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UWB microwave imaging for breast cancer detection: Many-core, GPU, or FPGA?

Published:28 March 2014Publication History
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Abstract

An UWB microwave imaging system for breast cancer detection consists of antennas, transceivers, and a high-performance embedded system for elaborating the received signals and reconstructing breast images. In this article we focus on this embedded system. To accelerate the image reconstruction, the Beamforming phase has to be implemented in a parallel fashion. We assess its implementation in three currently available high-end platforms based on a multicore CPU, a GPU, and an FPGA, respectively. We then project the results applying technology scaling rules to future many-core CPUs, many-thread GPUs, and advanced FPGAs. We consider an optimistic case in which available resources increase according to Moore's law only, and a pessimistic case in which only a fraction of those resources are available due to a limited power budget. In both scenarios, an implementation that includes a high-end FPGA outperforms the other alternatives. Since the number of effectively usable cores in future many-cores will be power-limited, and there is a trend toward the integration of power-efficient accelerators, we conjecture that a chip consisting of a many-core section and a reconfigurable logic section will be the perfect platform for this application.

References

  1. S. Ahmed, A. Schiessl, F. Gumbmann, M. Tiebout, S. Methfessel, and L. Schmidt. 2012. Advanced microwave imaging. Microwave Mag. 13, 6, 26--43.Google ScholarGoogle ScholarCross RefCross Ref
  2. Altera. 2011. Using floating-point fpgas for dsp in radar. WP-01156-1.0, Altera White Paper.Google ScholarGoogle Scholar
  3. S. Asano, T. Maruyama, and Y. Yamaguchi. 2009. Performance comparison of FPGA, GPU and CPU in image processing. In Proceedings of the International Conference on Field Programmable Logic and Applications. IEEE, 126--131.Google ScholarGoogle Scholar
  4. M. Birk, M. Zapf, M. Balzer, N. Ruiter, and J. Becker. 2012. A comprehensive comparison of GPU and FPGA-based acceleration of reflection image reconstruction for 3d ultrasound computer tomography. J. Real-Time Image Process. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. O. Bockenbach, H. Bartsch, and S. Schubert. 2008. Implementing real-time adaptive filtering for medical applications on the cell processor. In Proceedings of SPIE.Google ScholarGoogle Scholar
  6. E. Bond, X. Li, S. Hagness, and B. Van Veen. 2003. Microwave imaging via space-time beamforming for early detection of breast cancer. IEEE Trans. Antennas Propag. 51, 8, 1690--1705.Google ScholarGoogle ScholarCross RefCross Ref
  7. S. Borkar. 2010. The exascale challenge. In Proceedings of the International Symposium on VLSI Design, Automation and Test.Google ScholarGoogle ScholarCross RefCross Ref
  8. G. Caffarena and D. Menard. 2012. Many-core parallelization of fixed-point optimization of VLSI circuits through GPU devices. In Proceedings of the Conference on Design and Architectures for Signal and Image Processing. 1--8.Google ScholarGoogle Scholar
  9. M. R. Casu, M. Ruo Roch, S. V. Tota, and M. Zamboni. 2011. A NoC-based hybrid message-passing/shared-memory approach to CMP design. Microprocess. Microsyst. 35, 2, 261--273. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. C.-H. Chang and J. Ji. 2010. Compressed sensing mri with multichannel data using multicore processors. Magn. Reson. Med. 64, 4, 1135--1139.Google ScholarGoogle ScholarCross RefCross Ref
  11. J. Chase, B. Nelson, J. Bodily, Z. Wei, and D. Lee. 2008. Real-time optical flow calculations on FPGA and GPU architectures: a comparison study. In Proceedings of the International Symposium on Field-Programmable Custom Computing Machines. IEEE, 173--182. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. J. Cong, M. A. Ghodrat, M. Gill, B. Grigorian, and G. Reinman. 2012. Architecture support for accelerator-rich cmps. In Proceedings of the 49th Design Automation Conference. 843--849. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. E. Cota, P. Mantovani, M. Petracca, M. R. Casu, and L. P. Carloni. 2013. Accelerator memory reuse in the dark silicon era. IEEE Comput. Archit. Lett. 99.Google ScholarGoogle Scholar
