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An Automatic Functional Coverage for Digital Systems Through a Binary Particle Swarm Optimization Algorithm with a Reinitialization Mechanism

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Abstract

At present, functional verification represents the most expensive part of the digital systems design. Moreover, different problems such as: clock synchronization, code compatibility, simulation automation, new design methodologies, proper use of coverage metrics, among others represent challenges in this area. The automated test vector generation is involved in these problems. In this work, an automated functional test sequences generation for digital systems based on the use of coverage models and a binary Particle Swarm Optimization algorithm with a reinitialization mechanism (BPSOr) is described. Also, a comparison with other meta-heuristic algorithms such as: Genetic algorithms (GA) and pseudo-random generation is presented using different fitness functions, coverage models and devices under verification. The main strategy is based on the combination of the simulation and meta-heuristic algorithms to test the device behavior through the generation of test vector sequences. According to the results, the proposed test generation method represents a good alternative to increase the functional coverage during the automated functional verification of block-level digital systems verification.

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References

  1. AlRashidi MR, El-Hawary ME (2009) A survey of particle swarm optimization applications in electric power systems. IEEE Trans Evol Comput 13(4):913–918. doi:10.1109/TEVC.2006.880326

    Article  Google Scholar 

  2. Afshinmanesh F, Marandi A, Rahimi-Kian A (2005) A novel binary particle swarm optimization method using artificial immune system EUROCON 2005 - The international conference on computer as a tool, pp 217–220. doi:10.1109/EURCON.2005.1629899

    Chapter  Google Scholar 

  3. Bose M, Shin J, Rudnick EM, Dukes T, Abadir M (2001) A genetic approach to automatic bias generation for biased random instruction generation. In: Proceedings of the IEEE evolutionary computation, pp 442–448. doi:10.1109/CEC.2001.934425

    Google Scholar 

  4. Cruz AM, Fernandez RB, Lozano HM, Salinas MR, Villas LA (2015) Automated functional test generation for digital systems through a compact binary differential evolution algorithm. J Electron Test 31 (4):361–380. doi:10.1007/s10836-015-5540-6

    Article  Google Scholar 

  5. Corno F, Reorda MS, Squillero G, Manzone A, Pincetti A (2000) Automatic test bench generation for validation of RTL-level descriptions: an industrial experience. In: Proceedings of the IEEE conference on design, automation and test in Europe (DATE), pp 385–389. doi:10.1109/DATE.2000.840300

    Chapter  Google Scholar 

  6. Chen W, Wang L-C, Bhadra J, Abadir M (2013) Simulation knowledge extraction and reuse in constrained random processor verification. In: Proceedings of the 50th ACM/EDAC/IEEE design automation conference (DAC), Austin, TX, pp 1–6. doi:10.1145/2463209.2488881

    Google Scholar 

  7. Chen W-N, Zhang J, Chung HSH, Zhong W-L, Wu W-G, Shi Y-H (2010) A novel set-based particle swarm optimization method for discrete optimization problems. IEEE Trans Evol Comput 14(2):. doi:10.1109/TEVC.2009.2030331

  8. Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Sixth international symposium on micro machine and human science, pp 39–43

  9. Fine S, Ziv A (2003) Coverage directed test generation for functional verification using bayesian networks. In: Proceedings of the IEEE design automation conference (DAC), pp 286–291. doi:10.1145/775832.775907

    Google Scholar 

  10. Gent K, Hsiao MS (2013) Functional test generation at the RTL using swarm intelligence and bounded model checking. In: Proceedings IEEE 22nd Asian test symposium, pp 233–238. doi:10.1109/ATS.2013.51

    Google Scholar 

  11. Henzinger TA (1992) Symbolic model checking for real-time systems. LICS ’92. In: Proceedings of the 7th annual IEEE symposium on logic in computer science, pp 394–406. doi:10.1109/LICS.1992.185551

    Google Scholar 

  12. Hocine R, Kalla H, Kalla S, Arar C (2012) A methodology for verification of embedded systems based on SystemC. In: Proceedings of the IEEE international conference on complex systems (ICCS), pp 1–6. doi:10.1109/ICoCS.2012.6458557

    Google Scholar 

  13. Hertz S, Sheridan D, Vasudevan S (2013) Mining hardware assertions with guidance from static analysis. IEEE Trans Comput Aided Des Integr Circ Syst 32(6):952–965. doi:10.1109/TCAD.2013.2241176

