2024-03-29T12:48:12Zhttps://repositori.uji.es/oai/requestoai:repositori.uji.es:10234/1825082023-07-21T09:33:13Zcom_10234_7036com_10234_9col_10234_8620
00925njm 22002777a 4500
dc
BELLOCH, JOSE A.
author
Badía, José
author
Igual, Francisco D.
author
González, Alberto
author
Quintana-Orti, Enrique S.
author
2018-05
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 trade-offs
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.
BELLOCH, Jose A., et al. Optimized Fundamental Signal Processing Operations For Energy Minimization on Heterogeneous Mobile Devices. IEEE Transactions on Circuits and Systems I: Regular Papers, 2018, 65.5: 1614-1627.
http://hdl.handle.net/10234/182508
https://doi.org/10.1109/TCSI.2017.2761909
audio systems
signal synthesis
parallel architectures
parallel processing
heterogeneous (hybrid) systems
performance and energy efficiency
Optimized Fundamental Signal Processing Operations For Energy Minimization on Heterogeneous Mobile Devices