Skip to main content
SpringerLink
Log in
Book cover

International Conference on Computational Science and Its Applications

ICCSA 2020: Computational Science and Its Applications – ICCSA 2020 pp 845–858Cite as

Migration of Artificial Neural Networks to Smartphones

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12249))

Abstract

The paper explains the process of migration of an artificial neural network (ANN) to a smartphone device. It focuses on a situation when the ANN is already deployed on a desktop computer. Our goal is to describe the process of the migration of the network to a mobile environment. In the current system we have, images have to be scanned and fed to a computer that is applying the ANN. However, every smartphone has a camera that can be used instead of a scanner. Migration to such a device should save the overall processing time. ANNs in the field of computer vision have a long history. Despite that, mobile phones were not used as a target platform for ANNs because they did not have enough processing power. In the past years, smartphones have developed dramatically, and they have the processing power necessary for deploying ANNs now. Also, major mobile operating systems, Android and iOS, have included the support for the deployment.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Michel, E.: Using deep neural networks for automated speech recognition, p. 19 (2015)

    Google Scholar 

  2. Sriram, A., Jun, H., Gaur, Y., Satheesh, S.: Robust speech recognition using generative adversarial networks. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, pp. 5639–5643. IEEE (2018). https://doi.org/10.1109/ICASSP.2018.8462456

  3. Këpuska, V., Bohouta, G.: Next-generation of virtual personal assistants (Microsoft Cortana, Apple Siri, Amazon Alexa and Google Home). In: 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), pp. 99–103 (2018). https://doi.org/10.1109/CCWC.2018.8301638

  4. Shu, K., Sliva, A., Wang, S., Tang, J., Liu, H.: Fake news detection on social media: a data mining. Perspective (2017). https://doi.org/10.1145/3137597.3137600

    Article  Google Scholar 

  5. Ignatov, A., Kobyshev, N., Timofte, R., Vanhoey, K., Van Gool, L.: DSLR-quality photos on mobile devices with deep convolutional networks. In: Presented at the Proceedings of the IEEE International Conference on Computer Vision (2017)

    Google Scholar 

  6. Amin, S.M., Rodin, E.Y., Liu, A.-P., Rink, K., García-Ortiz, A.: Traffic prediction and management via RBF neural nets and semantic control. Comput. Aided Civil Infrastruct. Eng. 13, 315–327 (1998). https://doi.org/10.1111/0885-9507.00110

    Article  Google Scholar 

  7. Duan, Y., Lv, Y., Wang, F.-Y.: Travel time prediction with LSTM neural network. In: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, pp. 1053–1058. IEEE (2016). https://doi.org/10.1109/ITSC.2016.7795686

  8. LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1, 541–551 (1989). https://doi.org/10.1162/neco.1989.1.4.541

    Article  Google Scholar 

  9. Berger, A., Kostak, M., Maly, F.: Mobile AR solution for deaf people. In: Awan, I., Younas, M., Ünal, P., Aleksy, M. (eds.) MobiWIS 2019. LNCS, vol. 11673, pp. 243–254. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27192-3_19

    Chapter  Google Scholar 

  10. Berger, A., Vokalova, A., Maly, F., Poulova, P.: Google glass used as assistive technology its utilization for blind and visually impaired people. In: Younas, M., Awan, I., Holubova, I. (eds.) MobiWIS 2017. LNCS, vol. 10486, pp. 70–82. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65515-4_6

    Chapter  Google Scholar 

  11. El Bahi, H., Zatni, A.: Text recognition in document images obtained by a smartphone based on deep convolutional and recurrent neural network. Multimedia Tools Appl. 78(18), 26453–26481 (2019). https://doi.org/10.1007/s11042-019-07855-z

    Article  Google Scholar 

  12. Papageorgiou, C.P., Oren, M., Poggio, T.: A general framework for object detection. In: Sixth International Conference on Computer Vision (IEEE Cat. No. 98CH36271), pp. 555–562 (1998). https://doi.org/10.1109/ICCV.1998.710772

  13. Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999). https://doi.org/10.1109/ICCV.1999.790410

  14. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893 (2005). https://doi.org/10.1109/CVPR.2005.177

  15. Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1627–1645 (2010). https://doi.org/10.1109/TPAMI.2009.167

    Article  Google Scholar 

  16. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986). https://doi.org/10.1038/323533a0

    Article  MATH  Google Scholar 

  17. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014)

    Google Scholar 

  18. Girshick, R.: Fast R-CNN. In: Presented at the Proceedings of the IEEE International Conference on Computer Vision (2015)

    Google Scholar 

  19. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 28, pp. 91–99. Curran Associates, Inc. (2015)

    Google Scholar 

  20. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. arXiv:1506.02640 [cs] (2016)

  21. Redmon, J., Farhadi, A.: YOLO9000: Better, Faster, Stronger. arXiv:1612.08242 [cs] (2016)

  22. Redmon, J., Farhadi, A.: YOLOv3: An Incremental Improvement. arXiv:1804.02767 [cs] (2018)

  23. Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2

    Chapter  Google Scholar 

  24. Everingham, M., Eslami, S.M.A., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The Pascal visual object classes challenge: a retrospective. Int. J. Comput. Vis. 111(1), 98–136 (2014). https://doi.org/10.1007/s11263-014-0733-5