    Article  Google Scholar 

  14. Imková M, Kotásek Z (2015) Automation and optimization of coverage-driven verification. In: Proceedings of the euromicro conference on digital system design (DSD). Funchal, Madeira, Portugal, pp 87–94. doi:10.1109/DSD.2015.34

  15. Jian W, Chuanpei X (2008) Study on test generation of sequential circuits based on particle swarm optimization and ant algorithm International conference on computer science and software engineering, pp 149–152. doi:10.1109/CSSE.2008.953

    Google Scholar 

  16. Jerinic V, Langer J, Heinkel U, Muller D (2006) New methods and coverage metrics for functional verification. In: Proceedings of the design automation test in Europe conference. ISSN: 1530-1591, pp 1–6. doi:10.1109/DATE.2006.243901

    Google Scholar 

  17. Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. Systems, Man, and Cybernetics. Comput Cybern Simul 5(1):4104–4108. doi:10.1109/ICSMC.1997.637339

    Google Scholar 

  18. Kennedy J, Eberhart RC, Shi Y (2001) Swarm intelligence, 1st edn. Academic Press

  19. Katz Y, Rimon M, Ziv A, Shaked G (2011) Learning microarchitectural behaviors to improve stimuli generation quality. In: Proceedings of the 48th ACM/EDAC/IEEE design automation conference (DAC), New york, NY, pp 848–853

    Google Scholar 

  20. Khanesar MA, Teshnehlab M, Shoorehdeli MA (2007) A novel binary particle swarm optimization Mediterranean conference on control automation, 1-7. June 2007. MED ’07. doi:10.1109/MED.2007.4433821

  21. Kulkarni RV, Venayagamoorthy GK (2011) Particle swarm optimization in wireless-sensor networks: a brief survey. IEEE Trans Syst Man Cybern Part C (Appl Rev) 41(2):262–267. doi:10.1109/TSMCC.2010.2054080

    Article  Google Scholar 

  22. Li M, Gent K, Hsiao MS (2012) Design validation of RTL circuits using evolutionary swarm intelligence. In: Proceedings of the IEEE international test conference, Anaheim, CA, pp 1–8. doi:10.1109/TEST.2012.6401556

    Google Scholar 

  23. Li M, Hsiao MS (2009) An ant colony optimization technique for abstraction-guided state justification. In: Proceedings of the IEEE international test conference, pp 1–10. doi:10.1109/TEST.2009.5355676

    Google Scholar 

  24. Lisherness P, Lesperance N, Cheng K-T (Tim) (2013) Mutation analysis with coverage discounting. In: Design, automation test in Europe conference exhibition (DATE), pp 31–34. ISSN: 1530-1591. doi:10.7873/DATE.2013.021

  25. Liu L, Vasudevan S (2011) Efficient validation input generation in RTL by hybridized source code analysis. In: Proceedings of the design, automation & test in Europe, Grenoble, pp 1–6. doi:10.1109/DATE.2011.5763253

    Google Scholar 

  26. Marandi A, Afshinmanesh F, Shahabadi M, Bahrami F (2006) Boolean particle swarm optimization and its application to the design of a dual-band dual-polarized planar antenna IEEE international conference on evolutionary computation, pp 3212–3218. doi:10.1109/CEC.2006.1688716

    Google Scholar 

  27. Osman IH, Kelly JP (1996) Meta-heuristics: theory and applications. Springer US, Kluwer, Boston

    Book  MATH  Google Scholar 

  28. Oh Y-J, Song G-Y (2011) Simple hardware verification platform using SystemVerilog. In: Proceedings of the IEEE TENCON, pp 1414–1417. doi:10.1109/TENCON.2011.6129042

    Google Scholar 

  29. Pampara G, Franken N, Engelbrecht AP (2005) Combining particle swarm optimisation with angle modulation to solve binary problems. IEEE Congress Evol Comput 1(1):89–96. doi:10.1109/CEC.2005.1554671

    Google Scholar 

  30. Puri P, Hsiao MS (2015) Fast stimuli generation for design validation of RTL circuits using binary particle swarm optimization IEEE computer society annual symposium on VLSI, pp 573–578. doi:10.1109/ISVLSI.2015.26

    Google Scholar 

  31. Regimbal S, Lemire JF, Savaria Y, Bois G, Aboulhamid EM, Baron A (2003) Automating functional coverage analysis based on an executable specification. In: Proceedings of the the 3rd IEEE international workshop on system-on-chip for real-time applications, 2003, pp 228–234. doi:10.1109/IWSOC.2003.1213040

    Google Scholar 

  32. Rancea I, Sgarciu V (2008) Functional verification of digital circuits using a software system. In: Proceedings of the IEEE international conference on automation, quality and testing, robotics (AQTR), pp 152–157. doi:10.1109/AQTR.2008.4588725

    Google Scholar 

  33. Samarah A, Habibi A, Tahar S, Kharma N (2006) Automated coverage directed test generation using a cell-based genetic algorithm. In: Proceedings IEEE high-level design validation and test workshop, pp 19–26. doi:10.1109/HLDVT.2006.319996

    Google Scholar 

  34. Sharafi Y, Khanesar MA, Teshnehlab M (2013) Discrete binary cat swarm optimization algorithm 3rd international conference on computer, control communication (IC4), pp 1–6, (to appear in print), doi:10.1109/IC4.2013.6653754

    Google Scholar 

  35. Sadri J, Suen CY (2016) A genetic binary particle swarm optimization model IEEE international conference on evolutionary computation, pp 656–663. doi:10.1109/CEC.2006.1688373

    Google Scholar 

  36. Shen H, Wei W, Chen Y, Chen B, Guo Q (2008) Coverage directed test generation: godson experience. In: Proceedings IEEE Asian test symposium, pp 321–326. doi:10.1109/ATS.2008.42

    Google Scholar 

  37. Tasiran S, Keutzer K (2001) Coverage Metrics for Functional Validation of Hardware Designs. IEEE Des Test Comput 18(4):36–45. doi:10.1109/54.936247

    Article  Google Scholar 

  38. Ugarte I, Sanchez P (2005) Formal meaning of coverage metrics in simulation-based hardware design verification. In: 10th IEEE international high-level design validation and test workshop, pp 221–228. ISSN: 1552-6674. doi:10.1109/HLDVT.2005.1568841

  39. Vasudevan S, Sheridan D, Patel S, Tcheng D, Tuohy B, Johnson D (2010) GoldMine: automatic assertion generation using data mining and static analysis. In: Proceedings of the IEEE design, automation & test in Europe conference & exhibition (DATE 2010), pp 626–629. doi:10.1109/DATE.2010.5457129

    Google Scholar 

  40. Wagner I, Bertacco V, Austin T (2007) Microprocessor verification via feedback-adjusted markov models. IEEE Trans Comput Aided Des Integr Circ Syst 26(6):1126–1138. doi:10.1109/TCAD.2006.884494

    Article  Google Scholar 

  41. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82. doi:10.1109/4235.585893

    Article  Google Scholar 

  42. Wu H, Nie C, Kuo F-C, Leung H, Colbourn CJ (2015) A discrete particle swarm optimization for covering array generation. IEEE Trans Evol Comput 19(4):575–591. doi:10.1109/TEVC.2014.2362532

  43. Yeung SH, Chan WS, Ng KT, Man KF (2012) Computational optimization algorithms for antennas and RF/microwave circuit designs: an overview. IEEE Trans Ind Inform 8(2):216–227. doi:10.1109/TII.2012.2186821

    Article  Google Scholar 

  44. Yu X, Fin A, Fummi F, Rudnick EM (2002) A genetic testing framework for digital integrated circuits. In: Proceedings of the IEEE international conference on tools with artificial intelligence (ICTAI), pp 1–6. doi:10.1109/TAI.2002.1180847

    Google Scholar 

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Acknowledgements

This work has been partially supported by grants under agreements SIP-20170578 and SIP-20171527 of Secretaría de Investigación y Posgrado (SIP) del Instituto Politécnico Nacional (IPN), México. Also this work was supported by Instituto Politécnico Nacional, México, Consejo Nacional de Ciencia y Tecnología (CONACyT), México, and Sistema de Becas por Exclusividad (SIBE)-COFAA.

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Correspondence to Alfonso Martínez-Cruz.

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Martínez-Cruz, A., Barrón-Fernández, R., Molina-Lozano, H. et al. An Automatic Functional Coverage for Digital Systems Through a Binary Particle Swarm Optimization Algorithm with a Reinitialization Mechanism. J Electron Test 33, 431–447 (2017). https://doi.org/10.1007/s10836-017-5665-x

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