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Fast heterogeneous computing architectures for smart antennas.

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Title: Fast heterogeneous computing architectures for smart antennas.
Authors: Reichenbach, Marc1 marc.reichenbach@fau.de, Kasparek, Maximilian2 maximilian.kasparek@iis.fraunhofer.de, Häublein, Konrad1 konrad.haeublein@fau.de, Bauer, Jan Niklas1 jan.niklas.bauer@fau.de, Alawieh, Mohammad2 mohammad.alawieh@iis.fraunhofer.de, Fey, Dietmar1 dietmar.fey@fau.de
Source: Journal of Systems Architecture. May2017, Vol. 76, p76-88. 13p.
Subjects: Adaptive antennas, Physical training & conditioning, Sporting goods, Computer architecture, Software radio
Abstract: The usage of locating systems in sports elevates match and training analysis to a new level. By tracking players, balls and other sports equipment during matches or training, the performance of players can be analyzed, the training can be adapted and new strategies can be developed. The radio-based RedFIR system equips players and balls in soccer with miniaturized transmitters, while antennas distributed around the playing field receive the transmitted radio signals. A cluster computer processes these signals to determine the exact positions based on the signals’ Time Of Arrival (TOA) at the back end. While such a system works well, it is neither scalable nor inexpensive due to the required computing cluster. Also the relatively high power consumption of the GPU-based cluster is sub optimal. Moreover, high speed interconnects between the antennas and the cluster computers introduce additional costs and increase the installation effort. However, a significant portion of the computing performance is not required for the synthesis of the received data, but for the calculation of the unique TOA values of every receiver line. Therefore, in this paper we propose a smart sensor approach: By integrating some intelligence into the antenna (smart antenna), each antenna correlates the received signal independently of the remaining system and only a comparably small amount of resulting data is sent to the backend. While the idea is quite simple, the question of a well suited computer architecture to fulfill this task inside the smart antenna is more complex. Therefore, this paper provides an evaluation of embedded architectures, such as FPGAs, GPUs, ARM cores as well as a many core CPU (Epiphany), regarding processing performance and energy consumption. Additionally, we show that performance and energy consumption can be improved through heterogeneous computing techniques. Thereby, we are able to achieve the required 50.400 correlations per second in each smart antenna. As a result, the backend becomes lightweight, cheaper interconnects through data reduction are required and the system becomes more scalable, since most processing power is already integrated in the antenna. In addition, the evaluation results indicate that Software Defined Radio (SDR) approaches in general might benefit from a more diverse application of processing platforms. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Systems Architecture is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Fast heterogeneous computing architectures for smart antennas.
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  Data: <searchLink fieldCode="AR" term="%22Reichenbach%2C+Marc%22">Reichenbach, Marc</searchLink><relatesTo>1</relatesTo><i> marc.reichenbach@fau.de</i><br /><searchLink fieldCode="AR" term="%22Kasparek%2C+Maximilian%22">Kasparek, Maximilian</searchLink><relatesTo>2</relatesTo><i> maximilian.kasparek@iis.fraunhofer.de</i><br /><searchLink fieldCode="AR" term="%22Häublein%2C+Konrad%22">Häublein, Konrad</searchLink><relatesTo>1</relatesTo><i> konrad.haeublein@fau.de</i><br /><searchLink fieldCode="AR" term="%22Bauer%2C+Jan+Niklas%22">Bauer, Jan Niklas</searchLink><relatesTo>1</relatesTo><i> jan.niklas.bauer@fau.de</i><br /><searchLink fieldCode="AR" term="%22Alawieh%2C+Mohammad%22">Alawieh, Mohammad</searchLink><relatesTo>2</relatesTo><i> mohammad.alawieh@iis.fraunhofer.de</i><br /><searchLink fieldCode="AR" term="%22Fey%2C+Dietmar%22">Fey, Dietmar</searchLink><relatesTo>1</relatesTo><i> dietmar.fey@fau.de</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Systems+Architecture%22">Journal of Systems Architecture</searchLink>. May2017, Vol. 76, p76-88. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Adaptive+antennas%22">Adaptive antennas</searchLink><br /><searchLink fieldCode="DE" term="%22Physical+training+%26+conditioning%22">Physical training & conditioning</searchLink><br /><searchLink fieldCode="DE" term="%22Sporting+goods%22">Sporting goods</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+architecture%22">Computer architecture</searchLink><br /><searchLink fieldCode="DE" term="%22Software+radio%22">Software radio</searchLink>
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  Data: The usage of locating systems in sports elevates match and training analysis to a new level. By tracking players, balls and other sports equipment during matches or training, the performance of players can be analyzed, the training can be adapted and new strategies can be developed. The radio-based RedFIR system equips players and balls in soccer with miniaturized transmitters, while antennas distributed around the playing field receive the transmitted radio signals. A cluster computer processes these signals to determine the exact positions based on the signals’ Time Of Arrival (TOA) at the back end. While such a system works well, it is neither scalable nor inexpensive due to the required computing cluster. Also the relatively high power consumption of the GPU-based cluster is sub optimal. Moreover, high speed interconnects between the antennas and the cluster computers introduce additional costs and increase the installation effort. However, a significant portion of the computing performance is not required for the synthesis of the received data, but for the calculation of the unique TOA values of every receiver line. Therefore, in this paper we propose a smart sensor approach: By integrating some intelligence into the antenna (smart antenna), each antenna correlates the received signal independently of the remaining system and only a comparably small amount of resulting data is sent to the backend. While the idea is quite simple, the question of a well suited computer architecture to fulfill this task inside the smart antenna is more complex. Therefore, this paper provides an evaluation of embedded architectures, such as FPGAs, GPUs, ARM cores as well as a many core CPU (Epiphany), regarding processing performance and energy consumption. Additionally, we show that performance and energy consumption can be improved through heterogeneous computing techniques. Thereby, we are able to achieve the required 50.400 correlations per second in each smart antenna. As a result, the backend becomes lightweight, cheaper interconnects through data reduction are required and the system becomes more scalable, since most processing power is already integrated in the antenna. In addition, the evaluation results indicate that Software Defined Radio (SDR) approaches in general might benefit from a more diverse application of processing platforms. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Systems Architecture is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.sysarc.2016.11.004
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      – Code: eng
        Text: English
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        PageCount: 13
        StartPage: 76
    Subjects:
      – SubjectFull: Adaptive antennas
        Type: general
      – SubjectFull: Physical training & conditioning
        Type: general
      – SubjectFull: Sporting goods
        Type: general
      – SubjectFull: Computer architecture
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      – SubjectFull: Software radio
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      – TitleFull: Fast heterogeneous computing architectures for smart antennas.
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              Text: May2017
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