D. G. Chineery, S. J. Weber, and K. Keutzer, A functional validation technique: Biased-random simulation guided by observability-based coverage, in IEEE Int. Conference on Computer Design, Austin, TX, 2001, 82-88.
24. P. B. Wilson, and M. D. Macleod, Low implementation cost IIR digital filter design using genetic algorithms, in IEE/IEEE Workshop on Natural Algorithms in Signal Processing, Chelmsford, Essex, (1993), 41-48.
25. E. Zitzler, and L. Thiele, Multiobjective evolutionary algorithms: A comparative case study, and the strength Pareto approach, in IEEE Trans. on Evolutionary Computation, Vol. 3, IEEE Computational Intelligence Society, 1999, 257-271.
26. Nios II Hardware Development Tutorial, Development manual of Altera Inc., 2005. Available from: http://www.altera.com/literature/tt/tt_nios2_hardware_tutorial.pdf.