TX-RSA: A High Performance RSA Implementation Scheme on NVIDIA Tegra X2

Dong, Jiankuo; Guang, Fan; Zheng, Fangyu; Lin, Jingqiang; Xiao, Fu

doi:10.1007/978-3-030-86130-8_17

Cited by 4 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, Nvidia Tegra 4 mobile chip built on Cortex-A15 architecture is taken as the experiment platform. In particular, Nvidia Tegra 4 packs 72 customized GeForce GPU cores and a quad-core ARM Cortex-A15 CPU, reaching the maximum frequency of 1.9 GHz [34], and can dynamically activate either the single core or all four cores simultaneously through its built-in symmetric multi-processing architecture, in line with the resource consumption of current task. In this experiment, all four cores are activated by default.…”

Section: Experiments Configurationmentioning

confidence: 99%

Edge Intelligence-Assisted Asymmetrical Network Control and Video Decoding in the Industrial IoT with Speculative Parallelization

Yang,

Zhang,

Xia

et al. 2023

Symmetry

View full text Add to dashboard Cite

Industrial Internet of Things (IIoTs) has drawn significant attention in the industry. Among its rich applications, the field’s video surveillance deserves particular interest due to its advantage in better understanding network control. However, existing decoding methods are limited by the video coding order, which cannot be decoded in parallel, resulting in low decoding efficiency and the inability to process the massive amount of video data in real time. In this work, a parallel decoding framework based on the speculative technique is proposed. In particular, the video is first speculatively decomposed into data blocks, and then a verification method is designed to ensure the correctness of the decomposition. After verification, the data blocks having passed the validation can be decoded concurrently in the parallel computing platform. Finally, the concurrent decoding results are concatenated in line with the original encoding order to form the output. Experiments show that compared with traditional serial decoding ones, the proposed method can improve the performance by 9 times on average in the parallel computing environment with NVIDIA Tegra 4 chips, thus significantly enhancing the real-time video data’s decoding efficiency with guaranteed accuracy. Furthermore, proposed and traditional serial methods obtain almost the same peak signal-to-noise ratio (PSNR) and mean square error (MSE) metrics at different bit rates and resolutions, showing that the introduction of the speculative technique does not degrade the decoding accuracy.

show abstract

Section: Experiments Configurationmentioning

confidence: 99%