Electronic Science and Technology ›› 2024, Vol. 37 ›› Issue (5): 32-37.doi: 10.16180/j.cnki.issn1007-7820.2024.05.005

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Multiple Embedded AI Processors Low Latency Data Exchange Technology Based on PCIE Interface

WEI Xuan1,2, WEN Kailin1,3, LI Bin2, LIU Shutao2, CHU Jie1, CAI Jueping1   

  1. 1. School of Microelectronics,Xidian University,Xi'an 710071,China
    2. The 54th Research Institute of China Electronics Technology Group Corporation,Shijiazhuang 050081,China
    3. Suzhou Honghu Qiji Electronic Technology Co., Ltd.,Suzhou 215000,China
  • Received:2022-12-12 Online:2024-05-15 Published:2024-05-21
  • Supported by:
    National Natural Science Foundation of China(62274123);Key Industrial Innovation Chain Project of Shaanxi Key Research and Development Program(2021ZDLGY02-01)

Abstract:

In view of the conflict of task scheduling and data exchange between multiple embedded AI(Artificial Intelligence) processors and improving the reliability and efficiency of stack expansion of multiple AI processors, a high speed data exchange technology and data frame structure of multiple embedded AI processors with wormhole switching structure are proposed in this study. Based on the PCIE(PCI Express) high-speed data interface, the data is transmitted in the form of data unit, and the multi-weight decision algorithm is designed to avoid the conflict in data transmission and realize the concurrent multi-threading of the task.The FPGA(Field Programmable Gate Array) platform is designed and tested. The results show that the transmission bandwidth utilization efficiency of PCIE reaches more than 85%, the data exchange delay is less than 20 μs, and the average maximum delay time of interrupt task response is 8.775 μs.The technology is suitable for high-speed switching circuits with multi-processor collaboration and can be extended to hybrid PCIE and RapidIO switching circuit architectures.

Key words: embedded AI processors, data exchange, fast peripheral component interconnection, PCI Express, exchange switch, wormhole technology, data arbitration, multi-weighted decision making

CLC Number: 

  • TN919