An Efficient Implementation of Parallel Parametric HRTF Models for Binaural Sound Synthesis in Mobile Multimedia

Belloch, Jose A.; Ramos, Germán A.; Badía, Jordi; Cobos, Máximo

doi:10.1109/access.2020.2979489

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…Multimedia mobile applications were evaluated in Heterogeneous Mobile Multi-Core Processors [10]. A GPU-based SoC is also used in [11] for binaural sound sound synthesis. Our work introduced in this paper differs in that none of the previous efforts has tackled a massive audio system dealing with a huge number of channels in a network of powerful SoCs.…”

Section: Related Workmentioning

confidence: 99%

On the performance of a GPU-based SoC in a distributed spatial audio system

et al. 2021

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Many current System-on-Chip (SoC) devices are composed of lowpower multicore processors combined with a small graphics accelerator (or GPU) offering a trade-off between computational capacity and low-power consumption. In this context, spatial audio methods such as Wave Field Synthesis (WFS) can benefit from a distributed system composed of several SoCs that collaborate to tackle the high computational cost of rendering virtual sound sources. This paper aims at evaluating important aspects dealing with a distributed WFS implementation that runs over a network of Jetson Nano boards composed of embedded GPU-based SoCs: computational performance, energy efficiency, and synchronization issues. Our results show that the maximum efficiency is obtained when the WFS system operates the GPU frequency at 691.2 MHz, achieving 11 sources-per-Watt. Synchronization experiments using the NTP protocol show that the maximum initial delay of 10 milliseconds between nodes does not prevent us from achieving high spatial sound quality.

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Section: Related Workmentioning

confidence: 99%

On the performance of a GPU-based SoC in a distributed spatial audio system

et al. 2021

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“…GPUs entered the embedded domain to meet the growing demand for multimedia-based handheld and consumer devices such as smartphones [4]. More recently, mobile GPUs are increasingly being used to accelerate heavy workloads, for applications ranging from signal processing [5], to advanced driving assistance systems (ADAS) in cars [6], or to accelerate the computational requirements of deep neural networks [7].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Soft-Error Sensitivity in LU Decomposition Algorithms on Diverse GPUs

Leon,

Badia,

Belloch

et al. 2023

Preprint

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Graphics Processing Units (GPUs) have become integral to embedded systems and supercomputing centers due to their large memory, cutting-edge technology and high performace per watt. However, their susceptibility to transient errors necessitates a comprehensive analysis of error sensitivity, as well as the development of error mitigation techniques and fault-tolerant algorithms. This study focuses on evaluating the soft-error sensitivity of two distinct versions of LU decomposition algorithms implemented two on very different GPUs—a low-power SoC embedded GPU and a high-performance massively parallel GPU.Through extensive fault injection campaigns on both GPUs, we examine the vulnerability of the algorithms, identify error causes, and determine critical code components requiring enhanced protection. The experiments reveal that most single bit flip fault injections in the instruction results lead to erroneous outcomes or unrecoverable errors. Notably, efficient GPU resource utilization can increase the number of masked errors, thereby enhancing error resilience. Additionally, while different parts of the code exhibit similar error occurrence types and rates, the propagation of errors to elements within the result matrix differs significantly.

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