Energy-Efficient 3D Convolution Using Interposed Memory Accelerator eXtension 2 for Medical Image Processing | SpringerLink
Skip to main content

Energy-Efficient 3D Convolution Using Interposed Memory Accelerator eXtension 2 for Medical Image Processing

  • Conference paper
  • First Online:
Proceedings of 2023 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2023) (MICAD 2023)

Abstract

Energy-efficient medical image processing is crucial in mobile or remote healthcare situations where traditional GPU-based solutions are not feasible. Recently, three-dimensional (3D) image processing has gained significant importance in fields like computer vision, machine learning, natural language processing, and medical diagnosis. 3D convolution neural networks (CNN) have outperformed state-of-the-art in many visual recognition tasks, such as medical imaging, video processing and analysis, and 3D Object recognition. 3D CNNs excel at feature extraction but impose a computational burden, mainly from convolution layers. Their cubic complexity growth hinders speed and overall performance, requiring complexity reduction in these layers, as they dominate 3D CNN calculations. To tackle these challenges, we introduce a novel energy-efficient computational solution of the Interposed Memory Accelerator eXtension 2 (IMAX2), a Coarse-Grained Linear Array (CGLA) developed in our laboratory, which outperforms the RTX3090 by achieving 7.37 times the efficiency in Tops/W for 3D CNNs. Unlike fixed Application-Specific Integrated Circuits, IMAX2 offers remarkable flexibility for computations, making it a versatile choice for complex tasks. Specifically, we optimized the computational bottleneck of 3D convolutions within the U-Net architecture, a specialized CNN model designed for segmentation tasks. Our initial findings demonstrate that IMAX2 empowers medical image analysis with 3D CNNs while achieving superior energy efficiency. This study opens up new possibilities for Computer-Aided Diagnosis and AI-driven medical imaging solutions in settings where conventional high-power systems are impractical.

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

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /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
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 25167
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
JPY 31459
Price includes VAT (Japan)
  • 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. Hamidian, S., Sahiner, B., Petrick, N., Pezeshk, A.: 3D convolutional neural network for automatic detection of lung nodules in chest ct. In: Proceedings of SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013409 (2017)

    Google Scholar 

  2. Lan, Q., Wang, Z., Wen, M., Zhang, C., Wang, Y.: High performance implementation of 3D convolutional neural networks on a GPU. In: Computational Intelligence and Neuroscience 2017 (2017)

    Google Scholar 

  3. Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3D convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2015)

    Google Scholar 

  4. Kopuklu, O., Kose, N., Gunduz, A., Rigoll, G.: Resource efficient 3D convolutional neural networks. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops (2019)

    Google Scholar 

  5. Ji, S., Xu, W., Yang, M., Yu, K.: 3D convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 221–231 (2013)

    Article  Google Scholar 

  6. Zhang, C., Wu, D., Sun, J., Sun, G., Luo, G., Cong, J.: Energy-efficient CNN implementation on a deeply pipelined FPGA cluster. In: Proceedings of the 2016 International Symposium on Low Power Electronics and Design, pp. 326–331 (2016)

    Google Scholar 

  7. Ando, K., Takamaeda-Yamazaki, S., Ikebe, M., Asai, T., Motomura, M.: A multithreaded CGRA for convolutional neural network processing. Circ. Syst. 8(6), 149–170 (2017)

    Article  Google Scholar 

  8. Tanomoto, M., Takamaeda-Yamazaki, S., Yao, J., Nakashima, Y.: A CGRA-based approach for accelerating convolutional neural networks. In: 2015 IEEE 9th International Symposium on Embedded Multicore/Many-core Systems-on-Chip, pp. 73–80 (2015)

    Google Scholar 

  9. Keen, J.D., Keen, J.M., Keen, J.E.: Utilization of computer-aided detection for digital screening mammography in the united states, 2008 to 2016. J. Am. Coll. Radiol. 15(1), 44–48 (2018)

    Article  Google Scholar 

  10. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015, Part III. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  11. Wang, T., et al.: ICA-UNet: ICA inspired statistical UNet for real-time 3D cardiac cine MRI segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020, Part VI. LNCS, vol. 12266, pp. 447–457. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59725-2_43

    Chapter  Google Scholar 

  12. Cooley, C.Z., et al.: A portable scanner for magnetic resonance imaging of the brain. Nat. Biomed. Eng. 5(3), 229–239 (2021)

    Article  Google Scholar 

  13. Liu, L., et al.: A survey of coarse-grained reconfigurable architecture and design: taxonomy, challenges, and applications. ACM Comput. Surv. (CSUR) 52(6), 1–39 (2019)

    Article  Google Scholar 

  14. Github icanet. https://github.com/dewenzeng/icanet. Accessed 23 Oct 2023

  15. Bernard, O., et al.: Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans. Med. Imaging 37(11), 2514–2525 (2018)

    Article  Google Scholar 

  16. Imax2 document. http://archlab.naist.jp/proj-arm64/doc/emax6/emax6e.pdf. Accessed 23 Oct 2023

Download references

Acknowledgements

The research has been partly executed in response to the support of JSPS, KAKENHI Grant No. 21K11809, Japan. This work was also supported through the activities of VDEC, The University of Tokyo, in collaboration with NIHON SYNOPSYS G.K.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ren Imamura .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Imamura, R., Guangxian, Z., Thi, S.D., Pham, H.L., Zhang, R., Nakashima, Y. (2024). Energy-Efficient 3D Convolution Using Interposed Memory Accelerator eXtension 2 for Medical Image Processing. In: Su, R., Zhang, YD., Frangi, A.F. (eds) Proceedings of 2023 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2023). MICAD 2023. Lecture Notes in Electrical Engineering, vol 1166. Springer, Singapore. https://doi.org/10.1007/978-981-97-1335-6_6

Download citation

  • DOI: https://doi.org/10.1007/978-981-97-1335-6_6

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-1334-9

  • Online ISBN: 978-981-97-1335-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics