scholarly journals Remote TSN Frame Encapsulation by means of FPGA

Author(s):  
Jaime Jiménez ◽  
Igor Rodríguez ◽  
David Reguilón ◽  
Aitzol Zuloaga ◽  
Jesús Lázaro

Abstract TSN (Time-Sensitive Networking) has replaced outdated Fieldbus and non-deterministic Ethernet in the Industry 4.0. Field buses are not capable of providing neither connection for industry 4.0 IoT (Internet of Things) nor compatibility between different manufacturers. On the other hand, Ethernet is not able to ensure real-time. On the contrary, TSN guarantees real-time transmission, IoT and compatibility between devices. However, adapting to frequently changing needs makes TSN protocol evolve continuously. For this reason, devices for TSN analysis, such as PCs or not advanced frame analysis equipment are not able to process TSN packets at the speed that standard advances, discarding them as wrong frames. The integration of a System on Chip (SoC) that contains an FPGA (Field Programmable Gate Array) and a microcontroller, with capacity for reconfiguration and monitoring of the frames in the protocol, would be an ideal solution to this problem. This paper describes how to encapsulate TSN frames in Ethernet packets using an FPGA. Such Ethernet frames can subsequently be decapsulated, i.e. in a PC, and thus enable analysing TSN traffic in nonspecialized devices.

2020 ◽  
Vol 148 (4) ◽  
pp. 2468-2468
Author(s):  
Matthew Blunt ◽  
Hezekiah Austin ◽  
Trevor Vannoy ◽  
Tyler Davis ◽  
Ross Snider

2019 ◽  
Vol 30 (1) ◽  
pp. 107-116 ◽  
Author(s):  
Edwin González ◽  
Walter D. Villamizar Luna ◽  
Carlos Augusto Fajardo Ariza

Convolutional Neural Networks (CNNs) are becoming increasingly popular in deep learning applications, e.g. image classification, speech recognition, medicine, to name a few. However, the CNN inference is computationally intensive and demanding a large among of memory resources. In this work is proposed a CNN inference hardware accelerator, which was implemented in a co-processing scheme. The aim is to reduce the hardware resources and achieve the better possible throughput. The design was implemented in the Digilent Arty Z7-20 development board, which is based on System on Chip (SoC) Zynq-7000 of Xilinx. Our implementation achieved a  of accuracy for the MNIST database using only 12-bits fixed-point format. The results show that the co-processing scheme operating at a conservative speed of 100 MHz can identify around 441 images per second, which is about 17% times faster than a 650 MHz - software implementation. It is difficult to compare our results against other implementations based on Field-Programmable Gate Array (FPGA), because the others implementations are not exactly like ours. However, some comparisons, regarding the logical resources used and accuracy, suggest that our work could be better than previous works.


2019 ◽  
Vol 146 (4) ◽  
pp. 2879-2879
Author(s):  
Ross K. Snider ◽  
Trevor Vannoy ◽  
James Eaton ◽  
Matthew Blunt ◽  
E. Bailey Galacci ◽  
...  

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