    Article  Google Scholar 

  25. He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask R-CNN. Presented at the proceedings of the IEEE international conference on computer vision (2017)

    Google Scholar 

  26. Cai, Z., Vasconcelos, N.: Cascade R-CNN: delving into high quality object detection. Presented at the proceedings of the IEEE conference on computer vision and pattern recognition (2018)

    Google Scholar 

  27. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. Presented at the proceedings of the IEEE conference on computer vision and pattern recognition (2016)

    Google Scholar 

  28. Chao, Y.-W., Vijayanarasimhan, S., Seybold, B., Ross, D.A., Deng, J., Sukthankar, R.: Rethinking the faster R-CNN architecture for temporal action localization. Presented at the proceedings of the IEEE conference on computer vision and pattern recognition (2018)

    Google Scholar 

  29. Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollar, P.: Focal loss for dense object detection. Presented at the proceedings of the IEEE international conference on computer vision (2017)

    Google Scholar 

  30. Bhandari, R., Nambi, A.U., Padmanabhan, V.N., Raman, B.: Driving lane detection on smartphones using deep neural networks (2020). https://doi.org/10.1145/3358797

  31. Ignatov, A., et al.: AI benchmark: running deep neural networks on android smartphones. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11133, pp. 288–314. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11021-5_19

    Chapter  Google Scholar 

  32. Ignatov, A., et al.: AI benchmark: all about deep learning on smartphones in 2019. arXiv:1910.06663 [cs] (2019)

  33. Niu, W., Ma, X., Wang, Y., Ren, B.: 26 ms inference time for ResNet-50: towards real-time execution of all DNNs on smartphone. arXiv:1905.00571 [cs, stat] (2019)

  34. Begg, R., Hassan, R.: Artificial neural networks in smart homes. In: Augusto, J.C., Nugent, C.D. (eds.) Designing Smart Homes. LNCS (LNAI), vol. 4008, pp. 146–164. Springer, Heidelberg (2006). https://doi.org/10.1007/11788485_9

    Chapter  Google Scholar 

  35. Chollet, F., et al.: Keras (2015)

    Google Scholar 

  36. Why use Keras - Keras Documentation. https://keras.io/why-use-keras/. Accessed 25 Mar 2020

  37. Backend - Keras Documentation. https://keras.io/backend/. Accessed 25 Mar 2020

  38. TensorFlow. https://www.tensorflow.org/. Accessed 25 Mar 2020

  39. Abadi, M., et al.: TensorFlow: a system for large-scale machine learning. Presented at the 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16) (2016)

    Google Scholar 

  40. TensorFlow.js|Machine Learning for Javascript Developers. https://www.tensorflow.org/js. Accessed 25 Mar 2020

  41. TensorFlow Lite|ML for Mobile and Edge Devices. https://www.tensorflow.org/lite. Accessed 25 Mar 2020

  42. Case Studies and Mentions. https://www.tensorflow.org/about/case-studies. Accessed 25 Mar 2020

  43. PyTorch, https://www.pytorch.org. Accessed 30 Mar 2020

  44. PyTorch Mobile. https://pytorch.org/mobile/home/. Accessed 30 Mar 2020

  45. Get started with TensorFlow Lite. https://www.tensorflow.org/lite/guide/get_started?hl=cs. Accessed 01 Apr 2020

  46. ML Kit for Firebase. https://firebase.google.com/docs/ml-kit?hl=cs. Accessed 01 Apr 2020

  47. Neural Networks API|Android NDK. https://developer.android.com/ndk/guides/neuralnetworks?hl=cs. Accessed 01 Apr 2020

  48. Core ML|Apple Developer Documentation. https://developer.apple.com/documentation/coreml. Accessed 01 Apr 2020

  49. apache/incubator-mxnet. https://github.com/apache/incubator-mxnet. Accessed 01 Apr 2020

  50. tf-coreml/tf-coreml. https://github.com/tf-coreml/tf-coreml. Accessed 01 Apr 2020

Download references

Acknowledgement

This work and the contribution were supported by the project of Students Grant Agency – FIM, University of Hradec Kralove, Czech Republic.

Author information

Authors and Affiliations

  1. Faculty of Informatics and Management, University of Hradec Králové, Rokitanského 62, 50003, Hradec Králové, Czechia

    Milan Kostak, Ales Berger & Antonin Slaby

Authors
  1. Milan Kostak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ales Berger
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Antonin Slaby
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Milan Kostak .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Chair- Center of ICT/ICE, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Clayton School of Information Technology, Monash University, Clayton, VIC, Australia

    David Taniar

  7. Department of Information Science, Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

  11. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kostak, M., Berger, A., Slaby, A. (2020). Migration of Artificial Neural Networks to Smartphones. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12249. Springer, Cham. https://doi.org/10.1007/978-3-030-58799-4_61

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-58799-4_61

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58798-7

  • Online ISBN: 978-3-030-58799-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation