research-article

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

Authors:

Francesco Colonna,

Marco Crepaldi,

Danilo Demarchi,

Mariagrazia Graziano,

Maurizio ZamboniAuthors Info & Claims

ACM Transactions on Embedded Computing Systems (TECS), Volume 13, Issue 3s

Article No.: 109, Pages 1 - 22

https://doi.org/10.1145/2530534

Published: 28 March 2014 Publication History

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.

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Cited By

Ravikumar ASriraman HSaleena BPrakash B(2023)Selecting the optimal transfer learning model for precise breast cancer diagnosis utilizing pre-trained deep learning models and histopathology imagesHealth and Technology10.1007/s12553-023-00772-013:5(721-745)Online publication date: 19-Aug-2023
https://doi.org/10.1007/s12553-023-00772-0
Mansoori MCasu M(2023)Hardware Acceleration of Microwave Imaging AlgorithmsElectromagnetic Imaging for a Novel Generation of Medical Devices10.1007/978-3-031-28666-7_2(33-67)Online publication date: 30-Jun-2023
https://doi.org/10.1007/978-3-031-28666-7_2
Shah Karam SO’Loughlin DAsl B(2022)A novel sophisticated form of DMAS beamformer: Application to breast cancer detectionBiomedical Signal Processing and Control10.1016/j.bspc.2022.10351674(103516)Online publication date: Apr-2022
https://doi.org/10.1016/j.bspc.2022.103516
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Index Terms

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

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Published In

cover image ACM Transactions on Embedded Computing Systems

ACM Transactions on Embedded Computing Systems Volume 13, Issue 3s

Special Issue on Design Challenges for Many-Core Processors, Special Section on ESTIMedia'13 and Regular Papers

March 2014

403 pages

ISSN:1539-9087

EISSN:1558-3465

DOI:10.1145/2597868

Editors:
Sandeep K. Shukla
Virginia Tech
,
Masoud Daneshtalab
University of Turku, Finland
,
Maurizio Palesi
Kore University, Italy
,
Juha Plosila
University of Turku, Finland
,
Maurizio Palesi
Kore University, Italy
,
Todor Stefanov
Leiden University, The Netherlands

Issue’s Table of Contents

Copyright © 2014 ACM.

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 28 March 2014

Accepted: 01 September 2013

Revised: 01 June 2013

Received: 01 December 2012

Published in TECS Volume 13, Issue 3s

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Cited By

Ravikumar ASriraman HSaleena BPrakash B(2023)Selecting the optimal transfer learning model for precise breast cancer diagnosis utilizing pre-trained deep learning models and histopathology imagesHealth and Technology10.1007/s12553-023-00772-013:5(721-745)Online publication date: 19-Aug-2023
https://doi.org/10.1007/s12553-023-00772-0
Mansoori MCasu M(2023)Hardware Acceleration of Microwave Imaging AlgorithmsElectromagnetic Imaging for a Novel Generation of Medical Devices10.1007/978-3-031-28666-7_2(33-67)Online publication date: 30-Jun-2023
https://doi.org/10.1007/978-3-031-28666-7_2
Shah Karam SO’Loughlin DAsl B(2022)A novel sophisticated form of DMAS beamformer: Application to breast cancer detectionBiomedical Signal Processing and Control10.1016/j.bspc.2022.10351674(103516)Online publication date: Apr-2022
https://doi.org/10.1016/j.bspc.2022.103516
Sarwar ITurvani GCasu MTobon JVipiana FScapaticci RCrocco L(2018)Low-Cost Low-Power Acceleration of a Microwave Imaging Algorithm for Brain Stroke MonitoringJournal of Low Power Electronics and Applications10.3390/jlpea80400438:4(43)Online publication date: 1-Nov-2018
https://doi.org/10.3390/jlpea8040043
Riente FGiordano AVacca MGraziano M(2018)Exploring N3ASIC technology for microwave imaging architecturesIntegration10.1016/j.vlsi.2018.05.00362(395-405)Online publication date: Jun-2018
https://doi.org/10.1016/j.vlsi.2018.05.003
Pagliari DCasu MCarloni L(2017)Accelerators for Breast Cancer DetectionACM Transactions on Embedded Computing Systems10.1145/298363016:3(1-25)Online publication date: 28-Mar-2017
https://dl.acm.org/doi/10.1145/2983630
Pulimeno AVacca MCasu MTobon JVipiana FPagliari DSolimene RCarloni L(2017)Microwave Imaging for Breast Cancer Detection: A COTS-Based PrototypeApplications in Electronics Pervading Industry, Environment and Society10.1007/978-3-319-47913-2_4(25-34)Online publication date: 3-Jan-2017
https://doi.org/10.1007/978-3-319-47913-2_4
Al-Ayyoub MAlZu'bi SJararweh YAlsmirat M(2016)A GPU-based breast cancer detection system using Single Pass Fuzzy C-Means clustering algorithm2016 5th International Conference on Multimedia Computing and Systems (ICMCS)10.1109/ICMCS.2016.7905595(650-654)Online publication date: Sep-2016
https://doi.org/10.1109/ICMCS.2016.7905595
Pagliari DCasu MCartoni L(2015)Acceleration of microwave imaging algorithms for breast cancer detection via High-Level SynthesisProceedings of the 2015 33rd IEEE International Conference on Computer Design (ICCD)10.1109/ICCD.2015.7357152(475-478)Online publication date: 18-Oct-2015
https://dl.acm.org/doi/10.1109/ICCD.2015.7357152
Moll JKelly TByrne DSarafianou MKrozer VCraddock I(2014)Microwave radar imaging of heterogeneous breast tissue integrating a priori informationJournal of Biomedical Imaging10.1155/2014/9435492014(17-17)Online publication date: 1-Jan-2014
https://dl.acm.org/doi/10.1155/2014/943549

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