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Accelerators for Breast Cancer Detection

Accelerators for Breast Cancer Detection Algorithms used in microwave imaging for breast cancer detection require hardware acceleration to speed up execution time and reduce power consumption. In this article, we present the hardware implementation of two accelerators for two alternative imaging algorithms that we obtain entirely from SystemC specifications via high-level synthesis. The two algorithms present opposite characteristics that stress the design process and the capabilities of commercial HLS tools in different ways: the first is communication bound and requires overlapping and pipelining of communication and computation in order to maximize the application throughput; the second is computation bound and uses complex mathematical functions that HLS tools do not directly support. Despite these difficulties, thanks to HLS, in the span of only 4 months we were able to explore a large design space and derive about 100 implementations with different cost-performance profiles, targeting both a Field-Programmable Gate Array (FPGA) platform and a 32-nm standard-cell Application Specific Integrated Circuit (ASIC) library. In addition, we could obtain results that outperform a previous Register-Transfer Level (RTL) implementation, which confirms the remarkable progress of HLS tools. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Embedded Computing Systems (TECS) Association for Computing Machinery

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2017 ACM
ISSN
1539-9087
eISSN
1558-3465
DOI
10.1145/2983630
Publisher site
See Article on Publisher Site

Abstract

Algorithms used in microwave imaging for breast cancer detection require hardware acceleration to speed up execution time and reduce power consumption. In this article, we present the hardware implementation of two accelerators for two alternative imaging algorithms that we obtain entirely from SystemC specifications via high-level synthesis. The two algorithms present opposite characteristics that stress the design process and the capabilities of commercial HLS tools in different ways: the first is communication bound and requires overlapping and pipelining of communication and computation in order to maximize the application throughput; the second is computation bound and uses complex mathematical functions that HLS tools do not directly support. Despite these difficulties, thanks to HLS, in the span of only 4 months we were able to explore a large design space and derive about 100 implementations with different cost-performance profiles, targeting both a Field-Programmable Gate Array (FPGA) platform and a 32-nm standard-cell Application Specific Integrated Circuit (ASIC) library. In addition, we could obtain results that outperform a previous Register-Transfer Level (RTL) implementation, which confirms the remarkable progress of HLS tools.

Journal

ACM Transactions on Embedded Computing Systems (TECS)Association for Computing Machinery

Published: Mar 28, 2017

Keywords: Microwave imaging for breast cancer detection

References