Programming and Computer Software

, Volume 45, Issue 1, pp 12–17 | Cite as

Performance Evaluation of a Recognition System on the VLIW Architecture by the Example of the Elbrus Platform

  • E. E. LimonovaEmail author
  • N. A. BocharovEmail author
  • N. B. ParamonovEmail author
  • D. S. BogdanovEmail author
  • V. V. ArlazarovEmail author
  • O. A. SlavinEmail author
  • D. P. NikolaevEmail author


This paper overviews modern computing devices based on the Elbrus VLIW architecture and presents experimental results for the performance evaluation of the Smart IDReader document recognition system on these devices. Methods for speeding up the recognition system on the Elbrus platform are described, experimental estimates of the speedup improvements are presented, and the performance of various devices based on Elbrus processors is compared.



This work was supported by the Russian Foundation for Basic Research, project nos. 17-29-03170, 17-29-03297, and 18-07-01384.


  1. 1.
    Mastov, A., Konovalenko, I., and Grigoryev, A., Application of random ferns for non-planar object detection, Proc. 8th Int. Conf. Machine Vision, 2015.Google Scholar
  2. 2.
    Kopenkov, V.N. and Myasnikov, V.V., Detection and tracking of vehicles based on the video-registration information, Proc. 23rd Int. Conf. Computer Graphics, Visualization, and Computer Vision (WSCG), Plzen, 2015, pp. 65–69.Google Scholar
  3. 3.
    Arlazarov, V.V., Cognitive Form: A distributed system for stream recognition of standard forms of documents, Tr. konf. “Raspoznavanie obrazov i analiz izobrazhenii: novye informatsionnye tekhnologii” (ROAI) (Proc. Conf. Pattern Recognition and Image Analysis: New Information Technologies), Novgorod, 2002, pp. 41–45.Google Scholar
  4. 4.
    Arlazarov, V., Postnikov, V., and Sholomov, D., Cognitive Forms: A system for mass recognition of structured documents, Sb. tr. Inst. Sistemnogo Anal. Ross. Akad. Nauk (Proc. Inst. Syst. Anal. Russ. Acad. Sci.), Moscow: URSS, 2002, pp. 35–46.Google Scholar
  5. 5.
    Omondi, A.R. and Rajapakse, J.C., FPGA Implementations of Neural Networks, New York: Springer, 2006.CrossRefGoogle Scholar
  6. 6.
    Farabet, C., Poulet, C., Han, J.Y., and LeCun, Y., CNP: An FPGA-based processor for convolutional networks, Proc. Int. Conf. Field Programmable Logic and Applications, Prague, 2009, pp. 32–37.Google Scholar
  7. 7.
    Yang, Q., Wang, C., Gao, Y., Qu, H., and Chang, E.Y., Inertial sensors aided image alignment and stitching for panorama on mobile phones, Proc. 1st Int. Workshop Mobile Location-Based Service (MLBS), New York, 2011, pp. 21–30.Google Scholar
  8. 8.
    Luqman, M.M., Gomez-Kramer, P., and Ogier, J.-M., Mobile phone camera-based video scanning of paper documents, Revised Selected Papers (Proc. 5th Int. Workshop Camera-Based Document Analysis and Recognition (CBDAR), Washington, 2013), Iwamura, M. and Shafait, F., Eds., 2014, pp. 164–178.Google Scholar
  9. 9.
    Ramisa, A., Tapus, A., De Mantaras, R.L., and Toledo, R., Mobile robot localization using panoramic vision and combinations of feature region detectors, Proc. IEEE Int. Conf. Robotics and Automation (ICRA), Pasadena, 2008, pp. 538–543.Google Scholar
  10. 10.
    Kim, A.K., Bychkov, I.N., et al., Russian technologies “Elbrus” for personal computers, servers, and supercomputers, Sovrem. Inf. Tekhnol. IT Obraz., 2014, no. 10, pp. 39–50.Google Scholar
  11. 11.
    Kim, A.K., Perekatov, V.I., and Ermakov, S.G., Mikroprotsessory i vychislitel’nye kompleksy semeistva “El’brus” (Microprocessors and Computing Complexes of the Elbrus Family), St. Petersburg: Piter, 2013.Google Scholar
  12. 12.
    Ishin, P.A., Loginov, V.E., and Vasil’ev, P.P., Computation speedup using high-performance mathematical and multimedia libraries for the Elbrus architecture, Vestn. Vozdushn.-Kosm. Oborony, 2015, vol. 8, no. 4, pp. 64–68.Google Scholar
  13. 13.
    Bezmaternykh, P., Nikolaev, D., and Arlazarov, V.L., Textual blocks rectification method based on fast Hough transform analysis in identity documents recognition, Proc. 10th Int. Conf. Machine Vision (ICMV), Vienna, 2017.Google Scholar
  14. 14.
    Stahlberg, F. and Vogel, S., The QCRI recognition system for handwritten Arabic, Proc. Int. Conf. Image Analysis and Processing, 2015, pp. 276–286.Google Scholar
  15. 15.
    Chernov, T.S., Razumnyi, N.P., Kozharinov, A.S., and Arlazarov, V.V., Quality estimation of input images in video stream recognition systems, Inf. Tekhnol. Vychisl. Sist., 2017, no. 4, pp. 71–82.Google Scholar
  16. 16.
    Usilin, S., Nikolaev, D., Postnikov, V., and Schaefer, G., Visual appearance based document image classification, Proc. Int. Conf. Image Processing (ICIP), 2010, pp. 2133–2136.Google Scholar
  17. 17.