  14. H. Esmaeilzadeh, E. Blem, R. st. Amant, K. Sankaralingam, and D. Burger. 2011. Dark silicon and the end of multicore scaling. In Proceedings of the 38th Annual International Symposium on Computer Architecture. ACM, 365--376. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. E. Fear, S. Hagness, P. Meaney, M. Okoniewski, and M. Stuchly. 2002. Enhancing breast tumor detection with near-field imaging. Microwave Mag. 3, 1, 48--56.Google ScholarGoogle ScholarCross RefCross Ref
  16. N. Hardavellas, M. Ferdman, B. Falsafi, and A. Ailamaki. 2011. Toward dark silicon in servers. IEEE Micro 31, 4, 6--15. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. A. Hendy, M. Hassan, R. Eldeeb, D. Kholy, A.-B. Youssef, and Y. Kadah. 2009. PC-based modular digital ultrasound imaging system. In Proceedings of the IEEE International Ultrasonic Symposium. IEEE, 1330--1333.Google ScholarGoogle Scholar
  18. J. Holland, J. W. Horner, R. Kuning, and D. B. Oeffinger. 2011. Implementation of digital front end processing algorithms with portability across multiple processing platforms. In Proceedings of the 15th Annual Workshop on High Performance Embedded Computing. 2 pages.Google ScholarGoogle Scholar
  19. ITRS. 2011. ITRS, International Technology Roadmap for Semiconductors. http://www.itrs.net.Google ScholarGoogle Scholar
  20. S. Kondapalli, A. Madanayake, and L. Bruton. 2012. Digital architectures for uwb beam forming using 2d iir spatio-temporal frequency-planar filters. Int. J. Antennas Propagation, Article ID 234263, 1--19.Google ScholarGoogle Scholar
  21. X. Li and S. Hagness. 2001. A confocal microwave imaging algorithm for breast cancer detection. IEEE Microwave Wirel. Compon. Lett. 11, 3, 130--132.Google ScholarGoogle ScholarCross RefCross Ref
  22. A. Madanayake and L. T. Bruton. 2010. VLSI. In Tech, Chapter radio-frequency (RF) beamforming using systolic FPGA-based two dimensional (2D) IIR space-time filters.Google ScholarGoogle Scholar
  23. A. Pulimeno, M. Graziano, and G. Piccinini. 2012. Udsm trends comparison: From technology roadmap to ultrasparc niagara2. IEEE Trans. VLSI Syst. 20, 7, 1341--1346. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. D. Rivera, D. Schaa, M. Moffie, and D. Kaeli. 2007. Exploring novel parallelization technologies for 3-d imaging applications. In Proceedings of the International Symposium on Computer Architecture and High Performance Computing. IEEE, 26--33.Google ScholarGoogle Scholar
  25. J. A. Roden and S. D. Gedney. 2000. Convolutional pml (cpml): An efficient FDTD implementation of the CFS-PML for arbitrary media. Microwave Optical Tech. Let. 27, 334--339.Google ScholarGoogle ScholarCross RefCross Ref
  26. F. Schneider, A. Agarwal, Y. Yoo, T. Fukuoka, and Y. Kim. 2010. A fully programmable computing architecture for medical ultrasound machines. IEEE Trans. Inf. Technol. Biomed. 14, 2. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. R. Shams, P. Sadeghi, R. Kennedy, and R. Hartley. 2010. A survey of medical image registration on multicore and the GPU.IEEE Signal Process. Mag. 50.Google ScholarGoogle ScholarCross RefCross Ref
  28. H. So, J. Chen, B. Yiu, and A. Yu. 2011. Medical ultrasound imaging: To GPU or not to GPU? Micro 13, 5, 54--65. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. D. Theodoropoulos, G. Kuzmanov, and G. Gaydadjiev. 2011. Multi-core platforms for beamforming and wave field synthesis. IEEE Trans. Multimedia 13, 2. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. S. V. Tota, M. R. Casu, M. Ruo Roch, L. Rostagno, and M. Zamboni. 2010. Medea: a hybrid shared-memory/message-passing multiprocessor noc-based architecture. In Proceedings of the Design, Automation Test in Europe Conference Exhibition (DATE'10). 45--50. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. J. Treibig, G. Hager, H. Hofmann, J. Hornegger, and G. Wellein. 2012. Pushing the limits for medical image reconstruction on recent standard multicore processors. Int. J. High Perform. Comput. Appl. arXiv:1104.5243. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Uwcem. 2012. Uwcem numerical breast phantom repository. http://uwcem.ece.wisc.edu/home.htm.Google ScholarGoogle Scholar
  33. E. Zastrow, S. Davis, M. Lazebnik, F. Kelcz, B. Van Veen, and S. Hagness. 2008. Development of anatomically realistic numerical breast phantoms with accurate dielectric properties for modeling microwave interactions with the human breast. IEEE Trans. Biomed. Eng. 55, 12, 2792--2800.Google ScholarGoogle ScholarCross RefCross Ref
  34. K. Zeng, E. Bai, and G. Wang. 2007. A fast ct reconstruction scheme for a general multi-core pc. Int. J. Biomed, Imaging 1. Google ScholarGoogle ScholarDigital LibraryDigital Library

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