Skip to main content
SpringerLink
Log in

Resource and Energy Optimization Oriented Development of FPGA-Based Adaptive Logical Networks for Classification Problem

  • Chapter
  • First Online:
Green IT Engineering: Components, Networks and Systems Implementation

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 105))

Abstract

New FPGA-based devices with a high degree of reconfigurability that allow dynamic to change architecture and structure are created. These devices improve the efficiency of reconfigurable devices and systems according to the optimal criteria of hardware resources and power consumption, which are decisive for the Green IT Engineering. This class of devices for the implementation of technology the “Reconfigurable computing” is viewed. It is showed that in the dynamic logical structure devices, the configuration dynamically changing depending on the specific solving problem by transmitting information about any given current configuration. As a basic structure for construction of such devices the adaptive logical networks (ALN) are considered. ALN designed for solving of a wide class of problems by means of directly implementing algorithms and by direct mapping of the input data into output data. Unlike already known methods for the synthesis of multilevel logic scheme, two new approaches to the synthesis of such schemes are proposed. The first approach is based on the description of Boolean network by polynomials. In this case coefficients of the polynomial are given by Hadamard matrix. The second approach is based on the description of Boolean network by means Zhegalkin polynomials. An algorithm for partition of the range of Boolean functions is developed. This algorithm (for input data of arbitrary length and a threshold value) allows determining the types of logical functions for each level of ALN. The algorithm is based on the analysis of bit-by-bit of the threshold value for a predetermined threshold relation. The correctness of implementation this algorithm is proved. An example of solving the XOR problem in neural networks by means of a two-layer network is adduced. This network uses proposed by the threshold device and implemented on FPGA chip.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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. Kharchenko, V., Kondratenko, Y., Kacprzyk, J. (eds.): Green IT Engineering: Concepts, Models, Complex Systems Architectures, Studies in Systems, Decision and Control, Vol. 74. Springer, Berlin (2017). doi:10.1007/978-3-319-44162-7

  2. Kharchenko, V., Illiashenko, O.: Concepts of green IT engineering: taxonomy, principles and implementation. In: Kharchenko, V., Kondratenko, Y., Kacprzyk, J. (eds.) Green IT Engineering: Concepts, Models, Complex Systems Architectures, Studies in Systems, Decision and Control, Vol. 74, pp. 3–20. Springer, Berlin (2017). doi:10.1007/978-3-319-44162-7_1

  3. Palagin, A.V., Opanasenko, V.N.: Reconfigurable computing technology. J. Cybern. Syst. Anal. 43(5), 675–686 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  4. Estrin, G., Turn, R.: Parallel processing in a restructurable computer. IEEE Trans. Electron. Comput. EC–12 6, 747–755 (1963)

    Article  MATH  Google Scholar 

  5. Palagin, A.V., Opanasenko, V.N.: Design and application of the PLD-based reconfigurable devices. In: Adamski, M., Barkalov, A., Wegrzyn, M. (eds.) Design of Digital Systems and Devices. Lecture Notes in Electrical Engineering, Vol. 79, pp. 59–91. Springer, Berlin (2011)

    Google Scholar 

  6. Opanasenko, V.N., Kryvyi, S.L.: A direct adaptation problem of synthesis of logical nets. Int. J. Inf. Technol. Knowl. 8(1), 3–12 (2014). (in Russian)

    Google Scholar 

  7. Opanasenko, V.N., Kryvyi, S.L.: Synthesis of adaptive logical networks on the basis of Zhegalkin polynomials. J. Cybern. Syst. Anal. 51(6), 969–977 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  8. Bruck, J., Blaum, M.: Neural networks, error–correcting codes, and polynomials over the binary n–cube. J. IEEE Trans. Inf. Theory 35(5), 976–987 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  9. Kryvyi, S.L.: Algorithms for solving systems of linear Diophantine equations in integer domains. J. Cybern. Syst. Anal. 42(2), 163–175 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  10. Kryvyi, S.L.: Algorithms for solving systems of linear Diophantine equations in residue fields. J. Cybern. Syst. Anal. 43(2), 171–178 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  11. Opanasenko, V., Kryvyi, S.: Synthesis of multilevel structures with multiple outputs. In: CEUR Workshop Proceeding of 10th International Conference of Programming, UkrPROG 2016, Kyiv, Ukraine, Vol. 1631, Code 122904, pp. 32–37 (2016)

    Google Scholar 

  12. Opanasenko, V.N., Kryvyi, S.L.: Partitioning the full range of boolean functions based on the threshold and threshold relation. J. Cybern. Syst. Anal. 48(3), 459–468 (2012)

    Google Scholar 

  13. Palagin, A., Opanasenko, V., Kryvyi, S.: The structure of FPGA-based cyclic-code converters. J. Opt. Mem. Neural Netw. (Inf. Optics) 22(4), 207–216 (2013)

    Article  Google Scholar 

  14. Kondratenko, Y.P., Klymenko, L.P., Sidenko, I.V.: Comparative analysis of evaluation algorithms for decision-making in transport logistics. In: Jamshidi, M., Kreinovich, V., Kazprzyk, J. (eds.) Advance Trends in Soft Computing. Proceedings of WCSC. Series: Studies in Fuzziness and Soft Computing, Vol. 312, pp. 203–217 (2014)

    Google Scholar 

  15. Kondratenko, Y.P., Gordienko, E.: Implementation of the neural networks for adaptive control system on FPGA. In: Katalinic, B. (ed.) Annals of DAAAM for 2012 & Proceeding of 23rd DAAAM International Symposium on Intelligent Manufacturing and Automation, Vol. 23(1), pp. 0389–0392. DAAAM International, Vienna, Austria (2012)

    Google Scholar 

  16. Kondratenko, Y.P., Sidenko, Ie.V.: Decision-making based on fuzzy estimation of quality level for cargo delivery. In: Zadeh, L.A. et al. (eds.) Recent Developments and New Directions in Soft Computing. Studies in Fuzziness and Soft Computing, Vol. 317, pp. 331–344. Springer, Switzerland (2014)

    Google Scholar 

  17. Kondratenko, Y.P., Al Zubi, E.Y.M.: The optimization approach for increasing efficiency of digital fuzzy controllers. In: Annals of DAAAM for 2009 & Proceeding of the 20th International DAAAM Symposium. “Intelligent Manufacturing and Automation”, pp. 1589–1591. DAAAM International, Vienna, Austria (2009)

    Google Scholar 

  18. Kondratenko, Y.P., Korobko, O.V., Kozlov, O.V.: PLC-based systems for data acquisition and supervisory control of environment-friendly energy-saving technologies. In: Kharchenko, V., Kondratenko, Y., Kacprzyk, J. (eds.) Green IT Engineering: Concepts, Models, Complex Systems Architectures, Studies in Systems, Decision and Control, Vol. 74, pp. 247–267. Springer, Berlin (2017). doi:10.1007/978-3-319-44162-7_13

  19. Tyurin, S., Kamenskih, A.: Green logic: models, methods, algorithms. In: Kharchenko, V., Kondratenko, Y., Kacprzyk, J. (eds.) Green IT Engineering: Concepts, Models, Complex Systems Architectures, Studies in Systems, Decision and Control, Vol. 74, pp. 69–86. Springer, Berlin (2017). doi:10.1007/978-3-319-44162-7_4

  20. Drozd, A., Antoshchuk, S.: New on-line testing methods for approximate data processing in the computing circuit. In: Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS’2011, Prague, Czech Republic, Vol. 1, Code 87642, pp. 291–294 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Microprocessor Devices, V.M. Glushkov Institute of Cybernetics of the NASU, Glushkov Prospect 40, Kiev, 03187, Ukraine

    Alexander V. Palagin & Volodymyr M. Opanasenko

  2. Taras Shevchenko Kiev National University, Glushkov Prospect 4D, Kiev, 03187, Ukraine

    Sergey L. Kryvyi

Authors
  1. Alexander V. Palagin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Volodymyr M. Opanasenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sergey L. Kryvyi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Volodymyr M. Opanasenko .

Editor information

Editors and Affiliations

  1. National Aerospace University «KhAI» , Kharkiv, Ukraine

    Vyacheslav Kharchenko

  2. Intelligent Information Systems Department, Petro Mohyla Black Sea National University, Mykolaiv, Ukraine

    Yuriy Kondratenko

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Palagin, A.V., Opanasenko, V.M., Kryvyi, S.L. (2017). Resource and Energy Optimization Oriented Development of FPGA-Based Adaptive Logical Networks for Classification Problem. In: Kharchenko, V., Kondratenko, Y., Kacprzyk, J. (eds) Green IT Engineering: Components, Networks and Systems Implementation. Studies in Systems, Decision and Control, vol 105. Springer, Cham. https://doi.org/10.1007/978-3-319-55595-9_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-55595-9_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55594-2

  • Online ISBN: 978-3-319-55595-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation