Skip to main content
Springer Nature Link
Log in

Evolutionary Approximation of Edge Detection Circuits

  • Conference paper
  • First Online:
Genetic Programming (EuroGP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9594))

Included in the following conference series:

  • 1105 Accesses

Abstract

Approximate computing exploits the fact that many applications are inherently error resilient which means that some errors in their outputs can safely be exchanged for improving other parameters such as energy consumption or operation frequency. A new method based on evolutionary computing is proposed in this paper which enables to approximate edge detection circuits. Rather than evolving approximate edge detectors from scratch, key components of existing edge detector are replaced by their approximate versions obtained using Cartesian Genetic Programming (CGP). Various approximate edge detectors are then composed and their quality is evaluated using a database of images. The paper reports interesting edge detectors showing a good tradeoff between the quality of edge detection and implementation cost.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Esmaeilzadeh, H., Sampson, A., Ceze, L., Burger, D.: Neural acceleration for general-purpose approximate programs. Commun. ACM 58(1), 105–115 (2015)

    Article  Google Scholar 

  2. Fu, W., Johnston, M., Zhang, M.: Genetic programming for edge detection using multivariate density. In: Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, pp. 917–924. ACM (2013)

    Google Scholar 

  3. Golonek, T., Grzechca, D., Rutkowski, J.: Application of genetic programming to edge detector design. In: Proceedings of the 2006 IEEE International Symposium on Circuits and Systems, pp. 4683–4686. IEEE (2006)

    Google Scholar 

  4. Harding, S., Banzhaf, W.: Genetic programming on GPUs for image processing. Int. J. High Perform. Syst. Archit. 1(4), 231–240 (2008)

    Article  Google Scholar 

  5. Harris, C., Buxton, B.: Evolving edge detectors with genetic programming. In: Proceedings of the First Annual Conference on Genetic Programming, pp. 309–314 (1996)

    Google Scholar 

  6. Higuchi, T., Niwa, T., Tanaka, T., Iba, H., de Garis, H., Furuya, T.: Evolving hardware with genetic learning: a first step towards building a Darwin machine. In: Proceedings of the 2nd International Conference on Simulated Adaptive Behaviour, pp. 417–424. MIT Press (1993)

    Google Scholar 

  7. Hollingworth, G., Tyrrell, A.M., Smith, S.: Simulation of evolvable hardware to solve low level image processing tasks. In: Poli, R., Voigt, H.-M., Cagnoni, S., Corne, D.W., Smith, G.D., Fogarty, T.C. (eds.) EvoIASP 1999 and EuroEcTel 1999. LNCS, vol. 1596, pp. 46–58. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  8. Kogge, P.M., Stone, H.S.: A parallel algorithm for the efficient solution of a general class of recurrence equations. IEEE Trans. Comput. 22, 786–793 (1973)

    Article  MathSciNet  MATH  Google Scholar 

  9. Kulkarni, P., Gupta, P., Ercegovac, M.D.: Trading accuracy for power in a multiplier architecture. J. Low Power Electron. 7(4), 490–501 (2011)

    Article  Google Scholar 

  10. Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of the 8th International Conference on Computer Vision, vol. 2, pp. 416–423, July 2001

    Google Scholar 

  11. Miller, J.F.: Cartesian Genetic Programming. Springer-Verlag, Berlin (2011)

    Book  MATH  Google Scholar 

  12. Miller, J.F., Smith, S.L.: Redundancy and computational efficiency in cartesian genetic programming. IEEE Trans. Evol. Comput. 10(2), 167–174 (2006)

    Article  Google Scholar 

  13. Monajati, M., Fakhraie, S., Kabir, E.: Approximate arithmetic for low-power image median filtering. Circuits Syst. Signal Process. 34(10), 3191–3219 (2015)

    Article  Google Scholar 

  14. Nepal, K., Li, Y., Bahar, R.I., Reda, S.: Abacus: a technique for automated behavioral synthesis of approximate computing circuits. In: Proceedings of the Conference on Design, Automation and Test in Europe, DATE 2014, pp. 1–6. EDA Consortium (2014)

    Google Scholar 

  15. Priego, B., Bellas, F., Souto, D., Lopez-Pena, F., Duro, R.: Evolving cellular automata for detecting edges in hyperspectral images. In: 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–6. IEEE (2012)

