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.
- S. Ahmed, A. Schiessl, F. Gumbmann, M. Tiebout, S. Methfessel, and L. Schmidt. 2012. Advanced microwave imaging. Microwave Mag. 13, 6, 26--43.Google Scholar
Cross Ref
- Altera. 2011. Using floating-point fpgas for dsp in radar. WP-01156-1.0, Altera White Paper.Google Scholar
- 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 Scholar
- 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 Scholar
Digital Library
- 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 Scholar
- 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 Scholar
Cross Ref
- S. Borkar. 2010. The exascale challenge. In Proceedings of the International Symposium on VLSI Design, Automation and Test.Google Scholar
Cross Ref
- 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 Scholar
- 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 Scholar
Digital Library
- C.-H. Chang and J. Ji. 2010. Compressed sensing mri with multichannel data using multicore processors. Magn. Reson. Med. 64, 4, 1135--1139.Google Scholar
Cross Ref
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- 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 Scholar
- 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 Scholar
Digital Library
- 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 Scholar
Cross Ref
- N. Hardavellas, M. Ferdman, B. Falsafi, and A. Ailamaki. 2011. Toward dark silicon in servers. IEEE Micro 31, 4, 6--15. Google Scholar
Digital Library
- 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 Scholar
- 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 Scholar
- ITRS. 2011. ITRS, International Technology Roadmap for Semiconductors. http://www.itrs.net.Google Scholar
- 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 Scholar
- 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 Scholar
Cross Ref
- 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 Scholar
- 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 Scholar
Digital Library
- 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 Scholar
- 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 Scholar
Cross Ref
- 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 Scholar
Digital Library
- 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 Scholar
Cross Ref
- 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 Scholar
Digital Library
- D. Theodoropoulos, G. Kuzmanov, and G. Gaydadjiev. 2011. Multi-core platforms for beamforming and wave field synthesis. IEEE Trans. Multimedia 13, 2. Google Scholar
Digital Library
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- Uwcem. 2012. Uwcem numerical breast phantom repository. http://uwcem.ece.wisc.edu/home.htm.Google Scholar
- 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 Scholar
Cross Ref
- 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 Scholar
Digital Library
Index Terms
UWB microwave imaging for breast cancer detection: Many-core, GPU, or FPGA?
Recommendations
Microwave tomography for breast cancer detection on Cell broadband engine processors
Microwave tomography (MT) is a safe screening modality that can be used for breast cancer detection. The technique uses the dielectric property contrasts between different breast tissues at microwave frequencies to determine the existence of ...
Evaluation of a performance portable lattice Boltzmann code using OpenCL
IWOCL '14: Proceedings of the International Workshop on OpenCL 2013 & 2014With the advent of many-core computer architectures such as GPGPUs from NVIDIA and AMD, and more recently Intel's Xeon Phi, ensuring performance portability of HPC codes is potentially becoming more complex. In this work we have focused on one important ...
Potential of near-field microwave imaging in breast cancer detection utilizing tapered rectangular waveguide probes
Microwave imaging for medical applications has been of interest for many years. A novel near-field microwave non-invasive testing and evaluation (NIT&E) technique utilizing tapered rectangular waveguide probes is presented for breast cancer detection. ...






Comments