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Energy-efficient multi-task computing on MPSoCs: A case study from a memory perspective.

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Titel: Energy-efficient multi-task computing on MPSoCs: A case study from a memory perspective.
Autoren: Westphal, Rafael, Guntzel, Jose Luis, Santos, Luiz C. V.
Quelle: 2012 19th IEEE International Conference on Electronics, Circuits & Systems (ICECS 2012); 1/ 1/2012, p905-908, 4p
Abstract: To support growing data rates under low-power constraints, personal mobile devices rely on multi-core architectures which are challenged by increasing energy efficiency requirements. A mobile device is essentially a combination of two subsystems (a “PC” and a “radio”), which are often implemented as distinct multiprocessor systems-on-chip (MPSoC). The former supports multimedia processing and implements the end-user interface by relying on multi-thread computing under the management of a conventional operating system; the latter implements baseband processing by performing multi-task computing under the management of a real-time operating system. This paper focuses on the latter subsystem by describing a case study that estimates, from a memory perspective, the evolution of the energy efficiency for periodic multi-task computing as process technologies improve (90nm, 65nm, 45nm, and 32nm). The results show that real-time schedulability requirements limit the growth of throughput with increasing cache size in such a way that the extra cache consumption does not pay off. They also show that, in the context of multi-task computing, more than 89% of the energy spent in the memory subsystem is due to dynamic consumption. Such results allowed us to identify crucial cache optimizations to cope with increasing energy efficiency requirements. [ABSTRACT FROM PUBLISHER]
Copyright of 2012 19th IEEE International Conference on Electronics, Circuits & Systems (ICECS 2012) is the property of IEEE 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: Energy-efficient multi-task computing on MPSoCs: A case study from a memory perspective.
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  Data: <searchLink fieldCode="AR" term="%22Westphal%2C+Rafael%22">Westphal, Rafael</searchLink><br /><searchLink fieldCode="AR" term="%22Guntzel%2C+Jose+Luis%22">Guntzel, Jose Luis</searchLink><br /><searchLink fieldCode="AR" term="%22Santos%2C+Luiz+C%2E+V%2E%22">Santos, Luiz C. V.</searchLink>
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  Data: 2012 19th IEEE International Conference on Electronics, Circuits & Systems (ICECS 2012); 1/ 1/2012, p905-908, 4p
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: To support growing data rates under low-power constraints, personal mobile devices rely on multi-core architectures which are challenged by increasing energy efficiency requirements. A mobile device is essentially a combination of two subsystems (a “PC” and a “radio”), which are often implemented as distinct multiprocessor systems-on-chip (MPSoC). The former supports multimedia processing and implements the end-user interface by relying on multi-thread computing under the management of a conventional operating system; the latter implements baseband processing by performing multi-task computing under the management of a real-time operating system. This paper focuses on the latter subsystem by describing a case study that estimates, from a memory perspective, the evolution of the energy efficiency for periodic multi-task computing as process technologies improve (90nm, 65nm, 45nm, and 32nm). The results show that real-time schedulability requirements limit the growth of throughput with increasing cache size in such a way that the extra cache consumption does not pay off. They also show that, in the context of multi-task computing, more than 89% of the energy spent in the memory subsystem is due to dynamic consumption. Such results allowed us to identify crucial cache optimizations to cope with increasing energy efficiency requirements. [ABSTRACT FROM PUBLISHER]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of 2012 19th IEEE International Conference on Electronics, Circuits & Systems (ICECS 2012) is the property of IEEE 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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