Parallelization method of digital signal processing based on multi-core pipeline

Weiyi, Shuai; Yan, Di; Li, Xiaoyu; Ke, Sun

doi:10.1109/icct.2017.8359659

Cited by 1 publication

(1 citation statement)

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“…Signal processing needs to be redesigned [ 18 ]. At the same time, most of the multi-channel signal processing designed by FPGA are simple channel superpositions, as shown in Figure 1 , which wastes a lot of resources [ 19 , 20 ]. Therefore, this method is not suitable for scenarios with limited edge resources.…”

Section: Introductionmentioning

confidence: 99%

Resource-Saving Customizable Pipeline Network Architecture for Multi-Signal Processing in Edge Devices

Song

Qie

Hao

et al. 2022

Sensors

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With the development of the information age, the importance of edge computing has been highlighted in industrial site monitoring, health management, and fault diagnosis. Among them, the processing and computing of signals in edge scenarios is the cornerstone of realizing these scenarios. While the performance of edge devices has been dramatically improved, the demand for signal processing in the edge side has also ushered in explosive growth. However, the deployment of traditional serial or parallel signal processing architectures on edge devices has problems such as poor flexibility, low efficiency, and low resource utilization, making edge devices unable to exert their maximum performance. Therefore, this paper proposes a resource-saving customizable pipeline network architecture with a space-optimized resource allocation method and a coordinate addressing method for irregular topology. This architecture significantly improves the flexibility of multi-signal processing in edge devices, improves resource utilization, and further increases the performance potential of edge devices. Finally, we designed a comparative experiment to prove that the resource-saving and customizable pipeline network architecture can significantly reduce resource consumption under the premise of meeting real-time processing requirements.

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Section: Introductionmentioning

confidence: 99%