    Google Scholar 

  16. Sekanina, L., Harding, L.S., Banzhaf, W., Kowaliw, T.: Image processing and CGP. In: Miller, J.F. (ed.) Cartesian Genetic Programming, pp. 181–215. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  17. Sekanina, L., Vasicek, Z.: Approximate circuits by means of evolvable hardware. In: 2013 IEEE International Conference on Evolvable Systems. Proceedings of the 2013 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 21–28. IEEE CIS (2013)

    Google Scholar 

  18. Shi, K., Boland, D., Stott, E., Bayliss, S., Constantinides, G.: Datapath synthesis for overclocking: online arithmetic for latency-accuracy trade-offs. In: 51st ACM/EDAC/IEEE Design Automation Conference (DAC), pp. 1–6. IEEE (2014)

    Google Scholar 

  19. Sonka, M., Hlavac, V., Boyle, R.: Image Processing: Analysis and Machine Vision. Thomson-Engineering, Toronto (1999)

    Google Scholar 

  20. Torresen, J.: A scalable approach to evolvable hardware. Genet. Program Evolvable Mach. 3(3), 259–282 (2002)

    Article  MATH  Google Scholar 

  21. Ttofis, C., Hadjitheophanous, S., Georghiades, A., Theocharides, T.: Edge-directed hardware architecture for real-time disparity map computation. IEEE Trans. Comput. 62(4), 690–704 (2013)

    Article  MathSciNet  Google Scholar 

  22. Vasicek, Z., Sekanina, L.: An evolvable hardware system in Xilinx Virtex II Pro FPGA. Int. J. Innovative Comput. Appl. 1(1), 63–73 (2007)

    Article  Google Scholar 

  23. Vasicek, Z., Sekanina, L.: Formal verification of candidate solutions for post-synthesis evolutionary optimization in evolvable hardware. Genet. Program Evolvable Mach. 12(3), 305–327 (2011)

    Article  Google Scholar 

  24. Vasicek, Z., Sekanina, L.: Circuit approximation using single- and multi-objective cartesian GP. In: Machado, P., et al. (eds.) EuroGP. LNCS, vol. 9025, pp. 217–229. Springer International Publishing, Switzerland (2015)

    Google Scholar 

  25. Vasicek, Z., Sekanina, L.: Evolutionary approach to approximate digital circuits design. IEEE Trans. Evol. Comput. 19(3), 432–444 (2015)

    Article  Google Scholar 

  26. Yazdanbakhsh, A., Mahajan, D., Thwaites, B., Park, J., Nagendrakumar, A., Sethuraman, S., Ramkrishnan, K., Ravindran, N., Jariwala, R., Rahimi, A., Esmaeilzadeh, H., Bazargan, K.: Axilog: language support for approximate hardware design. In: Design, Automation Test in Europe Conference Exhibition (DATE 2015), pp. 812–817. IEEE (2015)

    Google Scholar 

  27. Zhang, Y., Rockett, P.I.: Evolving optimal feature extraction using multiobjective genetic programming: a methodology and preliminary study on edge detection. In: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, GECCO 2005, pp. 795–802. ACM (2005)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the Czech science foundation project GA16-17538S.

Author information

Authors and Affiliations

  1. Faculty of Information Technology, IT4Innovations Centre of Excellence, Brno University of Technology, Božetěchova 2, 612 66, Brno, Czech Republic

    Petr Dvoracek & Lukas Sekanina

Authors
  1. Petr Dvoracek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lukas Sekanina
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Petr Dvoracek .

Editor information

Editors and Affiliations

  1. Dalhousie University, Halifax, Nova Scotia, Canada

    Malcolm I. Heywood

  2. University College Dublin, Dublin, Ireland

    James McDermott

  3. Universidade Nova de Lisboa, Lisboa, Portugal

    Mauro Castelli

  4. CIUSC Dept Informatics Engg, University of Coimbra, Coimbra, Portugal

    Ernesto Costa

  5. Edinburgh Napier University, Edinburgh, United Kingdom

    Kevin Sim

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Dvoracek, P., Sekanina, L. (2016). Evolutionary Approximation of Edge Detection Circuits. In: Heywood, M., McDermott, J., Castelli, M., Costa, E., Sim, K. (eds) Genetic Programming. EuroGP 2016. Lecture Notes in Computer Science(), vol 9594. Springer, Cham. https://doi.org/10.1007/978-3-319-30668-1_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-30668-1_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30667-4

  • Online ISBN: 978-3-319-30668-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation