April  2014, 10(2): 383-396. doi: 10.3934/jimo.2014.10.383

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

1. 

School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, 5005, Australia, Australia

Received  October 2012 Revised  July 2013 Published  October 2013

Verification of semiconductor chip designs is commonly driven by single goal orientated measures. With increasing design complexities, this approach is no longer effective. We enhance the effectiveness of coverage driven design verifications by applying multi-objective optimization techniques. The technique is based on genetic evolutionary algorithms. Difficulties with conflicting test objectives and selection of tests to achieve multiple verification goals in the genetic evolutionary framework are also addressed.
Citation: Adriel Cheng, Cheng-Chew Lim. Optimizing system-on-chip verifications with multi-objective genetic evolutionary algorithms. Journal of Industrial & Management Optimization, 2014, 10 (2) : 383-396. doi: 10.3934/jimo.2014.10.383
References:
[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. doi: 10.3934/dcds.2011.31.1069.  Google Scholar

[2]

J. Bergeron, Writing Testbenches using SystemVerilog, $1^{st}$ edition, Springer Science + Business Media, New York, 1994. Google Scholar

[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. doi: 10.3934/jimo.2011.7.789.  Google Scholar

[4]

A. Cheng and C. C. Lim, Markov modeling and parameterization of genetic evolutionary test generation, J. Global Optim., 51 (2011), 743-751. doi: 10.1007/s10898-011-9682-5.  Google Scholar

[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. doi: 10.1109/DELTA.2008.31.  Google Scholar

[6]

A. Cheng, A. Parashkevov and C.-C. Lim, A software test program generator for verifying system-on-chips, in 10th IEEE Int. High Level Design Validation and Test Workshop (HLDVT'05), Napa Valley, CA, 2005, 79-86. doi: 10.1109/HLDVT.2005.1568818.  Google Scholar

[7]

C. A. C. Coello, A comprehensive survey of evolutionary-based multiobjective optimization techniques, Journal of Knowledge and Information Systems, 1 (1999), 269-308. doi: 10.1007/BF03325101.  Google Scholar

[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. Google Scholar

[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 Electronic Design Automation, 1996, 991-1000. doi: 10.1109/43.511578.  Google Scholar

[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. Google Scholar

[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. Google Scholar

[12]

D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Massachusetts, 1989. Google Scholar

[13]

J. Horn, N. Nafpliotis and D. E. Goldberg, A Niched Pareto genetic algorithm for multiobjective optimization, in Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Computation, Orlando, FL, 1994, 82-87. doi: 10.1109/ICEC.1994.350037.  Google Scholar

[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. Google Scholar

[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. doi: 10.3934/jimo.2011.7.365.  Google Scholar

[16]

G. Nativ, S. Mittermaier, S. Ur and A. Ziv, Cost evaluation of coverage directed test generation for the IBM mainframe, in Proceedings of the 2001 IEEE International Test Conference, Baltimore, MD, 2001, 793-802. doi: 10.1109/TEST.2001.966701.  Google Scholar

[17]

T. Ray and R. Sarker, EA for solving combined machine layout and job assignment problems, J. Ind. Manag. Optim., 4 (2008), 631-646. doi: 10.3934/jimo.2008.4.631.  Google Scholar

[18]

A. Samarah, A. Habibi, S. Tahar and N. Kharma, Automated coverage directed test generation using a cell-based genetic algorithm, in IEEE Int. High Level Design Validation and Test Workshop (HLDVT'06), Monterey, CA, 2006, 19-26. doi: 10.1109/HLDVT.2006.319996.  Google Scholar

[19]

E. Sanchez, M. Schillaci and G. Squillero, Evolutionary Optimization: The GP Toolkit, $1^{st}$ edition, Springer Science + Business Media, New York, 2011. Google Scholar

[20]

E. Sanchez and G. Squillero, Evolutionary techniques applied to hardware optimization problems: 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. doi: 10.1007/978-3-540-72377-6_13.  Google Scholar

[21]

N. Srinivas and K. Deb, Multiobjective optimization using nondominated sorting in genetic algorithms, Evolutionary Computation, 2 (1994), 221-248. doi: 10.1162/evco.1994.2.3.221.  Google Scholar

