Skip to main content
SpringerLink
Log in

Wrapper scan chain design algorithm for testing of embedded cores based on chaotic dragonfly algorithm

  • Research Paper
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

As embedded pre-designed and pre-validated cores in system-on-chip (SoC) designs have increased usage, the wrapper scan chain design (WSCD) for the embedded cores is one of the fundamental ways to reduce the SoC test time. In this paper, a chaotic dragonfly algorithm (CDA) for WSCD is proposed to minimize the test time of embedded cores by balancing the packaged scan chains (WSCs).Since the WSCD problem is non-continuous, we improve the dragonfly algorithm (DA) with integer coding to make it suitable for the WSCD problem. In order to improve population diversity and prevent falling into local optimum state, we introduce chaotic strategy into DA. Furthermore, a repaired operator that considers the specific knowledge is added to the DA. Since the CDA is a swarm intelligence method, it is expected to effectively solve the NP-hard problem. The experimental results on ITC’02 SoC benchmark show that the proposed algorithm can improve the balanced results and shorten the test time compared with the existing algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig.4
Fig. 5

Similar content being viewed by others

References

  1. Moreno E, Webber T, Marcon C, Moraes F, Calazans N (2014) MoNoC: A monitored network on chip with path adaptation mechanism. J Syst Architect 60(10):783–795

    Article  Google Scholar 

  2. Naghibi Jouybari H, Mohammadi K (2014) A low overhead, fault tolerant and congestion aware routing algorithm for 3D mesh-based network-on-chips. Microprocess Microsy 38(8):991–999

    Article  Google Scholar 

  3. Karmakar R, Chattopadhyay S (2015) Window-based peak power model and particle swarm optimization guided 3-dimensional bin packing for SoC test scheduling. Integr VLSI J 50:61–73

    Article  Google Scholar 

  4. Cota E, Liu C (2006) Constraint-driven test scheduling for NoC-based systems. IEEE T Comput Aid D 25(11):2465–2478

    Article  Google Scholar 

  5. Wagner FR, Cesário WO, Carro L, Jerraya AA (2004) Strategies for the integration of hardware and software IP components in embedded systems-on-chip. Integr VLSI J 37(4):223–252

    Article  Google Scholar 

  6. Hsin H, Chang E, Lin C, Wu AA (2014) Ant colony optimization-based fault-aware routing in mesh-based network-on-chip systems. IEEE T Comput Aid D 33(11):1693–1705

    Article  Google Scholar 

  7. Kim J, Hwang M, Roh S, Lee J, Lee K, Lee S and Yoo H (2004), On-chip network based embedded core testing, in: Proceedings of IEEE international SoC conference, 223–266

  8. Marinissen EJ, Goel SK and Lousberg M (2000), Wrapper design for embedded core test, in: International test conference, 911–920

  9. Zorian Y, Marinissen EJ and Dey S (1998), Testing embedded-core based system chips, in: International test conference, 130–143

  10. Iyengar V, Chakrabarty K, Marinissen EJ (2002) Test wrapper and test access mechanism co-optimization for system-on-chip. J Electron Test 18(2):213–230

    Article  Google Scholar 

  11. Pouget J, Larsson E, Peng Z (2005) Multiple-constraint driven system-on-chip test time optimization. J Electron Test 21(6):599–611

    Article  Google Scholar 

  12. Daoheng N, Hong W, Shiyuan Y, Benmao C, Yang J (2007) Re-optimization algorithm for SoC wrapper-chain balance using mean-value approximation. Tsinghua Sci Tech 12(S1):61–66

    Article  Google Scholar 

  13. Yang Y, Yefu C, Yu P (2011) Wrapper scan chain balance algorithm based on mean-value allowance. Chin J Sci Instr 32(10):2290–2296

    Google Scholar 

  14. Li-bao D, Li-yan Q, Yang Y, Xi-yuan P (2012) Wrapper scan chains balance algorithm base on twice-assigned method by the chains difference. Acta Electronica Sinica 40(2):338–343

    Google Scholar 

  15. Holland J (1992) Adaptation in natural and artificial systems. MIT Press, Cambridge

    Book  Google Scholar 

  16. Reddy KS, Sahoo SK (2015) An approach for FIR filter coefficient optimization using differential evolution algorithm. AEU Int J Electron C 69(1):101–108

    Article  Google Scholar 

  17. Goudos SK, Siakavara K, Sahalos JN (2015) Design of load-ended spiral antennas for RFID UHF passive tags using improved artificial bee colony algorithm. AEU Int J Electron C 69(1):206–214

    Article  Google Scholar 

  18. Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete and multi-objective problems. Neural Comput Appl 27(4):1053–1073

    Article  MathSciNet  Google Scholar 

  19. Angira R, Babu BV (2006) Optimization of process synthesis and design problems: a modified differential evolution approach. Chem Eng Sci 61(14):4707–4721

    Article  Google Scholar 

  20. Del Valle Y, Venayagamoorthy GK, Mohagheghi S, Hernandez JC, Harley RG (2008) Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE T Evolut Comput 12(2):171–195

    Article  Google Scholar 

  21. Wu W, Tsai M (2011) Application of enhanced integer coded particle swarm optimization for distribution system feeder reconfiguration. IEEE T Power Syst 26(3):1591–1599

    Article  Google Scholar 

  22. Wang B, Brown D, Zhang X, Li H, Gao Y, Cao J (2014) Polygonal approximation using integer particle swarm optimization. Inform Sci 278:311–326

    Article  MathSciNet  Google Scholar 

  23. Li J, Pan Q, Mao K (2015) A discrete teaching-learning-based optimisation algorithm for realistic flowshop rescheduling problems. Eng Appl Artif Intel 37:279–292

    Article  Google Scholar 

  24. Saremi S, Mirjalili SM, Mirjalili S (2014) Chaotic Krill Herd optimization algorithm. Procedia Technol 12:180–185

    Article  Google Scholar 

  25. Lu Y, Zhou J, Qin H, Wang Y, Zhang Y (2011) Chaotic differential evolution methods for dynamic economic dispatch with valve-point effects. Eng Appl Artif Intel 24(2):378–387

    Article  Google Scholar 

  26. Liu B, Wang L, Jin Y, Tang F, Huang D (2005) Improved particle swarm optimization combined with chaos. Chaos Soliton Fract 25(5):1261–1271

    Article  Google Scholar 

  27. Koupaei JA, Hosseini SMM, Ghaini FMM (2016) A new optimization algorithm based on chaotic maps and golden section search method. Eng Appl Artif Intel 50:201–214

    Article  Google Scholar 

  28. Javidi M, Hosseinpourfard R (2015) Chaos genetic algorithm instead genetic algorithm. Int Arab J Inform Technol 12(2):163–168

    Google Scholar 

  29. Zhang J, Lin S, Qiu W (2015) A modified chaotic differential evolution algorithm for short-term optimal hydrothermal scheduling. Int J Elec Power 65:159–168

    Article  Google Scholar 

  30. Alatas B (2010) Chaotic harmony search algorithms. Appl Math Comput 216(9):2687–2699

    MATH  Google Scholar 

  31. Gandomi AH, Yang X (2014) Chaotic bat algorithm J. Comput Sci 5(2):224–232

    MathSciNet  Google Scholar 

  32. Chen L (2014) Application of SVR with chaotic GASA algorithm to forecast Taiwanese 3G mobile phone demand. Neurocomputing 127:206–213

    Article  Google Scholar 

  33. Coelho LDS (2008) A quantum particle swarm optimizer with chaotic mutation operator. Chaos Soliton Fract 37(5):1409–1418

    Article  Google Scholar 

  34. Marinissen EJ, Iyengar V and Chakrabarty K (2002), A set of benchmarks for modular testing of SOCs, in: International test conference,519–528

Download references

Acknowledgements

This work is supported by National Natural Science Foundation of China (Grant No. 61861012), Guangxi Natural Science Foundation of China (Grant No. 2017GXNSFAA198021), Science Foundation of Guilin University of Aerospace Technology (XJ20KT09) and Guangxi Key Laboratory of Automatic Detecting Technology and Instruments (Grant No. YQ18109).

Author information

Authors and Affiliations

  1. School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin, 541004, China

    Tian Zhou

  2. School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, 541004, China

    Cong Hu, Aijun Zhu, Chuanpei Xu & Chunting Wan

Authors
  1. Tian Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cong Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aijun Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chuanpei Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chunting Wan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cong Hu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, T., Hu, C., Zhu, A. et al. Wrapper scan chain design algorithm for testing of embedded cores based on chaotic dragonfly algorithm. Evol. Intel. 15, 369–379 (2022). https://doi.org/10.1007/s12065-020-00513-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-020-00513-6

Keywords

Search

Navigation