Skip to main content
SpringerLink
Log in

A Compact Spectral Model for Convolutional Neural Network

  • Conference paper
  • First Online:
Proceedings of the Future Technologies Conference (FTC) 2022, Volume 1 (FTC 2022 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 559))

Included in the following conference series:

  • 748 Accesses

Abstract

The convolutional neural network (CNN) has gained widespread adoption in computer vision (CV) applications in recent years. However, the high computational complexity of spatial (conventional) CNNs makes real-time deployment in CV applications difficult. Spectral representation (frequency domain) is one of the most effective ways to reduce the large computational workload in CNN models, and thus beneficial for any processing platform. By reducing the size of feature maps, a compact spectral CNN model is proposed and developed in this paper by utilizing just the lower frequency components of the feature maps. When compared to similar models in the spatial domain, the proposed compact spectral CNN model achieves at least 24.11\(\times \) and 4.96\(\times \) faster classification speed on AT &T face recognition and MNIST digit/fashion classification datasets, respectively.

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

Access this chapter

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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

References

  1. Almasi, A.D., Wozniak, S., Cristea, V., Leblebici, Y., Engbersen, T.: Review of advances in neural networks: neural design technology stack. Nerocomputing 174, 31–41 (2016)

    Article  Google Scholar 

  2. Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine Learning, pp. 160–167. ACM (2008)

    Google Scholar 

  3. Simard, P.Y., Steinkraus, D., Platt, J.C.: Best practices for convolutional neural networks applied to visual document analysis. In: Proceedings of the 7th International Conference on Document Analysis and Recognition (ICDAR), vol. 3, pp. 958–963. IEEE (2003)

    Google Scholar 

  4. McNelis, P.D.: Neural Networks in Finance: Gaining Predictive Edge in the Market. Academic Press, Cambridge (2005)

    Google Scholar 

  5. Ahmad Radzi, S., Mohamad, K.H., Liew, S.S., Bakhteri, R.: Convolutional neural network for face recognition with pose and illumination variation. Int. J. Eng. Technol. (IJET) 6(1), 44–57 (2014)

    Google Scholar 

  6. Huang, P.S., He, X., Gao, J., Deng, L., Acero, A., Heck, L.: Learning deep structured semantic models for web search using clickthrough data. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, pp. 2333–2338. ACM (2013)

    Google Scholar 

  7. Rasti, P., Uiboupin, T., Escalera, S., Anbarjafari, G.: Convolutional neural network super resolution for face recognition in surveillance monitoring. In: Perales, F.J.J., Kittler, J. (eds.) AMDO 2016. LNCS, vol. 9756, pp. 175–184. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41778-3_18

    Chapter  Google Scholar 

  8. Yeap, Y.Y., Sheikh, U.U., Ab Rahman, A.A.: Image forensic for digital image copy move forgery detection. In: 14th IEEE International Colloquium on Signal Processing and Its Applications (CSPA), pp. 239–244. IEEE (2018)

    Google Scholar 

  9. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)

    Article  Google Scholar 

  10. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of the 3rd International Conference on Learning Representations, ICLR (2015)

    Google Scholar 

  11. Sermanet, P., et al.: A multirange architecture for collision-free off-road robot navigation. J. Field Robot. 26(1), 52–87 (2009)

    Article  Google Scholar 

  12. LeCun, Y., et al.: Handwritten digit recognition with a back-propagation network. In: Proceedings of the 2nd International Conference on Neural Information Processing Systems (NIPS), pp. 396–404. NeurIPS (1989)

    Google Scholar 

  13. Zhang, Q., Zhang, M., Chen, T., Sun, Z., Ma, Y., Yu, B.: Recent advances in convolutional neural network acceleration. Neurocomputing 323, 37–51 (2019)

    Article  Google Scholar 

  14. Amer, H., Ab Rahman, A., Amer, I., Lucarz, C., Mattavelli, M.: Methodology and technique to improve throughput of FPGA-based Cal dataflow programs: case study of the RVC MPEG-4 SP intra decoder. In: Proceedings of the IEEE Workshop on Signal Processing Systems (SiPS), pp. 186–191. IEEE (2011)

    Google Scholar 

  15. Ayat, S., Khalil-Hani, M., Ab Rahman, A., Abdellatef, H.: Spectral-based convolutional neural network without multiple spatial-frequency domain switchings. Neurocomputing 364, 152–167 (2019)

    Article  Google Scholar 

  16. Rizvi, S., Ab Rahman, A., Khalil-Hani, M., Ayat, S.: A low-complexity complex-valued activation function for fast and accurate spectral domain convolutional neural network. Indones. J. Electr. Eng. Inform. (IJEEI) 9(1), 173–184 (2021)

    Google Scholar 

  17. Liu, S., Luk, W.: Optimizing fully spectral convolutional neural networks on FPGA. In: Proceedings of the 19th IEEE International Conference on Field-Programmable Technology (ICFPT), pp. 39–47. IEEE (2020)

    Google Scholar 

  18. Watanabe, T., Wolf, D.: Image classification in frequency domain with 2SReLU: a second harmonics superposition activation function. Appl. Soft Comput. 112, 107851 (2021)

    Article  Google Scholar 

  19. Zhang, C., Li, P., Sun, G., Guan, Y., Xiao, B., Cong, J.: Optimizing FPGA-based accelerator design for deep convolutional neural networks. In: Proceedings of the 2015 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, pp. 161–170. ACM (2015)

    Google Scholar 

  20. Qiu, J., et al.: Going deeper with embedded FPGA platform for convolutional neural network. In: Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, pp. 26–35. ACM (2016)

    Google Scholar 

  21. Ristretto, G.P.: Hardware-oriented approximation of convolutional neural networks. arXiv preprint (arXiv:1605.06402). arXiv (2016)

  22. Denton, E.L., Zaremba, W., Bruna, J., LeCun, Y., Fergus, R.: Exploiting linear structure within convolutional networks for efficient evaluation. In: Proceedings of the Advances in Neural Information Processing Systems, pp. 1269–1277. NeurIPS (2014)

    Google Scholar 

  23. Han, S., Pool, J., Tran, J., Dally, W.: Learning both weights and connections for efficient neural network. In: Proceedings of the 28th International Conference on Neural Information Processing Systems (NIPS), pp. 1135–1143. NeurIPS (2015)

    Google Scholar 

  24. Shawahna, A., Sait, S.M., El-Maleh, A.: FPGA-based accelerators of deep learning networks for learning and classification: a review. IEEE Access 7, 7823–59 (2018)

    Article  Google Scholar 

  25. Zhang, X., Zou, J., Ming, X., He, K., Sun, J.: Efficient and accurate approximations of nonlinear convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and pattern Recognition (CVPR), pp. 1984–1992. IEEE (2015)

    Google Scholar 

  26. Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., Bengio, Y.: Binarized neural networks: Training deep neural networks with weights and activations constrained to +1 or -1. arXiv preprint (arXiv:1602.02830). arXiv (2016)

  27. Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., Bengio, Y.: Binarized neural networks. In: Proceedings of the Advances in Neural Information Processing Systems, pp. 4107–4115. NeurIPS (2016)

    Google Scholar 

  28. Liang, S., Yin, S., Liu, L., Luk, W., Wei, S.: FP-BNN: binarized neural network on FPGA. Neurocomputing 275, 1072–1086 (2018)

    Article  Google Scholar 

  29. Wu, Q., Lu, X., Xue, S., Wang, C., Wu, X., Fan, J.: SBNN: slimming binarized neural network. Neurocomputing 401, 113–122 (2020)

    Google Scholar 

  30. Mittal, S.: A survey of FPGA-based accelerators for convolutional neural networks. Neural Comput. Appl. 32(4), 1109–1139 (2018). https://doi.org/10.1007/s00521-018-3761-1

    Article  Google Scholar 

  31. Umuroglu, Y., et al.: A framework for fast, scalable binarized neural network inference. In: Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, pp. 65–74. ACM (2017)

    Google Scholar 

  32. Ma, X., et al.: An area and energy efficient design of domain-wall memory-based deep convolutional neural networks using stochastic computing. In: Proceedings of the 19th International Symposium on Quality Electronic Design (ISQED), pp. 314–321. IEEE (2018)

    Google Scholar 

  33. Li, J., et al.: Hardware-driven nonlinear activation for stochastic computing based deep convolutional neural networks. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN), pp. 1230–1236. IEEE (2017)

    Google Scholar 

  34. Li, Z., et al.: HEIF: highly efficient stochastic computing-based inference framework for deep neural networks. IEEE Trans. Comput.-Aid. Des. Integr. Circ. Syst. 38(8), pp. 1543–1556 (2019)

    Google Scholar 

  35. Abdellatef, H., Khalil-Hani, M., Shaikh-Husin, N., Ayat, S.: Accurate and compact convolutional neural network based on stochastic computing. Neurocomputing 471, 31–47 (2022)

    Article  Google Scholar 

  36. Qian, W., Li, X., Riedel, M.D., Bazargan, K., Lilja, D.J.: An architecture for fault-tolerant computation with stochastic logic. IEEE Trans. Comput. 60(1), 93–105 (2010)

    Article  MathSciNet  Google Scholar 

  37. Hayes, J.P.: Introduction to stochastic computing and its challenges. In: Proceedings of the 52nd ACM/IEEE Design Automation Conference (DAC), pp. 1–3. IEEE (2015)

    Google Scholar 

  38. Bottleson, J., Kim, S., Andrews, J., Bindu, P., Murthy, D.N., Jin, J.: clCaffe: OpenCL accelerated Caffe for convolutional neural networks. In: Proceedings of the IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 50–57. IEEE (2016)

    Google Scholar 

  39. Bareiss, E.H.: Numerical solution of linear equations with Toeplitz and vector Toeplitz matrices. Numerische Mathematik 13(5), 404–424 (1969)

    Google Scholar 

  40. Sze, V., Chen, Y.H., Yang, T.J., Emer, J.S.: Efficient processing of deep neural networks: a tutorial and survey. Proc. IEEE 105(12), 2295–2329 (2017)

    Article  Google Scholar 

  41. Winograd, S.: Arithmetic Complexity of Computations. SIAM (1980)

    Google Scholar 

  42. Lavin, A., Gray, S.: Fast algorithms for convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4013–4021. IEEE (2016)

    Google Scholar 

  43. Rippel, O., Snoek, J., Adams, R.P.: Spectral representations for convolutional neural networks. In: Proceedings of the 28th International Conference on Neural Information Processing Systems (NIPS), pp. 2449–2457. ACM (2015)

    Google Scholar 

  44. Ayat, S., Khalil-Hani, M., Ab Rahman, A.: Optimizing FPGA-based CNN accelerator for energy efficiency with an extended Roofline model. Turk. J. Electr. Eng. Comput. Sci. 26(2), 919–935 (2018)

    Article  Google Scholar 

  45. Niu, Y., et al.: SPEC2: SPECtral SParsE CNN accelerator on FPGAs. In: Proceedings of the 26th IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC), pp. 195–204. IEEE (2019)

    Google Scholar 

  46. Sun, W., Zeng, H., Yang, Y.-h., Prasanna, V.: Throughput-optimized frequency domain CNN with fixed-point quantization on FPGA. In: International Conference on ReConFigurable Computing and FPGAs (ReConFig), pp. 1–8. IEEE (2018)

    Google Scholar 

  47. Guan, B., Zhang, J., Sethares, W., Kijowski, R., Liu, F.: Spectral domain convolutional neural network. In: Proceedings of the 46th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2795–2799. IEEE (2021)

    Google Scholar 

  48. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  49. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint (arXiv:1502.03167). arXiv (2015)

  50. Vedaldi, A., Lenc, K.: MatConvNet: convolutional neural networks for MATLAB. In: Proceedings of the 23rd ACM International Conference on Multimedia (MM), pp. 689–692. ACM (2015)

    Google Scholar 

  51. Cong, J., Xiao, B.: Minimizing computation in convolutional neural networks. In: Proceedings of the 24th International Conference on Artificial Neural Networks (ICANN), pp. 281–290. IEEE (2014)

    Google Scholar 

Download references

Acknowledgment

The authors thank Universiti Teknologi Malaysia (UTM) for their support under the Research University Grant (GUP), grant number 16J83.

Author information

Authors and Affiliations

  1. Advanced Technologies Incubator Centre, Sharif University of Technology, Tehran, Iran

    Sayed Omid Ayat

  2. School of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Johor, Malaysia

    Shahriyar Masud Rizvi, Ab Al-Hadi Ab Rahman & Shahidatul Sadiah Abdul Manan

  3. Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon

    Hamdan Abdellatef

Authors
  1. Sayed Omid Ayat
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shahriyar Masud Rizvi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hamdan Abdellatef
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ab Al-Hadi Ab Rahman
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shahidatul Sadiah Abdul Manan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shahriyar Masud Rizvi .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ayat, S.O., Rizvi, S.M., Abdellatef, H., Ab Rahman, A.AH., Manan, S.S.A. (2023). A Compact Spectral Model for Convolutional Neural Network. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2022, Volume 1. FTC 2022 2022. Lecture Notes in Networks and Systems, vol 559. Springer, Cham. https://doi.org/10.1007/978-3-031-18461-1_7

Download citation

Publish with us

Policies and ethics

Search

Navigation