    Bocharov, N.A., Limonova, E.E., Paramonov, B.N., and Usilin, S., Optimization of a modified Viola–Jones method for the Elbrus computing architecture, Tr. Inst. Sistemnogo Anal. Ross. Akad. Nauk (Proc. Inst. Syst. Anal. Russ. Acad. Sci.), 2017, vol. 67, no. 4, pp. 10–21.Google Scholar
  18. 18.
    Chen, N. and Blostein, D., A survey of document image classification: Problem statement, classifier architecture, and performance evaluation, Int. J. Doc. Anal. Recognit., 2007, vol. 10, no. 1, pp. 1–16.CrossRefGoogle Scholar
  19. 19.
    Vorontsov, K.V., Additive regularization for topic models of text collections, Dokl. Math., 2014, vol. 89, no. 3, pp. 301–304.MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Junga, K., Kimb, K.I., and Jain, A.K., Text information extraction in images and video: A survey, Pattern Recognit., 2004, pp. 977–997.Google Scholar
  21. 21.
    Slugin, D.G. and Arlazarov, V.V., Detection of text fields in documents by using image processing methods, Tr. Inst. Sistemnogo Anal. Ross. Akad. Nauk (Proc. Inst. Syst. Anal. Russ. Acad. Sci.), 2017, vol. 67, no. 4, pp. 65–73.Google Scholar
  22. 22.
    Limonova, E., Ilin, D., and Nikolaev, D.P., Improving neural network performance on SIMD architectures, Proc. 8th Int. Conf. Machine Vision (ICMV), 2015, pp. 1–6.Google Scholar
  23. 23.
    Limonova, E., Sheshkus, A., and Nikolaev, D., Computational optimization of convolutional neural networks using separated filters architecture, Int. J. Appl. Eng. Res., 2016, vol. 11, no. 11, pp. 7491–7494.Google Scholar
  24. 24.
    Limonova, E., Sheshkus, A., Nikolaev, D.P., Ivanova, A., Il’in, D., and Arlazarov, V.L., Efficiency optimization of the first layers of deep convolutional neural networks, Vestn. RFFI, 2016, no. 4, pp. 84–96.Google Scholar
  25. 25.
    Limonova, E., Sheshkus, A., Ivanova, A., and Nikolaev, D.P., Convolutional neural network structure transformations for complexity reduction and speed improvement, Pattern Recognit. Image Anal., 2018, no. 1, pp. 24–33.Google Scholar
  26. 26.
    Ilin, D., Limonova, E., Arlazarov, V.V., and Nikolaev, D., Fast integer approximations in convolutional neural networks using layer-by-layer training, Proc. 9th Int. Conf. Machine Vision (ICMV), 2017, pp. 1–5.Google Scholar
  27. 27.
    Gavrikov, M.B., Pestryakova, N.V., Slavin, O.A., and Farsobina, V.V., Development of the polynomial regression method and its practical application to recognition problems, Preprint of Keldysh Inst. Appl. Math., 2006, no. 25, pp. 1–22.Google Scholar
  28. 28.
    Krizhevsky, A., Sutskever, I., and Hinton, G.E., Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, 2012.Google Scholar
  29. 29.
    Manzhikov, T.V., Slavin, O.A., Faradjev, I.A., and Janiszewski, I.M., Algorithms for correcting recognition results using N-grams, Pattern Recognit. Image Anal., 2007, vol. 4, no. 27, pp. 1054–6618. Google Scholar
  30. 30.
    Bulatov, K., Manzhikov, T., Slavin, O., Faradjev, I., and Janiszewski, I., Trigram-based algorithms for OCR result correction, 2017, vol. 10341, p. 1034100.
  31. 31.
    Llobet, R., Navarro-Cerdan, J.R., Perez-Cortez, J.-C., and Arlandis, J., OCR post-processing using weighted finite state transducers, Proc. 12th Int. Conf. Frontiers in Hadwriting Recognition, Kolkata, 2010, pp. 2021–2024.Google Scholar
  32. 32.
    Bulatov, K.B., Selecting the optimal strategy for combining the results of frame-by-frame symbol recognition in video stream, Inf. Tekhnol. Vychisl. Sist., 2017, no. 3, pp. 45–55.Google Scholar
  33. 33.
    Fiscus, J.G., A post-processing system to yield reduced word error rates: Recognizer output voting error reduction (ROVER), Proc. IEEE Workshop Automatic Speech Recognition and Understanding, 1997, pp. 347–354.Google Scholar
  34. 34.
    Bulatov, K., Arlazarov, V., Chernov, T., Slavin, O., and Nikolaev, D., Smart IDReader: Document recognition in video stream, Proc. 14th IAPR Int. Conf. Document Analysis and Recognition (ICDAR), Kyoto, Japan, 2017, pp. 39–44.Google Scholar
  35. 35.
    Arlazarov, V.V., Arlazarov, V.L., Bulatov, K., Nikola-ev, D.P., Polevoi, D., and Slavin, O., System for document recognition in video stream, Russian Federation’s State Register of Useful Models.Google Scholar
  36. 36.
    Programming system for Elbrus platforms and MTsST-R. Scholar
  37. 37.
    Intel Threading Building Blocks (Intel TBB). http:// Scholar

Copyright information

© Pleiades Publishing, Ltd. 2019

Authors and Affiliations

  1. 1.Institute for System Analysis, Federal Research Center “Computer Science and Control,” Russian Academy of SciencesMoscowRussia
  2. 2.Smart EnginesMoscowRussia
  3. 3.Moscow Center of SPARC Technologies (MCST)MoscowRussia
  4. 4.Bruk Institute of Electronic Control Machines (INEUM)MoscowRussia
  5. 5.Kharkevich Institute for Information Transmission Problems, Russian Academy of SciencesMoscowRussia

Personalised recommendations