[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. doi: 10.1109/ICEC.1996.542653.  Google Scholar

[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. doi: 10.1109/ICCD.2001.955007.  Google Scholar

[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. Google Scholar

[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. doi: 10.1109/4235.797969.  Google Scholar

[26]

, Nios II Hardware Development Tutorial,, Development manual of Altera Inc., (2005).   Google Scholar

show all references

References:
[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. doi: 10.3934/dcds.2011.31.1069.  Google Scholar

[2]

J. Bergeron, Writing Testbenches using SystemVerilog, $1^{st}$ edition, Springer Science + Business Media, New York, 1994. Google Scholar

[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. doi: 10.3934/jimo.2011.7.789.  Google Scholar

[4]

A. Cheng and C. C. Lim, Markov modeling and parameterization of genetic evolutionary test generation, J. Global Optim., 51 (2011), 743-751. doi: 10.1007/s10898-011-9682-5.  Google Scholar

[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. doi: 10.1109/DELTA.2008.31.  Google Scholar

[6]

A. Cheng, A. Parashkevov and C.-C. Lim, A software test program generator for verifying system-on-chips, in 10th IEEE Int. High Level Design Validation and Test Workshop (HLDVT'05), Napa Valley, CA, 2005, 79-86. doi: 10.1109/HLDVT.2005.1568818.  Google Scholar

[7]

C. A. C. Coello, A comprehensive survey of evolutionary-based multiobjective optimization techniques, Journal of Knowledge and Information Systems, 1 (1999), 269-308. doi: 10.1007/BF03325101.  Google Scholar

[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. Google Scholar

[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 Electronic Design Automation, 1996, 991-1000. doi: 10.1109/43.511578.  Google Scholar

[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. Google Scholar

[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. Google Scholar

[12]

D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Massachusetts, 1989. Google Scholar

[13]

J. Horn, N. Nafpliotis and D. E. Goldberg, A Niched Pareto genetic algorithm for multiobjective optimization, in Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Computation, Orlando, FL, 1994, 82-87. doi: 10.1109/ICEC.1994.350037.  Google Scholar

[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. Google Scholar

[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. doi: 10.3934/jimo.2011.7.365.  Google Scholar

[16]

G. Nativ, S. Mittermaier, S. Ur and A. Ziv, Cost evaluation of coverage directed test generation for the IBM mainframe, in Proceedings of the 2001 IEEE International Test Conference, Baltimore, MD, 2001, 793-802. doi: 10.1109/TEST.2001.966701.  Google Scholar

[17]

T. Ray and R. Sarker, EA for solving combined machine layout and job assignment problems, J. Ind. Manag. Optim., 4 (2008), 631-646. doi: 10.3934/jimo.2008.4.631.  Google Scholar

[18]

A. Samarah, A. Habibi, S. Tahar and N. Kharma, Automated coverage directed test generation using a cell-based genetic algorithm, in IEEE Int. High Level Design Validation and Test Workshop (HLDVT'06), Monterey, CA, 2006, 19-26. doi: 10.1109/HLDVT.2006.319996.  Google Scholar

[19]

E. Sanchez, M. Schillaci and G. Squillero, Evolutionary Optimization: The GP Toolkit, $1^{st}$ edition, Springer Science + Business Media, New York, 2011. Google Scholar

[20]

E. Sanchez and G. Squillero, Evolutionary techniques applied to hardware optimization problems: 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. doi: 10.1007/978-3-540-72377-6_13.  Google Scholar

[21]

N. Srinivas and K. Deb, Multiobjective optimization using nondominated sorting in genetic algorithms, Evolutionary Computation, 2 (1994), 221-248. doi: 10.1162/evco.1994.2.3.221.  Google Scholar

[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. doi: 10.1109/ICEC.1996.542653.  Google Scholar

[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. doi: 10.1109/ICCD.2001.955007.  Google Scholar

[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. Google Scholar

[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. doi: 10.1109/4235.797969.  Google Scholar

[26]

, Nios II Hardware Development Tutorial,, Development manual of Altera Inc., (2005).   Google Scholar

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