Optimized Fundamental Signal Processing Operations For Energy Minimization on Heterogeneous Mobile Devices

Numerous signal processing applications are emerging on both mobile and high-performance computing systems. These applications are subject to responsiveness constraints for user interactivity and, at the same time, must be optimized for energy efficiency. The increasingly heterogeneous power-versus-...

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Published inIEEE transactions on circuits and systems. I, Regular papers Vol. 65; no. 5; pp. 1614 - 1627
Main Authors Belloch, Jose A., Badia, Jose M., Igual, Francisco D., Gonzalez, Alberto, Quintana-Orti, Enrique S.
Format Journal Article
LanguageEnglish
Published New York IEEE 01.05.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN1549-8328
1558-0806
DOI10.1109/TCSI.2017.2761909

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Abstract Numerous signal processing applications are emerging on both mobile and high-performance computing systems. These applications are subject to responsiveness constraints for user interactivity and, at the same time, must be optimized for energy efficiency. The increasingly heterogeneous power-versus-performance profile of modern hardware introduces new opportunities for energy savings as well as challenges. In this line, recent systems-on-chip (SoC) composed of low-power multicore processors, combined with a small graphics accelerator (or GPU), yield a notable increment of the computational capacity while partially retaining the appealing low power consumption of embedded systems. This paper analyzes the potential of these new hardware systems to accelerate applications that involve a large number of floating-point arithmetic operations mainly in the form of convolutions. To assess the performance, a headphone-based spatial audio application for mobile devices based on a Samsung Exynos 5422 SoC has been developed. We discuss different implementations and analyze the tradeoffs between performance and energy efficiency for different scenarios and configurations. Our experimental results reveal that we can extend the battery lifetime of a device featuring such an architecture by a 238% by properly configuring and leveraging the computational resources.
AbstractList Numerous signal processing applications are emerging on both mobile and high-performance computing systems. These applications are subject to responsiveness constraints for user interactivity and, at the same time, must be optimized for energy efficiency. The increasingly heterogeneous power-versus-performance profile of modern hardware introduces new opportunities for energy savings as well as challenges. In this line, recent systems-on-chip (SoC) composed of low-power multicore processors, combined with a small graphics accelerator (or GPU), yield a notable increment of the computational capacity while partially retaining the appealing low power consumption of embedded systems. This paper analyzes the potential of these new hardware systems to accelerate applications that involve a large number of floating-point arithmetic operations mainly in the form of convolutions. To assess the performance, a headphone-based spatial audio application for mobile devices based on a Samsung Exynos 5422 SoC has been developed. We discuss different implementations and analyze the tradeoffs between performance and energy efficiency for different scenarios and configurations. Our experimental results reveal that we can extend the battery lifetime of a device featuring such an architecture by a 238% by properly configuring and leveraging the computational resources.
Author Badia, Jose M.
Gonzalez, Alberto
Igual, Francisco D.
Belloch, Jose A.
Quintana-Orti, Enrique S.
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SubjectTerms Audio systems
Batteries
Computation
Computer architecture
Convolution
Electronic devices
Embedded systems
Energy conservation
Energy efficiency
Energy management
Floating point arithmetic
Graphics processing units
Hardware
Headphones
heterogeneous (hybrid) systems
Microprocessors
Mobile computing
Mobile handsets
Neon
parallel architectures
parallel processing
performance and energy efficiency
Power consumption
Power efficiency
Power management
Service life assessment
Signal processing
signal synthesis
System on chip
Title Optimized Fundamental Signal Processing Operations For Energy Minimization on Heterogeneous Mobile Devices
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