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

Abstract

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.

A Survey of Trusted Computing Solutions Using FPGAs

“…Nowadays, FPGAs are considered a powerful device to deploy different applications using a hardware architecture described as intellectual property (IP), which means processing, memory, control, and communication that can connect multiple IPs to increase scalability. Due to FPGA' functionalities, mainly in processing large workloads efficiently, they are found in applications in the following areas: 5G open Radio Access Network (RAN) [20], prototype design [21], machine learning [22], deep learning [23], reinforcement learning [24], real-time face recognition [16], unmanned aerial vehicle control [25], multi-signal processing [26] and related to this review, trusted execution environments [27].…”